Neuromanagement: Neuroscience for Organizations 9781536195620

Citation preview

NEUROSCIENCE RESEARCH PROGRESS

NEUROMANAGEMENT NEUROSCIENCE FOR ORGANIZATIONS

No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services.

NEUROSCIENCE RESEARCH PROGRESS Additional books and e-books in this series can be found on Nova’s website under the Series tab.

NEUROSCIENCE RESEARCH PROGRESS

NEUROMANAGEMENT NEUROSCIENCE FOR ORGANIZATIONS

MICHELA BALCONI EDITOR

Copyright © 2021 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. We have partnered with Copyright Clearance Center to make it easy for you to obtain permissions to reuse content from this publication. Simply navigate to this publication’s page on Nova’s website and locate the “Get Permission” button below the title description. This button is linked directly to the title’s permission page on copyright.com. Alternatively, you can visit copyright.com and search by title, ISBN, or ISSN. For further questions about using the service on copyright.com, please contact: Copyright Clearance Center Phone: +1-(978) 750-8400 Fax: +1-(978) 750-4470 E-mail: [email protected].

NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the Publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

Library of Congress Cataloging-in-Publication Data ISBN: H%RRN

Published by Nova Science Publishers, Inc. † New York

CONTENTS Introduction

vii Michela Balconi

Part I:

Fundamentals

1

Chapter 1

Leaders’ Brains: How to Discover and Improve Them Michela Balconi

3

Chapter 2

Trusting and Rewarded Brains Michela Balconi

25

Chapter 3

To Be or Not to Be Moral in Organizations? Michela Balconi and Giulia Fronda

41

Chapter 4

Un-Stressed Mind: Neuroscientific Applications for Stress Management at the Workplace Michela Balconi and Laura Angioletti

67

Part II:

The Applications

83

Chapter 5

Neuroassessment: Neurometrics for Assessment in Organizations Michela Balconi

85

Contents

vi Chapter 6

Chapter 7

Chapter 8

Chapter 9

From the Evaluation of Executive Functions (EFs) to Neuroempowerment for Organizations Michela Balconi and Laura Angioletti Neurocognitive Enhancement in Organizations: Challenges and Opportunities Michela Balconi and Laura Angioletti Industry 4.0 and Automation: The Contribution of Applied Neuroscience Federico Cassioli, Davide Crivelli and Michela Balconi Digital-Learning for Organization: Insights from Cognitive Neuroscience Davide Crivelli and Michela Balconi

97

111

133

147

About the Editor

163

Index

165

INTRODUCTION

Michela Balconi, PhD International Research Center for Cognitive Applied Neuroscience (IrcCAN), Catholic University of the Sacred Heart, Milan, Italy Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy

Why a new discipline, such as that of neuromanagement? What added value does neuroscience applied to management introduce? An organizational neuroscience - i.e., neuromanagement - paradigm would bring three essential benefits, which work in the backstage of this Introduction. First, neuromanagement would help extend existing theories. Specifically, we observed that neuroscientific approach promises a new, deeper level of analysis for “old” concepts of organizational workplace. Consequently, neuroscientific investigations will add detail to our accounts of human behavior, while further linking our field more closely to other scientific disciplines. In so doing, organizational neuroscience will 

Corresponding Author’s E-mail: [email protected]

viii

Michela Balconi

promote a new spirit of analysis and research. Second, neuromanagement will encourage new research directions, by using specific and focused tools able to discover heterogeneous components of our behavior. Third, perspectives from neuromanagement could help scholars resolve existing conceptual disagreements of conflicts. Issues that are difficult to differentiate or resolve at one level of analysis may become more distinctive at the level of neural processing. As an example of the first type of benefit furnished by neuromanagement is the issue of the social environment and its relationship with the organizational skills, in order to explain how the neuroscientific approach can “inform” the organizational theories. Indeed, as a first milestone we can underline that organizational scientists recognize the importance of the social setting. Phenomena such as work climates, organizational cultures, and other aspects of the social setting exert well-documented effects on employee attitudes and behaviors. In the face of so much evidence, what room is left for biological incidence? Indeed, some scholars have found the evidence for situational effects so compelling that they have questioned whether any individual attribute - be it biological or otherwise – can appreciably affect workplace behavior. Neuromanagement proffers a unique perspective, suggesting that a neuroscientific analysis complements rather than supplants a social scientific one. Human beings are heavily influenced by their social setting because of their biology. Seen from this point of view, there is no contradiction between the coexistence of both social and biological influences. Indeed, the latter helps to generate the former. The volume deals with the theme of the applications of neuroscience to organizational contexts and management, evaluating the current impact, the potential for future developments, as well as the critical issues related to neuroscientific paradigms and investigation techniques typical of the neuroscience domain. The first part of the book focuses on the “neuroscientific mindset” for changing, considering, between the other, how leadership can be discovered and empowered by a neuroscientific approach; the neurophysiological components of trusting behavior in organization; the

Introduction

ix

role of moral behavior and decision-making in individual and organizational well-being, as well as the relationship between stress regulation and well-being in the workplace. The second part deals with the application of neuroscience to organization in term of: how neuromanagement allows us to evaluate and enhance individuals’ executive functions through neuroassessment protocols; some other issues deal with the challenge of applying novel neuroscientific techniques for neuroassessement; or with the development of new personal and interpersonal skills based on neuroempowerment or, in addition, the role of enhanced executive functions for management. The technology and innovative homo sapiens is also considered, elucidating the impact of 4.0 industry for automation or, finally, the effect digital learning in workplace. In other words, this book explores how to discover and “modify” human beings in organizations through their brains.

PART I: FUNDAMENTALS

In: Neuromanagement Editor: Michela Balconi

ISBN: 978-1-53619-562-0 © 2021 Nova Science Publishers, Inc.

Chapter 1

LEADERS’ BRAINS: HOW TO DISCOVER AND IMPROVE THEM Michela Balconi, PhD International Research Center for Cognitive Applied Neuroscience (IrcCAN), Catholic University of the Sacred Heart, Milan, Italy Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy

ABSTRACT Recent advances in neuroscience have allowed a better understanding of the phenomenon of leadership and its development. In particular, social and affective neuroscience have developed new neuroscientific methods and techniques that allow a more in-depth understanding of the implicit and explicit cognitive and emotional mechanisms characterizing different leadership styles, opening the field to new models such as that of “inspirational” and “generative” leadership. Therefore, the tools of neuroscience make it possible to analyze various phenomena underlying the style of leadership within the company 

Corresponding Author’s E-mail: [email protected].

4

Michela Balconi context, such as the emotional expression, the types of communication, and interpersonal relational skills. In particular, a recent neuroscience paradigm, the “hyperscanning”, has allowed analyzing the mechanisms of the body and brain synthonization between leader and employee during real interactional situations to better direct and develop excellent and functional styles of leadership.

1. DEFINITION OF LEADERSHIP AND NEUROLEADERSHIP Leadership can be considered a form of social capital involving the company’s members’ sharing, distribution, and connectivity. Indeed, as defined by Pearce and Conger (2003), shared leadership can be understood as a dynamic process in which individuals of the same or different hierarchical levels influence each other within the organization. Therefore, leadership turns out to be an essential issue in the organizational context, influenced by complex organizational dynamics and processes related to emotions, objectives, intentions, expectations, and cognitive biases, which have recently increased the interest in neuroscientific approaches. The interest in this neurosciencientific areas has been increased by the development of improved methods and protocols for the understanding of mechanisms underlying individuals’ interactions (Balconi and Canavesio 2013; Balconi, Cassioli, et al. 2019; Balconi and Vanutelli 2017; Balconi, Venturella, et al. 2019; Balconi et al. 2020; Paulus et al. 2009; Vanutelli et al. 2017). In particular, the field of social and affective neuroscience has been interested in empathic and emotional mechanisms underlying different leadership styles. Specifically, recently, this neuroscientific contribution has been possible to identify compelling leadership profiles observing their main characteristics, focusing in particular on two most widespread leadership models: the “inspirational” one and the “generative” one (Balconi, Fronda, et al. 2017; Waldman, Balthazard, and Peterson 2011).

Leaders’ Brains

5

2. LEADERSHIP’S STYLE ONE: THE “INSPIRATIONAL” LEADER Starting from the “inspirational” leadership model, several theories have observed that exceptional leaders, going beyond simple performanceversus-reward transactions, can have a possible impact on the work-team and the entire organization, pushing and motivating the creation of new visions and changes. In particular, the inspirational leaders’ vision is based on sustained ideological values provoking individuals’ greater energy, leading them to identify more with this type of vision (Conger and Kanungo 1998). This latter aspect has been underlined by many contemporary leadership theories that have emphasized the transformational, charismatic, and visionary paradigms of this style of leadership, which increases followers’ trust, intrinsic motivation, and admiration towards the leader. Therefore, the new vision of “inspirational” leadership can be seen as a socialized continuum versus the personalized one. In particular, the central elements that constitute this socialized vision are social responsibility, altruism, and the inclusion of responsible followers that allow responding to the group’s interests and needs and guarantee organizational success (House and Howell 1992). Therefore, the adoption of a socialized vision involves the implementation of processes and the achievement of results that are advantageous for the followers, the stakeholders, the community, and the country of residence of the organization. Contrary to the assumption of a socialized vision, the personalized one is characterized by a centrality of the leader’s figure, the importance of achieving company objectives, and the prevalence of competition. In this view, therefore, an obsession towards authority prevails. Thus, a socialized or personalized vision promotes the development of different relationships between leaders and followers. In particular, a neuroscientific research, using functional Magnetic Resonance Imaging (fMRI), has observed the neural correlates associated with leader-employee responses during inspirational statements (inspirational collective-oriented vs. non-

6

Michela Balconi

inspirational personal oriented; Howell and Shamir 2005) and shared group membership between followers and leaders (Haslam, Reicher, and Platow 2011). This research has made it possible to investigate the role of specific brain areas and networks in elaborating an inspirational style of leadership, providing a complete picture of the link between leaders and followers. Furthermore, this research has yielded new evidence on leadership’s neuroscience, compared to previous studies that have used different tools, such as electroencephalogram (EEG), to obtain information on brain connectivity mechanisms associated with leadership’s effectiveness (Waldman, Balthazard, and Peterson 2011). These neuroscientific studies, therefore, have allowed observing that leadership processes, in terms of social influence, appear to be determined by a fundamental basis concerning the categorization of oneself and others in terms of shared social identity (for example, as “we scholars leadership”). Indeed, it has been observed that when followers perceive that they belong to a group shared with the leader, his proposals more influence them, perceive him as a charismatic figure, support him and respond creatively to his ideas. Furthermore, the perception of belonging to a shared social group also influences the functioning of specific brain regions involved in the control of the semantic processing of inspirational messages oriented to the community. In this regard, cognitive psychology studies have demonstrated a phenomenon known as “confirmation bias” which illustrates how individuals more easily encode information according to their existing beliefs (Nickerson 1998). Specifically, information to which individuals are exposed is selectively encoded after being represented as schemas (cognitive categories representing prototypical instances of a given stimulus). This has also been applied to the field of leadership studies, where it has been observed that followers encode information received from leaders using previous schemas constructed about them (Shondrick, Dinh, and Lord 2010). In particular, from a neuroscientific point of view, the activation of specific brain areas, such as the right inferior parietal lobe, bilateral insula, and left superior temporal gyrus, was observed when the memories that followers have of leaders are resonant rather than dissonant. Moreover, the

Leaders’ Brains

7

phenomenon of self-representation has been explored by some studies that have used EEG-based power spectral analysis measures to observe any differences between leaders who have a more or less complex representation of themselves.

3. LADERSHIP’S STYLE TWO: OF THE “GENERATIVE” LEADER Therefore, as it emerged above, recent research has focused mainly on the different leadership characteristics to explore new management methods involving supportive and interpersonal exchange methods. In particular, great attention has been paid to “transformational” and “generative” leadership (Ashkanasy 2013; Balconi, Fronda, et al. 2017), observing how the effectiveness of this style of leadership is correlated with leaders’ great emotional understanding, balance, self-control, effective communication skills, intuition and foresight (Balthazard et al. 2012). The positive effects of a transformational and more cooperative leadership style have been demonstrated both on individuals’ performance, with a greater encouragement of relationships between colleagues, and on the entire organization (Judge and Piccolo 2004), with a greater individuals’ commitment towards the company (Bass and Bass 2009). Furthermore, a generative style of leadership appears to be correlated with a communication style involving others’ co-participation in decisionmaking, increasing the work team’s level of motivation and satisfaction. The effects of adopting a cooperative and generative style of leadership have also been investigated by some neuroscientific studies that have observed the activation of specific brain areas, such as the frontal lobes, which are good predictors of functional leadership and are more involved in interactional processes (Balthazard et al. 2012) and in monitoring executive functioning, such as planning, behavioral organization, and selfregulation. Furthermore, the frontal cortex organizes temporally internal

8

Michela Balconi

and external sensory information integration, regulating behavioral response patterns (Case 1992; Fuster 1999), that are essential for leaders, who must have a good ability to control and monitor their own and others’ behavior. In addition to supporting the organization of behavioral responses, the frontal cortex is also involved in the regulation of the social and affective components underlying interpersonal relationships (Levitan, Hasey, and Sloman 2000) and joint behaviors, such as the performance of cooperative social tasks (Balconi, Crivelli, and Vanutelli 2017; Balconi, Pezard, et al. 2017; Balconi and Pozzoli 2005). Contrarily to a generative leadership style, the authoritarian one is characterized by a more self-centered communication that involves good productivity but produces dissatisfaction and demotivation in employees. Indeed, it has been shown that leaders’ communication and emotional expression influence the level of employees’ motivation (Balconi and Venturella 2015). In particular, leaders’ emotional expression represents a fundamental element that appears to be influenced by the level of emotional intelligence that affects the choice of times and ways to inspire others (Goleman, Boyzatis, and McKee 2002). High capacities of emotional intelligence lead leaders to empathize more with their employees and express their emotions more functionally (Mayer, Salovey, and Caruso 2008). Specifically, these different capacities of leaders related to emotional expression, communication, and emotions’ management have been investigated in depth thanks to the use of neuroscientific approaches, which have allowed to explore the brain mechanisms related to the implementation of certain behavior by informing on the relative conscious and unconscious individuals’ mental processes (Balconi, Finocchiaro, and Campanella 2014; Balconi and Vanutelli 2016). In particular, neuroscience, thanks to the use of different neuroscientific tools such as EEG, functional Near-Infrared Spectroscopy (fNIRS), biofeedback, and Transcranial Magnetic Stimulation (TMS), have allowed to deepen the knowledge of these processes by functionally intervening on them into the organization.

Leaders’ Brains

9

Specifically, the EEG permits to record changes in individuals’ cortical activity by offering an excellent temporal resolution; the fNIRS allows informing about individuals’ hemodynamic activity, offering a good spatial resolution and investigating the greater involvement of certain brain areas in specific processes; the biofeedback permits measuring the variations in individuals’ peripheral activity (skin conductance, heart rate - HR, blood pressure, etc.), informing about their emotional involvement and arousal state; and the TMS allows to stimulate some specific brain regions by observing the functioning of different processes (Balconi and Venturella 2015). In addition to using these tools, recent developments in neuroscience have also made it possible to use a research paradigm, called “hyperscanning,” applied to the leadership field. Specifically, this paradigm allows to simultaneously record the activity of two individuals involved in an interpersonal or social interaction or during a task’s performance. In particular, several studies that have used different neuroscientific techniques, such as EEG, fNIRS, fMRI, and biofeedback, have demonstrated the possibility of hyperscanning to investigate the brain and peripheral synthonization and desynthonization mechanisms underlying different social interaction processes involving two or more individuals. Indeed, hyperscanning studies have been conducted in various social and interactional situations, such as communicative, cooperative, prosocial, and conflictual exchanges (Balconi, Fronda, and Bartolo 2020; Balconi, Fronda, and Vanutelli 2019; Balconi and Vanutelli 2016, 2017; Frischen, Bayliss, and Tipper 2007). In fact, given the effectiveness of this paradigm, its application has gone from the use of the observation of neural and bodily correlates involved in economic or interactive tasks to the investigation of the synthonization or desynthonization mechanisms in more real interaction contexts (Balconi, Fronda, and Vanutelli 2019, 2020; Balconi and Vanutelli 2017, 2018; Dumas et al. 2010; King-Casas et al. 2005; Lindenberger et al. 2009; Montague et al. 2002). Hyperscanning, therefore, through the biometric analysis of the neural and peripheral biosignals, can provide information on the mental and bodily strategies that are syntonic or not in inter-agents individuals. Thus, hyperscanning offers

10

Michela Balconi

information on the level of connectivity and interconnection between two brains or bodies (inter-cerebral and peripheral connectivity), informing about the social bond’s strength. In light of this evidence, the hyperscanning techniques provide a new approach to investigate the complexity of joint action, observing specific characteristics as spontaneity, multimodality, and reciprocity, which constitutes a big challenge for the neuroscientific field.

4. HOW LEADERSHIP STYLE AFFECTS EMPLOYEES’ EVALUATION Considering hyperscanning, the use of this paradigm applied to leadership has made it possible to investigate the neurophysiological correlates and the interactional dynamics associated with the employees’ evaluation, which is a critical process characterizing managerial direction. In particular, the application of this specific neuroscientific paradigm permits to observe the dynamics involved in the communication between leader and employee, investigating the use of different leadership styles, such as cooperative and authoritarian ones. Specifically, as demonstrated by previous studies (Balconi, Venturella, et al. 2019; Balconi et al. 2020), EEG and biofeedback’s integrated use has allowed identifying those behavioral and psychophysiological markers of the interaction between leader and employee, understanding the most functional leadership styles and communicative methods. Moreover, the application of hyperscanning has allowed observing the different mechanisms of cerebral and autonomic synthonization occurring between leaders and employees during the use of different styles of leadership, corporate roles, and performance evaluations. In particular, these synthonization mechanisms appear to be fundamental, representing the basis of solid and cooperative individuals’ interactions (Balconi, Bortolotti, and Gonzaga 2011; Balconi and Canavesio 2013). Specifically, for the investigation of synthonization mechanisms underlying different leadership’s characteristics and methods,

Leaders’ Brains

11

individuals (leader and employees) were asked to carry out, through a roleplaying technique, an evaluation interview, in which the leader was previously requested to use either an authoritarian or a participatory style of leadership. Some leaders were asked to use an authoritarian leadership style, characterized by more directive communication, while others were asked to use a participative leadership style. The levels of excitement or physiological discomfort perceived by individuals (leaders and employees) during the interaction have been observed with the simultaneous use of EEG and biofeedback in hyperscanning, which have allowed obtaining information about the effects that the leadership’s style of communication can have on the workers’ organism, health and stress levels. From this evidence, it has been observed that a generative and charismatic style of leadership, contrary to an authoritarian one, appears to be associated with a decrease in stress levels (De Hoogh and Den Hartog 2008) and a greater consonance among team members, due to the presence of a greater empathy (Preston and de Waal 2002; Vanutelli and Balconi 2015).

5. WHEN EMOTIONAL SYNTHONIZATION OCCURS BETWEEN LEADER AND EMPLOYEE In addition to evaluating the influence of an authoritarian or participatory style of leadership in the leader and employees’ brain synthonization, this study has investigated whether and how a quantitative evaluation of the performance could influence the relationship between leader and employees. Indeed, the presence of a score associated with performance evaluation could generate the perception of an asymmetric dynamic, with consequent negative feelings involving different cognitive and emotional processes. In particular, contrary to what was traditionally thought concerning the fact that evaluation can improve employees’ performance (Dixon, Rock, and Ochsner 2010), a meta-analysis by Kluger and DeNisi (1996) has shown how feedback improve individuals’ performance only in some

12

Michela Balconi

cases. This evidence is in line with the hypotheses of Rock (2008) that have explicated how feedback, providing a judgment on the ranking and a consequent sense of status, can represent a threat to individuals causing social pain experimentation and adverse working condition (Lieberman and Eisenberger 2008). Therefore, these findings have led companies to convert traditional feedback modalities into different performance evaluation forms, focusing on the other effects of quantitative or qualitative feedback. Indeed, it has been observed that, contrary to quantitative ones, qualitative feedbacks, more narrative, turn out to attract more individuals’ attention (Smither and Walker 2004). This evidence was also confirmed by study results that have demonstrated the presence of more negative individuals’ reactions correlated to the use of quantitative feedback, with significant effects on individuals’ cerebral synthonization levels. Differently, qualitative feedback has appeared more associated with individuals’ positive feelings, increased synthonization levels, and greater emotional involvement. Starting from this evidence, future research could use hyperscanning to investigate other critical organizational components, such as individual and team commitment and corporate condition’s understanding. Moreover, other constructs, such as moral issues and organizational changes related to gender, age, and other factors concerning the group’s composition, could be considered.

6. NEURAL SYNTHONIZATION: FROM SINGLE-BRAIN TO INTER-BRAIN CONNECTIVITY As reported in the previous paragraphs, several studies have investigated leaders’ and employees’ neural and behavioral mechanisms of synthonization to identify the most functional ways to conduct their relationship. Indeed, the development and application of techniques, such as hyperscanning, have allowed observing more directly interpersonal relationships by investigating the level of “brain synthonization” through

Leaders’ Brains

13

connectivity analyses. Specifically, these analyses can be conducted to observe the level of single individuals’ neural synthonization (single-brain connectivity) or two individuals’ brain synthonization (inter-brain connectivity), providing information on the synergy between inter-agents. In particular, concerning single-brain connectivity, the interconnectedness of different brain areas in single individuals can be measured through coherence. Specifically, this method allows to observe the coordination and communication of different brain regions and is used to observe complex behaviors requiring multiple cerebral areas, such as inspirational leadership (Cacioppo, Berntson, and Nusbaum 2008; Nolte, 2002). Typically, coherence is indicated in percentage. A high coherence, intended as a high degree of coordinated activity between two brain regions, is indicated with a percentage of 90%. Conversely, a low level of coherence understood as a poorly coordinated activity between two brain regions is marked with a percentage of 10%. In particular, the level of coherence concerning certain brain regions can inform about various behavioral phenomena. For example, high coherence levels in the right cerebral hemisphere appear to be correlated with greater integration of processes related to own and others’ emotions understanding and balance. Besides, the application of hyperscanning with the use of different techniques, such as EEG, fNIRS, fMRI, and magnetoencephalography (MEG), has also allowed observing the levels of inter-brain connectivity, understood as the synchronization of two oscillators that mutually regulate their ongoing rhythms during an interaction (Balconi and Molteni 2016). Specifically, inter-brain connectivity is configured as a fundamental indicator of individuals’ brain processes (Burgess 2013; Rosenblum et al. 2001) and occurs during the execution of complex behaviors requiring actions’ regulation and shared rules. Indeed, it has been observed that neurophysiological synthonization mechanisms can be used to evaluate the strength of two signals’ coupling occur in different contexts (Balconi and Vanutelli 2017). For example, EEG studies have shown a greater coherence during rhythmic, musical, and motor synchronization activities (Kawasaki et al. 2013; Konvalinka et al. 2014; Sänger, Müller, and Lindenberger 2012). Moreover, also game theory has highlighted these

14

Michela Balconi

synchronization mechanisms during cooperative activities and the use of computer-based laboratory paradigms (Astolfi et al. 2012; Balconi and Vanutelli 2016). In addition, biofeedback studies have observed the measurement of peripheral synthonization mechanisms of HR during conversations between spouses, interactions connected by touch, and social interactions based on trust and group cohesion (Chatel-Goldman et al. 2014; Gottman and Levenson 1986; Mitkidis et al. 2015; Strang et al. 2014). Evidence emerged from these studies have observed that these connectivity mechanisms have positive effects on communicative and understanding processes between inter-agents, improving their relation (Hasson et al. 2012) and promoting the implementation of cooperatives and prosocial behaviors (Mogan, Fischer, and Bulbulia 2017; Valdesolo and DeSteno 2011). In particular, inter-brain connectivity increases the level of individuals’ behavioral coordination by promoting greater affinity, involvement, social closeness, empathy, and cooperative behaviors (Bevilacqua et al. 2018; Dikker et al. 2017; Dumas et al. 2011; Fries 2005; Hasson et al. 2012; Varela et al. 2001).

7. NEUROSCIENCE AND LEADERSHIP: ADVANTAGES AND CRITICALITIES Evidence that emerged in previous paragraphs has allowed understanding and identify those characteristics, innate or cognitively mediated, necessary and characterizing good leadership skills. These characteristics and indicators of good and functional leadership styles can also be predicted by the EEG individuals’ activity. In particular, investigating these characteristics makes it possible to implement methods and interventions to improve the company’s leadership skills, enhancing the entire organization’s functioning. In particular, neuroleadership allows for better organizational change strategies, providing information on the basic brain mechanisms of learning and habit formation. Neuroleadership,

Leaders’ Brains

15

indeed, focuses on attention and learning’s expectations permits observing how individuals’ behaviors and attitudes are more easily modified by the facilitation of intuition’s moments. In this regard, specific tools, such as TMS or transcranial Direct Current Stimulation (tDCS), allow brain activity manipulation to strengthen specific ways of thinking directly. Also, less invasive tools such as EEG-based neurofeedback, allowing individuals to receive real-time feedback on their brain activity, could also be used to teach individuals to maintain their activity over time by implementing specific strategies and appropriate behaviors. Therefore, although these techniques could prove very useful, it is essential to carefully choose the behaviors to be trained to enhance specific skills to be implemented in a real work environment. Indeed, the neuroplasticity resulting from this training could be reversible and dissociated concerning the desired behavioral changes. Additionally, clinical and business applications of expensive and advanced technologies are susceptible to placebo effects, making them easily exploitable applications such as physician burn-out prevention. These aspects, therefore, underlines the importance of considering practical and ethical factors in the use of these techniques for improving some leadership skills. Certainly, an advantage represented by the use of these neuroscientific techniques is related to the fact that they allow investigating the unconscious processes at the basis of effective management relationships, which influence employees’ state of well-being and health. Moreover, these techniques enable investigating any disparities in interpersonal relationships and the provision of care, possible bullying attitudes, and resistance to adopting new interventions or protocols. Therefore, in light of this evidence, using these techniques could help investigate individuals’ unconscious prejudices and implicit attitudes to explain any contradictions between self-referencing perspectives and the implementation of specific behaviors, highlighting why logical discourse is often ineffective in promoting organizational change.

16

Michela Balconi

REFERENCES Ashkanasy, Neal M. 2013. “Neuroscience and Leadership: Take Care Not to Throw the Baby Out With the Bathwater.” Journal of Management Inquiry 22:311–13. doi:10.1177/1056492613478519. Astolfi, Laura, Jlenia Toppi, Gianluca Borghini, Giovanni Vecchiato, Eric J. He, Abhrajeet Roy, Febo Cincotti, et al. 2012. “Cortical Activity and Functional Hyperconnectivity by Simultaneous EEG Recordings from Interacting Couples of Professional Pilots.” Paper presented at the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, San Diego, United States, August 28September 1. Balconi, Michela, Adriana Bortolotti, and Ludovica Gonzaga. 2011. “Emotional Face Recognition, EMG Response, and Medial Prefrontal Activity in Empathic Behaviour.” Neuroscience Research 71:251–59. doi:10.1016/j.neures.2011.07.1833. Balconi, Michela, and Ylenia Canavesio. 2013. “Emotional Contagion and Trait Empathy in Prosocial Behavior in Young People: The Contribution of Autonomic (Facial Feedback) and Balanced Emotional Empathy Scale (BEES) Measures.” Journal of Clinical and Experimental Neuropsychology 35:41–48. doi:10.1080/13803395. 2012.742492. Balconi, Michela, Federico Cassioli, Giulia Fronda, and Maria Elide Vanutelli. 2019. “Cooperative Leadership in Hyperscanning. Brain and Body Synchrony during Manager-Employee Interactions.” Neuropsychological Trends 26:23–44. doi:10.7358/neur-2019-026bal2. Balconi, Michela, Davide Crivelli, and Maria Elide Vanutelli. 2017. “Why to Cooperate Is Better than to Compete: Brain and Personality Components.” BMC Neuroscience 18:1–15. doi:10.1186/s12868-0170386-8. Balconi, Michela, Roberta Finocchiaro, and Salvatore Campanella. 2014. “Reward Sensitivity, Decisional Bias, and Metacognitive Deficits in

Leaders’ Brains

17

Cocaine Drug Addiction.” Journal of Addiction Medicine 8:399–406. doi:10.1097/ADM.0000000000000065. Balconi, Michela, Giulia Fronda, and Angela Bartolo. 2020. “Affective, Social, and Informative Gestures Reproduction in Human Interaction: Hyperscanning and Brain Connectivity.” Journal of Motor Behavior 1–20. doi:10.1080/00222895.2020.1774490. Balconi, Michela, Giulia Fronda, Maria Rosaria Natale, and Enrico Rimoldi. 2017. “Perchè La Leadership Generativa. Il Contributo Delle Neuroscienze.” Ricerche di Psicologia [“Why Generating Leadership. The Contribution of Neuroscience”. Research of Psychology] 40:365– 83. doi:10.3280/rip2017-003007. Balconi, Michela, Giulia Fronda, and Maria Elide Vanutelli. 2019. “Donate or Receive? Social Hyperscanning Application with FNIRS.” Current Psychology 38:991–1002. doi:10.1007/s12144-019-00247-4. ———. 2020. “When Gratitude and Cooperation between Friends Affect Inter-Brain Connectivity for EEG.” BMC Neuroscience 21:1–12. doi:10.1186/s12868-020-00563-7. Balconi, Michela, and Erika Molteni. 2016. “Past and Future of NearInfrared Spectroscopy in Studies of Emotion and Social Neuroscience.” Journal of Cognitive Psychology 28:129–46. doi:10.1080/20445911.2015.1102919. Balconi, Michela, Laurent Pezard, Jean L. Nandrino, and Maria Elide Vanutelli. 2017. “Two Is Better than One: The Effects of Strategic Cooperation on Intra- and Inter-Brain Connectivity by FNIRS.” PLoS ONE 12:e0187652. doi:10.1371/journal.pone.0187652. Balconi, Michela, and Uberto Pozzoli. 2005. “Morphed Facial Expressions Elicited a N400 ERP Effect: A Domain-Specific Semantic Module?” Scandinavian Journal of Psychology 46:467–74. doi:10.1111/j.14679450.2005.00478.x. Balconi, Michela, and Maria Elide Vanutelli. 2016. “Competition in the Brain. The Contribution of EEG and FNIRS Modulation and Personality Effects in Social Ranking.” Frontiers in Psychology 7:1587. doi:10.3389/fpsyg.2016.01587.

18

Michela Balconi

———. 2017. “Interbrains Cooperation: Hyperscanning and SelfPerception in Joint Actions.” Journal of Clinical and Experimental Neuropsychology 39:607–20. doi:10.1080/13803395.2016.1253666. ———. 2018. “Functional EEG Connectivity during Competition.” BMC Neuroscience 19:1–11. doi:10.1186/s12868-018-0464-6. Balconi, Michela, and Irene Venturella. 2015. “Comunicazione, Emozioni e Neuromanagement.” In Neuroscienze e Management. Nuovi Strumenti Per La Professione Manageriale [“Communication, Emotions and Neuromanagement.” In Neuroscience and Management. New tools for Managerial Prefession], edited by Emanuela M. Salati and Attilio Leoni, 235–49. Milan: Guerini Next srl. Balconi, Michela, Irene Venturella, Giulia Fronda, Daniela De Filippis, Emanuela M. Salati, and Maria Elide Vanutelli. 2019. “To Rate or Not to Rate Autonomic Response and Psychological Well-Being of Employees during Performance Review.” Health Care Manager 38:179–86. doi:10.1097/HCM.0000000000000257. Balconi, Michela, Irene Venturella, Giulia Fronda, and Maria Elide Vanutelli. 2020. “Leader-Employee Emotional ‘Interpersonal Tuning’. An EEG Coherence Study.” Social Neuroscience 15:234–43. doi:10.1080/17470919.2019.1696226. Balthazard, Pierre A., David A. Waldman, Robert W. Thatcher, and Sean T. Hannah. 2012. “Differentiating Transformational and NonTransformational Leaders on the Basis of Neurological Imaging.” Leadership Quarterly 23:244–58. doi:10.1016/j.leaqua.2011.08.002. Bass, Bernard M., and Ruth Bass. 2009. The Bass Handbook of Leadership: Theory, Research, and Managerial Applications. New York: Simon and Schuster. Bevilacqua, Dana, Ido Davidesco, Lu Wan, Kim Chaloner, Jess Rowland, Mingzhou Ding, David Poeppel, and Suzanne Dikker. 2018. “Brain-toBrain Synchrony and Learning Outcomes Vary by Student–Teacher Dynamics: Evidence from a Real-World Classroom Electroencephalography Study.” Journal of Cognitive Neuroscience 31:401–11. doi:10.1162/jocn_a_01274.

