Relationships between meanings: Specifically with regard to trait concepts used in psychology. A model and the assessment of its validity [Reprint 2021 ed.] 9783112422106, 9783112422090

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RELATIONSHIPS BETWEEN MEANINGS

PSYCHOLOGICAL STUDIES

Editorial

Committee

J O H A N T. B A R E N D R E G T S I P K E D. F O K K E M A I N I C O H. F R I J D A J O H N P. VAN DE G E E R I A D R I A A N D. DE G R O O T MAUK M U L D E R r SIES W I E G E R S M A

MOUTON & CO - THE HAGUE - PARIS

RELATIONSHIPS BETWEEN MEANINGS specifically trait concepts

with regard to used in psychology

A M O D E L AND THE ASSESSMENT OF ITS VALIDITY

BY

JAN VASTENHOUW with a foreword Clyde H.

by

Coombs

MOUTON & CO - THE HAGUE - PARIS

© NO PART

O F THIS

BOOK

BY P R I N T , P H O T O P R I N T ,

MAY

M O U T O N & CO., 1 9 6 2 . BE TRANSLATED

OR

REPRODUCED

IN

ANY

M I C R O F I L M , OR ANY O T H E R MEANS, W I T H O U T PERMISSION FROM THE PUBLISHERS. PRINTED IN THE NETHERLANDS.

FORM,

WRITTEN

TO MY

PARENTS

FOREWORD

The area of psychological measurement during the period between the publication of Fechner's Elemente der Psychophysik (1860) and the publication of Thurstone's Law of Comparative Judgment (1927) and Spearman's Abilities of Man (1927) exhibited a slow but progressive development. Since then the rate of development gradually increased, until, since World War II especially, the current capability of the area of psychological measurement to analyze various kinds of data has increased by several orders of magnitude in the last fifteen years. One of the various kinds of data that has particular promise for the study of cognition is Similarities data. This is a kind of data in which the stimuli are mapped into points in a multidimensional space and the behavior of individuals with respect to the stimuli leads to measures of the distances between pairs of points. Only in the last ten years have models been developed which provide algorithms for the analysis of such data leading to a geometrical representation of the behavior in a Stimulus space. The potential significance and value of such models resides in the leverage they provide for an attack on the problem of the cognitive structure of individuals. A case in point is that of discovering the relations between the meanings of words for different individuals. That trait concepts, words like intelligent, sociable, practical, etc., have complex interrelations among their meanings for any individual is intuitively to be anticipated. The exploration of such interrelations is an exploration of individuals' implicit personality theories and clearly of importance in coming to an understanding of individual behavior. Mr. Jan Vastenhouw has chosen this very difficult area for his research and made substantial contributions to the basic measurement theory for the exploration of cognitive structure. This intimate combination of logical thinking and empirical observation in complex areas of behavior involves the most modern developments and is, I hope, the forerunner of a major step in the advancement of psychology as a quantitative rational science. CLYDE H . COOMBS

CONTENTS

Foreword 1. 2. 3. 4. 5. 6. 7.

Introduction Formulation of the problem Some previous investigations The investigation Results The significance of the model Summary

7 11 13 19 32 46 67 85

Appendices A. The postulate and derivations therefrom B. Frequency matrix C. Intransitivities D. Method of transformation E. The additive constant F. Referring to section 6.3 References