Leaders’ Brains

19

Burgess, Adrian P. 2013. “On the Interpretation of Synchronization in EEG Hyperscanning Studies: A Cautionary Note.” Frontiers in Human Neuroscience 7:881. doi:10.3389/fnhum.2013.00881. Cacioppo, John T., Gary G. Berntson, and Howard C. Nusbaum. 2008. “Neuroimaging as A New Tool in The Toolbox of Psychological Science.” Current Directions in Psychological Science 17:62–7. doi:10.1111/j.1467-8721.2008.00550.x. Case, Robbie. 1992. “The Role of the Frontal Lobes in the Regulation of Cognitive Development.” Brain and Cognition 20:51–73. doi:10.1016/0278-2626(92)90061-P. Chatel-Goldman, Jonas, Marco Congedo, Christian Jutten, and Jean Luc Schwartz. 2014. “Touch Increases Autonomic Coupling between Romantic Partners.” Frontiers in Behavioral Neuroscience 8:95. doi:10.3389/fnbeh.2014.00095. Conger, Jay Alden, and Rabindra N. Kanungo. 1998. Charismatic Leadership in Organizations. New York: SAGE Publications, Inc. De Hoogh, Annebel H. B., and Deanne N. Den Hartog. 2008. “Ethical and Despotic Leadership, Relationships with Leader’s Social Responsibility, Top Management Team Effectiveness and Subordinates’ Optimism: A Multi-Method Study.” Leadership Quarterly 19:297–311. doi:10.1016/j.leaqua.2008.03.002. Dikker, Suzanne, Lu Wan, Ido Davidesco, Lisa Kaggen, Matthias Oostrik, James McClintock, Jess Rowland, et al. 2017. “Brain-to-Brain Synchrony Tracks Real-World Dynamic Group Interactions in the Classroom.” Current Biology 27:1375–80. doi:10.1016/j.cub. 2017.04.002. Dixon, Phil, David Rock, and Kevin Ochsner. 2010. “Turn the 360 around.” NeuroLeadership Journal 3:78–86. Dumas, Guillaume, Fanny Lachat, Jacques Martinerie, Jacqueline Nadel, and Nathalie George. 2011. “From Social Behaviour to Brain Synchronization: Review and Perspectives in Hyperscanning.” IRBM 32:48–53. doi:10.1016/j.irbm.2011.01.002. Dumas, Guillaume, Jacqueline Nadel, Robert Soussignan, Jacques Martinerie, and Line Garnero. 2010. “Inter-Brain Synchronization

20

Michela Balconi

during Social Interaction.” PLoS ONE 5:e12166. doi:10.1371/ journal.pone.0012166. Fries, Pascal. 2005. “A Mechanism for Cognitive Dynamics: Neuronal Communication through Neuronal Coherence.” Trends in Cognitive Sciences 9:474–80. doi:10.1016/j.tics.2005.08.011. Frischen, Alexandra, Andrew P. Bayliss, and Steven P. Tipper. 2007. “Gaze Cueing of Attention: Visual Attention, Social Cognition, and Individual Differences.” Psychological Bulletin 133:694–724. doi:10.1037/0033-2909.133.4.694. Fuster, Joaquín M. 1999. “Synopsis of Function and Dysfunction of the Frontal Lobe.” Acta Psychiatrica Scandinavica, Supplement 99:51–7. doi:10.1111/j.1600-0447.1999.tb05983.x. Goleman, Daniel, Richard Boyzatis, and Annie McKee. 2002. “Emotional Intelligence: Leadership Competencies.” The New Leaders: Transforming the Art of Leadership into the Science of Results 23:273– 74. Gottman, John M., and Robert W. Levenson. 1986. “Assessing the Role of Emotion in Marriage.” Behavioral Assessment 8:31–48. Haslam, Alexander S., Stephen D. Reicher, and Michael J. Platow. 2011. The New Psychology of Leadership: Identity. Influence and Power. New York: Psychology Press. Hasson, Uri, Asif A. Ghazanfar, Bruno Galantucci, Simon Garrod, and Christian Keysers. 2012. “Brain-to-Brain Coupling: A Mechanism for Creating and Sharing a Social World.” Trends in Cognitive Sciences 16:114–21. doi:10.1016/j.tics.2011.12.007. House, Robert J., and Jane M. Howell. 1992. “Personality and Charismatic Leadership.” The Leadership Quarterly 3:81–108. doi:10.1016/10489843(92)90028-E. Howell, Jane M., and Boas Shamir. 2005. “The Role of Followers in the Charismatic Leadership Process: Relationships and Their Consequences.” Academy of Management Review 30:96–112. doi:10.5465/AMR.2005.15281435. Judge, Timothy A., and Ronald F. Piccolo. 2004. “Transformational and Transactional Leadership: A Meta-Analytic Test of Their Relative

Leaders’ Brains

21

Validity.” Journal of Applied Psychology 89:755–68. doi:10.1037/ 0021-9010.89.5.755. Kawasaki, Masahiro, Yohei Yamada, Yosuke Ushiku, Eri Miyauchi, and Yoko Yamaguchi. 2013. “Inter-Brain Synchronization during Coordination of Speech Rhythm in Human-to-Human Social Interaction.” Scientific Reports 3:1–8. doi:10.1038/srep01692. King-Casas, Brooks, Damon Tomlin, Cedric Anen, Colin F. Camerer, Steven R. Quartz, and P. Read Montague. 2005. “Getting to Know You: Reputation and Trust in a Two-Person Economic Exchange.” Science 308:78–83. doi:10.1126/science.1108062. Kluger, Avraham N., and Angelo DeNisi. 1996. “The Effects of Feedback Interventions on Performance: A Historical Review, a Meta-Analysis, and a Preliminary Feedback Intervention Theory.” Psychological Bulletin 119:254–84. doi:10.1037/0033-2909.119.2.254. Konvalinka, Ivana, Markus Bauer, Carsten Stahlhut, Lars Kai Hansen, Andreas Roepstorff, and Chris D. Frith. 2014. “Frontal Alpha Oscillations Distinguish Leaders from Followers: Multivariate Decoding of Mutually Interacting Brains.” NeuroImage 94:79–88. doi:10.1016/j.neuroimage.2014.03.003. Levitan, Robert D., Gary Hasey, and Leon Sloman. 2000. “Major Depression and the Involuntary Defeat Strategy: Biological Correlates.” In Subordination and Defeat: An Evolutionary Approach to Mood Disorders and Their Therapy, edited by Leon Sloman and Paul Gilbert, 95–118. Hillsdale: Lawrence Erlbaum Associates Publishers. Lieberman, Matthew D., and Naomi I. Eisenberger. 2008. “The Pains and Pleasures of Social Life: A Social Cognitive Neuroscience Approach.” NeuroLeadership Journal 1:38–43. Lindenberger, Ulman, Shu-Chen Li, Walter Gruber, and Viktor Müller. 2009. “Brains Swinging in Concert: Cortical Phase Synchronization While Playing Guitar.” BMC Neuroscience 10:1–12. doi:10.1186/1471-2202-10-22.

22

Michela Balconi

Mayer, John D., Peter Salovey, and David R. Caruso. 2008. “Emotional Intelligence: New Ability or Eclectic Traits?” American Psychologist 63:503–17. doi:10.1037/0003-066X.63.6.503. Mitkidis, Panagiotis, John J. McGraw, Andreas Roepstorff, and Sebastian Wallot. 2015. “Building Trust: Heart Rate Synchrony and Arousal during Joint Action Increased by Public Goods Game.” Physiology and Behavior 149:101–6. doi:10.1016/j.physbeh.2015.05.033. Mogan, Reneeta, Ronald Fischer, and Joseph A. Bulbulia. 2017. “To Be in Synchrony or Not? A Meta-Analysis of Synchrony’s Effects on Behavior, Perception, Cognition and Affect.” Journal of Experimental Social Psychology 72:13–20. doi:10.1016/j.jesp.2017.03.009. Montague, P. Read, Gregory S. Berns, Jonathan D. Cohen, Samuel M. McClure, Giuseppe Pagnoni, Mukesh Dhamala, Michael C. Wiest, et al. 2002. “Hyperscanning: Simultaneous FMRI during Linked Social Interactions.” NeuroImage 16:1159–64. doi:10.1006/nimg.2002.1150. Nickerson, Raymond S. 1998. “Confirmation Bias: A Ubiquitous Phenomenon in Many Guises.” Review of General Psychology 2:175– 220. doi:10.1037/1089-2680.2.2.175. Nolte, John. 2002. The Human Brain: An Introduction to Its Functional Anatomy. St. Louis: Mosby. Paulus, Martin P., Eric G. Potterat, Marcus K. Taylor, Karl F. Van Orden, James Bauman, Nausheen Momen, Genieleah A. Padilla, and Judith L. Swain. 2009. “A Neuroscience Approach to Optimizing Brain Resources for Human Performance in Extreme Environments.” Neuroscience and Biobehavioral Reviews 33:1080–88. doi:10.1016/ j.neubiorev.2009.05.003. Pearce, Craig Lewis, and Jay A. Conger. 2003. Shared Leadership: Reframing the Hows and Whys of Leadership. New York: SAGE Publications, Inc. Preston, Stephanie D., and Frans B. M. de Waal. 2002. “Empathy: Its Ultimate and Proximate Bases.” Behavioral and Brain Sciences 25:1– 20. doi:10.1017/S0140525X02000018. Rock, David. 2008. “SCARF: A Brain-Based Model for Collaborating with and Influencing Others.” NeuroLeadership Journal 1:44–52.

Leaders’ Brains

23

Rosenblum, Michael, Arkady Pikovsky, Jürgen Kurths, Carsten Schäfer, and Peter A. Tass. 2001. “Chapter 9 Phase Synchronization: From Theory to Data Analysis.” Handbook of Biological Physics 4:279–321. doi:10.1016/S1383-8121(01)80012-9. Sänger, Johanna, Viktor Müller, and Ulman Lindenberger. 2012. “Intraand Interbrain Synchronization and Network Properties When Playing Guitar in Duets.” Frontiers in Human Neuroscience 6:312. doi:10.3389/fnhum.2012.00312. Shondrick, Sara J., Jessica E. Dinh, and Robert G. Lord. 2010. “Developments in Implicit Leadership Theory and Cognitive Science: Applications to Improving Measurement and Understanding Alternatives to Hierarchical Leadership.” Leadership Quarterly 21:959–78. doi:10.1016/j.leaqua.2010.10.004. Smither, James W., and Alan G. Walker. 2004. “Are the Characteristics of Narrative Comments Related to Improvement in Multirater Feedback Ratings over Time?” Journal of Applied Psychology 89:575–81. doi:10.1037/0021-9010.89.3.575. Strang, Adam J., Gregory J. Funke, Sheldon M. Russell, Allen W. Dukes, and Matthew S. Middendorf. 2014. “Physio-Behavioral Coupling in a Cooperative Team Task: Contributors and Relations.” Journal of Experimental Psychology: Human Perception and Performance 40:145–58. doi:10.1037/a0033125. Valdesolo, Piercarlo, and David DeSteno. 2011. “Synchrony and the Social Tuning of Compassion.” Emotion 11:262–66. doi:10.1037/a0021302. Vanutelli, Maria Elide, and Michela Balconi. 2015. “Empathy and Prosocial Behaviours. Insights from Intra- and Inter-Species Interactions.” Rivista Internazionale Di Filosofia e Psicologia [International Journal of Philosophy and Psychology] 6:88–109. doi:10.4453/rifp.2015.0007. Vanutelli, Maria Elide, Laura Gatti, Laura Angioletti, and Michela Balconi. 2017. “Affective Synchrony and Autonomic Coupling during Cooperation: A Hyperscanning Study.” BioMed Research International 2017:3104564. doi:10.1155/2017/3104564.

24

Michela Balconi

Varela, Francisco, Jean Philippe Lachaux, Eugenio Rodriguez, and Jacques Martinerie. 2001. “The Brainweb: Phase Synchronization and LargeScale Integration.” Nature Reviews Neuroscience 2:229–39. doi:10.1038/35067550. Waldman, David A., Pierre A. Balthazard, and Suzanne J. Peterson. 2011. “Leadership and Neuroscience: Can We Revolutionize the Way That Inspirational Leaders Are Identified and Developed?” Academy of Management Perspectives 25:60–74. doi:10.5465/AMP.2011. 59198450.

In: Neuromanagement Editor: Michela Balconi

ISBN: 978-1-53619-562-0 © 2021 Nova Science Publishers, Inc.

Chapter 2

TRUSTING AND REWARDED BRAINS Michela Balconi, PhD International Research Center for Cognitive Applied Neuroscience (IrcCAN), Catholic University of the Sacred Heart, Milan, Italy Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy

ABSTRACT Trust represents a central part of all human relationships, including romantic partnerships, family life, business, politics, and other contexts of individuals’ life. Indeed, trusting behavior can be represented as an essential individual need to bond, relate, and develop supportive social relationships. Moreover, trust affects the organization’s ability to accomplish its objectives and meet strategic goals because it acts as a social connector for interpersonal relationships. In particular, the neuroscientific approach demonstrated that organizations sustaining high levels of trust have substantially greater engagement by colleagues. This indicates that organizational trust represents a valuable asset that can be measured and managed to sustain a competitive advantage over rivals, 

Corresponding Author’s E-mail: [email protected].

26

Michela Balconi demonstrating the possibility to support some processes that increase the trust behavior within the organizational context.

1. PERFORMANCE AND WELL-BEING: THE ROLE OF TRUST AND EMPATHY Trusting behavior is a fundamental need for individuals who feel the necessity to develop supportive social relationships with others. Trust, indeed, appears to be a fundamental element in any human relationship, considering family, sentimental, business, and political ones. However, how can trust be defined? Some preliminary definitions of trust will be proposed below. Firstly, trust can be defined as a set of behaviors, including acting in a manner related to another. Furthermore, trust can be defined as the belief that another individual is likely to act in a certain way. Thirdly, trust can be characterized as an abstract mental attitude regarding the reliability of another individual. Otherwise, trust can be defined as a feeling of security and trust that an individual feels. Finally, trust can be intended as a complex neural process involving several representations in a semantic container comprising emotions and beliefs. Trust, therefore, affects individuals’ lives and the organizations’ ability to achieve their goals because acting as a social lubricant facilitates interpersonal and social interactions (Zak and Knack 2001). In particular, organizations’ sociality and ordinary life require two key factors: identifying a common goal and developing sufficient trust within the working group (Barraza and Zak 2013). Indeed, as demonstrated by neuroscientific evidence, high levels of trust within the organizational context lead to greater motivation and commitment among colleagues. This demonstrates how, within the organizational context, trust represents a fundamental component that can be understood and quantified to sustain a competitive advantage over rivals and direct leadership practices that can directly influence individuals’ levels of trust.

Trusting and Rewarded Brains

27

Thus, policy design to promote trust is generalizable across organizations. In this regard, three main aspects can be considered in the description and promotion of trust behavior. Firstly, assuming that certain emotional states can induce individuals to act in a certain way (Zajonc 1998), trust can be considered an unconscious affective state. In this regard, for example, it has been shown that the investment decisions of venture capitalists while reading business plans are not only based on financial factors but also internal ones, such as expected internal rates of return. This lack of understanding and the speed with which managers make judgments about a company after reading a business plan suggests the involvement of an automatic process. Indeed, as neuroscience also shows, our brains frequently try to automate the most complex processes to increase cognitive efficiency. Secondly, to describe trust, it must be considered that this behavior occurs not only between people involved in long-term relationships but also short acquaintances or strangers (Zak 2005). This tendency is observable, for example, in travelers who place confidence in the airline pilot’s ability to lead them to their destination, despite not knowing him and having information about his experience. Furthermore, this is understandable observing groups similar in ethnicity, language, culture, and education that are more likely to trust others. This trust characteristic is essential as it helps in economic growth, transaction costs, and investment decisions. Thirdly, trust appears to be supported by two behavioral constructs consisting of prosocial behavior and empathy. In particular, prosocial behavior, influenced by individual, emotional, and social variables (Balconi and Canavesio 2013a, 2013b, 2014; Balconi and Vanutelli 2017; Carlo et al. 2003; Neff, Turiel, and Anshel 2002; Vanutelli and Balconi 2015), appears to be strongly correlated with the psychophysiological emotional reactivity, which allows one to provide one’s help or support to another individual (Balconi and Canavesio 2013a), and the ability to empathize with others’ emotional states (Balconi and Bortolotti 2012; Balconi, Bortolotti, and Gonzaga 2011; Batson 2009; Lamm, Batson, and Decety 2007; Spinella 2005). Some studies have

28

Michela Balconi

observed that the implementation of prosocial behaviors strengthens interpersonal relations and social connection between individuals (Nummenmaa et al. 2012), increasing their cooperative, behavioral, and brain tuning (Balconi, Fronda, and Vanutelli 2019, 2020; Nowak and Sigmund 2005). The positive effects of implementing prosocial behavior on individuals’ levels of cooperation and attunement have been demonstrated, for example, by a study by Balconi, Fronda, and Vanutelli (2019, 2020), which explored how a gift exchange increased the level of cerebral, peripheral, and behavioral cooperation between two interacting individuals during the performance of an attentional task. Given these positive effects, implementing prosocial behavior is also important within the working context, where leaders should see themselves as information aggregation devices. Indeed, individuals’ effective management capacity presupposes greater sensitivity and behavioral and emotional understanding to comprehend in advance others’ behaviors and needs without waiting for what will be established. Therefore, empathy turns out to be one of the main characteristics of an effective leader (Macaluso 2003) because the ability to empathize reduces uncertainty and satisfies workers’ needs by promoting conditions of trust and efficiency. In particular, empathy can be defined as the affective response to another individual’s emotional state (Decety and Jackson 2006; Preston and de Waal 2002) and concerns the ability to monitor and regulate one’s emotional states and the empathic resonance processes (Chauhan, Mathias, and Critchley 2008). Indeed, regulation and emotional recognition are the basis of empathy (Decety and Svetlova 2012). In particular, concerning emotional recognition, some studies have demonstrated a positive correlation between the ability to recognize emotional states expressed through facial expressions and empathic behavior (Balconi, Bortolotti, and Gonzaga 2011; Balconi and Canavesio 2013a, 2016; Balconi and Pozzoli 2009). High levels of empathy are closely connected with a more remarkable ability to recognize facial expressions, which are processed more quickly (Balconi and Bortolotti 2012; Goldman and Sripada 2005). The need to empathize is also important on the part of employees towards leaders. In this case, the increase in employee empathy towards

Trusting and Rewarded Brains

29

leaders occurs when the latter can clearly communicate the organization’s needs by increasing the willingness to cooperate to achieve the proposed objectives.

2. THE ROLE OF HORMONES IN TRUST Prosocial behavior and attitude appear to be directly related to a specific brain chemical: oxytocin (OT) (Barraza and Zak 2013). In particular, OT is configured as the biological basis of treating others well and the motivation to reciprocate. Indeed, OT’s production and its effects on the central and peripheral nervous systems increase individuals’ voluntary cooperative behavior. Hence, systems, organizational processes, policies, and leadership influence interpersonal relationships by facilitating or inhibiting OT release, which is configured as the neurochemical substrate of empathy and trust (Trust Game, TG; Berg, Dickhaut, and McCabe 1995). Indeed, some studies have observed how to simulate another individual’s mood results associated with a synthesis of OT and activate a neural circuit that uses OT receptors, increasing social cooperation. In particular, the involvement of OT in empathy and trust has been demonstrated by several studies that have used sequential cooperative dilemmas, such as TG. In particular, concerning empathy, the latter must be considered by referring to two distinct components: one cognitive and one emotional (Balconi and Bortolotti 2013; Balconi and Canavesio 2013a, 2016), that allow individuals to experience three different perspectives. The first is related to empathic distress, characterized by reactive and adverse feelings (e.g., anxiety, worry, and discomfort). The second concerns empathic concern (compassion), which is mainly associated with affective states. The third, regarding perspective-taking, concerns others’ mental states, mainly supported by a cognitive process. In particular, the effects of OT in empathic experience were observed in a Singer et al. (2008) study that used the empathy-for-pain paradigm and subsequent TG behavior. Specifically, these authors gave participants either 24 IU of OT

30

Michela Balconi

intranasally or a placebo before observing someone experiencing selfinduced pain or pain itself and then asked them to make decisions on the TG. The study results showed that OT does not affect brain regions previously associated with empathy for self-experienced pain or otherwitnessed pain. These results led authors to affirm that OT does not promote empathy, but it should be emphasized that this evidence only applies to a particular type of empathy (empathic distress). Conversely, other studies have observed that empathic concern and perspective-taking result from the release of endogenous OT (Barraza and Zak 2009). In this regard, it was observed if OT affects performance on the “Reading the Mind in the Eyes” (RMET), consisting of a task that measures the ability to read others’ emotional states, revealing a dysregulation in OT levels in individuals with social anxiety, borderline personality disorders, and aggressive traits who exhibit in-group bias instead of cooperation (Zak 2012). Moreover, some neuroeconomics studies have also investigated the release mechanisms of OT and its brain and behavioral effects in individuals, observing how OT produces a reduction in emotional anxiety levels and promotes cooperation obtaining beneficial social effects. Following this evidence, it is, therefore, possible to affirm that in the organizational context, high levels of trust and high content of OT represent a fundamental basis for the effective functioning of the entire organization. Indeed, the crucial role of trust in achieving common goals is also highlighted by for-and non-profit organizations. In light of this evidence, to increase the likelihood of achieving the organization’s goals, managers should be concerned about activating a climate of trust within the organization and recreating an environment where employees interact in a trusting way.

3. TRUSTING AND LEADERSHIP So how can trust within organizations be increased? Within organizations, it is possible to strengthen trustful behaviors based on some

Trusting and Rewarded Brains

31

managerial processes useful for promoting organizational well-being, developing cohesion and positive stimuli for strengthening individuals and collective resources. In particular, a recent model by Zak (2017) highlighted the role of these factors. Specifically, this model has underlined how, in the increase of organizational trust, a fundamental role is played by explicit recognitions, called “ovations,” which consist in the ability to recognize individuals’ successes adequately. Indeed, recognizing one’s successes by others induces OT synthesis and promotes dopamine release (Skuse and Gallagher 2009). In particular, the latter rewards for paying attention to something new in the surrounding environment and establishes pathways in the brain through which this new knowledge can be accessed in the future. In other words, the working principle of dopaminergic learning can be summarized as: “This is important, remember it and do it in the future.” In addition to explicit recognitions, expectations, which occur when colleagues take on challenges as a group, also result in being important for the release of OT and trust increasing among team members. The importance of challenge design has also been demonstrated at the neurobiological level, as the brain, functioning as an economic system, saves energy by producing only neurochemicals such as OT when necessary (Zak 2014). Hence, OT is more likely to be released and enhanced cooperative effectiveness is promoted when there is a compelling reason to work as a team. Therefore, to stimulate the group, managers should set challenging but achievable expectations to promote OT synthesis in their co-workers’ brains. Indeed, if the challenges and set goals are not feasible but impossible, they are stressful for collaborators by inhibiting OT synthesis through epinephrine release. In addition to explicit recognitions and expectations, yield, which occurs when colleagues choose how to perform a task, is also an essential factor for the OT release. In particular, yield concerns the acceptance of different execution methods, facilitates the control of work and reduces the perception of high workloads, improves their management and promotes a vision of error as a learning opportunity. Another critical factor for the release of OT and, therefore, for the promotion of trust is transfer ability,

32

Michela Balconi

which promotes the self-management of work by allowing collaborators to decide for what project work. Therefore, transfer ability allows developing a greater mastery of different skills, allowing collaborators to effectively use different competencies and experiences, reduce uncertainty and chronic stress, and increase teamwork motivation in completing projects (Zak 2014). High levels of commitment at work are not only promoted by professional development but also by personal growth. Indeed, the ability to bring energy and passion to working is inhibited by a dysfunctional personal life. For this reason, organizations that promote high levels of trust invest in the professional and personal growth of employees, subsidizing their growth opportunities and measuring them. In fact, when organizations invest in colleagues’ professional growth and development, this represents an important sign of trust. High-investment organizations have been shown to retain talented team members longer (Zak 2017). Furthermore, to foster high levels of trust, organizations need to be led by natural and reliable leaders. Instead of being directive, natural leaders ask for help, are open to discussion, and accept positive or negative decisions’ results. Indeed, as demonstrated by previous studies, vulnerable leaders promote OTs’ release in observers (Zak, Kurzban, and Matzner 2004, 2005) and appear more sympathetic, empathetic, and able to be forgiven when committed errors. However, vulnerable leaders only generate trust if they are perceived as competent. Conversely, incompetent leaders, who ask others for help, are not perceived as integrity and trustworthy. So, except in serious situations where leaders may impose the need for some changes, in other situations, leaders who admit not having all the answers create greater trust with the work team, involving their collaborators more in the realization of business objectives. Therefore, in light of this evidence, developing a climate of greater trust within the organizational context has positive effects on the work team. Indeed, in high-trust environments, people can discuss and resolve problems productively and respectfully, while in low-trust environments, conflict is institutionally arbitrated in a slow and costly manner.

Trusting and Rewarded Brains

33

Furthermore, low levels of trust lead individuals, who are primarily motivated by fear, to focus their energies on their protection and survival. In this case, mental resources are not used to focus on creativity and excellence but are spent on vigilance, safety, and survival. Indeed, the human brain, having limited resources, quickly adapts to the environment from which it is stimulated and automatically creates prejudices and behaviors that are difficult to eliminate. Finally, another essential factor for promoting trust is related to the empowerment of others. It has been observed how high levels of autonomy are connected with higher productivity and employee morale. Conversely, as a study of British employees observed, low levels of autonomy are more associated with the risk of developing cardiovascular disease and a higher mortality rate. Indeed, higher stress levels suppress OT and promote cortisol release, the primary human stress hormone, which produces a series of responses that damage the heart and other organs. Autonomy, therefore, appears to be an essential factor for the promotion of trust since, as the human being is not primarily configured as “homo economicus,” innately selfish, but as “homo reciprocans,” who trust others when trust is given. So the human brain works like this: it only trusts when trust is returned.

4. THE VALUE OF PROMISES IN PROMOTING TRUST In addition to the above, recent studies have observed that another critical factor in promoting trust appears to be the promise. Indeed, the promise was one of the primary means of establishing social contracts in society before developing complex social and legal systems. In fact, there were basic forms of cooperative agreements in early human society to maintain social connections, such as trust and cooperation. Like these forms of primitive agreement, the promise is expressed orally, not binding, but it conveys information on an individual’s reliability in social interactions. In fact, even in contemporary society, the promise, despite its non-exhaustive nature, but given its simplicity, validity, and efficiency,

34

Michela Balconi

represents the basis of various social exchanges. Keeping one’s word and then orally guaranteeing certain subsequent acts is configured as a powerful social norm, informing about a person’s reliability. The sense of trust and cooperative behavior at the basis of social relationships was investigated in behavioral and experimental economics research through the use of TG. In particular, the TG consists of a strategic game in which two individuals are involved, one of whom receives the role of “investor” and the other of “trustee.” The game requires that the investor be given a series of tokens that must decide whether to keep all of them or invest a part in the trustee, knowing that the tokens invested are multiplied. At this point, the trustee can decide whether to keep all the multiplied tokens or to repay the investor with a certain amount of tokens. In the TG experimental setting, players’ monetary exchanges are not directly affected by factors such as revenge, reputation, and punishment. This fact implies that there is, on a theoretical level, no valid economic reason that drives the trustee to return the payment received from the investor rationally. On these assumptions, referring to the Nash equilibrium, the investor should choose to keep all the tokens supplied for himself without investing them. Despite the predictions of classic game theory, it has been observed that many investors have invested considerable sums and many trustees have exhibited some degree of reciprocity (Declerck, Boone, and Emonds 2013). This shows how investors’ and trustees’ decisions can be influenced by social preference, trust, and reliability. Specifically, some neuroimaging studies have investigated the neural correlates of trustees’ decisions to keep or break a promise (Baumgartner et al. 2009). These studies found that breach of a promise causes emotional conflict related to non-obedience to social norms, as evidenced by increased activation of the dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (ACC), and amygdala. Furthermore, the deliberate violation of a promise can be predicted by the activation of the anterior insula, the ACC, and the inferior frontal gyrus before the action takes place. This evidence then informs us how the trustees perceive the promise and the breach of the promise. On the contrary, concerning investors, there is still no clear evidence about

Trusting and Rewarded Brains

35

cerebral correlates associated with decisions to keep or break a promise (Ma et al. 2014). Overall, the evidence from these studies demonstrates how making a promise promotes cooperative behavior and that individuals’ brains must believe in the need to cooperate to become rewarded brains to trust.

REFERENCES Balconi, Michela, and Adriana Bortolotti. 2012. “Empathy in Cooperative versus Non-Cooperative Situations: The Contribution of Self-Report Measures and Autonomic Responses.” Applied Psychophysiology Biofeedback 37:161–69. doi:10.1007/s10484-012-9188-z. ———. 2013. “Emotional Face Recognition, Empathic Trait (BEES), and Cortical Contribution in Response to Positive and Negative Cues. The Effect of RTMS on Dorsal Medial Prefrontal Cortex.” Cognitive Neurodynamics 7:13–21. doi:10.1007/s11571-012-9210-4. Balconi, Michela, Adriana Bortolotti, and Ludovica Gonzaga. 2011. “Emotional Face Recognition, EMG Response, and Medial Prefrontal Activity in Empathic Behaviour.” Neuroscience Research 71:251–59. doi:10.1016/j.neures.2011.07.1833. Balconi, Michela, and Ylenia Canavesio. 2013a. “Emotional Contagion and Trait Empathy in Prosocial Behavior in Young People: The Contribution of Autonomic (Facial Feedback) and Balanced Emotional Empathy Scale (BEES) Measures.” Journal of Clinical and Experimental Neuropsychology 35:41–8. doi:10.1080/13803395.2012. 742492. ———. 2013b. “Prosocial Attitudes and Empathic Behavior in Emotional Positive versus Negative Situations: Brain Response (ERPs) and Source Localization (LORETA) Analysis.” Cognitive Processing 14:63–72. doi:10.1007/s10339-012-0525-1. ———. 2014. “High-Frequency RTMS on DLPFC Increases Prosocial Attitude in Case of Decision to Support People.” Social Neuroscience 9:82–93. doi:10.1080/17470919.2013.861361.

36

Michela Balconi

———. 2016. “Is Empathy Necessary to Comprehend the Emotional Faces? The Empathic Effect on Attentional Mechanisms (Eye Movements), Cortical Correlates (N200 Event-Related Potentials) and Facial Behaviour (Electromyography) in Face Processing.” Cognition and Emotion 30:210–24. doi:10.1080/02699931.2014.993306. Balconi, Michela, Giulia Fronda, and Maria Elide Vanutelli. 2019. “A Gift for Gratitude and Cooperative Behavior: Brain and Cognitive Effects.” Social Cognitive and Affective Neuroscience 14:1317–27. doi:10.1093/ scan/nsaa003. ———. 2020. “When Gratitude and Cooperation between Friends Affect Inter-Brain Connectivity for EEG.” BMC Neuroscience 21:1–12. doi:10.1186/s12868-020-00563-7. Balconi, Michela, and Uberto Pozzoli. 2009. “Arousal Effect on Emotional Face Comprehension. Frequency Band Changes in Different Time Intervals.” Physiology and Behavior 97:455–62. doi:10.1016/j. physbeh.2009.03.023. Balconi, Michela, and Maria Elide Vanutelli. 2017. “Interbrains Cooperation: Hyperscanning and Self-Perception in Joint Actions.” Journal of Clinical and Experimental Neuropsychology 39:607–20. doi:10.1080/13803395.2016.1253666. Barraza, Jorge A., and Paul J. Zak. 2009. “Empathy toward Strangers Triggers Oxytocin Release and Subsequent Generosity.” Annals of the New York Academy of Sciences 1167:182–89. doi:10.1111/j.17496632.2009.04504.x. ———. 2013. “Oxytocin Instantiates Empathy and Produces Prosocial Behaviors.” In Oxytocin, Vasopressina and Related Peptides in the Regulation of Behavior, edited by Elena Choleris, Donald W. Pfaff, and Martin Kavaliers, 331–42. Cambridge, New York: Cambridge University Press. Batson, C. Daniel. 2009. “These Things Called Empathy: Eight Related But Distinct Phenomena.” In Social Neuroscience. The Social Neuroscience of Empathy, edited by Jean Decety and William Ickes, 3–15. Cambridge, MA: MIT Press.

Trusting and Rewarded Brains

37

Baumgartner, Thomas, Urs Fischbacher, Anja Feierabend, Kai Lutz, and Ernst Fehr. 2009. “The Neural Circuitry of a Broken Promise.” Neuron 64:756–70. doi:10.1016/j.neuron.2009.11.017. Berg, Joyce, John Dickhaut, and Kevin McCabe. 1995. “Trust, Reciprocity, and Social History.” Games and Economic Behavior 10:122–42. doi:10.1006/game.1995.1027. Carlo, Gustavo, Anne Hausmann, Stacie Christiansen, and Brandy A. Randall. 2003. “Sociocognitive and Behavioral Correlates of a Measure of Prosocial Tendencies for Adolescents.” Journal of Early Adolescence 23:107–34. doi:10.1177/0272431602239132. Chauhan, Bina, Christopher J. Mathias, and Hugo D. Critchley. 2008. “Autonomic Contributions to Empathy: Evidence from Patients with Primary Autonomic Failure.” Autonomic Neuroscience: Basic and Clinical 140:96–100. doi:10.1016/j.autneu.2008.03.005. Decety, Jean, and Philip L. Jackson. 2006. “A Social-Neuroscience Perspective on Empathy.” Current Directions in Psychological Science 15:54–8. doi:10.1111/j.0963-7214.2006.00406.x. Decety, Jean, and Margarita Svetlova. 2012. “Putting Together Phylogenetic and Ontogenetic Perspectives on Empathy.” Developmental Cognitive Neuroscience 2:1–24. doi:10.1016/j.dcn. 2011.05.003. Declerck, Carolyn H., Christophe Boone, and Griet Emonds. 2013. “When Do People Cooperate? The Neuroeconomics of Prosocial Decision Making.” Brain and Cognition 81:95–117. doi:10.1016/j.bandc.2012. 09.009. Goldman, Alvin I., and Chandra Sekhar Sripada. 2005. “Simulationist Models of Face-Based Emotion Recognition.” Cognition 94:193–213. doi:10.1016/j.cognition.2004.01.005. Lamm, Claus, C. Daniel Batson, and Jean Decety. 2007. “The Neural Substrate of Human Empathy: Effects of Perspective-Taking and Cognitive Appraisal.” Journal of Cognitive Neuroscience 19:42–58. doi:10.1162/jocn.2007.19.1.42. Ma, Qingguo, Huijian Fu, Tao Xu, Guanxiong Pei, Xiaojian Chen, Yue Hu, and Chao Zhu. 2014. “The Neural Process of Perception and

38

Michela Balconi

Evaluation for Environmental Hazards: Evidence From Event-Related Potentials.” Neuroreport, 25:607–11. doi:10.1097/WNR.000000000 0000147. Macaluso, Janet C. 2003. “Leading with Empathy Model What You Want to See in Others.” Executive Excellence 20:9. Neff, Kristin D., Elliot Turiel, and Daphne Anshel. 2002. “Reasoning about Interpersonal Responsibility When Making Judgments about Scenarios Depicting Close Personal Relationships.” Psychological Reports 90:723–42. doi:10.2466/pr0.2002.90.3.723. Nowak, Martin A., and Karl Sigmund. 2005. “Evolution of Indirect Reciprocity.” Nature 437:1291–98. doi:10.1038/nature04131. Nummenmaa, Lauri, Enrico Glerean, Mikko Viinikainen, Iiro P. Jääskeläinen, Riitta Hari, and Mikko Sams. 2012. “Emotions Promote Social Interaction by Synchronizing Brain Activity across Individuals.” Proceedings of the National Academy of Sciences of the United States of America 109:9599–604. doi:10.1073/pnas. 1206095109. Preston, Stephanie D., and Frans B. M. de Waal. 2002. “Empathy: Its Ultimate and Proximate Bases.” Behavioral and Brain Sciences 25:1– 20. doi:10.1017/S0140525X02000018. Singer, Tania, Romana Snozzi, Geoffrey Bird, Predrag Petrovic, Giorgia Silani, Markus Heinrichs, and Raymond J. Dolan. 2008. “Effects of Oxytocin and Prosocial Behavior on Brain Responses to Direct and Vicariously Experienced Pain.” Emotion 8:781–91. doi:10.1037/ a0014195. Skuse, David H., and Louise Gallagher. 2009. “DopaminergicNeuropeptide Interactions In The Social Brain.” Trends in Cognitive Sciences 13:27–35. doi:10.1016/j.tics.2008.09.007. Spinella, Marcello. 2005. “Prefrontal Substrates of Empathy: Psychometric Evidence in a Community Sample.” Biological Psychology 70:175–81. doi:10.1016/j.biopsycho.2004.01.005. Vanutelli, Maria Elide, and Michela Balconi. 2015. “Empathy and Prosocial Behaviours. Insights from Intra- and Inter-Species Interactions.” Rivista Internazionale Di Filosofia e Psicologia

Trusting and Rewarded Brains

39

[International Journal of Phylosophy and Psychology] 6:88–109. doi:10.4453/rifp.2015.0007. Zajonc, B. Robert. 1998. “Emotions.” In The Handbook of Social Psychology, edited by Daniel Todd Gilbert, Susan T. Fiske, and Gardner Lindzey, 591–632. Boston: McGraw-Hill. Zak, Paul J. 2005. “Trust: A Temporary Human Attachment Facilitated By Oxytocin.” Behavioral and Brain Sciences 28:368–69. doi:10. 1017/S0140525X05400060. ———. 2012. The Moral Molecule: The Source of Love and Prosperity. New York: Random House. ———. 2014. Why Your Brain Loves Good Storytelling. Cambridge, MA: Harvard Business Review. ———. 2017. “The Neuroscience of Trust.” Harvard Business Review 95:84–90. Zak, Paul J., and Stephen Knack. 2001. “Trust and Growth.” Economic Journal 111:295–321. doi:10.1111/1468-0297.00609. Zak, Paul J., Robert Kurzban, and William T. Matzner. 2004. “The Neurobiology of Trust.” Annals of the New York Academy of Sciences 1032:224–27. doi:10.1196/annals.1314.025. ———. 2005. “Oxytocin Is Associated with Human Trustworthiness.” Hormones and Behavior 48:522–27. doi:10.1016/j.yhbeh.2005.07.009.

In: Neuromanagement Editor: Michela Balconi

ISBN: 978-1-53619-562-0 © 2021 Nova Science Publishers, Inc.

Chapter 3

TO BE OR NOT TO BE MORAL IN ORGANIZATIONS? Michela Balconi*, PhD and Giulia Fronda International Research Center for Cognitive Applied Neuroscience (IrcCAN), Catholic University of the Sacred Heart, Milan, Italy Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy

ABSTRACT Moral decision-making is a complex process composed of different decisional systems supported by controlled, automatic, cognitive, and emotional processes. Specifically, moral decision-making regards the evaluation of the possible consequences, implications, and acceptability of our own decisions on others’ behavior. Neuroscience, thanks to the use of new and innovative methods, has allowed investigating the implicit and explicit correlates underlying moral decision-making in different contexts, such as the organizational one. Especially within the organizational context, neuroscience has proved useful for investigating *

Corresponding Author’s E-mail: [email protected].

42

Michela Balconi and Giulia Fronda the cerebral and peripheral mechanisms underlying the moral decision process through the use of classical paradigms consisting of monetary choices and social decision tasks. Therefore, the use of neuroscience in the investigation of company moral behavior has allowed highlighting the brain areas most involved in moral judgment and the analysis of decisions’ costs and benefits and the bodily responses associated with decisions more engaging for individuals.

1. THE MORAL DECISION-MAKING AS A DUAL PROCESS: METHODOLOGICAL ISSUES Recently, several fields of neuroscientific investigation have focused their interest on the moral decision process. Indeed, moral decision-making is configured as a complex process relating to the acceptability of decisions, judgments, or evaluations with moral implications (Garrigan, Adlam, and Langdon 2018). Therefore, moral decision-making refers to the area of ethical behavior that concerns the implementation of personal or directed towards others behaviors that are normatively appropriate, being the result of communicative, strengthening, and decision-making processes (Mayer et al. 2012). The multidimensionality of decision-making with moral implications has extended the interest in investigating this construct in various daily life situations, such as the company’s moral decisions. In particular, within the organizational context, the assumption or not of leaders’ ethical behavior can have positive or negative effects on the entire organizational culture, influencing employees and consumers’ behavior, their health and safety, and the well-being and quality of the entire organization (Balconi and Fronda 2019, 2020; Minas et al. 2014). The possible business implications and the impact of moral decisions have recently been explored by psychological and neuroscientific fields, interested in the neurobiological foundations of emotions underlying moral decision-making. In particular, the neuroscientific contribution to the study of moral emotions, through observing the brain structures involved in this process, has made it possible to investigate the individual and situational influencing variables. This

To Be or Not to Be Moral in Organizations?

43

investigation has allowed observing this construct more in-depth than previous studies, only focused on applying different theoretical guides in daily management decisions (Minas et al. 2014). Furthermore, the recent development of theoretical models investigating the individual and organizational factors influencing moral decision-making has shed light on company moral behavior. Firstly, it was thoroughly investigated whether moral reasoning can be considered a dependent variable or an antecedent of organizational behavior. In this regard, specifically, some studies referring to intuitionist decision-making models have proposed that decision-making does not derive from an a priori ethical reasoning but from the attribution of meaning that occurred due to the fact happened. Therefore, according to these models, decisions can be considered intuitively elaborated, without conscious awareness, on which rationalizations aimed at the appearance and social approval are subsequently built (Haidt and Bjorklund 2008; Sonenshein 2007). Secondly, moral decision-making has been investigated as a rational process. Concerning this point, while traditional psychological theories have emphasized the function of higher cognition in moral judgment (Kohlberg 1969), the role of emotions in moral decision-making has only recently been emphasized. This investigation has led to the development of new theories highlighting the moral decision process as a synthesis of cognition and emotion. In particular, according to this research flows, moral decision-making would be mediated by cognitive processes, supported by rational and deductive reasoning on costs/benefits of moral decisions, and by emotional ones, related to the evaluation of socially relevant stimuli as right or wrong (Brand, Labudda, and Markowitsch 2006; Greene et al. 2004; Loewenstein et al. 2001). In this approach, decision-makers can be considered as both rational and emotional agents. Therefore, this perspective, associated with neuroscience and neuroethics’ findings, has led to developing a model of moral decision-making double processing, which explains how decision-makers would refer to two processing modes in their decisions. In particular, the first, defined as XSystem, consists of an automatic and intuitive processing mode; the

44

Michela Balconi and Giulia Fronda

second, defined as C-System, would refer to a cognitive processing modality involving higher-order conscious reasoning processes. Specifically, it has been observed that the automatic and intuitive processing mode is considered as a means that speeds up the decisionmaking process in complex situations dictated by time pressure. On the contrary, the second elaboration method is more complex and implemented with hypothetical or real moral dilemmas without a ready solution (Oliveira-Souza, Zahn, and Moll 2015). Therefore, the processes, situational, and individuals variables underlying moral decision-making have been widely explored by neuroscientific studies through social decision-making tasks involving the use of monetary paradigms developed in the field of game theory (Sanfey 2007). Although useful for investigating players’ choice behavior, these paradigms have not allowed investigating the emotional correlates underlying moral decision-making thoroughly. For this reason, neuroscience has used social decision tasks such as the Ultimatum Game (UG) or its variants (Balconi and Fronda 2019; Sanfey et al. 2003), consisting of monetary paradigms that allow the investigation of fairness and altruistic behavior perception requiring two players (proposer, who proposes how to divide the sum of money, and respondent, who can accept or reject the offer) to divide a sum of money. Indeed, neuroscientific studies, using certain paradigms, have made it possible to investigate in depth the neurophysiological correlates and processes underlying moral decision-making, informing about interpersonal dynamics and elaboration and emotional mechanisms (Wagner et al. 2012). In particular, previous research has shown the advantages of using neuroscientific approaches with specific tools, such as psychophysiological, electrophysiological, and neuroimaging techniques, to observe brain and body mechanisms associated with moral decisionmaking. For example, some studies that have observed the brain correlates underlying moral decision-making funded a greater involvement of the ventromedial prefrontal cortex (VMPFC) and the dorsolateral prefrontal cortex (DLPFC). Specifically, the VMPFC is more implicated in social and

To Be or Not to Be Moral in Organizations?

45

cultural norms coding, moral value attribution, and mental representation. The DLPFC, instead, is mainly involved in cognitive control processes, utilitarian analysis, and problem-solving (Greene et al. 2004). In addition to brain activity, other neuroscientific studies have also investigated peripheral activity’s variations during moral decision-making, observing an increase in skin conductance response (SCR) and heart rate (HR) during unfairness perception (Balconi and Fronda 2019; Sarlo et al. 2013). Finally, a recent study by Balconi and Fronda (2019, 2020), with the use of electroencephalography (EEG), functional Near-Infrared Spectroscopy (fNIRS), and biofeedback, has investigated the neural and peripheral correlates associated with organizational moral decisionmaking, recording the activity of 14 managers during a moral decision task. This task, consisting of a modified version of the UG, proposed individuals to make decisions concerning three different choice contexts (professional, company, and prosocial) and three types of offers proposed (fair for the respondent, unfair for the respondent, and neutral) (Figure 1). In particular, the contexts of choice proposed managers to accept or reject a sum of money for various reasons: the first (professional) for a job done together with a colleague; the second (company) for the introduction of some benefits in the working environment; the third (prosocial) to financially help a relative of a sick colleague. A different brain and peripheral activity has emerged from the study results concerning the different contexts of choice and offers proposed. Specifically, an increase in frontal EEG activity has emerged during moral decision-making. A different frontal hemispheric lateralization has emerged concerning the various offers’ types, following individuals’ emotional evaluation and personal interests. Instead, regarding hemodynamic response, a greater involvement of VMPFC, DLPFC, and superior temporal sulcus was observed during moral decision-making. Finally, autonomic activity has revealed an increase in skin conductance and cardiovascular activity concerning individuals’ emotional response and cognitive assessment processes.

46

Michela Balconi and Giulia Fronda

Figure 1. Representation of the modified version of the UG used for the investigation of moral decision-making (Balconi and Fronda 2019, 2020).

2. COGNITION AND EMOTION IN MORAL DECISIONMAKING: THE ROLE OF EMPATHY AT THE WORKPLACE In recent years, therefore, neuroscience has made it possible to investigate in-depth the processes underlying moral decision-making and the role of emotions and empathy in fair and unfair organizational decisions (Balconi, Finocchiaro, and Campanella 2014). In particular, the perception of fairness, understood as adaptive mechanisms driven by justice and cooperation, and unfairness, associated with the experimentation of negative emotions involving the rejection of unfair offers, has been extensively investigated. Specifically, unfairness perception, which consists of an aversion to inequality perceived by individuals, has attracted particular attention within the organizational context (Strobel et al. 2011; White et al. 2014). From a neuroscientific point of view, indeed, it has been observed that unfair offers activate those brain regions, such as the striatal cerebral area, involved in individuals’ gratification and reward mechanisms. In particular, a study by Tabibnia, Satpute, and Lieberman (2008), using a paradigm similar to Sanfey and colleagues (2003), has demonstrated the involvement of cerebral areas and structures involved in reward mechanisms, like the insula, and the presence

To Be or Not to Be Moral in Organizations?

47

of negative emotions during unfairness perception, that always led individuals to the rejection of unfair offers. On the contrary, individuals who accepted unfair offers showed the neural reward network’s activation, observable from an increase of ventrolateral prefrontal cortex activity. Indeed, participants who accepted unfair offers ignored their negative emotions to perceive an economic gain that was not satisfactory. On the contrary, this study has observed how the fairness perception, consisting of an automatic, intuitive, and intrinsically rewarding process which almost always led to accepting fair offers, caused greater activation of different brain areas and structures, like the amygdala, the VMPFC, and the ventral striatum. Moreover, Hsu, Anen, and Quartz (2008) have investigated the association between fairness and efficiency of distributional outcomes by asking participants to distribute limited resources among third parties. This study has revealed that efficiency and fairness were elaborated in different cerebral areas. In particular, efficiency was associated with the putamen’s activity, while fairness and unfairness perception was associated with the insula’s activity. Indeed, as demonstrated by this study, options perceived as unfair produced greater activation of the insula and were not selected even when they produced more efficient results than fair options. Therefore, this study has demonstrated how fairness perception results from moral intuition and emotional processes, rather than ethical principles and cognitive considerations on economic efficiency. Furthermore, the fairness and unfairness perception appears to be mediated by empathic processes, essential for moral behavior (Van Vugt et al. 2011). In particular, empathy, as a cognitively and emotionally mediated behavioral response of an individual towards another (Balconi and Bortolotti 2012; Batson 2010; Pavlovich and Krahnke 2012), appears to influence moral decision-making above within the organizational context. In this regard, some researchers have investigated the effects of emotions such as empathy and compassion in moral decision-making (Eisenbeiss, Maak, and Pless 2014; Hofmann and Baumert 2010). For example, Mencl and May (2009) have observed how empathy is configured as a moral sentiment, highly correlated with greater awareness

48

Michela Balconi and Giulia Fronda

of the possible negative consequences of stakeholders’ decisions. Besides, Eisenbeiss, Maak, and Pless (2014) have also demonstrated that empathy and compassion represent the basis for compassionate solutions to moral dilemmas. Therefore, within the organizational context, empathy allows to investigate choices’ social benefits better, observing the implications, consequences, and responsibilities that decisions can have on others’ wellbeing. Low levels of empathy, indeed, lead to less consideration of decisions’ moral implications and the use of a utilitarian decision-making process. In particular, concerning the role of empathy in morality, the involvement of specific brain regions has been observed, such as the insula, which is part of the cerebral cortex, and the anterior cingulate cortex (ACC) and the amygdala (Decety and Lamm 2006), belonging to the limbic system. Specifically, the ACC, located within the forehead and behind the brain’s frontal lobe, has connections with the limbic system and the prefrontal cortex and is involved in emotional mechanisms, reward anticipation, decision-making, and impulse control. On the other hand, the amygdala is located deep in the brain and is involved in the processing of emotional stimuli and others’ emotions sharing (Adolphs et al. 1994). This evidence highlights how moral decision-making represents a complex process. To better investigate moral cognition, some authors such as Greene et al. (2001) have observed some individuals’ neural response during different dilemmas, from non-moral ones to variations of the “trolley dilemma”. This dilemma, specifically, requires individuals to choose whether to save the lives of five people from being hit by a cart by sacrificing one. In particular, this study has revealed the activation of specific brain areas, such as the angular gyrus, the posterior cingulate gyrus, and the medial frontal gyrus, during moral dilemmas with a high degree of personal involvement. Furthermore, it was found that during the personal condition, the areas associated with cognition were less involved. Therefore, different brain regions support cognitive and emotional mechanisms involved in moral decision-making and the regulation of emotions and thoughts appears to be fundamental for a good moral decision process (Waldman et al. 2017).

To Be or Not to Be Moral in Organizations?

49

3. THE INFLUENCE OF UNCONSCIOUS PROCESSES IN MORAL DECISION-MAKING In addition to cognitive and emotional mechanisms, moral decisionmaking appears to be strongly influenced by unconscious and implicit cerebral processes (Burns and Bechara 2007). Indeed, individuals’ behaviors result from the interaction between different systems and neurophysiological processes influencing our behaviors’ directionality (Johnson 2004). This antecedent is essential because it allows understanding how logic behaviors are often different from their real causes, pushing individuals to take decisive actions in uncertain conditions driven by subjective realities different from objective ones. These mechanisms are essential in moral decision-making and can have significant consequences, especially within the organizational context. Therefore, this fact can lead to a logical, consistent, and predictive description of individual future results’ intentions and actions, but to inaccurately describe the different mechanisms underlying our behaviors. Moreover, this fact explains why many individuals cannot communicate their decision-making processes that occur unconsciously and are therefore inaccessible to conscious thinking. It also informs why it is often difficult for individuals to modify and recognize their discriminatory behaviors. Indeed, individuals tend to underestimate the role of the implicit process by emphasizing conscious deliberation and intention. In this regard, neuroscience observes that most of what is perceived as a unified experience is the result of conscious and unconscious neural processes, which are not always unified (Balconi and Pozzoli 2005; Blackmore 2005), leading individuals to believe to know the real reasons behind their actions also driven by unconscious processes. This process is defined as the “binding problem” related to the awareness that individuals’ perceptions, thoughts, and decisions derive from unitary processes, although the role of disunited and unconscious neural mechanisms. In particular, the perception of this sense of unity is often adaptive as it allows individuals to simplify their experience, facing life situations with greater confidence. However, this phenomenon has other implications. Specifically, individuals’ thought

50

Michela Balconi and Giulia Fronda

internal functioning, being inaccessible to direct introspection or external relationship, cannot be assessed using ordinary self-observation procedures (Sanfey et al. 2003).

4. THE NEURAL CORRELATES OF MORALITY AND MORAL DECISION-MAKING In the context of morality, fairness and unfairness perception appears to be associated with the activation of different brain areas (Tabibnia, Satpute, and Lieberman 2008). In particular, fairness perception activates specific brain regions, such as the left lingual gyrus, the left hippocampus, and the bilateral insula (Rilling, King-Casas, and Sanfey 2008). On the contrary, unfairness perception activates the anterior cingulate cortex and the DLPFC, involved in processes concerning the detection of cognitive conflicts and the set objectives’ control. In particular, both the DLPFC, involved in short and long-term benefit choices’ evaluation, and the VMPFC, involved in decisions’ judgment and emotional processing, appear to be mainly involved in moral decision-making (Hare et al. 2010; Levy and Glimcher 2011). Other studies, using functional Magnetic Resonance Imaging (fMRI) to investigate the brain responses associated with judging others’ positive, negative, and neutral actions and considering decisions’ moral implications, have observed the implication of other cerebral areas in moral decision-making. In particular, during decisions about a goal to be finalized appear to be involved the DLPFC, the superior medial prefrontal cortex (SMPFC), the parietal lobe (Amodio and Frith 2006; Jack, Greenwood, and Schapper 2012), the premotor and sensorimotor cortex, and the striatum (Balleine, Delgado, and Hikosaka 2007; Poldrack et al. 2001). Most of these structures are located in the prefrontal cortex (PFC), situated in the brain’s front behind the forehead. Specifically, the PFC is divided into DLPFC, ventrolateral (VPFC), and orbitofrontal (OFC) and is involved in the control and monitoring higher cognitive functions. The DLPFC, located in the upper part of the PFC and connected to the brain areas of attention and cognition, is involved in the

To Be or Not to Be Moral in Organizations?

51

cognitive processes associated with the decision, and it appears to be activated when individuals consider multiple sources of information to make a decision, especially in uncertain conditions, in which activation of the right side of the DLPFC is observed. On the contrary, the VPFC, located on the side of the forehead, appears to be associated with the brain regions involved in emotional processes. Finally, the OFC, located above the eyes, is involved in decisions involving the recovery of partially observable consequences. Furthermore, this brain region appears to be involved in integrating emotional information from limbic areas, useful for determining the value of decisions’ results. The VMPFC and ACC are also involved in integrating cognition and emotion (Decety and Svetlova 2012) (Figure 2).

5. HOW PERSONAL OR OTHER INTERESTS INFLUENCE MORAL DECISION-MAKING As emerged in the previous paragraphs, fairness perception has been thoroughly investigated by some studies that have observed its association with self-referential thinking and the theory of mind (ToM) (Robertson et al. 2007). In particular, the latter consists of a partially automatic process that allows individuals to infer and simulate others’ mental states, feeling empathy. Indeed, ToM appears to be particularly associated with empathy, but allows us to simulate, in addition to others’ emotional responses, also their intentions, beliefs, and objectives (Gallagher and Frith 2003).

Figure 2. The figure represents the main cortical and subcortical brain areas involved in decision-making.

52

Michela Balconi and Giulia Fronda

The influence of ToM in fairness perception has been investigated, for example, by a study by Singer et al. (2006) that has observed how individuals were more likely to sacrifice their personal gain in order to punish injustice only when victims of injustice were perceived as loyal players. Furthermore, recent studies have observed how moral sensitivity to fairness is based both on individuals’ emotional responses, but also on cognitive inferences concerning others’ emotional states. Some studies, in particular, have reported the activation of different brain regions concerning the presentation of fairness’ moral dilemmas and economic gain, highlighting a cerebral modular model. Specifically, different research has shown a greater involvement of individuals concerning injustice towards themselves rather than others (Tabibnia, Satpute, and Lieberman 2008). Indeed, it has been observed that often, although individuals are concerned that others are being treated fairly, they ignore the unfair treatment of members of the external group (Clayton and Opotow 2003). Furthermore, it was observed that the fairness perception also derives from unconscious brain processes that strongly influence this construct, emphasizing once again how implicit processes influence equity’s considerations. Personal interest is configured as the main human drive and their consideration is fundamental in the sphere of morality concerning the possible implications within the organizational context. Therefore, given the centrality of the self in individual concerns, the fairness perception and personal interests’ processing should be mediated by the same brain regions. In this regard, a study by Ferraro, Pfeffer, and Sutton (2005) has shown that leaders’ preconceptions about human nature can influence the organizational management’s strategies. Specifically, if leaders are motivated by self-centered and personal interests, they design management systems based on this aspect. In addition to leaders’ preconceptions, the fairness elaboration for oneself and others can also influence the implicit automatic reactions to unfairness perception within the organizational context (Frith and Frith 2008). Indeed, it has been observed that the neural processing of the fairness perception in different social contexts can be influenced by implicit negative attitudes.

To Be or Not to Be Moral in Organizations?

53

Within the organizational context, diversity within the work team and the company can represent a barrier to the fairness perception. For example, the perception of extraneousness concerning the reference group would disable individuals’ implementation of empathic responses and justice’s development. This fact explains why, at times, unjust and discriminatory actions are taken towards some individuals of a particular group. Thus, neuroscience, by deepening the investigation of some theoretical aspects, allows us to follow and suggest new research directions, leading to consider new elements compared to classical theories. For example, motivation and organizational behavior theories have always considered mostly explicit individuals’ behaviors and decisions, despite the presence of evidence regarding the role of implicit and unconscious goals and processes that can influence motivational drive and individuals’ behavior (Latham, Stajkovic, and Locke 2010). Thus, neuroscience allows us to understand that brain research must largely consider the existence of unconscious processes, which should be considered by organizations along with deliberative ones.

6. THE INFLUENCE OF SELF-MONITORING SKILLS AND MORAL SELF IN MORAL DECISION-MAKING As noted above, self-monitoring plays an essential role in morality. In particular, self-monitoring consists of the individual’s ability to seek internally or externally stimuli to implement appropriate behaviors concerning a given situation. Besides, self-monitoring also consists in the individual’s ability to exercise conscious control over their expression, presentation, and the manifestation of emotional expressions and behaviors (Snyder 1974). Within the organizational context, self-monitoring allows leaders to refer to their values to guide future decisions or look for appropriate behavioral signals in the external situation. In addition to the leader’s self-monitoring capabilities, the ethical organizational climate concerns the rules implemented for resolving ethical issues and is

54

Michela Balconi and Giulia Fronda

characterized by ethical decision norms and the reference group used by organizational members. Finally, the organizational ethical climate is influenced by ethical leadership, which is the concept of the “moral self”. The latter is characterized as a multicomponent phenomenon composed of stable affective and cognitive components that influence moral thoughts and actions and is driven by a series of behaviors, such as the disposition of moral judgment deriving from neural activity that influences leaders’ ideology (Lee, Senior, and Butler 2012; Senior, Lee, and Butler 2011). The moral self is supported by the “default mode network” (DMN), involved in the processes of moral reasoning that influence leadership and ethical ideology (Boyatzis, Rochford, and Jack 2014; Koenigs et al. 2007), which can be seen from the relativist orientation as the set of ethical norms and moral behaviors that depend on the individual situation. On the contrary, the idealist orientation considers ethical ideology as the ensemble of moral principles and universal ethical rules that must be respected in all contexts. Therefore, according to the relativist orientation, individuals would decide whether to follow specific principles based on the importance attributed to their personal interests and goals. Relativist leaders, therefore, show themselves more inconsistent in making moral decisions, with a lesser ability to establish principles and norms within the group. On the contrary, according to the idealistic orientation, individuals would decide in all situations, concerning moral and ethical principles, not to cause harm to others, even without pursuing their gains. Therefore, in predicting future ethical leadership skills, the interaction between neural activity and ideological/cognitive aspects is considered. Previous studies, indeed, have already observed that some leaders’ characteristics, such as personal dispositions, orientations, and personality traits, helped define future leadership skills. For example, a study by Brown and Treviño (2006) has predicted future leadership skills by measuring leaders’ personalities. Other studies, instead, have considered moral identity and moral reasoning skills (Jordan et al. 2013; Mayer et al. 2012). In addition to these, a study by Waldman and colleagues (2017) has predicted future leadership skills, social cognition, and organizational behavior by recording individuals’ neural activity using a neuroscientific

To Be or Not to Be Moral in Organizations?

55

approach. Indeed, the use of a neuroscientific approach allows understanding better the neurophysiological basis of leadership skills (Healey and Hodgkinson 2014; Powell 2011), observing the intrinsic or responsive brain activity that informs about individuals’ mental and behavioral functioning (Balconi, Finocchiaro, and Campanella 2014; Balconi, Grippa, and Vanutelli 2015; Balconi, Vanutelli, and Grippa 2017; Raichle and Snyder 2007). The intrinsic brain activity is beneficial for observing individuals’ cognitive, emotional, and behavioral differences, such as ideology, ethical leadership, self-awareness, and self-regulation, which are better measured as stable traits than responses to temporary stimuli (Buckner and Carroll 2006). Specifically, we need to consider three aspects of brain activity that allow us to identify a reliable ethical leadership profile. The first concerns the ethical leadership profile most involved in this process and associated with some leadership characteristics; the second considers the neural connectivity; the third is related to hemispheric asymmetry. Referring in particular to the first aspect, the main involvement of the frontal area in the composition of the leader’s socialized vision was observed (Waldman, Balthazard, and Peterson 2011). In addition to the frontal cortex, the involvement of complex brain networks, such as DMN, has also been observed, involving in social cognition, self-projection, decision-making, and moral judgment (Buckner and Carroll 2006; Lindquist et al. 2012). The DMN, indeed, is composed of different brain regions such as the medial prefrontal cortex, the medial, lateral, and inferior parietal cortices, the medial temporal lobe, and the posterior cingulate (Raichle 2010). Referring to neural connectivity, on the other hand, intended as the synchronous activity of different brain regions, it can inform about cognition and leadership behavior (Balconi et al. 2019, 2020; Buckner, Andrews-Hanna, and Schacter 2008; Hannah et al. 2013). Neural connectivity can inform about the degree of similarity of simultaneous neurophysiological signals in two distinct brain regions (Thatcher, North, and Biver 2008). Some studies have shown that brain connectivity informs about different processes, such as monitoring the external environment,

56

Michela Balconi and Giulia Fronda

attention, moral awareness, and individuals’ processing and social competence (Buckner, Andrews-Hanna, and Schacter 2008; Schreiner et al. 2014). Finally, referring to hemispheric asymmetry, the latter informs about the processes, thinking, and processing behavior of the right and left hemispheres. In this regard, Hellige (1990) observed a greater activation of the left hemisphere in the rational and analytical evaluation processes underlying moral decision-making. Other studies, on the other hand, have shown the involvement of the right and left hemisphere in the processes of emotional processing, with greater activation of the right hemisphere in emotional regulation and moral judgment (Balconi, Grippa, and Vanutelli 2015; Bennet and Bennet 2008; Cacioppo, Berntson, and Nusbaum 2008). Therefore, emotional regulation is a fundamental element for ethical leadership, which is negatively affected by inadequate emotional management skills, leading to freezing in the face of intense moral situations and engaging in inappropriate behavior. A good corporate ethical climate is influenced by the organizational ethical culture, the individual components of the leader and their cerebral attitude.

REFERENCES Adolphs, Ralph, Daniel Tranel, Hanna Damasio. & Antonio R. Damasio. (1994). “Impaired Recognition of Emotion in Facial Expressions Following Bilateral Damage to the Human Amygdala.” Nature, 372, 669–72. doi:10.1038/372669a0. Amodio, David M. & Chris D. Frith. (2006). “Meeting of Minds: The Medial Frontal Cortex and Social Cognition.” Nature Reviews Neuroscience, 7, 268–77. doi:10.1038/nrn1884. Balconi, Michela. & Adriana Bortolotti. (2012). “Empathy in Cooperative versus Non-Cooperative Situations: The Contribution of Self-Report Measures and Autonomic Responses.” Applied Psychophysiology Biofeedback, 37, 161–69. doi:10.1007/s10484-012-9188-z. Balconi, Michela, Federico Cassioli, Giulia Fronda. & Maria Elide Vanutelli. (2019). “Cooperative Leadership in Hyperscanning. Brain

To Be or Not to Be Moral in Organizations?

57

and Body Synchrony during Manager-Employee Interactions.” Neuropsychological Trends, 26, 23–44. doi:10.7358/neur-2019-026bal2. Balconi, Michela, Roberta Finocchiaro. & Salvatore Campanella. (2014). “Reward Sensitivity, Decisional Bias, and Metacognitive Deficits in Cocaine Drug Addiction.” Journal of Addiction Medicine, 8, 399–406. doi:10.1097/ADM.0000000000000065. Balconi, Michela. & Giulia Fronda. (2019). “Physiological Correlates of Moral Decision-Making in the Professional Domain.” Brain Sciences, 9, 229. doi:10.3390/brainsci9090229. ———. (2020). “Morality and Management: An Oxymoron? FNIRS and Neuromanagement Perspective Explain Us Why Things Are Not like This.” Cognitive, Affective and Behavioral Neuroscience, 20, 1336–48. doi:10.3758/s13415-020-00841-1. Balconi, Michela, Elisabetta Grippa. & Maria Elide Vanutelli. (2015). “What Hemodynamic (FNIRS), Electrophysiological (EEG) and Autonomic Integrated Measures Can Tell Us about Emotional Processing.” Brain and Cognition, 95, 67–76. doi:10.1016/ j.bandc.2015.02.001. Balconi, Michela. & Uberto Pozzoli. (2005). “Morphed Facial Expressions Elicited a N400 ERP Effect: A Domain-Specific Semantic Module?” Scandinavian Journal of Psychology, 46, 467–74. doi:10.1111/j.14679450.2005.00478.x. Balconi, Michela, Maria Elide Vanutelli. & Elisabetta Grippa. (2017). “Resting State and Personality Component (BIS/BAS) Predict the Brain Activity (EEG and FNIRS Measure) in Response to Emotional Cues.” Brain and Behavior, 7, e00686. doi:10.1002/brb3.686. Balconi, Michela, Irene Venturella, Giulia Fronda. & Maria Elide Vanutelli. (2020). “Leader-Employee Emotional ‘Interpersonal Tuning’. An EEG Coherence Study.” Social Neuroscience, 15, 234– 43. doi:10.1080/17470919.2019.1696226. Balleine, Bernard W., Mauricio R. Delgado. & Okihide Hikosaka. (2007). “The Role of the Dorsal Striatum in Reward and Decision-Making.”

58

Michela Balconi and Giulia Fronda

Journal of Neuroscience, 27, 8161–65. doi:10.1523/JNEURO SCI.1554-07.2007. Batson, C. Daniel. (2010). “Empathy-Induced Altruistic Motivation.” In Prosocial Motives, Emotions, and Behavior: The Better Angels of Our Nature, edited by Mario Mikulincer and Phillip R. Shaver, 15–34. Washington, DC: American Psychological Association. Bennet, Alex. & David Bennet. (2008). “The Human Knowledge System: Music and Brain Coherence.” VINE, 38, 277–95. doi:10.1108/ 03055720810904817. Blackmore, Susan. (2005). Consciousness: A Very Short Introduction. Oxford: Oxford University Press. Boyatzis, Richard E., Kylie Rochford. & Anthony I. Jack. (2014). “Antagonistic Neural Networks Underlying Differentiated Leadership Roles.” Frontiers in Human Neuroscience, 8, 114. doi:10.3389/ fnhum.2014.00114. Brand, Matthias, Kirsten Labudda. & Hans J. Markowitsch. (2006). “Neuropsychological Correlates of Decision-Making in Ambiguous and Risky Situations.” Neural Networks, 19, 1266–76. doi:10.1016/ j.neunet.2006.03.001. Brown, Michael E. & Linda K. Treviño. (2006). “Ethical Leadership: A Review and Future Directions.” Leadership Quarterly, 17, 595–616. doi:10.1016/j.leaqua.2006.10.004. Buckner, Randy L., Jessica R. Andrews-Hanna. & Daniel L. Schacter. (2008). “The Brain’s Default Network: Anatomy, Function, and Relevance to Disease.” Annals of the New York Academy of Sciences, 1124, 1–38. doi:10.1196/annals.1440.011. Buckner, Randy L. & Daniel C. Carroll. (2006). “Self-Projection and The Brain.” Trends in Cognitive Sciences, 11, 49–57. doi:10.1016/ j.tics.2006.11.004. Burns, Kelly. & Antoine Bechara. (2007). “Decision Making and Free Will: A Neuroscience Perspective.” Behavioral Sciences and the Law 25:263–80. doi:10.1002/bsl.751. Cacioppo, John T., Gary G. Berntson. & Howard C. Nusbaum. (2008). “Neuroimaging as a New Tool in the Toolbox of Psychological

To Be or Not to Be Moral in Organizations?

59

Science: Commentary.” Current Directions in Psychological Science, 17, 62–7. doi:10.1111/j.1467-8721.2008.00550.x. Clayton, Susan. & Susan Opotow. (2003). “Justice and Identity: Changing Perspectives on What Is Fair.” Personality and Social Psychology Review, 7, 298–310. doi:10.1207/S15327957PSPR0704_03. Decety, Jean. & Claus Lamm. (2006). “Human Empathy through the Lens of Social Neuroscience.” The Scientific World Journal, 6, 1146–63. doi:10.1100/tsw.2006.221. Decety, Jean. & Margarita Svetlova. (2012). “Putting Together Phylogenetic and Ontogenetic Perspectives on Empathy.” Developmental Cognitive Neuroscience, 2, 1–24. doi:10.1016/ j.dcn.2011.05.003. Eisenbeiss, Silke Astrid, Thomas Maak. & Nicola M. Pless. (2014). “Leader Mindfulness and Ethical Decision Making.” In Advances in Authentic and Ethical Leadership, edited by Linda L. Neider and Chester A. Schriesheim, 191–208. Charlotte, NC: Information Age Publishing. Ferraro, Fabrizio, Jeffrey Pfeffer. & Robert I. Sutton. (2005). “Economics Language and Assumptions: How Theories Can Become SelfFulfilling.” Academy of Management Review, 30, 8–24. doi:10.5465/AMR.2005.15281412. Frith, Chris D. & Uta Frith. (2008). “Implicit and Explicit Processes in Social Cognition.” Neuron, 60, 503–10. doi:10.1016/ j.neuron. 2008.10.032. Gallagher, Helen L. & Christopher D. Frith. (2003). “Functional Imaging of ‘Theory of Mind’.” Trends in Cognitive Sciences, 7, 77–83. doi:10.1016/S1364-6613(02)00025-6. Garrigan, Beverley, Anna L. R. Adlam. & Peter E. Langdon. (2018). “Moral Decision-Making and Moral Development: Toward an Integrative Framework.” Developmental Review, 49, 80–100. doi:10.1016/j.dr.2018.06.001. Greene, Joshua D., R. Brian Sommerville, Leigh E. Nystrom, John M. Darley. & Jonathan D. Cohen. (2001). “An FMRI Investigation of

60

Michela Balconi and Giulia Fronda

Emotional Engagement in Moral Judgment.” Science, 293, 2105–08. doi:10.1126/science.1062872. Greene, Joshua D., Leigh E. Nystrom, Andrew D. Engell, John M. Darley. & Jonathan D. Cohen. (2004). “The Neural Bases of Cognitive Conflict and Control in Moral Judgment.” Neuron, 44, 389–400. doi:10.1016/j.neuron.2004.09.027. Haidt, Jonathan. & Fredrik Bjorklund. (2008). “Social Intuitionists Reason, as Normal Part of Conversation.” In Moral Psychology Volume 2. The Cognitive Science of Morality: Intuition and Diversity, edited by Walter Sinnott-Armstrong, 241–54. Cambridge, MA: MIT Press. Hannah, Sean T., Pierre A. Balthazard, David A. Waldman, Peter L. Jennings. & Robert W. Thatcher. (2013). “The Psychological and Neurological Bases of Leader Self-Complexity and Effects on Adaptive Decision-Making.” Journal of Applied Psychology, 98, 393– 411. doi:10.1037/a0032257. Hare, Todd A., Colin F. Camerer, Daniel T. Knoepfle. & Antonio Rangel. (2010). “Value Computations in Ventral Medial Prefrontal Cortex during Charitable Decision Making Incorporate Input from Regions Involved in Social Cognition.” Journal of Neuroscience, 30, 583–90. doi:10.1523/JNEUROSCI.4089-09.2010. Healey, Mark P. & Gerard P. Hodgkinson. (2014). “Rethinking the Philosophical and Theoretical Foundations of Organizational Neuroscience: A Critical Realist Alternative.” Human Relations, 67, 765–92. doi:10.1177/0018726714530014. Hellige, Joseph B. (1990). “Hemispheric Asymmetry.” Annual Review of Psychology, 41, 55–80. doi:10.1146/annurev.ps.41.020190.000415. Hofmann, Wilhelm. & Anna Baumert. (2010). “Immediate Affect as a Basis for Intuitive Moral Judgement: An Adaptation of the Affect Misattribution Procedure.” Cognition and Emotion, 24, 522–35. doi:10.1080/02699930902847193. Hsu, Ming, Cédric Anen. & Steven R. Quartz. (2008). “The Right and the Good: Distributive Justice and Neural Encoding of Equity and Efficiency.” Science, 320, 1092–95. doi:10.1126/science.1153651.

To Be or Not to Be Moral in Organizations?

61

Jack, Gavin, Michelle Greenwood. & Jan Schapper. (2012). “Frontiers, Intersections and Engagements of Ethics and HRM.” Journal of Business Ethics, 111, 1–12. doi:10.1007/s10551-012-1427-y. Johnson, Steven. (2004). Mind Wide Open: Your Brain and The Neuroscience of Everyday Life. New York: Scribner. Jordan, Jennifer, Michael E. Brown, Linda K. Treviño. & Sydney Finkelstein. (2013). “Someone to Look Up To: Executive-Follower Ethical Reasoning and Perceptions of Ethical Leadership.” Journal of Management, 39, 660–83. doi:10.1177/0149206311398136. Koenigs, Michael, Liane Young, Ralph Adolphs, Daniel Tranel, Fiery Cushman, Marc Hauser. & Antonio Damasio. (2007). “Damage to the Prefrontal Cortex Increases Utilitarian Moral Judgements.” Nature, 446, 908–11. doi:10.1038/nature05631. Kohlberg, Lawrence. (1969). “Stage and Sequence: The CognitiveDevelopmental Approach To Socialization.” In Handbook of Socialization Theory, edited by David A. Goslin, 347–480. New York, NY: Rand McNally. Latham, Gary P., Alexander D. Stajkovic. & Edwin A. Locke. (2010). “The Relevance and Viability of Subconscious Goals in the Workplace.” Journal of Management, 36, 234–55. doi:10.1177/0149206309350777. Lee, Nick, Carl Senior. & Michael J. R. Butler. (2012). “The Domain of Organizational Cognitive Neuroscience: Theoretical and Empirical Challenges.” Journal of Management, 38, 921–31. doi:10.1177/0149206312439471. Levy, Dino J. & Paul W. Glimcher. (2011). “Comparing Apples and Oranges: Using Reward-Specific and Reward-General Subjective Value Representation in the Brain.” Journal of Neuroscience, 31, 14693–707. doi:10.1523/JNEUROSCI.2218-11.2011. Lindquist, Kristen A., Tor D. Wager, Hedy Kober, Eliza Bliss-Moreau. & Lisa Feldman Barrett. (2012). “The Brain Basis of Emotion: A MetaAnalytic Review.” Behavioral and Brain Sciences, 35, 121–43. doi:10.1017/S0140525X11000446.

62

Michela Balconi and Giulia Fronda

Loewenstein, George, Elke U. Weber, Christopher K. Hsee. & Ned Welch. (2001). “Risk as Feelings.” Psychological Bulletin, 127, 267–86. doi:10.1037/0033-2909.127.2.267. Mayer, David M., Karl Aquino, Rebecca L. Greenbaum. & Maribeth Kuenzi. (2012). “Who Displays Ethical Leadership, and Why Does It Matter? An Examination of Antecedents and Consequences of Ethical Leadership.” Academy of Management Journal, 55, 151–71. doi:10.5465/amj.2008.0276. Mencl, Jennifer. & Douglas R. May. (2009). “The Effects of Proximity and Empathy on Ethical Decision-Making: An Exploratory Investigation.” Journal of Business Ethics, 85, 201–26. doi:10.1007/s10551-0089765-5. Minas, Randall, Robert Potter, Alan Dennis, Valerie Bartelt. & Soyoung Bae. (2014). “Putting on the Thinking Cap: Using NeuroIS to Understand Information Processing Biases in Virtual Teams.” Journal of Management Information Systems, 30, 49–82. doi:10.2753/MIS0742-1222300403. Oliveira-Souza, Ricardo, Roland Zahn. & Jorge Moll. (2015). “Neural Correlates of Human Morality: An Overview.” In A Multidisciplinary Perspective, edited by Jean Decety and Thalia Wheatley, 197–220. Cambridge, MA: MIT Press. Pavlovich, Kathryn. & Keiko Krahnke. (2012). “Empathy, Connectedness and Organisation.” Journal of Business Ethics, 105, 131–37. doi:10.1007/s10551-011-0961-3. Poldrack, Russell A., Jill Clark, E. Juliana Paré-Blagoev, Daphna Shohamy, J. Cabero Moyano, Catherine E. Myers. & Mark A. Gluck. (2001). “Interactive Memory Systems in The Human Brain” Nature, 414, 546–50. doi:10.1017/S0140525X0800472X. Powell, Thomas C. (2011). “Neurostrategy.” Strategic Management Journal, 32, 1484–99. doi:10.1002/smj.969. Raichle, Marcus E. (2010). “Two Views of Brain Function.” Trends in Cognitive Sciences, 14, 180–90. doi:10.1016/j.tics.2010.01.008.

To Be or Not to Be Moral in Organizations?

63

Raichle, Marcus E. & Abraham Z. Snyder. (2007). “A Default Mode of Brain Function: A Brief History of an Evolving Idea.” NeuroImage, 37, 1083–90. doi:10.1016/j.neuroimage.2007.02.041. Rilling, James K., Brooks King-Casas. & Alan G. Sanfey. (2008). “The Neurobiology of Social Decision-Making.” Current Opinion in Neurobiology, 18, 159–65. doi:10.1016/j.conb.2008.06.003. Robertson, Diana, John Snarey, Opal Ousley, Keith Harenski, F. Du Bois Bowman, Rick Gilkey. & Clinton Kilts. (2007). “The Neural Processing of Moral Sensitivity to Issues of Justice and Care.” Neuropsychologia, 45, 755–66. doi:10.1016/ j.neuropsychologia. 2006.08.014. Sanfey, Alan G. (2007). “Social Decision-Making: Insights from Game Theory and Neuroscience.” Science, 318, 598–602. doi:10.1126/ science.1142996. Sanfey, Alan G., James K. Rilling, Jessica A. Aronson, Leigh E. Nystrom. & Jonathan D. Cohen. (2003). “The Neural Basis of Economic Decision-Making in the Ultimatum Game.” Science, 300, 1755–58. doi:10.1126/science.1082976. Sarlo, Michela, Lorella Lotto, Daniela Palomba, Simona Scozzari. & Rino Rumiati. (2013). “Framing the Ultimatum Game: Gender Differences and Autonomic Responses.” International Journal of Psychology, 48, 263–71. doi:10.1080/00207594.2012.656127. Schreiner, Matthew J., Katherine H. Karlsgodt, Lucina Q. Uddin, Carolyn Chow, Eliza Congdon, Maria Jalbrzikowski. & Carrie E. Bearden. (2014). “Default Mode Network Connectivity and Reciprocal Social Behavior in 22q11. 2 Deletion Syndrome.” Social Cognitive and Affective Neuroscience, 9, 1261–67. doi:10.1093/scan/nst114. Senior, Carl, Nick Lee. & Michael Butler. (2011). “Organizational Cognitive Neuroscience.” Organization Science, 22, 804–15. doi:10.1287/orsc.1100.0532. Singer, Tania, Ben Seymour, John P. O’Doherty, Klaas E. Stephan, Raymond J. Dolan. & Chris D. Frith. (2006). “Empathic Neural Responses Are Modulated by the Perceived Fairness of Others.” Nature, 439, 466–69. doi:10.1038/nature04271.

64

Michela Balconi and Giulia Fronda

Snyder, Mark. (1974). “Self-Monitoring of Expressive Behavior.” Journal of Personality and Social Psychology, 30, 526–37. doi:10.1037/ h0037039. Sonenshein, Scott. (2007). “The Role of Construction, Intuition, and Justification in Responding to Ethical Issues at Work: The Sensemaking-Intuition Model.” Academy of Management Review, 32, 1022–40. doi:10.5465/AMR.2007.26585677. Strobel, Alexander, Jan Zimmermann, Anja Schmitz, Martin Reuter, Stefanie Lis, Sabine Windmann. & Peter Kirsch. (2011). “Beyond Revenge: Neural and Genetic Bases of Altruistic Punishment.” NeuroImage, 54, 671–80. doi:10.1016/j.neuroimage.2010.07.051. Tabibnia, Golnaz, Ajay B. Satpute. & Matthew D. Lieberman. (2008). “The Sunny Side of Fairness: Preference for Fairness Activates Reward Circuitry (and Disregarding Unfairness Activates Self-Control Circuitry): Research Article.” Psychological Science, 19, 339–47. doi:10.1111/j.1467-9280.2008.02091.x. Thatcher, Robert W., Duane M. North. & Carl J. Biver. (2008). “Development of Cortical Connections as Measured by EEG Coherence and Phase Delays.” Human Brain Mapping, 29, 1400–15. doi:10.1002/hbm.20474. Van Vugt, Eveline, John Gibbs, Geert Jan Stams, Catrien Bijleveld, Jan Hendriks. & Peter Van Der Laan. (2011). “Moral Development and Recidivism: A Meta-Analysis.” International Journal of Offender Therapy and Comparative Criminology, 55, 1234–50. doi:10.1177/0306624X11396441. Wagner, Ullrich, Lisa Handke, Denise Dörfel. & Henrik Walter. (2012). “An Experimental Decision-Making Paradigm to Distinguish Guilt and Regret and Their Self-Regulating Function via Loss Averse Choice Behavior.” Frontiers in Psychology, 3, 431. doi:10.3389/ fpsyg.2012.00431. Waldman, David A., Pierre A. Balthazard. & Suzanne J. Peterson. (2011). “Leadership and Neuroscience: Can We Revolutionize the Way That Inspirational Leaders Are Identified and Developed?” Academy of

To Be or Not to Be Moral in Organizations?

65

Management Perspectives, 25, 60–74. doi:10.5465/ AMP.2011. 59198450. Waldman, David A., Danni Wang, Sean T. Hannah. & Pierre A. Balthazard. (2017). “A Neurological and Ideological Perspective of Ethical Leadership.” Academy of Management Journal, 60, 1285– 1306. doi:10.5465/amj.2014.0644. White, Stuart F., Sarah J. Brislin, Stephen Sinclair. & James R. Blair. (2014). “Punishing Unfairness: Rewarding or The Organization of A Reactively Aggressive Response?.” Human Brain Mapping, 35, 2137– 47. doi:10.1002/hbm.22316.

In: Neuromanagement Editor: Michela Balconi

ISBN: 978-1-53619-562-0 © 2021 Nova Science Publishers, Inc.

Chapter 4

UN-STRESSED MIND: NEUROSCIENTIFIC APPLICATIONS FOR STRESS MANAGEMENT AT THE WORKPLACE Michela Balconi*, PhD and Laura Angioletti International Research Center for Cognitive Applied Neuroscience (IrcCAN), Catholic University of the Sacred Heart, Milan, Italy Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy

ABSTRACT This contribution outlined recent strategies and technology-based training for stress management interventions (SMIs) at the workplace, dedicated to professionals at each level. Specific attention has been given to mindfulness-based interventions (MBIs), wearable brain-and-body sensing technologies, biofeedback (BF) and neurofeedback (NF) training, and virtual reality. Despite the promising evidence at the neural, psychophysiological, cognitive, and behavioral level of a neuroscientific *

Corresponding Author’s E-mail: [email protected].

68

Michela Balconi and Laura Angioletti approach combining MBI and BF/NF training, there are still limited opportunities in the application of SMIs at the workplace. Introducing valuable and intensive stress management training for professionals at the workplace, with the support of wearable technologies, may help to contain health-related complications due to stress. It may also improve the physical and psychological well-being of the workforce, with a large impact in standard welfare intervention and productivity.

1. EFFECTS OF STRESS IN THE WORKPLACE Stress consists of non-specific individual organism response to an internal or external stimulus that, thanks to its intensity or duration, involves implementing an adaptive behavior to restore a condition of homeostasis and cope with the stimulus (Selye 1975). This definition underlines how stress can be considered a primary adaptive response to an event perceived by the individual as positive or negative, although it is also influenced by contextual factors and cognitive events’ evaluations (Matthews, Lin, and Wohleber 2017). Indeed, by emphasizing stress as an adaptive response, the latter allows individuals, who perceive to have sufficient capacity and resources to respond to contextual requests, to implement controlled responses and problemsolving. In this sense, therefore, stress is essential to allow the body to cope with a situation or event in a suitable manner. On the contrary, when the stress responses are too severe or prolonged too long over time, they become dysfunctional due to a failure of the physiological adaptation and homeostatic regulation mechanisms (Dhabhar 2014). Indeed, contrary to the optimal ones located at the center of the stress curve, extremely high or low-stress levels correlate with the inability to implement adaptive responses and a worsening of cognitive performance. Furthermore, it has been shown that chronic exposure to high levels of stress can have serious clinical implications, altering mental abilities, consuming cognitive resources, and worsening individuals’ performance (Chrousos 2009). These physiological and psychological responses also occur when individuals experience everyday stressful situations, such as conducting an

Un-Stressed Mind

69

exam (Saleh, Camart, and Romo 2017), which can significantly affect subjective self-perception, performance, and well-being. This underlines how stress is experienced in different social and daily life situations, such as working contexts. In particular, what can be said about stress at the workplace? Indeed, within the working context, all workers are subject to experiencing stress levels. However, especially those who hold managerial positions, which require the performance of tasks characterized by a high cognitive load, are subject to continually experience stressful situations. Indeed, these professionals’ work performance is characterized and influenced by high workload, high responsibility, and demand to achieve excellent results (Mohr and Wolfram 2010; Schieman and Glavin 2016). Therefore, the relevance of the topic of stress within the working context leads to the need to expand systematic research on the topic. Currently, existing studies underline the negative impact of professional stress on managers’ mood, perceived health, and performance effectiveness (Schieman and Glavin 2016). In particular, an interesting study by Cavanaugh et al. (2000) analysed the self-report stress differentiating it between challenge stress, related to work stress reported following demanding job requests, and hindrance stress, which can be defined as the self-report stress related to job demands or work circumstances that involve excessive or undesirable constraints interfering with individual’s ability to achieve valuable goals. In particular, this study showed that self-report stress, depending on the individual’s assessment of the situation as a positive challenge or a negative obstacle, correlates differently with job satisfaction and job search behavior. This evidence demonstrates how the investigation of the possible implications of stress and the ways to manage it represent a fundamental topic within the working context, having an impact on successful work performance, personal well-being (Balconi et al. 2017; Crivelli and Balconi 2017; Little, Simmons, and Nelson 2007), and compromising individuals’ quality of life due to interpersonal conflicts and interference with private life (Schieman and Glavin 2016). Indeed, working stress has been observed to increase cardiovascular risk and alter cardiovascular activity’s neural regulation (Backé et al. 2012). In particular, stress alters

70

Michela Balconi and Laura Angioletti

the functionality of the endocrine and immune systems, thus increasing individual susceptibility to various diseases (Chandola, Heraclides, and Kumari 2010), and influences the reactivity and autonomic regulation, with increased heart rate (HR), blood pressure, and reduced vagal tone in working and resting conditions. The reduction of vagal tone, in particular, reduces the ability of the parasympathetic system to downregulate the autonomic arousal associated with various conditions, such as that of chronic suffering (Lucini et al. 2007). Furthermore, sustained exposure to stressful conditions also affects individuals’ neural response and the effectiveness of the cognitive systems involved in attention regulation (Ptak 2012). Specifically, the hyperactivation caused by stress affects the ability to correctly implement certain cognitive processes such as executive, decision-making, attentional and mnemonic ones and the processes of self-monitoring and emotional regulation (Arnsten 2015; Girotti et al. 2018). Considering the effects of stress on individuals’ neural and psychophysiological response, it is therefore important to intervene and train the ability to capitalize on the initial increase in stress-related psychophysical reactivity through the use of appropriate stress management techniques or interventions, preventing any dysfunctional consequences (Subhani et al. 2018), increasing the effectiveness of individuals’ neural, cognitive and behavioral reactions to stressful situations and promoting an improvement in subjective well-being. Specifically, these SMIs have been classified as primary when they address the source of stress in the workplace and involve the organizational or group plan; secondary, when they consider the individual level of stress of the employee; and tertiary, when they are aimed at improving levels of stress already existing in individual members of the organization (Le Fevre, Kolt, and Matheny 2006; Quick, Quick, and Nelson 1998). Given the specific purposes, primary interventions require the organization’s active involvement and the activation of support groups. In contrast, secondary interventions include different approaches, such as somatic ones, relaxation techniques, meditation and visualization, cognitive exercise, psychotherapy, biofeedback techniques, or a multimodal

Un-Stressed Mind

71

combination of these approaches. These secondary interventions do not eliminate work-related and organizational stressors but focus on organizational members’ reactions to stress and possible ways to manage these stressors. The idea behind these SMIs is that individuals’ positive or negative reaction to stress depends on the evaluation of the event and on the methods to cope with the stress factors (Le Fevre, Kolt, and Matheny 2006). The next paragraph describes the potential of some of these secondary SMIs integrated with some neuroscientific tools’ contribution.

2. TECHNIQUES AND INTERVENTIONS FOR THE MANAGEMENT OF STRESS IN THE COMPANY Among the secondary SMIs, techniques and methods inherited from the neuroscientific field applied to the managerial field can be considered. In particular, concerning the management of work stress, using an integrated approach involving several techniques, as mindfulness-based interventions supported by wearable neurofeedback devices, has proved useful and effective. Among the most common protocols for stress management in the company context, relaxation techniques, cognitive-behavioral psychological training, and meditation practices (Richardson and Rothstein 2008) have found to be effective for the negative influence of exposure to stress factors, which can be translated into health risks and worsening of cognitive performance. In particular, MBIs are functional and effective in reducing stress levels related to clinical and non-clinical contexts (Creswell 2017). Indeed, several literature studies have shown that mindfulness causes a significant modulation of the neurophysiological markers associated with stress. Furthermore, this technique was advantageous within the workplace for stress management (Janssen et al. 2018; Ravalier, Wegrzynek, and Lawton 2016). Although some methodological limitations (e.g., work on

72

Michela Balconi and Laura Angioletti

long-term outcomes and effects, follow-up) and negative results (Bartlett et al. 2019; Jamieson and Tuckey 2017; Vonderlin et al. 2020), MBIs are effective in improving well-being, awareness, and job satisfaction by causing a reduction in individuals’ stress levels, burnout, mental distress development, and somatic disorders (Vonderlin et al. 2020). In addition to MBIs, recently, well-designed app-based wearable devices have also been identified as possible options for work stress management by facilitating the awareness processes of individuals, allowing recording physiological responses in real-time, as indicators of stress levels, and informing individuals when to act (Patel, Asch, and Volpp 2015). The advantages reported by these systems are that they act as a reminder of SMIs, monitor change over time by increasing motivation in practice and the achievement of significant progress, alerting the worker to the moment of stress, and helping him implement better stress management strategies promoting a more adaptive response. Specifically, it was observed that these wearable devices, which involve the integration of elementary functions (for example, heart rate sensors, app notifications, and tactile vibrations) and cognitive and physiological experimental processes, can reduce stress levels and increase the attentional levels of employees (Fallon, Spohrer, and Heinzl 2019, 229–39), by acting as facilitators and not drivers of behavior change (Patel, Asch, and Volpp 2015). Despite this early evidence, research on the effectiveness of these stress monitoring devices in the workplace should be supported by more significant effectiveness. Instead, this was found for more advanced wearable body and brain-sensing devices that have proven effective in reducing stress levels, causing benefits to employees and managers (Balconi and Crivelli 2019; Balconi, Fronda, and Crivelli 2019; Balconi et al. 2018; Crivelli et al. 2019). Besides MBIs and well-designed app-based wearable devices, another accessible, low-cost, and easy-to-use system for applying work stress reduction and management interventions is BF. This tool, in particular, has proved effective in improving psychological (self-report work stress scale) and stress indicators (i.e., electromyography, temperature, cortisol and heart rate variability, HRV) in the workplace (De Witte, Buyck, and Van

Un-Stressed Mind

73

Daele 2019; Kotozaki et al. 2014; Murphy, Nimmo-Smith, and Lawrence 2003; Sutarto, Wahab, and Zin 2012). Specifically, this tool’s effectiveness with wearable devices on self-reported stress has been demonstrated in clinical and non-clinical settings (Goessl, Curtiss, and Hofmann 2017). For example, a study by Munafò, Patron, and Palomba (2016) has demonstrated the positive impact of a BF intervention for respiratory sinus arrhythmia (RSA) of five weekly sessions for 45 minutes on a sample of sixteen managers with high-level job responsibilities, with a decrease in resting heart rate activity, anxiety levels and an improvement in the quality of life. Moreover, this tool’s effectiveness in reducing stress levels has also been demonstrated in combination with MBIs (Brinkmann et al. 2020). In particular, some studies, which have observed the conscious and unconscious mechanisms of MBIs supported by an NF wearable device in emotion regulation and stress management, have found the effectiveness of these new approaches, which integrate mental training practices with wearable brain sensing devices, in improving individuals’ cognitive performance and stress management (Balconi and Crivelli 2019; Balconi et al. 2017; Bhayee et al. 2016; Crivelli et al. 2019). Specifically, these studies have shown how MBIs can help regulate affective responses, improving negative emotions management. Furthermore, this integrated protocol has also proved beneficial for the empowerment of stress management skills and increased neurocognitive efficiency in stressful professional contexts with people occupying high managerial levels characterized by high responsibilities and managerial duties (Crivelli et al. 2019). These studies’ results have observed decreased stress levels after two intensive weeks of intervention with NF. In particular, at the end of NF treatment, a decrease in stress, anxiety, anger, and mental fatigue and an increase in electrophysiological markers of relaxation and effective recovery from the stress response has been observed. In addition, a significant increase in vagal tone has emerged, with an increase in HRV both at rest and during exposure to a cognitive stress factor. The increase in HRV values indicates that the intense MBIs with NF promoted effective psychophysiological reactivity and homeostatic mechanisms with observable effects on the physiological response markers to stress.

74

Michela Balconi and Laura Angioletti

Moreover, this increase in HRV was also observed in rest conditions, highlighting how these skills trained by constant practice have positive implications in daily life. Indeed, this index reflects the effect of stressors on individuals and is configured as a reliable metric that shows the capacity of physiological coping skills with practical consequences for the assessment and intervention on stress management in different contexts (Subhani et al. 2018). A decrease in self-perceived stress, reported levels of anxiety and anger, and mental fatigue was also found at a psychological level. Therefore, these studies show how this protocol can have positive effects on single individuals and the entire organization. Indeed, it was observed that managerial staff stress has negative impacts on working and interpersonal life and on well-being, productivity, and effectiveness of the working team and the entire organization (Little, Simmons, and Nelson 2007). Furthermore, extensive literature has also shown the relationship between occupational stress and cardiovascular disease (Backé et al. 2012; Collins, Karasek, and Costas 2005; Eller, Kristiansen, and Hansen 2011). Considering the positive and promising results of the implementation of this protocol, short in terms of overall duration and timing of the daily practices required, it could prove to be a useful tool for those professionals, with high work duties and limited time, for whom traditional stress management programs may be too demanding and easily abandoned. Finally, although some studies have shown the effectiveness of virtual reality (VR) treatments in reducing stress and anxiety, only a review by Naylor, Ridout, and Campbell (2020) and other exploratory studies (Ahmaniemi et al. 2017; Straßmann et al. 2019; Thoondee and Oikonomou 2018; Yin et al. 2019) have demonstrated its usefulness in the workplace. Specifically, these studies have adopted both subjective and objective outcome measures reporting higher relaxation effects and lower stress levels in participants. In particular, individuals responded positively to the possibility of using VR to promote a condition of relaxation and interruption of stress control by immersing in the scene, thus distracting themselves from thoughts and work tasks (Ahmaniemi et al. 2018; Thoondee and Oikonomou 2018). In this regard, a more positive mental state was observed after the condition of relaxation, no feeling of simulator

Un-Stressed Mind

75

sickness, and the importance of the intervention’s adaptability to the user’s choice has emerged (Straßmann et al. 2019). The biophilic and natural design of VR also promoted a reduction in stress and improved individuals’ creativity (Yin et al. 2019). Despite this few pieces of evidence, the issue of using VR for the management of work stress deserves a broader investigation. Therefore, future studies and research should focus on the standardization of possible scientific protocols, methodologically and statistically complete, that adopt a robust randomized controlled study design that includes an objective measure (such as HR, HRV, or neural indices) to observe the regulation of work stress through virtual reality interventions.

CONCLUSION As demonstrated in the previous paragraphs, in which some effective neuroscientific techniques and interventions in the management of work stress have been described, the introduction of effective and intensive training designed, supported by the use of wearable devices, could help reduce stress levels of professionals at risk by decreasing negative health effects. It also lowers potential costs for the company and improves the workforce’s physical and psychological well-being, with limited investment in standard welfare interventions. Furthermore, a reduction in stress levels with a consequent improvement in professionals’ well-being would positively affect management levels for the work team and the entire organization, increasing its productivity. Despite the potential that emerged from applying these tools, the number of studies demonstrating their effectiveness is still limited. It would be necessary to expand the research and literature on this topic, further corroborating the current empirical observations. Furthermore, it might be helpful to verify these protocols’ effectiveness and the robustness of these practices’ effects even on samples of managers from distinct organizations or different categories of high-risk professionals. Finally, it could also help collect data on work experience, company climate,

76

Michela Balconi and Laura Angioletti

employee productivity, level of satisfaction, and work autonomy (Mühlhaus and Bouwmeester 2016). The inclusion and observation of these factors could help build a clearer picture of the effect that personalized training could have on the organization’s top positions at the group and company level and better estimate the related potential economic and psychological benefits.

REFERENCES Ahmaniemi, Teemu, Harri Lindholm, Kiti Muller. & Tapio Taipalus. (2017). “Virtual Reality Experience as a Stress Recovery Solution in Workplace.” Paper presented at the IEEE Life Sciences Conference (LSC), Sydney, Australia, December 13–15. Arnsten, Amy F. T. (2015). “Stress Weakens Prefrontal Networks: Molecular Insults to Higher Cognition.” Nature Neuroscience, 18, 1376–85. doi:10.1038/nn.4087. Backé, Eva Maria, Andreas Seidler, Ute Latza, Karin Rossnagel. & Barbara Schumann. (2012). “The Role of Psychosocial Stress at Work for the Development of Cardiovascular Diseases: A Systematic Review.” International Archives of Occupational and Environmental Health, 85, 67–79. doi:10.1007/s00420-011-0643-6. Balconi, Michela. & Davide Crivelli. (2019). “Wearable Devices for SelfEnhancement and Improvement of Plasticity: Effects on Neurocognitive Efficiency.” In Quantifying and Processing Biomedical and Behavioral Signals, edited by Anna Esposito, Marcos Faundez-Zanuy, Francesco Carlo Morabito, and Eros Pasero, 11–22. Cham: Springer International Publishing. Balconi, Michela, Davide Crivelli, Giulia Fronda. & Irene Venturella. (2018). “Neuro-Rehabilitation and Neuro-Empowerment by Wearable Devices. Applications to Well-Being and Stress Management.” Paper presented at the 4th International Conference on Neurorehabilitation, Pisa, Italy, October 16–20.

Un-Stressed Mind

77

Balconi, Michela, Giulia Fronda. & Davide Crivelli. (2019). “Effects of Technology-Mediated Mindfulness Practice on Stress: Psychophysiological and Self-Report Measures.” Stress, 22, 200–9. doi:10.1080/10253890.2018.1531845. Balconi, Michela, Giulia Fronda, Irene Venturella. & Davide Crivelli. (2017). “Conscious, Pre-Conscious and Unconscious Mechanisms in Emotional Behaviour. Some Applications to the Mindfulness Approach with Wearable Devices.” Applied Sciences (Switzerland), 7, 1280. doi:10.3390/app7121280. Bartlett, Larissa, Angela Martin, Amanda L. Neil, Kate Memish, Petr Otahal, Michelle Kilpatrick. & Kristy Sanderson. (2019). “A Systematic Review and Meta-Analysis of Workplace Mindfulness Training Randomized Controlled Trials.” Journal of Occupational Health Psychology, 24, 108–26. doi:10.1037/ocp0000146. Bhayee, Sheffy, Patricia Tomaszewski, Danie H. Lee, Graeme Moffat, Lou Pino, Sylvain Moreno. & Norma A. S. Farb. (2016). “Attentional and Affective Consequences of Technology Supported Mindfulness Training: A Randomised, Active Control, Efficacy Trial.” BMC Psychology, 4, 1–14. doi:10.1186/s40359-016-0168-6. Brinkmann, Amelie Edith, Sophia Antonia Press, Eduard Helmert, Martin Hautzinger, Inna Khazan. & Jan Vagedes. (2020). “Comparing Effectiveness of HRV-Biofeedback and Mindfulness for Workplace Stress Reduction: A Randomized Controlled Trial.” Applied Psychophysiology Biofeedback, 45, 307–22. doi:10.1007/s10484-02009477-w. Cavanaugh, Marcie A., Wendy R. Boswell, Mark V. Roehling. & John W. Boudreau. (2000). “An Empirical Examination of Self-Reported Work Stress among U.S. Managers.” Journal of Applied Psychology, 85, 65– 74. doi:10.1037/0021-9010.85.1.65. Chandola, Tarani, Alexandros Heraclides. & Meena Kumari. (2010). “Psychophysiological Biomarkers of Workplace Stressors.” Neuroscience and Biobehavioral Reviews, 35, 51–7. doi:10.1016/ j.neubiorev.2009.11.005.

78

Michela Balconi and Laura Angioletti

Chrousos, George P. (2009). “Stress and Disorders of the Stress System.” Nature Reviews Endocrinology, 5, 374–81. doi:10.1038/ nrendo. 2009.106. Collins, Sean M., Robert A. Karasek. & Kevin Costas. (2005). “Job Strain and Autonomic Indices of Cardiovascular Disease Risk.” American Journal of Industrial Medicine, 48, 182–93. doi:10.1002/ajim.20204. Creswell, J. David. (2017). “Mindfulness Interventions.” Annual Review of Psychology, 68, 491–516. doi:10.1146/annurev-psych-042716-051139. Crivelli, Davide. & Michela Balconi. (2017). “Agentività e competenze sociali: riflessioni teoriche e implicazioni per il management.” Ricerche Di Psicologia [“Agency and Social Skills: Theoretical Remarks and Implications for Management.” Research of Psychology], 40, 349–63. doi:10.3280/RIP2017-003006. Crivelli, Davide, Giulia Fronda, Irene Venturella. & Michela Balconi. (2019). “Stress and Neurocognitive Efficiency in Managerial Contexts: A Study on Technology-Mediated Mindfulness Practice.” International Journal of Workplace Health Management, 12, 42–56. doi:10.1108/IJWHM-07-2018-0095. De Witte, Nele A. J. De, Inez Buyck. & Tom Van Daele. (2019). “Combining Biofeedback with Stress Management Interventions: A Systematic Review of Physiological and Psychological Effects.” Applied Psychophysiology Biofeedback, 44, 71–82. doi:10.1007/ s10484-018-09427-7. Dhabhar, Firdaus S. (2014). “Effects of Stress on Immune Function: The Good, the Bad, and the Beautiful.” Immunologic Research, 58, 193– 210. doi:10.1007/s12026-014-8517-0. Eller, Nanna Hurwitz, Jesper Kristiansen. & Ase Marie Hansen. (2011). “Long-Term Effects of Psychosocial Factors of Home and Work on Biomarkers of Stress.” International Journal of Psychophysiology, 79, 195–202. doi:10.1016/j.ijpsycho.2010.10.009. Fallon, Monica, Kai Spohrer. & Armin Heinzl. (2019). “Wearable Devices: A Physiological and Self-Regulatory Intervention for Increasing Attention in the Workplace.” In Information Systems and

Un-Stressed Mind

79

Neuroscience, edited by Fred D. Davis, Jan vom Brocke, PierreMajorique Léger, and Adriane B. Randolph, 229–39. Cham: Springer. Girotti, Milena, Samantha M. Adler, Sarah E. Bulin, Elizabeth A. Fucich, Denisse Paredes. & David A. Morilak. (2018). “Prefrontal Cortex Executive Processes Affected by Stress in Health and Disease.” Progress in Neuro-Psychopharmacology and Biological Psychiatry, 85, 161–79. doi:10.1016/j.pnpbp.2017.07.004. Goessl, Vera C., Joshua E. Curtiss. & Stefan G. Hofmann. (2017). “The Effect of Heart Rate Variability Biofeedback Training on Stress and Anxiety: A Meta-Analysis.” Psychological Medicine, 47, 2578–86. doi:10.1017/S0033291717001003. Jamieson, Stephanie D. & Michelle R. Tuckey. (2017). “Mindfulness Interventions in the Workplace: A Critique of the Current State of the Literature.” Journal of Occupational Health Psychology, 22, 180–93. doi:10.1037/ocp0000048. Janssen, Math, Yvonne Heerkens, Wietske Kuijer, Beatrice Van Der Heijden. & Josephine Engels. (2018). “Effects of Mindfulness-Based Stress Reduction on Employees’ Mental Health: A Systematic Review.” PLoS ONE, 13, e0191332. doi:10.1371/ journal. pone.0191332. Kotozaki, Yuka, Hikaru Takeuchi, Atsushi Sekiguchi, Yuki Yamamoto, Takamitsu Shinada, Tsuyoshi Araki, Kei Takahashi., et al. (2014). “Biofeedback-Based Training for Stress Management in Daily Hassles: An Intervention Study.” Brain and Behavior, 4, 566–79. doi:10.1002/brb3.241. Le Fevre, Mark Le, Gregory S. Kolt. & Jonathan Matheny. (2006). “Eustress, Distress and Their Interpretation in Primary and Secondary Occupational Stress Management Interventions: Which Way First?.” Journal of Managerial Psychology, 21, 547–65. doi:10.1108/ 02683940610684391. Little, Laura M., Bret L. Simmons. & Debra L. Nelson. (2007). “Health among Leaders: Positive and Negative Affect, Engagement and Burnout, Forgiveness and Revenge.” Journal of Management Studies, 44, 243–60. doi:10.1111/j.1467-6486.2007.00687.x.

80

Michela Balconi and Laura Angioletti

Lucini, Daniela, Silvano Riva, Paolo Pizzinelli. & Massimo Pagani. (2007). “Stress Management at the Worksite: Reversal of Symptoms Profile and Cardiovascular Dysregulation.” Hypertension, 49, 291–97. doi:10.1161/01.HYP.0000255034.42285.58. Matthews, Gerald, Jinchao Lin. & Ryan Wohleber. (2017). “Personality, Stress and Resilience: A Multifactorial Cognitive Science Perspective.” Psihologijske Teme, 26, 139–62. doi:10.31820/pt.26.1.6. Mohr, Gisela. & Hans Joachim Wolfram. (2010). “Stress Among Managers: The Importance of Dynamic Tasks, Predictability, and Social Support in Unpredictable Times.” Journal of Occupational Health Psychology, 15, 167–79. doi:10.1037/a0018892. Mühlhaus, Julia. & Onno Bouwmeester. (2016). “The Paradoxical Effect of Self-Categorization on Work Stress in a High-Status Occupation: Insights from Management Consulting.” Human Relations, 69, 1823– 52. doi:10.1177/0018726715626255. Munafò, Marianna, Elisabetta Patron. & Daniela Palomba. (2016). “Improving Managers’ Psychophysical Well-Being: Effectiveness of Respiratory Sinus Arrhythmia Biofeedback.” Applied Psychophysiology Biofeedback, 41, 129–39. doi:10.1007/s10484-0159320-y. Murphy, Fionnuala C., Ian Nimmo-Smith. & Andrew D. Lawrence. (2003). “Functional Neuroanatomy of Emotions: A Meta-Analysis.” Cognitive, Affective and Behavioral Neuroscience, 3, 207–33. doi:10.3758/CABN.3.3.207. Naylor, Matthew, Brad Ridout. & Andrew Campbell. (2020). “A Scoping Review Identifying the Need for Quality Research on the Use of Virtual Reality in Workplace Settings for Stress Management.” Cyberpsychology, Behavior, and Social Networking, 23, 506–18. doi:10.1089/cyber.2019.0287. Patel, Mitesh S., David A. Asch. & Kevin G. Volpp. (2015). “Wearable Devices as Facilitators, Not Drivers, of Health Behavior Change.” JAMA - Journal of the American Medical Association, 313, 459–60. doi:10.1001/jama.2014.14781.

Un-Stressed Mind

81

Ptak, Radek. (2012). “The Frontoparietal Attention Network of the Human Brain: Action, Saliency, and a Priority Map of the Environment.” Neuroscientist, 18, 502–15. doi:10.1177/1073858411409051. Quick, Jonathan D., James Campbell Quick. & Debra L. Nelson. (1998). “The Theory of Preventive Stress Management in Organizations.” In Theories of Organizational Stress, edited by Cary L. Cooper, 246–68. Oxford: Oxford University Press. Ravalier, Jermaine M., Paulina A. Wegrzynek. & Simon Lawton. (2016). “Systematic Review: Complementary Therapies and Employee WellBeing.” Occupational Medicine, 66, 428–36. doi:10.1093/ occmed/kqw047. Richardson, Katherine M. & Hannah R. Rothstein. (2008). “Effects of Occupational Stress Management Intervention Programs: A MetaAnalysis.” Journal of Occupational Health Psychology, 13, 69–93. doi:10.1037/1076-8998.13.1.69. Saleh, Dalia, Nathalie Camart. & Lucia Romo. (2017). “Predictors of Stress in College Students.” Frontiers in Psychology, 8, 19. doi:10.3389/fpsyg.2017.00019. Schieman, Scott. & Paul Glavin. (2016). “The Pressure-Status Nexus and Blurred Work–Family Boundaries.” Work and Occupations, 43, 3–37. doi:10.1177/0730888415596051. Selye, Hans. (1975). “Stress and Distress.” Comprehensive Therapy, 1, 9– 13. doi:10.1177/0022002185016004003. Subhani, Ahmad Rauf, Nidal Kamel, Mohamad Naufal Mohamad Saad, Nanda Nandagopal, Kenneth Kang. & Aamir Saeed Malik. (2018). “Mitigation of Stress: New Treatment Alternatives.” Cognitive Neurodynamics, 12, 1–20. doi:10.1007/s11571-017-9460-2. Sutarto, Purwandini Auditya, Muhammad Nubli Abdul Wahab. & Nora Mat Zin. (2012). “Resonant Breathing Biofeedback Training for Stress Reduction among Manufacturing Operators.” International Journal of Occupational Safety and Ergonomics, 18, 549–61. doi:10.1080/10803548.2012.11076959. Straßmann, Carolin, Sabrina C. Eimler, Alexander Arntz, Dustin Keßler, Sarah Zielinski, Gabriel Brandenberg, Vanessa Dümpel. & Uwe

82

Michela Balconi and Laura Angioletti

Handmann. (2019). “Relax Yourself - Using Virtual Reality to Enhance Employees’ Mental Health and Work Performance.” Paper presented at the Conference on Human Factors in Computing Systems, Glasgow Scotland, May 8–13. Thoondee, Krsna Das. & Andreas Oikonomou. (2018). “Using Virtual Reality to Reduce Stress at Work.” Paper presented at the Computing Conference, London, United Kingdom, July 18–20. Vonderlin, Ruben, Miriam Biermann, Martin Bohus. & Lisa Lyssenko. (2020). “Mindfulness-Based Programs in the Workplace: A MetaAnalysis of Randomized Controlled Trials.” Mindfulness, 11, 1579–98. doi:10.1007/s12671-020-01328-3. Yin, Jie, Nastaran Arfaei, Piers MacNaughton, Paul J. Catalano, Joseph G. Allen. & John D. Spengler. (2019). “Effects of Biophilic Interventions in Office on Stress Reaction and Cognitive Function: A Randomized Crossover Study in Virtual Reality.” Indoor Air, 29, 1028–39. doi:10.1111/ina.12593.

PART II: THE APPLICATIONS

In: Neuromanagement Editor: Michela Balconi

ISBN: 978-1-53619-562-0 © 2021 Nova Science Publishers, Inc.

Chapter 5

NEUROASSESSMENT: NEUROMETRICS FOR ASSESSMENT IN ORGANIZATIONS Michela Balconi, PhD International Research Center for Cognitive Applied Neuroscience (IrcCAN), Catholic University of the Sacred Heart, Milan, Italy Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy

ABSTRACT The term “neuroassessment” refers to the application of neurometrics, neuroscientific methods and techniques for the assessment practices conducted in the organizational contexts. So far, despite the significance of dynamic cognitive and social processes, as well as their potential function as precursors to self-regulation and other key professional soft skills, the neuromanagement area lacks an overarching structure for assessing these processes in the professional contexts. Therefore, a neurocognitive triadic model for the evaluation of individual professional potential and neuroenhancement has been here proposed. 

Corresponding Author’s E-mail: [email protected].

86

Michela Balconi Within this framework, two application examples of neuroassessment to the evaluation of the potential practices and the professional interpersonal interactions in the company have been discussed. These examples intended to emphasize the advantages of integrating the neuroscientific perspective into the organizational contexts, provide an overview of what has been done so far, and stimulate new ideas on what could be done in the companies contexts in the future.

1. INTEGRATING NEUROSCIENCE DISCIPLINE INTO THE COMPANY FOR ASSESSMENT The primary goal of the neuroscience applied to the managerial context is the definition of an organizational neuroscientific perspective capable of understanding and integrating the cognitive, emotional, and relational processes underlying the actions of individuals in specific organizational areas. The discipline of neuromanagement was indeed conceived from the necessity to comprehend mental processes subserving motives, attitudes, and behaviors of professionals in organizations, with the ultimate aim of predicting, modifying, and enhancing them (Balconi and Venturella 2017; Balconi et al., 2017; Murray and Antonakis 2019). Despite the neuromanagement discipline is relatively young and constantly evolving to adapt to changing professional challenges, also the “mere” integration of social neuroscience and organizational disciplines in the company field already proved to be useful for theoretical, methodological, and technical purposes. There are at least three key reasons for which the integration of social neuroscience and neuromanagement in the organizational field constitute an added value. To begin with the first reason, neuroscience has the principal aim of exploring and understanding brain functioning and its higher-order cognitive and social mechanisms.

Neuroassessment

87

Neuroscientific disciplines application is based on the integration of multiple levels of analysis - from the explicit level of information (“overt” behavior and self-reported subjective experiences) to the implicit information processing (the “covert” central and peripheral physiological mechanisms), the last goes hand in hand with the first level and support it. Secondly, the neuroscience approach frequently employs methodologies and tools that have been demonstrated to effectively tap on mental processes that govern self-regulation, social skills, and higher cognition even in the workplace. This aspect could allow the development and identification of mission-critical professions, as well as the effective design of Human Resources (HR) management practices and policies. Finally, the discipline of neuromanagement may provide a sustainable avenue for better defining and developing specific protocols that can be used to set new practices for the assessment in a variety of ways, ranging from assessing the potential, to the assessment of the work performance, until the implementation of new pathways for cognitive, emotional, and relationship skills enhancement. Whereas in the current chapter, the focus will be above all on the advantages of integrating the neuroscientific perspective into the assessment procedures carried out in companies. Indeed with the term “neuroassessment,” we refer to the application of neurometrics, neuroscientific methods and techniques, and neuroscience-based tools for the assessment practices conducted in the organizational contexts. Conscious of the ethical issues and implications of neurocognitive applications on individuals and community, as well as the advantages and challenges of implementing it specifically in environments oriented to the promotion of well-being (Fronda, Crivelli, and Balconi 2019), newly developed cognitive neuroempowerment protocols hold promise for improving organizational effectiveness and efficiency and will be addressed in the following chapters.

88

Michela Balconi

2. THE TRIADIC MODEL (TRM MODEL) FOR NEUROASSESSMENT AND NEUROEMPOWERMENT So far, despite the significance of dynamic cognitive and social processes, as well as their potential function as precursors to selfregulation and other key professional soft skills, the neuromanagement area lacks an overarching structure for examining these processes in the professional context. To fill this gap, we recently proposed a triadic model for the evaluation of individual professional potential and neuroenhancement (Balconi, Angioletti, and Crivelli 2020), according to which professionals’ potential can be examined by considering three main areas of competencies, which are include in the TRM Model (Technical, Relational, Metacognitive Model): (i) technical-analytical skills, (ii) metacognitive skills, and (iii) relational skills (Figure 1). Starting from the first cluster of competencies, technical skills regard a precise function and business area and they are based on an individual’s educational and work experience (Dessler 2016; Silzer and Church 2009). The logical reasoning involved in solving a specific problem is an example of these skills. Whereas analytical skills, in contrast to technical skills, have a lower degree of specificity in terms of organizational roles, since they regard domain-independent skills such as selective attention, cognitive flexibility, work-load capacity, and working memory, which are needed in transversal areas of applications. The second level consider the relational skills, which are a central component in most accessible models on the determinants of talent in organizations, include the individual capability to consider, comprehend and track own and others’ affective states (emotional empathy component), as well as others’ point of view, values, and intentions, in terms of the capacity of perspective-taking (cognitive empathy component). They also enclosed the ability to manage social interactions, as well as the inclination towards interpersonal relationships. These features belong to the overall

Neuroassessment

89

construct of emotional intelligence and may lead to the success of a professional in organizational and social life. Lastly, cluster of competencies encompasses metacognitive skills that are higher-order cognitive processes that, in addition to being required for efficient and autonomous daily functioning, become increasingly important as the scope of the acting context rises, all the way up to the highest levels of professional life. They include the implementation of complex behavioral and cognitive strategies, understanding of one’s own cognitive and affective processes, and the adoption of supervision skills over mental process information to track and manage them, allowing for proper cognitive resource allocation (Dunning, Heath, and Suls 2018). Therefore, in the current model, metacognitive skills, in which also executive functions (EFs) are partly included, enclose strategic planning, problemsolving, decision-making, learning flexibility, creativity, self-awareness (e.g., the ability to assess subjective strong and weak points); and ability to focus on intrinsic motivational drives.

Figure 1. Triadic model (TRM Model: Technical, Relational, Metacognitive Model) for the evaluation of individual professional potential and neuroenhancement (retrieved from Balconi 2020).

90

Michela Balconi

It worth noting that on a theoretical level, the internal structure of the triadic model and the interdependent set of skills that constitute its three clusters is transversely connoted by specific mental processes that constitute the core of human EFs – i.e., inhibition mechanisms, working memory, and information-processing capacity (Diamond 2013; Miyake et al., 2000) – consistent with latest integrated accounts of EFs and selfregulation (Hofmann, Schmeichel, and Baddeley 2012). While on a practical level, the model may be used to profile high-level competencies and hard/soft skills needed for optimal job-related performance, as well as to build and incorporate new integrated protocols for talent growth and neurocognitive empowerment in the workplace. To date, the model has already proven to be a useful framework for mapping the progress and the effects of group-based interventions on stress management and neurocognitive efficiency for senior management roles (Crivelli, Fronda, and Balconi 2019; Crivelli et al., 2019; Fronda, Crivelli, and Balconi 2019), as well as customized age-management interventions based on tailored neurocognitive empowerment protocols for EFs assisted by wearable neurotechnologies.

3. NEUROASSESSMENT APPLIED TO THE EVALUATION OFTHE HUMAN POTENTIAL The term “potential” is usually used to mean that a person possesses the attributes (i.e., motivation, talents, abilities, experiences, and other skills) necessary to successfully perform and participate in wider or different positions in the organization at some point in the future. In this sense, the “potential” specifically applies to future growth opportunities instead of just current performance concerns. Its evaluation refers to the assessment (i.e., the identification) of a person’s latent abilities and skills, which he or she may or may not be aware of. Potential assessment is a future-oriented appraisal and the main aim of potential assessment, as future-oriented appraisal, is to define and analyze

Neuroassessment

91

the potential of workers to assume higher roles and duties (i.e., responsibilities) in the operational applications of an organization, while also achieving the following objectives:    

to educate workers about their potential prospects; to assist the company in creating a management succession plan; to update training and recruiting activities; to inform employees about the work that needs to be done to boost their job opportunities.

Potential assessment assists in the detection of what could happen in the future so that it can be driven and focused toward individual and organizational development and goals. Figure 2 shows a graphical representation of employee potential evaluation, as future-oriented process. For the organization to be in an adequate position to set up and realize the potential evaluation system, it proves necessary that the functions, the qualities required to perform these functions, indicators of these qualities, and mechanisms for generating these indicators are clear and well-defined. The establishment of this situation requires clarity in the policy of the organization and the systematization of its endeavors. Among the widely spread methods to investigate the potential in an individual (or in a group of people), there are assessment practices and peer-appraisal, psychological and psychometric scales/tests, or interactive management games, like role-playing.

Figure 2. Representation of a possible model for the potential evaluation of the employee.

92

Michela Balconi

If the organization invests and believes in the development of the human resources that compose it, it should work to generate a climate of openness among its members: this climate is necessary and essential to facilitate employees in understanding their strengths and weaknesses and to create real development opportunities. A good and effective potential assessment system should provide each employee with the chance to be informed and to deepen the outcome of his/her assessment. Each employee should be helped to understand which requirements are actually necessary to perform the role for which he/she aspires and thinks to have the potential, the methods and criteria used by the organization to assess the potential and, as anticipated, the detailed outcome of the evaluation. These procedures take place during periodic counseling and guidance sessions operated and promoted by the personnel department or managers concerned. An organization that intends to develop a good potential assessment system must strive to continually provide such opportunities to the employee so that he/she is able i) to recognize his/her strengths and weaknesses, and ii) to develop realistic self-perception and plan his/her career and development. Nonetheless, in the traditional models of assessment some limitations can be identified, that are mainly linked to, firstly, the absence of systematic considerations related to relational aspects, secondly, to the lack of differentiation between the cognitive functions mentioned above in the triadic model (and in particular there is a lack of specific evaluation of metacognitive functions); thirdly, there is an underestimation of the impact of these skills on one’s team and extended workgroup, two situations in which the potential can be limited. Therefore, a key aspect that deserves more attention is that the contextual impact considered as the condition closest to the individual (i.e., the working group) or in the broad sense (i.e., the organization and its social constraints) should be adequately taken into account and included when addressing potential evaluation.

Neuroassessment

93

4. NEUROASSESSMENT APPLIED TO THE INTERPERSONAL INTERACTIONS As far as concern the evaluation of the interpersonal interactions in the organizational contexts, the necessary conditions for assessing the performance related to the relational level were recently evaluated in a neuroscientific protocol developed by Balconi and colleagues (Balconi unpublished). In particular, this protocol exploited the potential of the hyperscanning technique described in chapter 1. Specifically, by collecting the inter-brains synchronous behavior of two interagents in the role of sales manager and manager of management (a total of twelve couples of individuals) it was displayed how the context of interaction (consisting of short interactive scripts) could modify the synchronous response of the two brains. The variations in terms of higher or lower neural and interpersonal tuning in relation to the overt and covert purposes of the interactive exchange were observed. With regard to the neuroscientific methodology and neurometrics adopted during the assessment, the hemodynamic and peripheral responses of the individuals involved in the interpersonal interactive exchange were measured through the use of functional Near-Infrared Spectroscopy (fNIRS) and biofeedback applied in hyperscanning. In particular, for the interactive exchange, scripts consisting of possible scenarios and plots were created based on problematic and recurring situations within the organizational context of belonging (such as the use of an app to track the tasks of sales and documentation relating to the reporting policy for company sales). At the end of the experimental procedure, multiple levels of analysis were performed starting from the overt communication level. Indeed, the application of discourse analysis, performed on a standard basis, allowed to map the semantic areas discussed by the managers and made it possible to highlight the relevant topics addressed during the role-playing.

94

Michela Balconi

In addition, the correlation of this semantic mapping with the pattern of hemodynamic and autonomic responses of the individuals, allowed to identify the variations of inter-cerebral and body tuning mechanisms of the two interagents in relation to the topics and roles discussed and filled during the interactive exchange. Put in other words, the application of the hyperscanning technique allowed to skip from the single brain analysis to the two interacting brain neuroassessment and, by the simultaneously record the activity of the two individuals involved in the interaction, allowed to create connectivity maps relating to the brain and autonomic activity of the interagents by observing how they tuned (i.e., how they were synchronized at the brain-and-body level) according to the various topics addressed during the communicative interaction. In another recent work, the analogous neuroscientific approach was used to examine the corporate brand image in a sample of bank employees recruited from an Italian banking institution. The sample underwent semistructured interviews, and realistic role-playing of business contexts while interpersonal neurometrics (brain-to-brain coupling, through an electroencephalographic hyperscanning paradigm) were collected. As general consideration on the different set of results, the covert emotional and representational aspects that emerged from the analyses have disclosed a wide range of attitudes and resistances toward the brand, some of which were in direct contrast to explicit and overt representations. Overall, the applied examples described in the previous paragraphs intend to emphasize the potential of the application of neurometrics, neuroscientific methods and techniques for the various assessment procedures conducted in the organizational contexts, from the evaluation of the potential to the assessment of relational dynamics. As a take-home message, they also intend to provide an overview of what has been done so far in organizational contexts and to stimulate the reflection on what could be done in the companies in the future.

Neuroassessment

95

REFERENCES Balconi, Michela, and Irene Venturella. 2017. “Neuromanagement. What about Emotion and Communication.” Neuropsychological Trends 21:9-21. doi: https://10.7358/neur-2017-021-balc. Balconi, Michela, Laura Angioletti, and Davide Crivelli. 2020. “NeuroEmpowerment of Executive Functions in the Workplace: The Reason Why.” Frontiers in Psychology 11:1519. doi: https://10.3389/ fpsyg.2020.01519. Balconi, Michela, Maria Rosaria Natale, Nadia Benabdallah, and Davide Crivelli. 2017. “New Business Models: The Agents and Inter-Agents in a Neuroscientific Domain.” Neuropsychological Trends 21:53-63. doi: https://10.7358/neur-2017-021-nata. Balconi, Michela. 2020. “From the Executive Functions To Neuroempowerment Programs. New Perspectives for Neuroassessment.” In Neuromanagement. People and Organizations, edited by Michela Balconi, 55-65. Milano: LED Edizioni Universitarie [Milan: LED University Editions]. Crivelli, Davide, Giulia Fronda, and Michela Balconi. 2019. “Neurocognitive Enhancement Effects of Combined MindfulnessNeurofeedback Training in Sport.” Neuroscience 412:83-93. doi: https://10.1016/j.neuroscience.2019.05.066. Crivelli, Davide, Giulia Fronda, Irene Venturella, and Michela Balconi. 2019. “Supporting Mindfulness Practices with Brain-Sensing Devices. Cognitive and Electrophysiological Evidences.” Mindfulness 10:30111. doi: https://10.1007/s12671-018-0975-3. Dessler, Gary. 2016. Fundamentals of Human Resource Management. Boston, MA: Pearson. Diamond, Adele. 2013. “Executive Functions.” Annual Review of Psychology 64:135-68. doi: https://10.1146/annurev-psych-113011143750. Dunning, David, Chip Heath, and Jerry M. Suls. 2018. “Reflections on Self-Reflection: Contemplating Flawed Self-Judgments in the Clinic,

96

Michela Balconi

Classroom, and Office Cubicle.” Perspectives on Psychological Science 13:185-89. doi: https://10.1177/1745691616688975. Fronda, Giulia, Davide Crivelli, and Michela Balconi. 2019. “Neurocognitive Enhancement: Applications and Ethical Issues.” NeuroRegulation 6:161-161. doi: https://10.15540/nr.6.3.161. Hofmann, Wilhelm, Brandon J. Schmeichel, and Alan D. Baddeley. 2012. “Executive Functions and Self-Regulation.” Trends in Cognitive Sciences 16:174-80. doi: https://10.1016/j.tics.2012.01.006. Miyake, Akira, Naomi P. Friedman, Michael J. Emerson, Alexander H. Witzki, Amy Howerter, and Tor D. Wager. 2000. “The Unity and Diversity of Executive Functions and Their Contributions to Complex ‘Frontal Lobe’ Tasks: A Latent Variable Analysis.” Cognitive Psychology 41:49-100. doi: https://10.1006/cogp.1999.0734. Murray, Micah M., and John Antonakis. 2019. “An Introductory Guide To Organizational Neuroscience.” Organizational Research Methods 22:6-16. doi: https://10.1177/1094428118802621. Silzer, Rob, and Allan H. Church. 2009. “The Pearls and Perils of Identifying Potential.” Industrial and Organizational Psychology 2:377-412. doi: https://10.1111/j.1754-9434.2009.01163.x.

In: Neuromanagement Editor: Michela Balconi

ISBN: 978-1-53619-562-0 © 2021 Nova Science Publishers, Inc.

Chapter 6

FROM THE EVALUATION OF EXECUTIVE FUNCTIONS (EFS) TO NEUROEMPOWERMENT FOR ORGANIZATIONS Michela Balconi1,2,, PhD and Laura Angioletti1,2 1

International Research Center for Cognitive Applied Neuroscience (IrcCAN), Catholic University of the Sacred Heart, Milan, Italy 2 Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy

ABSTRACT The interest of the neuroscientific approach, recently, is aimed to implement methodologies and tools to investigate the mental processes underlying different individuals’ abilities, such as self-regulation, social 

Corresponding Author’s E-mail: [email protected].

98

Michela Balconi and Laura Angioletti skills, and higher cognition at the workplace. Indeed, neuroscience allows investigating the human mind and its own higher cognitive and social functions, called Executive Functions (EFs), by integrating several analyses, including overt behavior and covert neurophysiological correlates. Therefore, neuromanagement allows to evaluate and enhance individuals’ EFs controlling different top-down mental processes, as inhibition, working memory, attention, learning, pianification and cognitive flexibility. Particularly, neuroscience applied to the management field could offer valid evidence useful to the implementation of specific protocols for the development of new evaluation potential and performances and the enhancement of individuals’ cognitive, emotional and relationship skills.

1. THE WHAT AND WHY OF DEEPENING EXECUTIVE FUNCTIONS IN ORGANIZATIONS In a challenging environment like the competitive market in which we find ourselves, companies can gain and retain a competitive edge by focusing on the identification and development of valuable human capital (Collings, Mellahi and Cascio 2019; Obisi 2011). For the successful achievement of pre-determined work goals and for taking advantage of the ability to react adequately to workplace demands, both effective and versatile cognitive and social functioning are required. Moreover, the current professional challenges necessitated a high degree of flexibility and adaptability to new circumstances, as well as the ability to find innovative solutions to problems, be proficient in handling workrelated stressors and be successful in communicating and building positive interpersonal relationships (Balconi, Fronda et al. 2017; Balconi and Venturella 2017). A consistent panel of functions constitutes the reservoir of abilities that can be used to assess the performance and potential of growth of the Human Resources (HR) in the company, and that can be strengthened using neuroscience methods, for example neurocognitive techniques for neuroempowerment. This panel encompasses the EFs, that, according to former studies, play a crucial role in job performance, since successful

From the Evaluation of Executive Functions (EFs) …

99

professionals are characterized by high social, cognitive, and executive functioning (Bailey 2007; Willoughby and Blair 2016). But what is meant for EFs? EFs are a family of top-down mental processes that, among the others, include behavioral control (for example the self-regulation and interference control), cognitive flexibility, and working memory (Diamond 2013; Miyake et al. 2000). They are high-level cognitive functions that promote goal-directed behavior and are needed for sustained focusing, attention resource management, automatic responses, and rapid and versatile response to changing environmental demands. Another collection of complex cognitive functions (i.e., reasoning, planning, decision-making [DM], innovation, and problem-solving) is considered essential for professional achievement and optimal workplace efficiency. Taken as a whole, these mental functions form a valuable “mental resource” for adapting to continuously evolving situations and unexpected challenges. In particular, according to the self-regulatory model of Hofmann, Schmeichel, and Baddeley (2012), three main functions, that are working memory ability, behavioral inhibition, and task-switching, serve as the basis for developing an active representation of multiple self-regulatory objectives. Indeed, these functions are the foundation for adapting cognitive resources to individual objectives while actively inhibiting distracters, suppressing maladaptive and mindless conducts, managing excessive affective reactions, and regulating dysfunctional distress behaviors. The ability to self-regulate is then intimately related to EFs, as is the ability to reflexively become aware of own communication, relational, and affective schemata, as well as to perceive others’ mental states. It is also worth noting that EFs orchestrate all complex behaviors and sustain both emotion regulation and social competencies, which are essential for effective interpersonal relationships, group dynamics, and adaptive stress management (Cacioppo and Cacioppo 2020). Besides EFs deserve special attention with implications for both assessment and HR development programs because they also enable the growth of a domainindependent repertoire of soft skills, for instance, interpersonal and

100

Michela Balconi and Laura Angioletti

communication efficacy, and empathy (intended as the ability to interpret and understand others’ intentions, desires, and affective states) in both its cognitive and emotional components. Considering the complexity of this family of functions, it is not surprising that their neural substrates coincide with the most evolved part of the central nervous system, that is the prefrontal cortex (PFC), as shown in figure 1. Indeed, the PFC is a highly integrated structure, whose functioning is the target of neuroscientific-based assessment and neuroempowerment protocols. Given these premises, it is understandable the reason why the demand for assessment procedures and empowerment protocols dedicated to the EFs is growing rapidly.

Figure 1. Representation of the multiple executive processes regulated by the PFC structures.

From the Evaluation of Executive Functions (EFs) …

101

Nevertheless, despite the importance of EFs as the foundation for complex cognitive and social processes, as well as their potential position as precursors to self-regulation and other core professional soft skills, the neuromanagement area lacks an overarching structure for exploring EFs at work. The triadic model devised by Balconi, Angioletti, and Crivelli (2020) described in chapter 5 on the neuroscientific methods for the company’s assessment practices stands as a first attempt to concretely systematize the crucial skills to be evaluated and strengthened in organizational contexts, and it transversely includes, within the various clusters that compose it, the EFs. To offer an overview of the relevance of EFs for professionals’ cognitive, affective, and relational functioning, some recent protocols for the investigation and development of EFs at the workplace will be described in the present chapter. Through some recent examples of protocols developed and applied in the neuroscientific field, the challenges and future opportunities for the research and practical implementation of effective integration of neuroscientific models and methods with the development of human resources will be discussed.

2. NEUROSCIENCE-BASED TOOL FOR ASSESSING EFS IN THE COMPANY As we underlined in the previous paragraph, EFs include higher-level cognitive processes implicated in the formation of successful goal-directed behavior, encompassing planning and initiating behaviors, anticipating (positive and negative) consequences of actions, and the ability to adjust behaviors based on environmental feedback. Specifically, planning, judgment, set-shifting, anticipation, reasoning, the suppression of unnecessary information, the inhibition of inappropriate responses and DM could be considered as crucial cognitive processes required for the

102

Michela Balconi and Laura Angioletti

successful completion of any complex behavioral or cognitive task, including work daily duties. We recently developed a neuroscience-based tool for assessing EFs, but more specifically DM, in the organizational context. DM is a composited process constituted of cognitive and emotional factors that determine the selection of an action between different possible alternatives. The discipline that, in the neuroscience field, has so far dealt with further deepening the DM processes in organizations is neuroeconomics. It deals with investigating the proximate causes of DM behavior, i.e., the causes that are immediately responsible and those of a specific type of choice, as well as the neurophysiological and behavioral correlates associated with it (Camerer, Loewenstein and Prelec 2005; Glimcher 2003). Among the most in-depth topics of the neuroeconomic discipline, there are a broad variety of processes and phenomena, including the search and acquisition of rewards, DM processes in conditions of risk and uncertainty, DM strategies, the delay mechanisms of gratification, learning, cooperation and competition processes, moral DM dynamics, and the game theory. Among the others, DM is particularly relevant for the companies since it involves each different level and sector of an organization, from the individual employee (at any professional rank), who must face ordinary decisions related to the management of his/her daily work commitments and goals, up to the widest system, in which the members of the organization as a group should decide policies, strategic action plans and relevant management changes that may influence the entire company. Having adequate DM skills in the company allows an individual to have the self-confidence – typically owned by professionals who know they have the adequate tools for taking a decision – to be able to make the best choice with a low probability of error. This means knowing how to choose the most effective and efficient solution for the organization, which may have a great impact on its performance and consequently on its competitiveness. Despite the relevance and the impact of functional DM capability into the organizational context, to date, to the best of our knowledge there are no neuroscience-based tools that explore professionals’ DM ability,

From the Evaluation of Executive Functions (EFs) …

103

considering all its complex facets and components. Neuroscientific literature provides several psychometric tests, behavioral tasks, and techniques to measure the DM process, and those have been previously adopted in the organizational contexts (Butler et al. 2016), for evaluating specific categories of DM, such as financial (Frydman and Camerer 2016) or moral DM (Balconi and Fronda 2019, 2020) in top managers. However, a comprehensive tool for assessing the different dimensions of DM ability of professionals is actually lacking. Therefore in the recent tool for the neuroscientific study of DM processes developed in my laboratory, together with the research team I supervised, the construct of DM has been explored as a process of managing thought and action, which is developed based on the analysis of the context and the related information available, and that, if functionally implemented, it is characterized by effective and efficient actions, that allows individuals to make choices that have a reduced risk of damage or failure. The structure of the tool is based on a dynamic and modular process approach, which takes into account different levels of DM, which are independent one from each other but intrinsically correlated. It comprehends five domains, evaluating the following DM dimensions: 1) the decisional styles; 2) the decisional strategies; 3) the decisional effectiveness; 4) the decisional awareness; 5) the decisional metacognition (figure 2).

Figure 2. Graphical description of the five domains composing the tool for DM evaluation.

104

Michela Balconi and Laura Angioletti

Going in-depth within the tool definition, the decisional styles measured in the first domain of the tool are well-established skills and competencies conceived as a stable trait of the individual: these are components difficult to change and resistant to the context. Before DM style has been defined as “a habitual pattern individuals use in decisionmaking”, and “the learned, habitual response pattern exhibited by an individual when confronted by a decision situation” (Scott and Bruce 1995). Therefore, in the present tool DM styles evaluation was placed as the first domain to investigate, since it assesses the structural and relatively dynamic level of DM. Instead, the second domain determines the repertoire of decisional strategies, that is the knowledge of how to represent, plan and adopt complex plans of DM strategies, with attention to the context and internalexternal requests: therefore, these components are adaptable to the context and flexible. This second domain assesses the recursive functional level of the DM process. The decisional effectiveness can be examined through the third domain: it appraises the know-how to act with effective, timely, and balanced decisions with respect to internal-external costs. Indeed it explores the efficacy and the evidence-based level of DM. The fourth domain concerns the estimation of decisional awareness, intended as the awareness of mental processes, representations, the plan implemented, and the organizational role in the DM process: in this sense, it evaluates the extended awareness level of the DM process. Finally, the fifth and last domain is dedicated to the decisional metacognition that explores the degree of self-awareness and selfregulation in full awareness of the decision. It assesses the level of EFs regulations and this fifth domain consists of the maximum degree of selfawareness in decision, achievable with full DM maturity, which allows management of it in full autonomy. Each domain can be administered independently from the others, and it is composed of distinct subcomponents that aim at assessing distinct aspects of the domain-specific dimension. These subcomponents have been operationalized and investigated in multiple ways, through questionnaires

From the Evaluation of Executive Functions (EFs) …

105

and scales, scripts (i.e., descriptions of real situations connected to the professional context in which the individual should make a decision), as well as cognitive-behavioral tasks and neuroscientific measures. Regarding the application of the tool, it can be easily administered via a digital interface, and behavioral indices (such as reaction times and accuracy rate) can be collected for each level of measure and individually taken into consideration in the final evaluation. Given the specificity of this measuring tool, the future challenges and opportunities for neuromanagement research could be the integration of neuroscientific techniques in the evaluation of DM in professional contexts, but also the practical implementation of an effective neurocognitive protocol for the empowerment of this key mental process.

3. APPLIED NEUROCOGNITIVE PROTOCOLS FOR “NEUROEMPOWERING” EFS As mentioned above, there is a widespread necessity for valuable, effective, and expendable protocols aimed not only at evaluating but also at enhancing professionals’ EFs and DM skills in the neuromagement field. To attempt to meet this need, we have recently designed and tested an innovative neurocognitive protocol of intervention based on wearable neurotechnologies and specially developed for improving stress management abilities, attentional focus, executive control processes in highly-demanding professional contexts, with managers occupying toplevel ranks (Crivelli et al. 2019a). Specifically, this neuroempowerment protocol involves the combination of mindfulness-based practice and EFs enhancement, thanks to the support of wearable neurofeedback (NF) system managed via an app on a smartphone. In the last few years, several neuroscientific research validated its effectiveness in both experimental and applied contexts (Balconi, Crivelli and Angioletti 2019; Balconi, Fronda and Crivelli 2019; Balconi, Natale et al. 2017; Crivelli et al. 2019a, 2019b).

106

Michela Balconi and Laura Angioletti

Regarding its specific application in the managerial context, the feasibility and efficacy of the neuroempowerment protocol have been tested on a sample of sixteen managers (Crivelli et al. 2019a). The neuroempowerment protocol was based on breathing awareness practices derived from mindfulness practice, which were supported by a dedicated wearable NF device – namely, a non-invasive electroencephalographic (EEG) recording system connected to a smartphone app that was devised to support mental practices and help foster self-awareness and selfregulation via real-time acoustic feedbacks on changes of the EEG signature of practicer’s mindset. The training period lasted two weeks and was constituted by brief daily activities, delivered in incremental sessions. After the training period, the group of managers who took part in the experimental NF sessions displayed greater neurocognitive efficiency during challenging cognitive tasks, improved electrophysiological markers of relaxation and attentional focus, and improved autonomic markers of parasympathetic recovery when exposed to cognitive stressors. They also showed lower levels of perceived stress, anxiety, anger, and mental fatigue. In addition to the proportional innovativeness of the protocol applied in this study, this project can also be considered one of the first systematic investigations on the effects of a neurotechnologymediated empowerment protocol in an organization and with top management professionals. This first experimentation was, however, mainly aimed at only two fundamental skills: self-awareness and executive control of resources for attention and stress reaction. Instead, the implementation of a revised neuroempowerment protocol that goes deep into the proposed triadic model for talent evaluation and development (Balconi, Angioletti and Crivelli 2020) has been the subject of our most recent applied research activities. This revised protocol was designed to help individuals to train jointly the three core clusters of competencies of the triadic model and, in particular, to provide a customized training opportunity for aging senior managers to empowering their analytical, executive, metacognitive, and social skills. Concerning the practical application of the protocol, the

From the Evaluation of Executive Functions (EFs) …

107

integrated training phase begins with a comprehensive evaluation of the individual’s cognitive, executive, and affective functioning, after which the previously mentioned NF training through the wearable device is integrated into a training schedule with tasks dedicated to both i) technical and analytical skills (cognitive flexibility, working memory, and reasoning), ii) metacognitive skills (problem-solving, multitasking, and creativity), and iii) relational skills (perspective-taking, social selfawareness, and self-regulation), the three cornerstones of the triadic model. At the end of the neurocognitive training, findings suggested a greater neurocognitive efficiency featured by an improvement in working memory performance, cognitive flexibility, problem-solving, inhibitory control, self-awareness, and self-regulation, as well as a reduction in perceived stress levels both at the behavioral and neurophysiological level. These application examples can promote the future development of targeted protocols for the training of a specific subset of functions and neuroempowerment aimed at specific populations, interested in promoting their neurocognitive well-being. Furthermore, in high-level professional contexts, these neurocognitive protocols may be considered as a feasible alternative solution for preventive age management interventions. To conclude, together with the development of effective neurocognitive protocols for the empowerment of the DM process in the organizational field, one of the current challenge for neuroscientific research will consist of keeping up with the digitization process in the companies and translating these assessment procedures and neuroempowerment practices into a digital modality of use, which can also be administered and applied remotely or via online systems.

REFERENCES Bailey, Charles E. 2007. “Cognitive Accuracy and Intelligent Executive Function in the Brain and in Business”. Annals of the New York Academy of Sciences, 1118:122 - 41. doi:10.1196/annals.1412.011.

108

Michela Balconi and Laura Angioletti

Balconi, Michela, Angioletti, Laura and Crivelli, Davide. 2020. “NeuroEmpowerment of Executive Functions in the Workplace: The Reason Why”. Frontiers in Psychology, 11:1519. doi:10.3389/ fpsyg.2020. 01519. Balconi, Michela, Crivelli, Davide and Angioletti, Laura. 2019. “Efficacy of a Neurofeedback Training on Attention and Driving Performance: Physiological and Behavioral Measures”. Frontiers in Neuroscience, 13:996. doi:10.3389/fnins.2019.00996. Balconi, Michela, and Fronda, Giulia. 2019. “Physiological Correlates of Moral Decision-Making in the Professional Domain”. Brain Sciences, 9:229. doi:10.3390/brainsci9090229. ———. 2020. “Morality and Management: An Oxymoron? FNIRS and Neuromanagement Perspective Explain Us Why Things are not like This”. Cognitive, Affective and Behavioral Neuroscience, 20:1336 48. doi:10.3758/s13415-020-00841-1. Balconi, Michela, Fronda, Giulia and Crivelli, Davide. 2019. “Effects of Technology-Mediated Mindfulness Practice on Stress: Psychophysiological and Self-Report Measures”. Stress, 22:200 - 9. doi:10.1080/10253890.2018.1531845. Balconi, Michela, Fronda, Giulia, Venturella, Irene and Crivelli, Davide. 2017. “Conscious, Pre-Conscious and Unconscious Mechanisms in Emotional Behaviour. Some Applications to the Mindfulness Approach with Wearable Devices”. Applied Sciences, (Switzerland) 7:1280. doi:10.3390/app7121280. Balconi, Michela, Natale, Maria Rosaria, Benabdallah, Nadia and Crivelli, Davide. 2017. “New Business Models: The Agents and Inter-Agents in a Neuroscientific Domain”. Neuropsychological Trends, 21:53 - 63. doi:10.7358/neur-2017-021-nata. Balconi, Michela and Venturella, Irene. 2017. “Neuromanagement. What about Emotion and Communication”. Neuropsychological Trends, 21:9 - 21. doi:10.7358/neur-2017-021-balc. Butler, Michael J. R., O’Broin, Holly L. R., Lee, Nick and Senior, Carl. 2016. “How Organizational Cognitive Neuroscience Can Deepen Understanding of Managerial Decision-Making: A Review of the

From the Evaluation of Executive Functions (EFs) …

109

Recent Literature and Future Directions”. International Journal of Management Reviews, 18:542 - 59. doi:10.1111/ijmr.12071. Cacioppo, Stephanie and Cacioppo, John T. 2020. Introduction to Social Neuroscience. Princeton, NY: Princeton University Press. Camerer, Colin, Loewenstein, George and Prelec, Drazen. 2005. “Neuroeconomics: How Neuroscience Can Inform Economics”. Journal of Economic Literature, 43:9 - 64. doi:10.1257/ 00220510 53737843. Collings, David G., Mellahi, Kamel and Cascio, Wayne F. 2019. “Global Talent Management and Performance in Multinational Enterprises: A Multilevel Perspective”. Journal of Management, 45:540 - 66. doi:10.1177/0149206318757018. Crivelli, Davide, Fronda, Giulia, Venturella, Irene and Balconi, Michela. 2019a. “Stress and Neurocognitive Efficiency in Managerial Contexts: A Study on Technology-Mediated Mindfulness Practice”. International Journal of Workplace Health Management, 12:42 - 56. doi:10.1108/IJWHM-07-2018-0095. ———. 2019b. “Supporting Mindfulness Practices with Brain-Sensing Devices. Cognitive and Electrophysiological Evidences”. Mindfulness, 10:301 - 11. doi:10.1007/s12671-018-0975-3. Diamond, Adele. 2013. “Executive Functions”. Annual Review of Psychology, 64:135 - 68. doi:10.1146/annurev-psych-113011-143750. Frydman, Cary and Camerer, Colin F. 2016. “The Psychology and Neuroscience of Financial Decision Making”. Trends in Cognitive Sciences, 20:661 - 75. doi:10.1016/j.tics.2016.07.003. Glimcher, Paul W. 2003. “The Neurobiology of Visual-Saccadic Decision Making”. Annual Review of Neuroscience, 26:133 - 79. doi:10.1146/ annurev.neuro.26.010302.081134. Hofmann, Wilhelm, Schmeichel, Brandon J. and Baddeley, Alan D. 2012. “Executive Functions and Self-Regulation”. Trends in Cognitive Sciences, 16:174 - 80. doi:10.1016/j.tics.2012.01.006. Miyake, Akira, Friedman, Naomi P., Emerson, Michael J., Witzki, Alexander H., Howerter, Amy and Wager, Tor D. 2000. “The Unity and Diversity of Executive Functions and Their Contributions to

110

Michela Balconi and Laura Angioletti

Complex ‘Frontal Lobe’ Tasks: A Latent Variable Analysis”. Cognitive Psychology, 41:49 - 100. doi:10.1006/cogp.1999.0734. Obisi, Chris. 2011. “Employee Performance Appraisal and Its Implication for Individual and Organizational Growth”. Australian Journal of Business and Manegement Research, 1:92 - 7. Scott, Susanne G. and Bruce, Reginald A. 1995. “Decision-Making Style: The Development and Assessment of a New Measure”. Educational and Psychological Measurement, 55:818 - 31. doi:10.1177/ 0013164495055005017. Willoughby, Michael T. and Blair, Clancy B. 2016. “Measuring Executive Function in Early Childhood: A Case for Formative Measurement”. Psychological Assessment, 28:319 - 30. doi:10.1037/pas0000152.

In: Neuromanagement Editor: Michela Balconi

ISBN: 978-1-53619-562-0 © 2021 Nova Science Publishers, Inc.

Chapter 7

NEUROCOGNITIVE ENHANCEMENT IN ORGANIZATIONS: CHALLENGES AND OPPORTUNITIES Michela Balconi, PhD and Laura Angioletti International Research Center for Cognitive Applied Neuroscience (IrcCAN), Catholic University of the Sacred Heart, Milan, Italy Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy

ABSTRACT Neurocognitive enhancement is one of the possible options proposed by neuroscience discipline to optimize and maximize workers’ neurocognitive efficiency and behavioral performance at the workplace. It involves the adoption of neuroscience techniques able to influence the activity of neural structures and networks sub-serving cognitive skills and supporting cognitive performance. In this contribution the combined integration of non-invasive neurofeedback wearable device with 

Corresponding Author’s E-mail: [email protected].

112

Michela Balconi and Laura Angioletti mindfulness-based practice proves to be the current fittest and more appropriate mental training for all professionals in the company context. Three applied protocols will be described to highlight how neuroenhancement could promote well-being at work by increasing neurocognitive efficiency of workers (at every level, from junior to senior positions), and consequently it could augment workers’ quality of life by improving their performance. Last but not least, neuroenhancement could also act as a preventive intervention for age management at the workplace, given its efficacy in preventing cognitive decline and strengthening attention regulation.

1. WHAT IS NEUROENHANCEMENT AND WHAT KIND OF ADDED VALUE IN ORGANIZATIONS? Today, companies are increasingly investing in training on the concepts of lifelong learning, continuous training, and workers’ well-being and are looking for solutions and methods to promote growth in the company. At the same time, professionals of all levels (from junior to senior positions) are required to deliver maximum results with minimum effort, not waste time and energy, and optimize their processes and performance. One of the most current and frequent questions that companies ask to the world of research and innovation is: how is it possible to promote this continuous improvement of oneself and one’s performance in organizations? The assumption behind neurocognitive enhancement is that the empowerment of neural efficiency and cognitive ability can be applied through the entire life span by activating (or probably reactivating) the cortico-subcortical networks that mediate cognitive functions. This process can promote brain plasticity, which is described as the capacity of neural structures to reinforce existing connections and form new ones as a result of learning and experience (Balconi, Fronda, et al. 2017). Among the others, the neuroscience discipline proposes neurocognitive enhancement as one of the possible options for “boosting the organizations’ mind.” Neurocognitive self-enhancement can be defined as

Neurocognitive Enhancement in Organizations

113

“a voluntary attempt to improve one’s cognitive abilities and behavioral performance, employing neuroscience techniques capable of influencing the activity of the neural structures and neural networks that serve these skills and support performance cognitive” (Balconi, Fronda, et al. 2017).

The main aim of neurocognitive enhancement approaches is to modulate brain cognitive functioning in order to increase performance and achieve an optimal level of functioning in a given task, as well as to facilitate flexible behaviors in response to the external context (Agar 2013). Interventions for neurocognitive enhancement may target one (or more) cognitive processes and functions of the neural information processing system (for example, attention, memory, perception, and learning), as well as some cognitive strategies (Chapman and Mudar 2013). As mentioned above in the definition of neuroenhancement, what characterizes and distinguishes neuroenhancement from cognitive enhancement is the adoption of neuroscience-specific methods and techniques, which will be described in the next section. Conceiving the concept of neurocognitive enhancement along a continuum, three different possible interpretations can be attributed to it: in fact, it can be conceived in terms of increase, decrease, and optimization of performance. According to the first definition of “increase” of performance, neurocognitive enhancement refers to interventions aimed at increasing the functioning of cognitive abilities compared to normal function (Bostrom and Sandberg 2009). Secondly, the improvement of individual well-being can also be obtained by decreasing the functionality of the capacity or its effects (Earp et al. 2014), for example by reducing a specific cognitive-emotional function that is hyperactivated. Thirdly, neuroenhancement interventions can be aimed at optimizing specific cognitive functions to support and improve performance in daily activities (Anand et al. 2011). As for the transferability of neuroenhancement effects to daily life activities, previous research has shown that even the simple application of

114

Michela Balconi and Laura Angioletti

cognitive training aimed at improving cognitive functions is effective for the development of distinct and new performances during daily activities (Anguera et al. 2013; Au et al. 2015; Chapman and Mudar 2013; Dahlin et al. 2008; Nyberg et al. 2003), so that the tools of neuroscience, acting at the level of brain plasticity, can support and maintain this improvement even for a longer time. Given these premises necessary to understand what is meant by neuroenhancement, a question spontaneously arises: what is the added value of applying neurocognitive enhancement in an organization? There are at least two reasons why the application of neurocognitive enhancement in a company can be found to be an advantageous approach with considerable potential. First, by increasing the neurocognitive efficiency of workers (at all levels, from junior to senior positions), can consequently increase and improve their work performance, and this could have effects in terms of satisfaction and work well-being, up to the increase the quality of life of workers. Second, applying neurocognitive enhancement interventions in the company could act as a preventive intervention for age management in senior workers, given its effectiveness in preventing cognitive decline. Besides, it should be emphasized that the adoption of neuroscience tools brings with it a series of advantages that are sometimes less evident because they act on some “implicit” variables, which can be measured with the tools of neuroscience themselves. Some of these advantages are for example: 



the possibility of obtaining an objective evaluation and the measurability of the markers (neurophysiological and psychophysiological) of the improvement; in people with normal or high-level cognitive performance (i.e., without clinical impairments), there is a ceiling effect in terms of behavioral performance for which it seems that after a certain level of cognitive stimulation there is no more room for improvement. Instead, with the tools of neuroscience, it is possible to measure the effects of neurocognitive enhancement that occur at the

Neurocognitive Enhancement in Organizations





115

neurophysiological and psychophysiological level, and which indicate that a profound change has occurred (for example at the level of brain structures and functions). This structural and functional change (which is nothing more than an effect of brain plasticity) lays the foundation for the development of new abilities and modalities in the person; the recording of objective neurophysiological and psychophysiological parameters allows objective comparability between different groups of performers and between the performance of the same person detected at two different times; the neuroscience tools allow the monitoring of the specific training effects which are long-lasting.

Therefore, adopting the tools of neuroscience to apply neuroenhancement interventions in the company means working with a view of promoting the well-being of the person in the long term and with a view of growth for the company. It also means that the organization takes seriously the safety of applying neuroenhancement and how it could have a virtuous impact on group productivity, economy, and job satisfaction.

2. NEUROSCIENTIFIC METHODS AND TOOLS FOR NEUROENHANCEMENT FOR PROFESSIONALS Despite many neurocognitive enhancement techniques were originally developed for clinical applications, they are now increasingly applied for the empowerment of healthy individuals in different fields of interest. Several studies have shown the beneficial effects of neurocognitive enhancement on health promotion, autonomy, and success (Bostrom and Sandberg 2009; Clark and Parasuraman 2014; Greely 2008) increase in learning and acquiring skills in complex tasks related to demanding professions (Coffman, Clark, and Parasuraman 2014; Parasuraman and

116

Michela Balconi and Laura Angioletti

McKinley 2014), but also in work engagement (Liu et al. 2020), and leadership and job performance (Bartz 2018; King and Haar 2017). But what are the methods and tools that neuroscience provides to promote neurocognitive enhancement in the company? Neuroscience studies have focused on various forms of interventions that can boost brain function and human capacities within the context of neurocognitive enhancement (Cohen Kadosh 2014; Cinel, Valeriani, and Poli 2019). The ability of non-invasive cognitive enhancement treatments to cause neuromodulation or neurostimulation effects on the brain was investigated with particular attention and interest. To get more specific, on the one hand, multiple studies have shown the effectiveness of NonInvasive Brain Stimulation techniques (NIBS), such as transcranial Electrical Stimulation (tES) and Transcranial Magnetic Stimulation (TMS), in enhancing cognitive performance (Balconi and Crivelli 2020; Brunoni et al. 2012). Various research, on the other hand, has shown the results of awareness-based approaches, such as mindfulness, that facilitate cognitive enhancement by regulating cognitive and emotional processes (Balconi, Fronda, et al. 2017; Bhayee et al. 2016). Between the NIBS technique, also the neurofeedback (NF) technique is effective in enhancing cognitive efficiency in healthy people (Gruzelier 2014), and potentially reveals itself as a more adequate and suitable technique for the professional context. Let us briefly explain how the NF technique works. The fundamental concept of NF can be thought of as a loop. Indeed, NF monitors individual brain function, then processes the brain patterns of interest (e.g., alpha waves for relaxation), and delivers audio or video feedback stimuli relevant to the activity of processed cortical rhythms to the individual. The feedback can be positive or negative, based on whether the person has managed to reach the desired mental state, corresponding to the cortical rhythms set on the NF. And thanks to the principle of operant conditioning, the person learns from time to time how to achieve the desired mental state (e.g., relaxation, marked by the presence of alpha wave). In a nutshell, NF devices capture electroencephalographic (EEG) brain waves and provide real-time feedback on a person’s mind-body state behavior (Gruzelier

Neurocognitive Enhancement in Organizations

117

2014). Through this informative feedback, individuals become aware of their mind-body state and can learn to self-regulate their cortical function using NF. Wearable NF technologies now make it possible for even inexperienced practitioners to gain access to implicit markers of their internal neural and bodily states (e.g., EEG rhythms) and process this knowledge on a conscious level. If compared to conventional NF, the added benefit of new NF wearable devices is found in their high versatility, low cost, and portability. The reliability of NF wearable devices in terms of signal quality was previously compared to EEG signal and found to have a high-quality level and accurate feedback (Balconi, Fronda, et al. 2017; Bhayee et al. 2016). The results and effectiveness of a mental training protocol aided by NF wearable brain-sensing devices revealed that the devices assisted practitioners in training and optimizing the efficiency of attention management, control, and concentrating skills. Reaction times during complex cognitive tasks are reduced without a loss of accuracy as a result of these effects. Furthermore, an increase in Event-Related Potentials (ERPs) marking early attention orientation and cognitive function was observed at the central nervous system level (Balconi and Crivelli 2019). Wearable NF neurotechnology adoption may be a viable way to implement neurocognitive enhancement in the workplace, provided that the systems are functional and simple to use, the feedback interface is user-friendly, and the system is suitable for practitioners at all levels. In summary, the main advantage of the NF technique is that it is based on engaging the participant by recognizing to him/her an active role; indeed, by applying the principles of operant conditioning, the participant must learn new cognitive strategies through positive or negative feedback. In this way, this technique promotes brain plasticity and cognitive empowerment by actively training both the participants’ self-awareness and active control over the physiological correlates of their cognitive abilities.

118

Michela Balconi and Laura Angioletti

On the contrary, tES and TMS do not necessarily require the active involvement of the individual receiving the stimulation, because they are based on the externally induced modulation of the ongoing neural activity (Enriquez-Geppert, Huster, and Herrmann 2013). Previous theoretical accounts suggested that because participants are directly involved in researching and consolidating personalized strategies to intentionally modulate their own neurophysiological activity, NF empowerment interventions might have more effective results on long-term maintenance of training effects. Recently, the desire to develop one’s capacity and improve cognitive performance has resulted in the emergence and expansion of mental fitness programs. Indeed, in addition to NIBS, recent research showed that neurocognitive enhancement can be promoted by mental awareness-based practices, such as mindfulness. Mindfulness is a unique type of mental training based on selfobservation and awareness practices that are centered on the present and require deliberate intentional focusing on and acknowledgment of one’s bodily sensations, mental states, and emotions, as well as mental nonjudgement and moment-by-moment living (Kabat-Zinn 2003). More and more confirmations are obtained from studies that focus on the effects of mental training and meditation practice, which highlight the potential of these practices in modulating both manifest behavior and implicit neuroand psycho-physiological activity (Quaglia et al. 2016) and in inducing short and long-term effects of empowerment on the cognitive and emotion regulation abilities of those who practice (Balconi, Fronda, et al. 2017; Keng, Smoski, and Robins 2011). Recently, mindfulness-based interventions (MBI) have also proved an opportunity to increase individual psychological well-being (Balconi, Fronda, et al. 2017; Crivelli et al. 2019b; Keng, Smoski, and Robins 2011). This discipline favors the perception and conscious acceptance of the individual’s mental states and related physiological feelings (Keng, Smoski, and Robins 2011). Furthermore, previous research has demonstrated the effectiveness of awareness training also on various cognitive functions, such as self-regulation of attention and sustained

Neurocognitive Enhancement in Organizations

119

attention (Balconi, Crivelli, and Angioletti 2019; Crivelli et al. 2019b), preventing work memory decline (Jha et al. 2017), reducing cognitive reactivity and mental rumination (Raes et al. 2009) and decreasing physiological stress reactivity markers (Balconi, Fronda, and Crivelli 2019; Crivelli, Fronda, and Balconi 2019). For a discussion of the effects of integrated MBI on work-related stress, see chapter 4. Mindfulness’s adoption for self-empowerment in non-clinical environments, such as the workplace, has risen exponentially, since it helps the practitioner to train concentration, monitoring, and attention skills by engaging and sustaining a particular conscious and attentive mindset (Bartlett et al. 2019). A brief summary of the key strategies of non-invasive neuroenhancement, ranging from neuroscientific approaches to mindfulness-based mental exercise, was presented in the preceding paragraphs. In general, it is proposed that a combination of these approaches should be used to achieve greater neuroempowering results. These methods may also be beneficial in developing employees’ ability to deal with day-to-day challenges at work. Mindfulness may be useful for staying mentally present in the here and now and completing tasks, and its combination with neurotechnology may aid in improving knowledge of one’s abilities and supporting problem-solving abilities. In line with this suggestion to combine multiple types of intervention, we previously proved the effectiveness of an MBI combined with neurofeedback techniques (Balconi, Fronda, et al. 2017). Indeed, the adaptive improvements in neurocognitive function and brain connectivity that are caused by mental training could be boosted even further by offering individuals who practice more useful knowledge about how their psychophysical states are modulated as a result of practice. In the next sections, it will be explained how this strengthening effect becomes possible in professional contexts, thanks to the application of integrated technology-mediated mindfulness-based protocols.

120

Michela Balconi and Laura Angioletti

3. NEUROENHANCEMENT AT THE WORKPLACE: A TRAINING PROTOCOL APPLIED TO MANAGERS The first comprehensive report of the technology-mediated mindfulness-based protocol implementation in an organization and with top management professionals is listed below. Going into the specifics of the methodology, we developed and implemented a technology-mediated mental training protocol to empower neurocognitive performance in high-stress professional settings, with managers occupying top-level positions. An experimental sample of top managers was recruited based on their structured organizational roles, with a particular role as managers heading a team of at least ten employees and at least five years of people management experience. They belonged to some of the major corporations that represented primary national or foreign corporations in a variety of industries (services, transportation, food, consulting, and advertising, to name a few). The innovative neurocognitive protocol, that managers have experimented, specifically combines the practice of mindfulness and a wearable NF system managed via smartphone (Balconi and Crivelli 2019). The training protocol has been validated by previous research in both experimental and applied contexts (Balconi and Crivelli 2019; Balconi, Fronda, et al. 2017; Crivelli, Fronda, and Balconi 2019; Crivelli et al. 2019a, 2019b). Following the training course, scheduled for a duration of two weeks, the findings revealed a substantial improvement in the participants’ information processing performance during cognitive activities, as well as an increase in electrophysiological markers of the mind-brain system’s ability to focus and reactivity, and a reduction in mental exhaustion. This evidence is consistent with what is known about the impact of MBI on cognitive ability in the literature (Hommel and Colzato 2017; Lutz et al. 2008), implying that this type of mental exercise, even as a form of neurocognitive empowerment, can be beneficial.

Neurocognitive Enhancement in Organizations

121

Neurometric results support this interpretation even further. Managers showed improved target indices at the end of the protocol, showing a change from a distressed to a relaxed-focused attitude. A shift that indicates a more effective containment of hyperactivation’s carry-over effect outside of the workplace. This interpretation is also supported by the localization of the observed effects on electrophysiological activity. In fact, the frontal and parietal lobes are considered to be the central hubs of a large neural network that mediates cognitive function, attention regulation, and the selection of relevant environmental information (Ptak 2012). This is a skill that is especially important for efficiently self-regulating and adapting our actions to dynamic environments, such as fluid and highdemanding business environments (Balconi, Natale, et al. 2017; Crivelli and Balconi 2017). As a result of the training of concentration and attention orientation skills, we believe that the MBI supported by the NF training protocol had a positive impact on the efficiency of participants’ neurocognitive functioning. Taking everything into consideration, combining mindfulnessbased mental training with the benefits of a novel brain-sensing wearable technology allows for overcoming conventional methods’ flaws (e.g., significant time investment) and maximizing training opportunities and outcomes. At the end of the description of this example of neurocognitive intervention protocol applied to managers, we intend to highlight the strengths of this approach and of this applied research project, which proves to be highly ecological and applicable to various organizational contexts. The available and previously described results underline significant practical implications for professionals who intend to plan interventions to improve cognitive efficiency in the workplace, but also to enable the skills to manage work-related stress. Indeed, it seems that combining traditional approaches with highly usable and non-invasive technological devices can shorten the efforts and time required to obtain measurable improvements in cognitive and affective regulation skills even in professionals exposed to high work demands and with little time available. This reduction of the

122

Michela Balconi and Laura Angioletti

“dose” of practice and the users’ commitment, therefore, translates into a reduction in monetary costs and time to implement a training protocol of this type and the engagement of the participants, their active role, favors the levels of motivation and reduces the dropout rate. This makes it possible to design and offer easily accessible and replicable training opportunities in various organizational contexts by exploiting economies of scale and transferability.

4. PREVENTIVE NEUROCOGNITIVE INTERVENTIONS FOR AGE MANAGEMENT In Italy, a systemic increase in job quotas in favor of professionals over 50 has been reported over the last decade. These developments also created new challenges for older employees in terms of preserving their success and subjective well-being. Another clear benefit of using neuroenhancement protocols in the workplace, as previously discussed, is age management interventions. The neuroscientific study we described in the previous section demonstrated the effectiveness of NF protocols on stress reduction and cognitive efficiency in a sample of senior managers, opening up the possibility of applying interventions of this type in the context of age management. Starting from this evidence, we have recently developed an integrated neurocognitive training protocol for the empowerment of cognitive, metacognitive, and social functions in managers over 50. A first pilot case underwent an intensive three-week protocol that included the following three set of training:  

neurofeedback training via a wearable device; cognitive training focused on cognitive flexibility, working memory, multitasking, reasoning, creativity and problem-solving;

Neurocognitive Enhancement in Organizations 

123

metacognitive-social training focused on perspective-taking, selfawareness, and self-regulation (Balconi, Angioletti, and Crivelli 2020).

A pre- and post-training multilevel evaluation was used to assess the training’s results. The identification of neurometric (EEG) and autonomic indicators (Heart Rate, HR; Heart Rate Variability, HRV) at rest and during the execution of cognitively complex tasks has been coupled with the detection of psychometric, neuropsychological, and behavioral outcome steps. The EEG and autonomic task-related markers (Eventrelated Potentials - ERPs - N200 and P300, alpha and beta band power, HR) demonstrated a pattern of higher neurocognitive efficiency. Both at a behavioral and neurocognitive level, the preliminary results suggested an increase in working memory performance, cognitive flexibility, problemsolving, inhibitory control, self-awareness, and self-regulation, as well as a decrease in perceived stress levels. The potential of the integrated intensive protocol as a viable choice for preventive age management approaches in high-level professional contexts is highlighted by this first pilot study and the first promising evidence that resulted from it.

5. OPPORTUNITIES AND ACTUAL CHALLENGES The debate over the potential and opportunities of various methods and techniques for neuroenhancement in the workplace was fueled primarily by the increasing complexity and competitiveness in both social and professional contexts, as well as the drive for ever-greater performance. In this chapter, the opportunities, potentials, and usefulness of neurocognitive enhancement applied in different contexts such as managerial and age management in senior workers have been emphasized in quite a few points. Several studies have highlighted the effectiveness of neurocognitive enhancement techniques in improving cognitive processes.

124

Michela Balconi and Laura Angioletti

Of great interest for the professional work context, some studies have observed the possibility, provided by neurocognitive improvement techniques, to produce positive social effects in individuals, accompanied by the improvement of some cognitive functions such as working memory, attention, and cognition and the improvement of performance and successful activities (Balconi and Pozzoli 2005; Sahakian 2007). Concerning current challenges, the discipline of neuroethics has recently focused on the ethical implications of neuroenhancement treatments at various levels, including safety, justice, autonomy, fairness, morality, and possible negative consequences for society and professional contexts (Farah et al. 2004; Fronda, Balconi, and Crivelli 2018; Fronda, Crivelli, and Balconi 2019). When proposing neuroenhancement interventions in an organization, two key issues should be addressed from an ethical standpoint. On the one hand, these interventions have been judged as a threat to interindividual equity and tend to suppress interindividual differences (Butcher 2003). On the other hand, previous studies have criticized the neurocognitive enhancement of healthy individuals as an intervention that can change the personality of the individual by removing the characteristics that represent the unique personality traits of individuals (Farah 2005; Wolpe 2002). Given these legitimate concerns, we believe that it is necessary to increase research in this area to make an objective balance of the advantages and risks associated with the application of neuroenhancement techniques in the company. To do this, organizations must “open their doors” to groups of research trained on these issues and invest in projects, even of short duration, which applies neurocognitive enhancement in the company. Adopting the tools of neuroscience to apply neuroenhancement interventions in the company means working with a view of promoting the well-being of the employees in the long-term and with a view of growth for the company. It also means that the organization takes seriously the safety of applying neuroenhancement, but also its virtuous impact on group productivity, economy, and job satisfaction. Concluding with a take-home message, we indicate that, considering the strengths and weaknesses of neuroenhancement applications, but also

Neurocognitive Enhancement in Organizations

125

its related ethical issues, the use of integrated neuroenhancement protocols that combine mental training (awareness-based practices or cognitive training) and non-invasive techniques (such as NF) could perhaps be the most suitable, effective, appropriate and sustainable practice for organizational contexts.

REFERENCES Agar, Nicholas. 2013. Truly Human Enhancement: A Philosophical Defense of Limits. Truly Human Enhancement: A Philosophical Defense of Limits. Cambridge, MA: The MIT Press. Anand, Raksha, Sandra B. Chapman, Audette Rackley, Molly Keebler, Jennifer Zientz, and John Hart. 2011. “Gist Reasoning Training in Cognitively Normal Seniors.” International Journal of Geriatric Psychiatry 26:961–68. doi:10.1002/gps.2633. Anguera, Joaquin A., Jacqueline Boccanfuso, Jody L. Rintoul, Omar AlHashimi, Farshid Faraji, Jacki Janowich, Edmund Kong, et al. 2013. “Video Game Training Enhances Cognitive Control in Older Adults.” Nature 501:97–104. doi:10.1038/nature12486. Au, Jacky, Ellen Sheehan, Nancy Tsai, Greg J. Duncan, Martin Buschkuehl, and Susanne M. Jaeggi. 2015. “Improving Fluid Intelligence with Training on Working Memory: A Meta-Analysis.” Psychonomic Bulletin and Review 22:366–77. doi:10.3758/s13423014-0699-x. Balconi, Michela, Laura Angioletti, and Davide Crivelli. 2020. “NeuroEmpowerment of Executive Functions at the Workplace: The Reason Why.” Frontiers in Psychology 11:1519. doi:10.3389/fpsyg. 2020.01519. Balconi, Michela, and Davide Crivelli. 2019. “Wearable Devices for SelfEnhancement and Improvement of Plasticity: Effects on Neurocognitive Efficiency.” In Quantifying and Processing Biomedical and Behavioral Signals, edited by Anna Esposito, Marcos

126

Michela Balconi and Laura Angioletti

Faundez-Zanuy, Francesco Carlo Morabito, and Eros Pasero, 11–22. Cham: Springer International Publishing. ———. 2020. “Transcranial Magnetic Stimulation for Cognitive Neurosciences: Applications and Open Questions.” In Non Invasive Brain Stimulation in Psychiatry and Clinical Neurosciences, edited by Bernardo Dell’Osso, and Giorgio Di Lorenzo, 191–208. Cham: Springer. Balconi, Michela, Davide Crivelli, and Laura Angioletti. 2019. “Efficacy of a Neurofeedback Training on Attention and Driving Performance: Physiological and Behavioral Measures.” Frontiers in Neuroscience 13:996. doi:10.3389/fnins.2019.00996. Balconi, Michela, Giulia Fronda, and Davide Crivelli. 2019. “Effects of Technology-Mediated Mindfulness Practice on Stress: Psychophysiological and Self-Report Measures.” Stress 22:200–9. doi:10.1080/10253890.2018.1531845. Balconi, Michela, Giulia Fronda, Irene Venturella, and Davide Crivelli. 2017. “Conscious, Pre-Conscious and Unconscious Mechanisms in Emotional Behaviour. Some Applications to the Mindfulness Approach with Wearable Devices.” Applied Sciences (Switzerland) 7:1280. doi:10.3390/app7121280. Balconi, Michela, Maria Rosaria Natale, Nadia Benabdallah, and Davide Crivelli. 2017. “New Business Models: The Agents and Inter-Agents in a Neuroscientific Domain.” Neuropsychological Trends 21:53–63. doi:10.7358/neur-2017-021-nata. Balconi, Michela, and Uberto Pozzoli. 2005. “Morphed Facial Expressions Elicited a N400 ERP Effect: A Domain-Specific Semantic Module?.” Scandinavian Journal of Psychology 46:467–74. doi:10.1111/j.14679450.2005.00478.x. Bartlett, Larissa, Angela Martin, Amanda L. Neil, Kate Memish, Petr Otahal, Michelle Kilpatrick, and Kristy Sanderson. 2019. “A Systematic Review and Meta-Analysis of Workplace Mindfulness Training Randomized Controlled Trials.” Journal of Occupational Health Psychology 24:108–26. doi:10.1037/ocp0000146.

Neurocognitive Enhancement in Organizations

127

Bartz, David E. 2018. “Managers Utilizing Mindfulness and Flow to Maximize Job Performance and Satisfaction.” International Journal of Education and Social Science 5:1–5. Bhayee, Sheffy, Patricia Tomaszewski, Daniel H. Lee, Graeme Moffat, Lou Pino, Sylvain Moreno, and Norman A. S. Farb. 2016. “Attentional and Affective Consequences of Technology Supported Mindfulness Training: A Randomised, Active Control, Efficacy Trial.” BMC Psychology 4:1–14. doi:10.1186/s40359-016-0168-6. Bostrom, Nick, and Anders Sandberg. 2009. “Cognitive Enhancement: Methods, Ethics, Regulatory Challenges.” Science and Engineering Ethics 15:311–41. doi:10.1007/s11948-009-9142-5. Brunoni, Andre Russowsky, Michael A. Nitsche, Nadia Bolognini, Marom Bikson, Tim Wagner, Lotfi Merabet, Dylan J. Edwards, et al. 2012. “Clinical Research with Transcranial Direct Current Stimulation (TDCS): Challenges and Future Directions.” Brain Stimulation 5:175– 95. doi:10.1016/j.brs.2011.03.002. Butcher, James. 2003. “Cognitive Enhancement Raises Ethical Concerns.” The Lancet 362:132–133. doi:10.1016/s0140-6736(03)13897-4. Chapman, Sandra Bond, and Raksha Anand Mudar. 2013. “Discourse Gist: A Window into the Brain’s Complex Cognitive Capacity.” Discourse Studies 15:519–33. doi:10.1177/1461445613501444. Cinel, Caterina, Davide Valeriani, and Riccardo Poli. 2019. “Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects.” Frontiers in Human Neuroscience 13:1–24. doi:10.3389/fnhum.2019.00013. Clark, Vincent P., and Raja Parasuraman. 2014. “Neuroenhancement: Enhancing Brain and Mind in Health and in Disease.” NeuroImage 85:889–94. doi:10.1016/j.neuroimage.2013.08.071. Coffman, Brian A., Vincent P. Clark, and Raja Parasuraman. 2014. “Battery Powered Thought: Enhancement of Attention, Learning, and Memory in Healthy Adults Using Transcranial Direct Current Stimulation.” NeuroImage 85:895–908. doi:10.1016/j.neuroimage. 2013.07.083.

128

Michela Balconi and Laura Angioletti

Cohen Kadosh, Roi. 2014. The Stimulated Brain: Cognitive Enhancement Using Non-Invasive Brain Stimulation. The Stimulated Brain: Cognitive Enhancement Using Non-Invasive Brain Stimulation. Cambridge, MA: Academic Press. Crivelli, Davide, and Michela Balconi. 2017. “Agentività e competenze sociali: riflessioni teoriche e implicazioni per il management.” Ricerche Di Psicologia [“Agency and Social Skills: Theoretical Remarks and Implications for Management.” Research of Psychology] 40:349–363. doi:10.3280/RIP2017-003006. Crivelli, Davide, Giulia Fronda, and Michela Balconi. 2019. “Neurocognitive Enhancement Effects of Combined MindfulnessNeurofeedback Training in Sport.” Neuroscience 412:83–93. doi:10.1016/j.neuroscience.2019.05.066. Crivelli, Davide, Giulia Fronda, Irene Venturella, and Michela Balconi. 2019a. “Stress and Neurocognitive Efficiency in Managerial Contexts: A Study on Technology-Mediated Mindfulness Practice.” International Journal of Workplace Health Management 12:42–56. doi:10.1108/IJWHM-07-2018-0095. ———. 2019b. “Supporting Mindfulness Practices with Brain-Sensing Devices. Cognitive and Electrophysiological Evidences.” Mindfulness 10:301–11. doi:10.1007/s12671-018-0975-3. Dahlin, Erika, Lars Nyberg, Lars Bäckman, and Anna Stigsdotter Neely. 2008. “Plasticity of Executive Functioning in Young and Older Adults: Immediate Training Gains, Transfer, and Long-Term Maintenance.” Psychology and Aging 23:720–30. doi:10.1037/a0014296. Earp, Brian D., Anders Sandberg, Guy Kahane, and Julian Savulescu. 2014. “When Is Diminishment a Form of Enhancement? Rethinking the Enhancement Debate in Biomedical Ethics.” Frontiers in Systems Neuroscience 8:12. doi:10.3389/fnsys.2014.00012. Enriquez-Geppert, Stefanie, René J. Huster, and Christoph S. Herrmann. 2013. “Boosting Brain Functions: Improving Executive Functions with Behavioral Training, Neurostimulation, and Neurofeedback.” International Journal of Psychophysiology 88:1–16. doi:10.1016/ j.ijpsycho.2013.02.001.

Neurocognitive Enhancement in Organizations

129

Farah, Martha J. 2005. “Neuroethics: The Practical and the Philosophical.” Trends in Cognitive Sciences 9:34–40. doi:10.1016/j.tics.2004.12.001. Farah, Martha J., Judy Illes, Robert Cook-Deegan, Howard Gardner, Eric Kandel, Patricia King, Eric Parens, Barbara Sahakian, and Paul Root Wolpe. 2004. “Neurocognitive Enhancement: What Can We Do and What Should We Do?” Nature Reviews Neuroscience 5:421–25. doi:10.1038/nrn1390. Fronda, Giulia, Michela Balconi, and Davide Crivelli. 2018. “Neuroethical Implications of Neurocognitive Enhancement in Managerial Professional Contexts.” Journal of Cognitive Enhancement 2:356–63. doi:10.1007/s41465-018-0100-5. Fronda, Giulia, Davide Crivelli, and Michela Balconi. 2019. “Neurocognitive Enhancement: Applications and Ethical Issues.” NeuroRegulation 6:161–161. doi:10.15540/nr.6.3.161. Greely, Henry. 2008. “Towards Responsible Use of Cognitive-Enhancing Drugs by the Healthy.” Nature 456:702–5. doi:10.1038/456872a. Gruzelier, John H. 2014. “EEG-Neurofeedback for Optimising Performance. III: A Review of Methodological and Theoretical Considerations.” Neuroscience and Biobehavioral Reviews 44:159–82. doi:10.1016/j.neubiorev.2014.03.015. Hommel, Bernhard, and Lorenza S. Colzato. 2017. “Meditation and Metacontrol.” Journal of Cognitive Enhancement 1:115–21. doi:10.1007/s41465-017-0017-4. Jha, Amishi P., Joanna E. Witkin, Alexandra B. Morrison, Nina Rostrup, and Elizabeth Stanley. 2017. “Short-Form Mindfulness Training Protects Against Working Memory Degradation over High-Demand Intervals.” Journal of Cognitive Enhancement 1:154–71. doi:10.1007/ s41465-017-0035-2. Kabat-Zinn, Jon. 2003. “Mindfulness-Based Interventions in Context: Past, Present, and Future.” Clinical Psychology: Science and Practice 10:144–56. doi:10.1093/clipsy/bpg016. Keng, Shian Ling, Moria J. Smoski, and Clive J. Robins. 2011. “Effects of Mindfulness on Psychological Health: A Review of Empirical

130

Michela Balconi and Laura Angioletti

Studies.” Clinical Psychology Review 31:1041–56. doi:10.1016/ j.cpr.2011.04.006. King, Elizabeth, and Jarrod M. Haar. 2017. “Mindfulness and Job Performance: A Study of Australian Leaders.” Asia Pacific Journal of Human Resources 55:298–319. doi:10.1111/1744-7941.12143. Liu, Shengmin, Huanhuan Xin, Li Shen, Jianjia He, and Jingfang Liu. 2020. “The Influence of Individual and Team Mindfulness on Work Engagement.” Frontiers in Psychology 10:2928. doi:10.3389/fpsyg. 2019.02928. Lutz, Antoine, Heleen A. Slagter, John D. Dunne, and Richard J. Davidson. 2008. “Attention Regulation and Monitoring in Meditation.” Trends in Cognitive Sciences 12:163–69. doi:10.1016/j.tics.2008. 01.005. Nyberg, Lars, Johan Sandblom, Sari Jones, Anna Stigsdotter Neely, Karl Magnus Petersson, Martin Ingvar, and Lars Bäckman. 2003. “Neural Correlates of Training-Related Memory Improvement in Adulthood and Aging.” Proceedings of the National Academy of Sciences of the United States of America 100:13728–33. doi:10.1073/pnas. 1735487100. Parasuraman, Raja, and Richard A. McKinley. 2014. “Using Noninvasive Brain Stimulation to Accelerate Learning and Enhance Human Performance.” Human Factors 56:816–24. doi:10.1177/00187208145 38815. Ptak, Radek. 2012. “The Frontoparietal Attention Network of the Human Brain: Action, Saliency, and a Priority Map of the Environment.” Neuroscientist 18:502–15. doi:10.1177/1073858411409051. Quaglia, Jordan T., Sarah E. Braun, Sara P. Freeman, Michael A. McDaniel, and Kirk Warren Brown. 2016. “Meta-Analytic Evidence for Effects of Mindfulness Training on Dimensions of Self-Reported Dispositional Mindfulness.” Psychological Assessment 28:803–18. doi:10.1037/pas0000268.

Neurocognitive Enhancement in Organizations

131

Raes, Filip, J. Mark G. Williams, and Dirk Hermans. 2009. “Reducing Cognitive Vulnerability to Depression: A Preliminary Investigation of Memory Specificity Training (MEST) in Inpatients with Depressive Symptomatology.” Journal of Behavior Therapy and Experimental Psychiatry 40:24–38. doi:10.1016/j.jbtep.2008.03.001. Sahakian, Barbara. 2007. “Professor’s Little Helper.” Nature 450:1157–59. Wolpe, Paul Root. 2002. “Treatment, Enhancement, and the Ethics of Neurotherapeutics.” Brain and Cognition 50:387–95. doi:10.1016/ S0278-2626(02)00534-1.

In: Neuromanagement Editor: Michela Balconi

ISBN: 978-1-53619-562-0 © 2021 Nova Science Publishers, Inc.

Chapter 8

INDUSTRY 4.0 AND AUTOMATION: THE CONTRIBUTION OF APPLIED NEUROSCIENCE Federico Cassioli, Davide Crivelli, PhD and Michela Balconi, PhD International Research Center for Cognitive Applied Neuroscience (IrcCAN), Catholic University of the Sacred Heart, Milan, Italy Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy

ABSTRACT The fourth industrial revolution focuses on automation and on designing intelligent and responsive systems. Industry 4.0 represents a long-term development strategy, where efficiency is increased via the integrated analysis of large streams of data and the optimization of human-technology interactions. 

Corresponding Author’s E-mail: [email protected].

134

Federico Cassioli, Davide Crivelli and Michela Balconi In the following work, we highlight the possible contribution of applied neuroscience to Industry 4.0 and to the industrial revolution, reviewing the role of neuroergonomics in the design of novel systems, which will be required to be fully responsive and collaborative. Future technology should therefore include and consider data on neurocognitive, emotional, and attentional states of the worker. We believe that the rapid growth of wearable and non-invasive neuro-devices and sensors will grant neuroscience a paramount importance in the designing, research, and development processes of human-centered technology.

1. WHAT NEW Industry 4.0 was theoretically introduced in Germany in 2011, as an economic and development target (Gilchrist 2016). Subsequently, the term has rapidly spread to currently connote the industrial research agendas and development plans in many countries worldwide. Scientific and professional communities link the concept to the fourth revolution in the modern industry, following the (i) introduction of mechanical production, (ii) the mass production with the support of electricity towards the early twentieth century, and finally (iii) the adoption of information technology (IT) in organizational and production systems, during the 70s. The fourth industrial revolution is characterized by a focus on the automation, aiming at designing intelligent and responsive systems that can effectively increase their efficiency thanks to the integrated analysis of large and heterogeneous flows of data (Cassioli and Balconi 2020), to the adoption of new technologies for management and analysis of information (including artificial intelligence, machine learning, and cloud systems) as well as for human-machine communication (Human-Machine Interaction HMI) and communication between machines and artificial systems (Internet of Things - IoT; Machine-to-Machine communication - M2M). As reported by Roblek, Meško, and Krapež (2016) and Posada et al., (2015), Industry 4.0 has five main peculiarities: (i) optimization and customization of production, mostly using digital resources; (ii) automation and adaptation of management processes; (iii) effective humanmachine interaction (HMI); (iv) design and implementation of value-added

Industry 4.0 and Automation

135

services and businesses; and (v) automated data exchange and communication. More specifically, the 4.0 revolution facilitates interconnection and computerization and provides high product customization supported by IT, making the production chain automatic, flexible, and more efficient by, for example, facilitating communication between departments, supporting the creation of smart factories and optimizing their productivity thanks to functional IoT applications, or developing innovative business models (Shafiq et al., 2016). Industry 4.0 represents a long-term strategic development objective on a national and international level and a new vision on business management and production. Also, the extent of this transformation is supposed to have significant consequences on a social and individual level for the worker, promoting the spread of smart technologies, Artificial Intelligence, and advanced and responsive wearable devices in many contexts (Oztemel and Gursev 2020).

2. THE CONTRIBUTION OF APPLIED NEUROSCIENCE TO INDUSTRY 4.0 AND THE INDUSTRIAL REVOLUTION In the recent years, the driving force of the 4.0 revolution has promoted systematic research and development (R&D) activities on wearable technology, smart sensors, machine learning, cloud computing and the IoT. Even if the path for a complete transformation is still long and the achievement of set objectives far, this trend has led to important advances in the fields of robotics and automation in industrial and organizational contexts, providing new inputs for innovation, also for the management (Oztemel and Gursev 2020; Villalba-Diez et al., 2019). This process was positively influenced by the contribution of interpretative models, methods, and tools derived and taken from neuroscientific disciplines. The potential of neuroscience as an interpretative and exploratory perspective aiming at reaching a deeper understanding of mental processes, and the opportunities it offers for

136

Federico Cassioli, Davide Crivelli and Michela Balconi

monitoring and assessing cognitive functions and implicit responses even in real life contexts, have provided new lifeblood to methodological and theoretical issues. For example, possible application of it might be useful in the design of intelligent artificial agents with decision-making and problem-solving skills, or for the optimization and automation of strategic resource management processes, and the improvement of the humanmachine interaction.

2.1. Biometric Data for Interfaces and Artificial Supportive Agents In the Industry 4.0, new technologies, such as integrated biometric devices capable of non-invasively detecting different physiological responses while a professional is carrying out his/her activity, are attracting particular attention since they offer the possibility of analyzing behaviors and physiological activations, in order to extract models and identify the latent patterns that could facilitate optimal performances (Lohmeyer and Meboldt 2016). This possibility acquires even more value in the context of Industry 4.0, where the areas of action are made more complex by the need to integrate human work and the collaborative contribution of robots and automated systems. Among the neuroscientific investigation tools, biosignal detection systems - both referred to central nervous system (electroencephalography - EEG, funtional Near-Infrared Spectroscopy fNIRS) and peripheral nervous system (heart rate - HR, electrodermal activity - EDA, electromyography activity - EMG, pupillary response and visual behavior) - and behavioral data such as gaze patterns and eyetracking data (e.g., number-of-fixation, time-to-first-fixation and pupil diameter) support the analysis and classification of experiences, automatic responses, emotions, and mental states associated with the performance of a task, and are able to provide valuable information for the development of artificial solutions for production. Specifically, Borgianni and colleagues (2018) proposed an interesting reflection on the use of eye-tracking systems in the analysis of design processes in the engineering field. This

Industry 4.0 and Automation

137

technique has already found application for the evaluation of humancomputer interfaces for industrial software production (Zülch and Stowasser 2003). In the daily programme of activities, working memory plays an important role because it is a cognitive system with a limited capacity that can temporarily hold information, devoted to support the reasoning, decision-making, and behavioral planning processes. Working in a complex production environment requires the professional to grasp and evaluate different sources of information and to carefully select relevant information keeping it in the working memory, and then coordinate the appropriate response actions. In this context, wearable eye-tracking systems offer the advantage of a lower sensitivity to movement artifacts and therefore are suitable for on-field applications and in situations where a big amount of movements is required to the worker. In fact, monitoring professionals during the designing and creation phases using eye-tracking technology allows to achieve a better understanding of the processes in place and of the management of the mental workload during the activity, giving the opportunity to evaluate their impact and hypothesize interventions to optimize the workstation and the digital interface. The same information can then be used for the development of artificial worker assistance systems, resulting in improved performance and optimized visual behavior (Stork and Schubö 2010); or for programming responsive supportive systems capable of projecting instructions, perceptual reminders, and visual information on the worktable according to the degree of attention of the users (Wallhoff et al., 2010). In addition, the use of eye-tracking techniques has interesting practical implications in the safety field, for the anticipation and prevention of operator errors. Finally, as shown by a recent trend in applied research, eye-tracking techniques can be integrated with the use of augmented reality systems both to investigate the usability and to use the gaze patterns as a modality input in the interaction with the device during assembly or maintenance processes (Renner and Pfeiffer 2017).

138

Federico Cassioli, Davide Crivelli and Michela Balconi

2.2. Operational Excellence: How to Monitor Automation of Strategical Resource Management Processes with EEG In the framework of Industry 4.0, operational excellence represents a process-oriented approach to management, aimed at the optimization and maximization of value flows, towards a standardization of activities and the reduction of internal process variability. In order to pursue these objectives, the involved managerial workers must constantly face evolving challenges and exercise their decision-making abilities in complex and changing contexts. Although these strategic reasoning models are critical and have a significant impact on company performance, the discriminating characteristics that systematically make a stable winning model are still an object of debate. Consequently, the creation of artificial agents capable of managing such functions or facilitate the decision maker by optimizing the decision process is still an object of study and speculation. Villalba-Diez and colleagues (2019) suggest that this debate could benefit from the contribution of neuroscience and from the use of EEG to qualify and monitor in real time the decision-making and strategic problem-solving processes in authentic work situations. In particular, the EEG technique proved to be useful for the automated classification of cognitive tasks (Di Flumeri et al., 2019), of alertness levels (X. Zhang et al., 2017), of states of focused attention, emotions and stress responses (Ahn, Ku, and Kim 2019; Masood and Farooq 2019), and of cognitive workload (Y. Zhang and Shen 2019). Related to the Industry 4.0, the challenge is represented by the definition of intelligent algorithms, which should be able to classify and qualify the complex EEG responses of a human decision maker in a short time and make them understandable to other artificial agent, enhancing the human-machine interaction. In addition, information on these processes and on the electrophysiological activity could be given back by the artificial agent to the decision maker in the form of feedback, thus promoting her/his metacognitive awareness and initiating a virtuous circle of self-empowerment (Balconi, Angioletti, and Crivelli 2020; Balconi et al., 2017). Using correlational analysis and deep learning techniques

Industry 4.0 and Automation

139

applied to EEG responses detected during process management tasks, Villalba-Diez et al., (2019) observed how these tasks can be considered executive tasks, mediated mainly by the prefrontal cortex (PFC) and associated with decision-making, self-regulation mechanisms, and working memory, but also how it is possible to distinguish the electrophysiological signatures of tasks and modalities of different process management. These studies suggest that, even in a business environment in which the complexity of processes is high and constantly increasing, it is possible, by integrating a non-invasive EEG activity detector with a machine learning system, to implement a classifier capable of estimating the appearance of functional decision-making models based on the situation and providing information to the users.

2.3. Human-Robot-Interaction and Neuroergonomics: The “Co-Bot” Example In some cases, conventional robotic automation systems fail to consider production demand, especially when the target product is variable, the production itself requires flexibility, and the intervention of human workers is needed to overcome these limitations. This integration requires interaction and collaboration between man and machine that is only possible by introducing intelligent interfaces that allow adaptive support during production. In this area of Industry 4.0, one of the most critical aspects concerns the optimization of the man-machine relationship, looking for a good fit among the dynamic work allocation between the human and robot worker, the role of intelligent support needed, and the inherent limitations of cognition as the workload increases. Also, the automation of a process adds value when the efficiency of the machine is enhanced by the human cognitive skills and flexibility. The equilibrium between machine automation and human workers unlocks efficiency and it is even more important for safety reasons in collaborative robotic (co-bot) technology. The main difference of this new type of technology compared to the previous one is the augmented level of

140

Federico Cassioli, Davide Crivelli and Michela Balconi

interaction with the human being which would bring benefits to the workers. In fact, as suggested by Hashemi-Petroodi and colleagues (2020), the inclusion of collaborative systems, which are reliable and tirelessness, might reduce human cognitive load, preventing their involvement in unergonomic and risky task and allowing human to better use of creativity and intelligence. Yet, to date, in the industries fully collaborative co-bot are still not systematically present or tend not to be directly interacting with workers, being separated by barriers or working in sequential collaboration conditions.

2.4. New Challenges and Perspectives: The Applications to Neuroergonomics In this light, some added value could be brought by neuroergonomics – defined as the study of physiological responses and behavior in work contexts, aiming at aligning technological and human skills in order to increasing efficiency (Parasuraman and Rizzo 2007). In fact, by improving the understanding of neural correlates underlying human performance in complex and real tasks, it might be possible to design safer and more efficient technologies and work environments. In particular, according to the authors, the results of field surveys obtained by combining different investigation tools (for example, behavioral indices, EEG and motiontracking) could help to design and adapt human-machine interfaces to the constraints of human perception, processing skills, and cognitive control, as well as specific individual characteristics (such as, for example, work expertise), providing important guidelines and constraints for the presentation of information. The introduction of this type of adaptive and responsive systems could plausibly reduce the human error rates and help optimizing and regulating the production performance according to criteria based on neuroscientific evidence. The adopted methods in neuroergonomics research combine the classic measurement of behavioural performance with neuroimaging techniques (although they are less used, as they are not portable), EEG and event-

Industry 4.0 and Automation

141

related potentials (ERPs), motion-tracking and eye-tracking. For example, ERPs are useful for studying mental workload (Wickens 1990), motion tracking techniques can be used to study the interaction between a human and a robotic artificial agent, in order to analyze and model the presence of coordination patterns in the movements and subsequently program the artificial agent so that both the human worker and the robotic agent mutually predict and anticipate the following phases, ensuring higher safety standards (Hägele, Schaaf, and Helms 2002). For example, related to the mental load, the use of slow and fast wave increases and ratios in frontal and central brain areas were already adopted to study cognitive and emotional planning (Wang et al., 2020). Also, emotional states are generally inferred via frontal asymmetry (Balconi and Mazza 2010), theta beta ratio and Hjorth parameters (Mowla et al., 2020). Furthermore, a quality cycle for the optimization of co-bot system was proposed (Cassioli, Fronda, and Balconi 2021), where a co-bot system is firstly tested via simulations of real industrial tasks while data is collected and subsequently used to enhance the human-robot interaction (HMI) by developing novel models. Lastly, robotic agents could be programmed by implementing imitation learning modules – a learning modality based on the analysis of an operator’s motor patterns, aimed at detecting human movement and automatically identifying its purpose – so as to optimize their patterns of action, adaptation, and interaction with the environment.

CONCLUSION As indicated by Briken (2020), it is evident that organizations in the future will be facing conditions where the production process will be more intelligent, flexible, adaptable, autonomous and sensor based. Furthermore, it is plausible that future management and production systems will not only be based on current Industry 4.0 standards but adopt fully automated systems and adaptive robotic technology, characterized by human-like behavior patterns. As we showed via the example of co-bot, neuroscience

142

Federico Cassioli, Davide Crivelli and Michela Balconi

could add value in the industrial environment and more in particular in the Industry 4.0 era, where the understating of human condition is a necessity for the guarantee of productivity, safety and efficiency. By adopting a neuroergonomics perspective, it could be possible to enhance the relation between human and robotic systems aiming at the development of optimized HMI. More in general, the analysis of the literature on the 4.0 revolution suggests a rapid and systematic growth in the field of wearable neurotechnology, augmented reality and artificial intelligence, with a growing role for neuroscience in the designing process and research and development activities. Nevertheless, it should be strongly emphasized that, despite some cases or specific conditions related to commercial applications based on the principles of Industry 4.0, the common implementation of wearable devices and cyber-physical systems is not fully developed and advanced in the organizational and industrial sector. For this reason, to make industrial systems more intuitive and suitable for humans, further technological advancements have to be made. In addition, some ethical issues relating, for example, to the concept of “cybernetic control” (Raffetseder, Schaupp, and Staab 2017) and possible decreases in autonomy, authenticity and self-efficacy at work were highlighted (Butollo, Jürgens, and Krzywdzinski 2019), which are issues that will have to be carefully addressed.

REFERENCES Ahn, Joong Woo, Yunseo Ku, and Hee Chan Kim. 2019. “A Novel Wearable EEG and ECG Recording System for Stress Assessment.” Sensors 19:1991. doi:10.3390/s19091991. Balconi, Michela, Laura Angioletti, and Davide Crivelli. 2020. “NeuroEmpowerment of Executive Functions in the Workplace: The Reason Why.” Frontiers in Psychology 11:1519. doi:10.3389/fpsyg.2020. 01519.

Industry 4.0 and Automation

143

Balconi, Michela, Giulia Fronda, Irene Venturella, and Davide Crivelli. 2017. “Conscious, Pre-Conscious and Unconscious Mechanisms in Emotional Behaviour. Some Applications to the Mindfulness Approach with Wearable Devices.” Applied Sciences (Switzerland) 7:1280. doi:10.3390/app7121280. Balconi, Michela, and Guido Mazza. 2010. “Lateralisation Effect in Comprehension of Emotional Facial Expression: A Comparison Between EEG Alpha Band Power and Behavioural Inihibition (BIS) and Activation (BAS) Systems.” Laterality: Asymmetries of Body, Brain and Cognition 15:361–84. doi:10.1080/13576500902886056. Borgianni, Yuri, Erwin Rauch, Lorenzo Maccioni, and Benedikt Gregor Mark. 2018. “User Experience Analysis in Industry 4.0 – The Use of Biometric Devices in Engineering Design and Manufacturing”. Paper presented at the 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM, Bangkok, Thailand, December 16–19. doi:10.1109/IEEM.2018.8607367. Briken, Kendra. 2020. “Welcome in the Machine: Human-Machine Relations and Knowledge Capture” Capital & Class 44:159–71. doi:10. 1177/0309816819899418. Butollo, Florian, Ulrich Jürgens, and Martin Krywdzinsky. 2019. “From Lean Production to Industry 4.0: More Autonomy for Employees?” In Digitalization in Industry, edited by David Seibt, Simon Schaupp, and Uli Meyer, 61–80. Cham: Springer International Publishing. Cassioli, Federico, and Michela Balconi. 2020. “Big Data: Neuroscience at the Service of Management.” In Neuromanagement. People and Organizations, edited by Michela Balconi, 113–22. Milano: LED Edizioni Universitarie [Milan: LED University Editions]. Cassioli, Federico, Giulia Fronda, and Michela Balconi. 2021. “HumanCo-Bot Interaction and Neuroergonomics: Co-Botic Vs Robotic Systems.” Frontiers in Robotics and AI, 8:77. doi:10.3389/ frobt.2021.659319. Di Flumeri, Gianluca, Pietro Aricò, Gianluca Borghini, Nicolina Sciaraffa, Antonello Di Florio, and Fabio Babiloni. 2019. “The Dry Revolution: Evaluation of Three Different EEG Dry Electrode Types in Terms of

144

Federico Cassioli, Davide Crivelli and Michela Balconi

Signal Spectral Features, Mental States Classification and Usability.” Sensors 19:1365 doi:10.339/s19061365. Gilchrist, Alasdair. 2016. Industry 4.0: The Industrial Internet of Things. New York: Apress Media, LCC. doi:10.1007/978-1-4842-2047-4. Hägele, Martin, Walter Schaaf, and Evert Helms. 2002. “Robot Assistants at Manual Workplaces – Effective Co-Operation and Safety Aspects.” Proceedings of the 33rd International Symposium on Robotics, ISR 7:97. doi:10.1007/978-3-540-31509-4_29. Hashemi-Petroodi, Ehsan, Simon Thevenin, Sergey Kovalev, and Alexandre Dolguia. 2020. “Operationas Management Issues in Design and Control of Hybrid Human-Robot Collaborative Manufacturing Systems: A Survey.” Annual Reviews in Control 49:264–76. doi:10.1016/j.arcon trol.2020.04.009. Lohmeyer, Quentin, and Mirko Meboldt. 2016. “The Integration of Quatitative Biometric Measures and Experimental Design Research – Approaches, Perspectives, Applications.” In Experimental Design Research, edited by Philip Cash, Tino Stankovic, and Mario Štorga, 97–112. Cham: Spinger International Publishing. Masood, Naveen, and Humera Farooq. 2019. “Investigating EEG Patterns for Dual-Stimuli Induced Human Fear Emotional State.” Sensors 19:522. doi:10.3390/s19030522. Mowla, Rakibul, Racheal Cano, Katie Dhuyvetter, and David Thompson. 2020. “Affective Brain-Computer Interfaces: A Tutorial to Choose Perfomance Measuring Metric.” ArXiv 4:1–12. doi:10.1016/j.comp biomed.2020104001. Öztemel, Ercan, and Samet Gursev. 2020. “Literature Review of Industry 4.0 and Related Technologies.” Journal of Intelligent Manufacturing 31:127–82. doi:10.1007/s10845-018-1433-8. Parasuraman, Raja, and Matthew Rizzo. 2007. Neuroergonomics: The Brain at Work. Oxford: Oxford University Press. Posada, Jorge, Carlos Toro, Iñigo Barandiaran, David Oyarzun, Didier Stricker, Raffaele de Amicis, Eduardo B. Pinto Peter Eisert, Jürgen Döllner, and Ivan Vallarino. 2015. “Visual Computing as A Key Enabling Technology for Industry 4.0 and Industrial Internet.” IEEE

Industry 4.0 and Automation

145

Computer Graphics and Applications 35:26–40. doi:10.1109/ MCG. 2015.45. Raffetseder, Eva-Maria, Simon Schaupp, and Philipp Staab. 2017 “Kybernetik Und Kontrolle: Algorithmische Arbeitssteurung Und Betriebliche Herrshaft.” [“Cybernetics And Control: Algorithmic Work Control And Operational Mastery.”] PROKLA 47:229–48. doi:10. 32387/prokla.v47i187.143. Renner, Patrick, and Thies Pfeiffer. 2017. “Attention Guiding Techniques Using Peripheral Vision and Eye Tracking for Feedback in Augmented-Reality-Based Assistance Systems” Proceedings of the 2017 IEEE Symposium on 3D User interfaces (3DUI) 1:186–94. doi:10.1109/ 3dui.2017.7893338. Roblek, Vasja, Maja Meško, and Alojz Krapež. 2016. “A Complex View of Industry 4.0.” Sage Open 6:1–12. doi:10.1177/2158244016653987. Shafiq, Syed Imran, Cesar Sanin, Edward Szczerbicki, and Carlos Toro. 2016. “Virtual Engineering Factory: Creating Experience Base for Industry 4.0.” Cybernectis and Systems 47:32–47. doi:10.1080/ 01969722.2016.1128762. Stork, Sonja, and Anna Schubo. 2010. “Human Cognition in Manual Assembly: Theories and Applications.” Advanced Engineering Informatics 24:320–28. doi:10.1016/j.aei.2010.05.010. Villalba-Diez, Javier, Xaiochen Zheng, Daniel Schimidt, and Martin Molina. 2019. “Characterization of Industry 4.0. Learn Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning.” Sensors 19:2841. doi:10.3390/s19132841. Wallhoff, Frank, Blume Jurgen, Alexander Bannat, Wolfgang Rosel, Claus Lenzc, and Alois Knollc. 2010. “A Skill-Based Approach Towards Hybrid Assembly.” Advanced Engineering Informatics 24:329–39. doi:10.1016/j.aei.2010.05.013. Wang, Zhongting, Ling Luo, Chanyu Zou, Linghua Ran, and Haimei Wu. 2020. “Research on The Influence of Dimension of Visual Grouping Cue for Guiding Sign Recognition.” Advances in Intelligent Systems and Computing 2:390–96. doi:10.1007/978-3-030-20473-0_38.

146

Federico Cassioli, Davide Crivelli and Michela Balconi

Wickens, Christopher. 1990. “Applications of Event-Related Potential Research to Problems in Human Factors.” In Event-Related Potentials: Basic and Applied Issues, edited by John Rohrbaugh, John Parasuraman, and Ray Johnson, 301–9. Oxford: Oxford University Press. Zhang, Yihong, and Yuou Shen. 2019. “Parallel Mechanism of Spectral Feature-Enchanced Maps in EEG-Based Cognitive Workload Classification.” Sensors 19:808. doi:10.3390/s19040808. Zhang, Xiaoliang, Jiali Li, Yugang Liu, Zutao Zhang, Zhuojun Wang, Dianyuan Luo, Xiang Zhou, et al., 2017. “Design of A Fatifue Detection System for High-Speed Trains Based on Driver Vigilance Using A Wireless Wearable EEG.” Sensors 17:486. doi:10.3390/s17030486. Zülch, Gert, and Sascha Stowasser. 2003. “Eye Tracking for Evaluating Industrial Human-Computer Interfaces.” In The Mind’s Eye: Cognitive and Applied Aspects of Eye Movement Research, edited by Jukka Hyönä, Ralph Radach, and Heiner Deubel, 531–53. North Holland: Elsevier Science.

In: Neuromanagement Editor: Michela Balconi

ISBN: 978-1-53619-562-0 © 2021 Nova Science Publishers, Inc.

Chapter 9

DIGITAL-LEARNING FOR ORGANIZATION: INSIGHTS FROM COGNITIVE NEUROSCIENCE Davide Crivelli, PhD and Michela Balconi, PhD International Research Center for Cognitive Applied Neuroscience (IrcCAN), Catholic University of the Sacred Heart, Milan, Italy Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy

ABSTRACT Recently, advances in technology and learning methods have allowed the development of new training practices within the workplace. This was made possible, in particular, by digital-learning, which allowed trainers and users to share knowledge in any place and time. Furthermore, digitallearning has made it possible to give rise to more economical, practical, and realistic training addressing many people at the same time. The possibilities offered by digital-learning have also been explored by 

Corresponding Author’s E-mail: [email protected].

148

Davide Crivelli and Michela Balconi neuroscience, interested in applying neuroscientific models of learning and cognition to implement increasingly effective and useful e-learning programs. In addition, the contribution of neuroscience in improving digital-learning also includes the possible use of neuroscientific techniques for the assessment and monitoring of learning outcome and quality. This, therefore, leads to an increasingly deepening of the investigation of neuroscientific methods and techniques that could be useful for improving the digital-learning field.

1. THE CONTRIBUTION OF COGNITIVE AND EDUCATIONAL NEUROSCIENCE TO WORKPLACE DIGITAL-LEARNING The quest towards a renewed approach to organizational and human resources management based on complex competences and centered on the person requires, as suggested by Nilsson and Ellström (2012), a dynamic adaptation of management practice, in which efficient professional education and talent development programs represent a crucial factor for success. Technological advances and progresses in lifelong learning models and methods allowed, in the past years, to tackle such issues and develop novel practices for training even at the workplace. In particular, digital-learning understood as the ensemble of learning models, practices, and technological means used to distribute multimedia educational and training contents and to foster distance learning processes - gave trainers and users the opportunity to engage with learning anywhere and almost at any time and to readily share knowledge. Furthermore, thanks to digital-learning, training becomes more efficient and cost-effective - i.e., more individuals are able to access training “simultaneously” - as well as more consistent and realistic - thanks to the use, for example, of videos and similar realistic or immersive materials taken from field experiences and actual casehistories. Notably, the innovation trend that came along with the development and spread of digital-learning practices was likely promoted also by the hot debate that, since almost two decades ago, began to promote critical

Digital-Learning for Organization

149

thinking on traditional learning theories and methods by looking for a fruitful integration of cognitive-affective neuroscience discoveries, as well as explicative models, with pedagogical and education traditions (Bowers 2016; Bruer 1997; Gabrieli 2016; Howard-Jones et al. 2016). As claimed in 2011 by Frith and colleagues “Education is about enhancing learning, and neuroscience is about understanding the mental processes involved in learning. This common ground suggests a future in which educational practice can be transformed by science, just as medical practice was transformed by science about a century ago” (Frith et al. 2011, 5). While it has to be acknowledged that scientific literature and properly studied cases of neuroscience-informed digital-learning practices at the workplace are still scarce, we do think that we are now at the right time to definitely promote both research on neural and physiological markers of online, as well as traditional, learning processes in organizational contexts, and development of innovative digital-learning programs, which might - as an example - rely on a proper brain-centered, besides person-centered, approach. Besides holding many promises, devising and developing online learning programs and materials require commitment and resources and should therefore be “done right” (Rossett 2002), by keeping in focus the target learners and the cognitive as well as neurofunctional correlates of the learning process. Cognitive and educational neuroscience provide valuable explanatory models concerning the learning brain, which might help shaping professional training opportunities included into the company HR development plan and designing efficient digital-learning modules focused on target learners. Namely, neuroscience might provide further insight with respect to the following critical issues:   

the way multimedia information is captured; the way the learning system deal with new information in online learning contexts; the way information is transcribed into knowledge and stored.

150

Davide Crivelli and Michela Balconi

Figure 1. The contribution of neuroscience to the creation and evaluation of efficient digital-learning and development programs at the workplace: a synoptic model of core processes and application fields.

In addition, besides the contribution of neuroscience to the creation of efficient training and development programs even at the workplace (see Figure 1), such discipline also offers a unique opportunity to enrich the monitoring and assessment of learning process and outcomes via objective measures, which might complement behavioural and subjective evaluation of the effects of such programs. We will focus on this interesting opportunity in the last paragraph.

2. THE WAY MULTIMEDIA INFORMATION IS CAPTURED, PROCESSED, ASSIMILATED, AND TRANSCRIBED INTO KNOWLEDGE Firstly, learners, in order to take in new information and build transferrable knowledge, have to attend to salient information, manipulate and repeatedly experience the coding of such information, and process it taking into account different contexts, their cognitive schemata, and their

Digital-Learning for Organization

151

previous experience. Attention is the complex mechanism that manages if and how information is captured and made accessible to the cognitive system of a learner. Therefore, what trainees experience and learn is very much a function of what they attend to. Such field evidence find its explication in the neurocognitive model of attention filter (Kastner and Pinsk 2004; Treisman 1964), according to which the multiple data entering the central nervous system in parallel via the sensory systems during a task are initially held in a temporary buffer, where they are quickly analyzed for basic physical properties, and then “filtered” so that only those signals that are salient and/or relevant to the ongoing task are further processed. Cognitive neuroscience provides the basic knowledge to properly qualify and quantify such filtering and focusing processes especially when they develop in complex and multimodal digital-learning contexts. As an example, neuroscience studies had long since demonstrated that attention can modulate sensory and perceptual processing since its very early steps (Kastner and Pinsk 2004) and that the effect of such filtering on information-processing and behaviour can be objectively measured, monitored, and predicted. Notably the outcome of such filtering and salience attribution process, mediated by the prefrontal-insular-cingulate salience network (Menon and Uddin 2010), is also guided by motivational and affective factors, which explains the strong causal connection between engagement in learning experience, captivating multimedia, learning outcomes, and memorability (Hew 2016). Those findings have remarkable practical implications for the presentation of information in digital-learning and might therefore help content creators and trainers in designing efficient online and multimedia materials. Focusing, as an example, on the use of multimedia, research on multisensory stimulation suggests that the combination of verbal, pictorial and acoustic stimuli might stimulate the growth of new neural connections via the creation of multi-sensory experiences (Mayer and Moreno 2002). Yet, in order to avoid being stuck into oversimplified rules of thumb, it has also to be acknowledged that even if the redundancy of information elicited via different perceptual and cognitive representations seems to

152

Davide Crivelli and Michela Balconi

represent an added value for knowledge creation and consolidation of learning, neuroscience evidence highlighted that multiple representations also have their cognitive and neural costs in terms of both encoding effort and integration of multimodal information - a demand that might turn against the learner (Fisher, Hopp, and Weber 2019). Linked to this last point, how to create optimal training programs and materials actually is one of the first and yet still debated topic in lifelong education and professional education research, with peculiar reference to multimedia and technology-based digital-learning. At the heart of such debate lies the Cognitive Load Theory (CLT; Sweller 1994), which, despite a few critics (see de Jong 2010), played a relevant role in innovating instructional design for traditional and digital-learning applications, as well as evaluation of online and multimedia learning environments (Lane and D’Mello 2019; Martin 2014). The model grounds on cognitive theories supported by neuroscientific evidence regarding two specific types of mnestic functions - namely, working memory (WM) and long-term memory (LTM) - and their interaction and interdependence (Kirschner, Ayres, and Chandler 2011; Sweller 1994). In the CLT, WM especially plays a pivotal role, in that it allows for temporary storage and manipulation of the information necessary to complete the ongoing cognitive tasks that enable proper learning. The learning process is therefore hindered or blocked when the WM becomes overwhelmed by too much information and processing, a particularly relevant risk in professional digital-learning contexts where much of the learning process is transferred to multimedia materials and the deprived relational contexts taxes the trainee’s cognitive system. According to the CLT, three core components influence the cognitive load imposed by a learning task: intrinsic, extraneous, and germane load (Sweller 2010). Intrinsic load is determined by the complexity of the learning tasks and topics. Extraneous load is determined by suboptimal instructional procedures. Germane load, instead, depends on learners’ WM resources that are currently available to deal with the learning experience. While the first, it being intrinsically linked to the object of learning, cannot be directly altered by the trainer or instructional designer, both extraneous

Digital-Learning for Organization

153

and germane load can be facilitated by the learning content creator and the trainer. Indeed, optimal performance and outcomes in organizational training, especially when delivered online, can be fostered by finding the right balance between keeping multimedia materials and activities sufficiently challenging but still within the cognitive capacities of the learners. As an example, if the learning object is notably complex and present high intrinsic load, trainers might foster the learning process and learning motivation by providing clear information and embedded support (thus limiting the extraneous and germane loads) and by creating a facilitating and engaging learning environment, so to capitalize on the involvement of prefrontal executive and motivational networks in consolidating knowledge and competence and making the learning experience more pleasant. Finally, neuroscientific models also provide a framework to understand the processes that lead to knowledge and competence consolidation during learning and skills development. In that regard, neuroplasticity - mirroring the ability of our brain to constantly change depending on environmental stimulations - is the neural base of the notable adaptability of our neurocognitive system. Namely, experience-dependent plasticity following the Hebbian principle stating that “neurons that fire together wire together” (Hebb 1961) - is now deemed as a phenomenon that connote the whole life-span (Lövdén et al. 2010), though characterized by different sensitivity and efficiency depending on age and other individual and environmental factors. Furthermore, it is considered the enabling factor of the brain ability to store the results of learning in the form of memories. Mnestic traces - i.e., the way information is stored in the central nervous system in the form of cell assemblies - are therefore created by experience and depend on learning processes. Research in cognitive and clinical neuroscience helped to point out the basic principles of neural plasticity that foster learning in both the damaged and the healthy brain (Kleim and Jones 2008). Again, the critical analysis of such pieces of evidence, might provide a few recommendations for lifelong educational practice and design of digital-learning programs. In particular, in line with the “Use it and improve it” principle, efficient

154

Davide Crivelli and Michela Balconi

digital-learning programs have to allow the learner to be an active agent of the learning process. Feeling agency - understood as the sense of being in charge of your own behaviour and of feeling actively involved in an experience (Crivelli and Balconi 2010, 2017) - during the learning process is a strong promoter of learning performance. Indeed, fostering the processing of new information and experience via active participation and first-hand individual and group-works notwithstanding the physical distance would help encoding them via deeper mechanisms, making them personally meaningful, and easing their integration within the learner’s knowledge framework. Furthermore, by facilitating learners’ awareness of their learning activities and their active role in them, it is possible to foster their selfreflection and metacognitive skills, which will in turn contribute towards effective and flexible learning ability. Again, in line with the “Salience matters” principle of neuroplasticity, efficient digital-learning materials and experiences should be created taking into account that learning is cumulative and personal (Ellis and Goodyear 2010). Namely, since the learner’s knowledge framework will influence how and how much new information is included in learning and sense-making processes and since the learner constructs its own knowledge in a unique way based on previous experiences, interests, and beliefs, the salience, personal relevance and professional implications of the digital-learning experience as well as the links between digital-learning topics/activities and previous neighbouring knowledge and expertise should be clearly highlighted, so to strengthen an adaptive learning process. Finally, creators of digital-learning contents should keep in mind that, in line with the “Specificity” and “Transference” principles of neural plasticity, learning needs to be situated and goal-directed in order to be efficient (Ellis and Goodyear 2010). The learning processes and outcomes, especially in professional training and HR development programs, are significantly influenced by the context in which learning is taking place, would it be physical or virtual. Therefore, pragmatism, authenticity, and professional relevance of training tasks and learning contexts have gradually become core and mandatory requisites in content design (Ring

Digital-Learning for Organization

155

and Mathieux 2002), in particular for organizational digital-learning programs (e.g., concerning safety, industrial design, communication efficiency) and especially when such programs are delivered at distance. And again, in order to ensure effectiveness of learning experiences especially when they are mediated and when interpersonal dynamics are likely looser, such as in digital-learning - training/development goals need to be clearly set, shared, and understood by the learners.

3. ASSESSMENT OF DIGITAL-LEARNING EFFICACY USING NEUROMETRIC AND PHYSIOLOGICAL PERFORMANCE MARKERS In order to evaluate and optimize the quality of a learning experience it is necessary to monitor and efficiently assess the progress and the final outcome of the learning process. That need becomes even more relevant in digital-learning applications, where the learner is often given much autonomy, and in professional contexts, where training and development opportunities acquire a peculiar strategic relevance. By observing, measuring, testing, and reflecting on what happens during the training period, learners and trainers may co-operate to adjust, if needed, strategies, technologies, and learning activities to achieve the intended outcomes. Notwithstanding well-known limitations of observational and selfreport outcome measures (Antonenko et al. 2010; Lane and D’Mello 2019), assessment of learning performance and outcomes is, to date, still usually based on behavioural and psychometric tools. Nonetheless, recent technological and methodological progresses in neuroscientific tools and the diffusion of portable non-invasive and less-expensive devices for physiological measures are creating novel opportunities for objective measurement of physiological markers of learning even at the workplace and in ecological contexts. According to a recent review by Lane and D’Mello (2019), a wide range of technologies have been put under test, including depth-sensing

156

Davide Crivelli and Michela Balconi

cameras, electrodermal activity (EDA) recording, electroencephalography (EEG), posture detectors, eye- and head-tracking cameras, and mousepressure sensors. Here we will focus on one of the most promising - and yet still not systematically explored - developments in the field: the use of EEG to monitor and measure cognitive load for the purpose of adjusting the digital-learning experience and assessing its cognitive cost and efficacy online. EEG is a non-invasive technique used to investigate the electrophysiological signature of cognitive processes by monitoring both tonic and event-related fluctuations of scalp electrical potential linked to cortical activity. Interestingly, the long since known association between alpha and theta EEG oscillations and task demand (Klimesch, Schack, and Sauseng 2005), has been observed even in the still limited investigations of electrophysiological markers of complex learning processes. For example, Gerlič and Jaušovec (1999), in a seminal work on multimedia contents, showed that desynchronization of alpha EEG band might mirror the cognitive load induced by various multimedia formats relative to the areas of the cortex involved in processing modality-specific input, as well as inter-individual differences in managing such load. Again, the potential of alpha neurometrics as marker for inter-individual differences in learning efficiency was further supported by comparing experts and novices (Gerlič and Jaušovec 2001). Then, Antonenko and Niederhauser (2010) by focusing on hypertexts, showed that EEG can serve as an online continuous measure of cognitive load detecting its subtle fluctuations even when self-report measures of mental effort do not detect any difference in experienced cognitive load. In particular, the authors proved that desynchronization of alpha oscillations together with synchronization of theta waves mark higher cognitive effort, and that node previews likely improve the effectiveness of hypertexts. This is an example of how the integration of fine-grained neuroscientific measures might support optimal design of multimedia contents and complex learning environments. Such potential is further suggested by a recently-published investigation focused on educational videos (Castro-Meneses, Kruger, and Doherty 2020). By varying the linguistic complexity of professional-quality video lectures, the

Digital-Learning for Organization

157

authors showed that theta power modulation might act as objective measure of cognitive load in the context of online and blended learning, which heavily rely on videos, particularly for measurement of fluctuations in instantaneous load. While it has to be acknowledged that the contribution of EEG and other neuroscientific techniques to assessment and monitoring of digitallearning process and outcomes needs to be further explored, available data and investigations in neighbouring application areas (e.g., Balconi, Angioletti, and Crivelli 2020) seem to outline valuable food for thought. Also, as pointed out by Castro-Meneses and colleagues (2020), the real potential of EEG applications to digital-learning resides in the online measurement of moment-to-moment fluctuation of learner’s cognitive load, which could be used to design tailored instructions and responsive learning environments or simply to detect obstacles in learning experience and associated cognitive processes.

CONCLUSION We have opened this chapter by reminding the role of professional education and talent development as necessary, strategic, and crucial investment in terms of organizational success and performance. In highly flexible and complex working context, like the ones that are daily experienced in many organizations and companies, digital-learning programs - and, especially, remote learning ones - were deemed as a sort of panacea in the development of novel efficient and cost-effective practices for workplace training. Methodological remarks concerning the integration of cognitiveaffective neuroscience discoveries and techniques with traditional learning theories and methods have, across the last years, more and more found the interest of professional trainers and professional development consultants. While systematic research on neuroscience-informed digital-learning practices at the workplace is yet scant, the attention given to such topic and the remarkable technological and theoretical advances in the field of

158

Davide Crivelli and Michela Balconi

applied neuroscience provide the right context in which to foster neuroscientific investigation on traditional, digital, and remote learning practices in organizations, as well as the development of innovative digitallearning programs based on a brain/person-centered approach. Available evidence and theoretical remarks have begun to highlight the implications of neuroscientific models of cognition and learning processes for the design of effective and engaging digital-learning programs, as also the potential of neuroscientific techniques for the accurate monitoring and objective assessment of learning outcomes and quality. Forthcoming challenges in this promising field would likely concern the integration of multiple methods - e.g., non-invasive optical imaging of neural activity, measurement of autonomic arousal, estimation of attentional fatigue via eye-tracking - to monitor the dynamics of the learning experience or as supports in the digital-learning process in real or virtual environments, or even the use of wearable neuroscientific tools - such as the neurofeedback to promote the enhancement of learning skills in workplace training.

REFERENCES Antonenko, Pavlo D., and Dale S. Niederhauser. 2010. “The Influence of Leads on Cognitive Load and Learning in a Hypertext Environment.” Computers in Human Behavior 26:140-50. doi:10.1016/j.chb.2009.10. 014. Antonenko, Pavlo D., Fred Paas, Roland Grabner, and Tamara van Gog. 2010. “Using Electroencephalography to Measure Cognitive Load.” Educational Psychology Review 22:425-38. doi:10.1007/s10648-0109130-y. Balconi, Michela, Laura Angioletti, and Davide Crivelli. 2020. “NeuroEmpowerment of Executive Functions in the Workplace: The Reason Why.” Frontiers in Psychology 11:1519. doi:10.3389/fpsyg.2020. 01519.

Digital-Learning for Organization

159

Bowers, Jeffrey S. 2016. “The Practical and Principled Problems with Educational Neuroscience.” Psychological Review 123:600-12. doi:10. 1037/rev0000025. Bruer, John T. 1997. “Education and the Brain: A Bridge Too Far.” Educational Researcher 24:4-16. doi:10.3102/0013189X026008004. Castro-Meneses, Leidy J., Jan Louis Kruger, and Stephen Doherty. 2020. “Validating Theta Power as an Objective Measure of Cognitive Load in Educational Video.” Educational Technology Research and Development 68:181-202. doi:10.1007/s11423-019-09681-4. Crivelli, Davide, and Michela Balconi. 2010. “Agency and Inter-Agency, Action and Joint Action: Theoretical and Neuropsychological Evidences.” In: Neuropsychology of the Sense of Agency. From Consciousness to Action, edited by Michela Balconi, 107-22. New York: Springer-Verlag. ———. 2017. “Agentività e competenze sociali: riflessioni teoriche e implicazioni per il management.” Ricerche Di Psicologia [“Agency and Social Skills: Theoretical Remarks and Implications for Management.” Research in Psychology] 40:349-363. doi:10.3280/ RIP2017-003006. de Jong, Ton. 2010. “Cognitive Load Theory, Educational Research, and Instructional Design: Some Food for Thought.” Instructional Science 38:105-34. doi:10.1007/s11251-009-9110-0. Ellis, Robert A., and Peter Goodyear. 2010. Students’experiences of eLearning in Higher Education: The Ecology of Sustainable Innovation. New York: RoutledgeFalmer. Fisher, Jacob Taylor, Frederic René Hopp, and René Weber. 2019. “Modality-Specific Effects of Perceptual Load in Multimedia Processing.” Media and Communication 7:149-65. doi:10.17645/mac. v7i4.2388. Frith, Uta, Dorothy Bishop, Colin Blakemore, Sarah-Jayne Blakemore, Brian Butterworth, Usha Goswami, Paul A. Howard-Jones, et al. 2011. Neuroscience: Implications for Education and Lifelong Learning. London: The Royal Society.

160

Davide Crivelli and Michela Balconi

Gabrieli, John D. E. 2016. “The Promise of Educational Neuroscience: Comment on Bowers (2016).” Psychological Review 123:613-19. doi: 10.1037/rev0000034. Gerlič, Ivan, and Norbert Jaušovec. 1999. “Multimedia: Differences in Cognitive Processes Observed with EEG.” Educational Technology Research and Development 47:5-14. doi:10.1007/BF02299630. ———. 2001. “Differences in EEG Power and Coherence Measures Related to the Type of Presentation: Text versus Multimedia.” Journal of Educational Computing Research 25:177-95. doi:10.2190/YDWYU3FJ-4LY4-LYND. Hebb, Donald Olding. 1961. “Distinctive Features of Learning in the Higher Animal.” In: Brain Mechanisms and Learning, edited by Jean F. Delafresnaye, 37-46. New York: Oxford University Press. Hew, Khe Foon. 2016. “Promoting Engagement in Online Courses: What Strategies Can We Learn from Three Highly Rated MOOCS.” British Journal of Educational Technology 47:320-41. doi:10.1111/bjet. 12235. Howard-Jones, Paul A., Daniel Ansari, Brian Butterworth, Bert De Smedt, Diana Laurillard, and Michael S. C. Thomas. 2016. “The Principles and Practices of Educational Neuroscience: Comment on Bowers (2016).” Psychological Review 123:620-27. doi:10.1037/rev0000036. Kastner, Sabine, and Mark A. Pinsk. 2004. “Visual Attention as a Multilevel Selection Process.” Cognitive, Affective, & Behavioral Neuroscience 4:483-500. doi:10.3758/CABN.4.4.483. Kirschner, Paul A., Paul Ayres, and Paul Chandler. 2011. “Contemporary Cognitive Load Theory Research: The Good, the Bad and the Ugly.” Computers in Human Behavior 27:99-105. doi:10.1016/j.chb.2010.06. 025. Kleim, Jeffrey A., and Theresa A. Jones. 2008. “Principles of ExperienceDependent Neural Plasticity: Implications for Rehabilitation after Brain Damage.” Journal of Speech, Language, and Hearing Research 51:S225-39. doi:10.1044/1092-4388(2008/018). Klimesch, Wolfgang, Bärbel Schack, and Paul Sauseng. 2005. “The Functional Significance of Theta and Upper Alpha Oscillations for

Digital-Learning for Organization

161

Working Memory: A Review.” Experimental Psychology 52:99-108. doi:10.1027/1618-3169.52.2.99. Lane, H. Chad, and Sidney K. D’Mello. 2019. “Uses of Physiological Monitoring in Intelligent Learning Environments: A Review of Research, Evidence, and Technologies.” In: Mind, Brain and Technology. Educational Communications and Technology: Issues and Innovations, edited by Thomas D. Parsons, Lin Lin, and Debbie Cockerham, 67-86. Cham: Springer. Lövdén, Martin, Lars Backman, Ulman Lindenberger, Sabine Schaefer, and Florian Schmiedek. 2010. “A Theoretical Framework for the Study of Adult Cognitive Plasticity.” Psychological Bulletin 136:659-76. doi:10.1037/a0020080. Martin, Stewart. 2014. “Measuring Cognitive Load and Cognition: Metrics for Technology-Enhanced Learning.” Educational Research and Evaluation 20:592-621. doi:10.1080/13803611.2014.997140. Mayer, Richard E., and Roxana Moreno. 2002. “Animation as an Aid to Multimedia Learning.” Educational Psychology Review 14:87-99. doi:10.1023/A:1013184611077. Menon, Vinod, and Lucina Q. Uddin. 2010. “Saliency, Switching, Attention and Control: A Network Model of Insula Function.” Brain Structure and Function 214:655-67. doi:10.1007/s00429-010-0262-0. Nilsson, Staffan, and Per-Erik Ellstrom. 2012. “Employability and Talent Management: Challenges for HRD Practices.” European Journal of Training and Development 36:26-45. doi:10.1108/030905912111926 10. Ring, G., and G. Mathieux. 2002. “The Key Components of Quality Learning.” Paper presented at the ASTD Techknowledge 2002 Conference, Las Vegas, Nevada, February 4-7. Rossett, Allison. 2002. The ASTD E‐learning Handbook: Best Practices, Strategies, and Case Studies for an Emerging Field. New York: McGraw‐Hill. Sweller, John. 1994. “Cognitive Load Theory, Learning Difficulty, and Instructional Design.” Learning and Instruction 4: 295-312. doi:10. 1016/0959-4752(94)90003-5.

162

Davide Crivelli and Michela Balconi

———. 2010. “Element Interactivity and Intrinsic, Extraneous, and Germane Cognitive Load.” Educational Psychology Review 22:12338. doi:10.1007/s10648-010-9128-5. Treisman, Anne M. 1964. “Selective Attention in Man.” British Medical Bulletin 20:12-16. doi:10.1093/oxfordjournals.bmb.a070274.

ABOUT THE EDITOR Michela Balconi, PhD, is Professor of “Psychophysiology and Cognitive Neuroscience”, “Neuropsychology”, “Neuropsychology of Well-being in the Lifespan” and “Neuromarketing and Psychology of Advertising” at the Faculty of Psychology of the Catholic University of the Sacred Heart, Milan and Brescia. Head of the Research Unit in Affective and Social Neuroscience, Director of the International Research Center for Cognitive Applied Neuroscience (IrcCAN). Founder and Editor-in-Chief of the Neuropsychological Trends journal. She introduced new neuroscientific methods to analyze human interactions in the social neuroscience field and her research interests mainly concern cognitive neuroscience and psychophysiology. In agreement with the relevance of proper integration between the body and the mind, she has studied and introduced new methods to analyze and explore the relationship between affective, communication, cognitive processes and physiological markers using multiple neuroscientific techniques applied in different context, such as management one.

INDEX A adaptation, 68, 134, 141, 148 adaptive stress management, 99 affective neuroscience, 3, 4, 36, 63, 149, 157 age, 12, 90, 107, 112, 114, 122, 123, 153 age management interventions, 107, 122 agency, 78, 128, 154, 159 anterior cingulate cortex, 34, 48, 50 anxiety, 29, 30, 73, 74, 106 artificial agents, 136, 138 artificial intelligence, 134, 135, 142 assessment, 45, 69, 74, 85, 87, 90, 91, 92, 93, 94, 99, 100, 101, 107, 148, 150, 155, 157, 158 assessment procedures, 87, 94, 100, 107 attention filter, 151 attention orientation, 117, 121 attribution, 43, 45, 151 augmented reality, 137, 142 automation, vi, ix, 133, 134, 135, 136, 138, 139 autonomic activity, 45, 94 autonomic indicators, 123

autonomy, 33, 76, 104, 115, 124, 142, 155 awareness, 47, 49, 56, 72, 103, 104, 106, 116, 118, 125, 138, 154

B behavioral change, 15 behavioral tasks, 103, 105 behaviors, viii, 8, 14, 15, 26, 28, 30, 33, 42, 49, 53, 86, 99, 101, 113, 136 benefits, vii, 42, 43, 45, 72, 76, 121, 140 biofeedback, 8, 9, 10, 11, 14, 45, 67, 70, 93 blood pressure, 9, 70 borderline personality disorder, 30 brain, 4, 6, 7, 8, 9, 11, 12, 13, 14, 28, 29, 30, 31, 42, 44, 45, 46, 47, 48, 50, 51, 52, 53, 55, 67, 72, 73, 86, 94, 112, 113, 114, 115, 116, 117, 119, 120, 121, 141, 149, 153, 158 brain activity, 15, 45, 55 brain cognitive functioning, 113 brain functioning, 86 brain structure, 42, 115 brain tuning, 28 brain-sensing devices, 72, 95, 109, 117, 128

166

Index

business environment, 121, 139 business management, 135 business model, 135 businesses, 135

C cardiovascular disease, 33, 74 central nervous system, 100, 117, 136, 151, 153 challenges, 31, 86, 87, 98, 99, 101, 105, 119, 122, 124, 138, 158 climate, 30, 32, 53, 56, 75, 92 clinical application, 115 cloud, 134, 135 co-bot, 139, 141, 143 cognition, 37, 43, 48, 50, 51, 55, 87, 98, 124, 139, 148, 158 cognitive abilities, 113, 117 cognitive biases, 4 cognitive capacities, 153 cognitive effort, 156 cognitive flexibility, 88, 98, 99, 107, 122, 123 cognitive function, 50, 92, 99, 112, 113, 114, 117, 118, 121, 124, 136 cognitive load, 69, 140, 152, 156, 157 cognitive load theory, 152, 159, 160, 161 cognitive performance, 68, 71, 73, 111, 114, 116, 118 cognitive process, 29, 43, 51, 70, 89, 101, 113, 123, 156, 157 cognitive processing, 44 cognitive psychology, 6 cognitive representations, 151 cognitive skills, 111, 139 cognitive system, 70, 137, 151, 152 cognitive tasks, 106, 117, 138, 152 coherence, 13, 18, 20, 57, 58, 64, 160 commitment, 7, 12, 26, 32, 122, 149

communication, 7, 8, 10, 11, 13, 93, 100, 134, 135, 155 communication skills, 7 communicative methods, 10 company climate, 75 complexity, 10, 100, 123, 139, 152, 156 connectivity, 4, 6, 10, 13, 14, 55, 94, 119 content design, 154 continuous improvement, 112 continuous training, 112 cooperation, 28, 29, 30, 33, 46, 102 creativity, 33, 75, 89, 107, 122, 140

D daily work commitments, 102 decision task, 42, 44, 45 decisional awareness, 103, 104 decisional effectiveness, 103, 104 decisional metacognition, 103, 104 decisional strategies, 103, 104 decision-making, ix, 7, 41, 42, 43, 44, 45, 47, 48, 49, 50, 51, 55, 57, 58, 60, 62, 63, 64, 70, 89, 99, 104, 108, 110, 136, 137, 138, 139 decision-making process, 42, 44, 48, 49 deductive reasoning, 43 default mode network, 54, 63 dependent variable, 43 depth, 3, 8, 43, 44, 46, 102, 104, 155 desynchronization, 156 detection, 50, 91, 123, 136 digital-learning, vi, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158 dorsolateral prefrontal cortex, 34, 44

E economic efficiency, 47 economic growth, 27 economics, 34

Index economies of scale, 122 education, 27, 148, 149, 152, 157 educational neuroscience, 148, 149, 159, 160 electroencephalogram, 6 electroencephalographic, 94, 106, 116 electroencephalography, 45, 136, 156 electromyography, 72, 136 electrophysiological markers, 73, 106, 120, 156 emotion, 43, 51, 73, 99, 118 emotion regulation, 73, 99, 118 emotional conflict, 34 emotional information, 51 emotional intelligence, 8, 20, 22, 89 emotional processes, 11, 41, 47, 51, 116 emotional responses, 51, 52 emotional state, 27, 28, 30, 52, 141 emotional stimuli, 48 emotional synthonization, 11 empathic concern, 29 empathic distress, 29 empathy, 11, 14, 16, 22, 23, 26, 27, 28, 29, 35, 36, 37, 38, 46, 47, 51, 56, 58, 59, 62, 88, 100 employee productivity, 76 employees, 8, 10, 11, 12, 15, 28, 30, 32, 33, 42, 72, 91, 92, 94, 119, 120, 122, 124 empowerment, 33, 73, 76, 90, 95, 100, 105, 106, 107, 108, 112, 115, 117, 118, 119, 120, 122, 125, 138, 142, 158 empowerment of others, 33 engagement, 25, 60, 79, 116, 122, 130, 151, 160 environment, 30, 31, 33, 45, 98, 137, 141, 142 environmental factors, 153 environments, 32, 87, 119, 121, 158 ethical behavior, 42 ethical implications, 124 ethical issues, 53, 87, 125, 142

167

event-related potentials, 36, 38, 117, 141, 146 evidence, viii, 6, 10, 11, 12, 15, 26, 30, 32, 34, 35, 48, 53, 67, 69, 72, 75, 98, 104, 120, 122, 123, 140, 151, 152, 153, 158 executive functioning, 7, 99 executive functions, vi, ix, 89, 95, 96, 97, 98, 108, 109, 125, 128, 142, 158 executive processes, 100 exercise, 53, 70, 119, 120, 138 expectations, 4, 15, 31 expertise, 140, 154 explicit level of information, 87 explicit recognitions, 31 exposure, 68, 70, 71, 73 external costs, 104 external environment, 55 external relations, 50 eye-tracking, 136, 137, 141, 158

F fairness, 44, 46, 47, 50, 51, 52, 53, 63, 64, 124 flexibility, 89, 98, 139 frontal cortex, 7, 8, 55 frontal lobe, 7, 48 functional Near-Infrared Spectroscopy, 8, 45, 93

G game theory, 13, 34, 44, 63, 102 goal-directed behavior, 99, 101 group dynamics, 99 group membership, 6 growth, 32, 90, 98, 99, 112, 115, 124, 134, 142, 151

168

Index H

health, 11, 15, 42, 68, 71, 75, 115 health effects, 75 health promotion, 115 hemispheric asymmetry, 55, 56, 60 higher cognition, 43, 76, 87, 98 high-stress professional settings, 120 human, vii, ix, 25, 26, 33, 52, 90, 92, 98, 101, 116, 133, 134, 136, 138, 139, 140, 141, 148 human behavior, vii human brain, 33 human capital, 98 human condition, 142 human nature, 52 human perception, 140 human resources, 87, 92, 98, 101, 130, 148 human-computer interfaces, 137, 146 human-machine interaction, 134, 136, 138 hyperscanning, 4, 9, 10, 11, 12, 13, 16, 17, 18, 19, 22, 23, 36, 56, 93, 94

I identification, 87, 90, 98, 123 implicit information processing, 87 individual characteristics, 140 individual differences, 156 individual employee, 102 individuals, ix, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 42, 44, 45, 46, 48, 49, 51, 52, 53, 54, 56, 68, 69, 70, 71, 72, 73, 74, 86, 87, 93, 94, 97, 103, 104, 106, 115, 117, 119, 124, 148 industrial revolution, 133, 134 industry 4.0, vi, 133, 134, 135, 136, 138, 139, 141, 142, 143, 144, 145 information processing, 87, 113, 120 information technology, 134

inhibition, 90, 98, 99, 101 innovation, 99, 112, 135, 148, 159 instructional design, 152, 159, 161 instructional procedures, 152 integration, 8, 13, 72, 86, 87, 101, 105, 111, 139, 149, 152, 154, 156, 157, 158 intelligent and responsive systems, 133, 134 inter-brain connectivity, 12, 13, 14, 17, 36 inter-cerebral and peripheral connectivity, 10 internet of things, 134, 144 interpersonal conflict, 69 interpersonal interactions, 86, 93 interpersonal relations, 8, 12, 15, 25, 28, 29, 88, 98, 99 intervention, 68, 73, 74, 75, 105, 112, 114, 119, 121, 124, 139 intuitionist decision-making, 43 investment, 27, 32, 75, 121, 157 issues, viii, ix, 12, 124, 136, 142, 148, 149

J job performance, 98, 116, 127, 130 job satisfaction, 69, 72, 115, 124

L leadership, viii, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 26, 29, 30, 54, 55, 56, 58, 59, 61, 62, 64, 65, 116 leadership characteristics, 7, 55 leadership models, 4 leadership style, 3, 4, 7, 8, 10, 11, 14 learners, 149, 150, 152, 154, 155 learning, ix, 14, 31, 89, 98, 102, 112, 113, 115, 141, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 161 learning efficiency, 156 learning environment, 152, 153, 156, 157 learning outcomes, 151, 158

Index learning process, 149, 150, 152, 153, 154, 155, 156, 157, 158 learning skills, 158 learning task, 152 left hemisphere, 56 levels of cooperation, 28 lifelong educational practice, 153 lifelong learning, 112, 148, 159 light, 10, 15, 30, 32, 43, 140

M management, vii, viii, ix, 7, 8, 15, 28, 31, 43, 52, 56, 68, 71, 72, 73, 74, 75, 78, 87, 90, 91, 93, 98, 102, 104, 106, 107, 112, 114, 117, 120, 122, 123, 128, 134, 135, 137, 138, 139, 141, 148, 159 measurement, 14, 140, 155, 157, 158 medical, 149 memory, 113, 119, 137 mental fatigue, 73, 74, 106 mental load, 141 mental processes, 8, 86, 87, 90, 97, 99, 104, 135, 149 mental representation, 45 mental state, 29, 51, 74, 99, 116, 118, 136 mental states, 29, 51, 99, 118, 136 metacognition, 103, 104 metacognitive skills, 88, 89, 107, 154 mindfulness, 59, 67, 71, 77, 78, 79, 82, 95, 105, 106, 108, 109, 112, 116, 118, 119, 120, 121, 126, 127, 128, 129, 130, 143 mindfulness-based interventions, 67, 71, 118, 129 moral behavior, ix, 42, 43, 47, 54 moral decision-making, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 56, 57, 59, 108 moral judgment, 42, 43, 54, 55, 56 moral reasoning, 43, 54 morality, 48, 50, 52, 53, 124 motion-tracking, 140, 141

169

motivation, 5, 7, 8, 26, 29, 32, 53, 58, 72, 90, 122, 153 multimedia, 148, 149, 151, 152, 153, 156 multimedia materials, 151, 152, 153

N natural leaders, 32 negative attitudes, 52 negative consequences, 48, 124 negative effects, 42 negative emotions, 46, 73 neural connection, 151 neural connectivity, 55 neural efficiency, 112 neuroassessment, v, ix, 85, 87, 88, 90, 93, 94, 95 neurobiological foundations of emotions, 42 neurocognitive protocol, 105, 107, 120 neuroempowering, 105, 119 neuroenhancement, 85, 88, 89, 112, 113, 114, 115, 119, 120, 122, 123, 124, 127 neuroergonomics, 134, 139, 140, 142, 143, 144 neurofeedback, 15, 67, 95, 105, 108, 111, 116, 119, 122, 126, 128, 129, 158 neuroimaging, 34, 44, 140 neuroleadership, 4, 14 neuromanagement, vii, viii, ix, 18, 57, 85, 86, 87, 88, 95, 98, 101, 105, 108, 143 neurometrics, v, 85, 87, 93, 94, 156 neurophysiological correlates, 10, 44, 98 neuroplasticity, 15, 153, 154 neuroscience, vii, viii, ix, 3, 4, 6, 8, 9, 27, 41, 43, 44, 46, 49, 53, 86, 87, 95, 98, 102, 111, 112, 113, 114, 115, 116, 124, 128, 134, 135, 138, 141, 142, 148, 149, 150, 151, 152, 153, 157

170

Index O

operant conditioning, 116, 117 opportunities, 32, 68, 90, 91, 92, 101, 105, 121, 122, 123, 135, 149, 155 optimization, 113, 133, 134, 136, 138, 139, 141 optimization of performance, 113 organizational behavior, 43, 53, 54 organizational change, 12, 14, 15 organizational context, viii, 4, 26, 30, 32, 41, 42, 46, 47, 49, 52, 53, 85, 87, 93, 94, 101, 102, 121, 122, 125, 135, 149 organizational culture, viii, 42 organizational development, 91 organizational neuroscientific perspective, 86 ovations, 31 oxytocin, 29, 36, 38, 39

P parietal lobe, 6, 50, 121 participants, 29, 47, 74, 117, 120, 121, 122 personal interest, 45, 52, 54 personalized vision, 5 perspective-taking, 29, 37, 88, 107, 123 physiological correlates, 117 physiological mechanisms, 87 planning, 7, 89, 99, 101, 137, 141 plasticity, 112, 114, 115, 117, 153, 154 positive correlation, 28 potential, viii, 71, 75, 85, 87, 88, 89, 90, 91, 92, 93, 94, 96, 98, 101, 114, 118, 123, 135, 146, 156, 157, 158 potential assessment, 90, 91, 92 prefrontal cortex, 34, 35, 44, 47, 48, 50, 55, 60, 61, 79, 100, 139 principles, 47, 54, 117, 142, 153, 154 problem-solving, 45, 68, 89, 99, 107, 119, 122, 123, 136, 138, 145

problem-solving skills, 136 professional development, 32, 157 professional education, 148, 152, 157 professional growth, 32 professionals, 67, 69, 74, 75, 86, 88, 99, 101, 102, 105, 106, 112, 120, 121, 122, 137 prosocial behavior, 14, 16, 27, 28, 29, 35, 36, 38 psychological well-being, 68, 75, 118 psychometric tests, 103

Q qualitative feedback, 12 quantitative evaluation, 11

R rational process, 43 reality, 67, 75, 137, 142 reasoning, 38, 43, 44, 54, 61, 88, 99, 101, 107, 122, 125, 137, 138 relational skills, 4, 88, 107 relevance, 69, 101, 102, 154, 155 reliability, 26, 33, 34, 117 resources, 33, 47, 68, 99, 106, 134, 149, 152 response, 8, 28, 45, 47, 48, 68, 70, 72, 73, 93, 99, 104, 113, 136, 137 responsive learning environments, 157 robotic agents, 141 rules, 13, 53, 54, 151

S safety, 33, 42, 115, 124, 137, 139, 141, 142, 155 salience attribution, 151 satisfaction, 7, 69, 72, 76, 114, 115, 124, 127

Index self-awareness, 55, 89, 104, 106, 107, 117, 123 self-confidence, 102 self-control, 7 self-efficacy, 142 self-empowerment, 119, 138 self-enhancement, 112 self-monitoring, 53, 70 self-monitoring skills, 53 self-observation, 50, 118 self-reflection, 154 self-regulation, 7, 55, 85, 87, 88, 90, 96, 97, 99, 101, 104, 106, 107, 109, 118, 123, 139 single-brain connectivity, 13 smart factories, 135 smart sensors, 135 social anxiety, 30 social benefits, 48 social capital, 4 social cognition, 54, 55 social competence, 56 social context, 52 social contract, 33 social decision tasks, 42, 44 social environment, viii social interaction, 9, 14, 26, 33, 88 social mechanisms, 86 social neuroscience, vii, 3, 17, 18, 25, 35, 36, 41, 57, 59, 67, 85, 86, 97, 109, 111, 133, 147, 163 social norms, 34 social relations, 25, 26, 34 social relationships, 25, 26, 34 social responsibility, 5 social skills, 78, 87, 98, 106, 128, 159 socialized vision, 5, 55 strategic problem-solving, 138 stress, v, ix, 11, 32, 33, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 90, 99, 105, 106, 107, 108, 109, 119, 120, 121, 122, 123, 126, 128, 138, 142

171

stress management, v, 67, 70, 71, 72, 73, 74, 76, 78, 79, 80, 81, 90, 99, 105 stress management techniques, 70 synthonization or desynthonization mechanisms, 9

T talent development, 148, 157 team members, 11, 31, 32 technical-analytical skills, 88 techniques, viii, ix, 3, 9, 12, 13, 15, 44, 70, 71, 75, 85, 87, 94, 98, 103, 105, 111, 113, 115, 116, 119, 123, 124, 125, 137, 138, 140, 148, 157, 158 technological advancement, 142 technology-mediated mindfulness-based protocol, 119, 120 theory of mind, 51 top managers, 103, 120 top-down, 98, 99 top-down mental processes, 98, 99 training, 15, 67, 71, 73, 75, 76, 91, 106, 107, 112, 114, 115, 117, 118, 119, 120, 121, 122, 123, 125, 147, 148, 149, 150, 152, 153, 154, 155, 157, 158 triadic model, 85, 88, 89, 90, 92, 101, 106 trusting behavior, viii, 25, 26

U unconscious processes, 15, 49, 53 unfairness, 45, 46, 47, 50, 52, 64, 65

V videos, 148, 156 virtual reality, 67, 74, 75, 76, 80, 82

172

Index W

wearable neurofeedback devices, 71 wearable technologies, 68 well-being, ix, 15, 31, 42, 48, 69, 72, 74, 75, 87, 107, 112, 113, 114, 115, 124 work autonomy, 76 work climate, viii work environment, 15, 140 workers, 11, 28, 31, 69, 91, 111, 112, 114, 123, 138, 139 workforce, 68, 75 working contexts, 69

working memory, 88, 90, 98, 99, 107, 122, 123, 124, 125, 129, 137, 139, 152, 161 workload, 69, 137, 138, 139, 141 workplace, vii, viii, ix, 67, 69, 70, 71, 72, 74, 87, 90, 98, 99, 101, 111, 117, 119, 121, 122, 123, 147, 148, 149, 150, 155, 157, 158 work-related stress, 98, 119, 121 work-related stressors, 98

X X-system, 43