87 98 99 103 107 108 Ill

1. I N T R O D U C T I O N

In an article, published in 1958, professor De Groot (6) makes a case for systematic research into the usefulness of trait concepts that frequently occur in personality descriptions. Considering the importance of personality descriptions in the field of applied psychology, it would be very useful to know, among other things, how far the terminology, used in personality descriptions, is valid with respect to a number of objective and so-called actual 'life-criteria'. It would also be useful to know, from the point of view of communication, to what extent a given description is open to more than one interpretation and therefore may lead to misunderstandings on the part of the reader of a report with regard to its intended contents. As a method of dealing with this problem, De Groot suggests making use of judgments which the members of a closely collaborating group have formed about each other. The advantages of this method are, in the first place, that the grounds upon which the judgments are based are about the same for all subjects (e.g. the work situation) and, secondly, that the persons making these judgments know each other about equally well. Undoubtedly these two requirements are essential, if the judgments of various judges are to be comparable. The Psychological Laboratory of the University of Amsterdam offered a favorable opportunity for such an investigation. In a certain stage of their study the students are required to collaborate intensively on a research project, in small groups of about ten, for some months. The first results of such an investigation were obtained from a group of students in spring 1959. In giving their judgments each judge was asked to rank the members of the group (including himself) according to the appropriateness of each trait concept. The judgments were given on each of sixty-six different trait concepts. It was now possible to determine the degree of concordance between the judgments of the various judges on each trait. The degree of this concordance varied considerably from trait to trait and moreover appeared to correlate with the stability of the judgments, r == .64 (the judging procedure being repeated after a time interval). In order to explain low measures of agreement the judging process was conceived as consisting of two distinct phases:

12

RELATIONSHIPS BETWEEN MEANINGS

1. the perceptual phase, during which an (as yet non-articulated) impression of the other is formed; 2. the phase of codification, in which this impression is specified by attributing a trait to a person. It is clear that both the perceptual phase and the phase of codification may lead to varying judgments. Two judges may have the same impression of a person, but their terminology, although equal, may bear different meanings, or, two judges may have a different impression of a person, though their terminology covers the same meaning. The result will be diverging judgments in both cases. From a preliminary investigation it was found that one of the main sources of disagreement may be the perceptual phase. One judge, for instance, was of the opinion that persons A and B showed a striking resemblance in the general structure of their personality, while another judge was convinced of the opposite. Socio-psychological factors undoubtedly played a prominent part in the formation of these impressions. In the present study, however, the main stress is laid on the codification phase, and in particular on the mutual relationships of various trait concepts as conceived by a judge.

2. FORMULATION OF T H E PROBLEM

2.1. INTRODUCTION

When one is in the process of forming a judgment of people in terms of personality characteristics it often happens that the judgment is based on insufficient data. It is known, for example, on the basis of a certain test that a person is an introvert. Is one therefore justified in drawing the conclusion that he will lack confidence in company? Or, if it is known that someone is intelligent and humorous, does this mean that he will also be resourceful? Most people have certain expectations in this respect which, although not usually based on the findings of investigations made by means of psychological tests, are nevertheless very definite. It is, as if certain associations exist between characteristics, which may be strong for one pair of characteristics and weak for another. Conclusions drawn about a person's personality traits may in part be explained on the basis of such associations. It was assumed in this investigation that 'associations' between trait concepts exist and an attempt was made to measure their strength.

2.2. THE NATURE OF THE BEHAVIOR TO BE INVESTIGATED

Our investigation concerned one aspect of the behavior of a judge in making a judgment on his fellows. In doing so he uses certain terms, such as intelligent, dominant, etc., to denote certain personality traits. In the present context the term 'personality trait' refers to the subjective content of such terms for the judge. Henceforth this subjective content will be known as the meaning of the term for the judge. We assume then that fixed relationships exist between these meanings for each judge and that it is possible, by means of these relationships, to explain certain aspects of his judgment behavior. The aim of our investigation was to measure the relative strength or intensity of these relationships and to explain the way in which the judge draws his conclusions on the basis of the results of such measurement.

14

RELATIONSHIPS BETWEEN MEANINGS

It is quite possible and even probable that different judges have different ideas about the meanings of the same terms. But this is of no significance with regard to our purpose. In order to avoid any possible misunderstandings we will again define what this study does and what it does not set out to investigate. We shall not investigate: 1. the agreement or disagreement between the subjective meaning of the terms and their 'dictionary' meaning; 2. the agreement or disagreement between the subjective meaning of the terms and their meaning in the language of professional psychologists; 3. how far the person judged in fact possesses the characteristics attributed to him. We shall investigate: 1. whether the behavior of the judge in his use of terms indicates constancy of meaning for the same term; 2. whether the use of terms points to the existence of fixed relationships between the subjective meanings of these terms; 3. whether a model can be constructed to describe the complex of these relationships; 4. whether on the basis of this model which we assume to be constructable predictions can be made about new judgment behavior of the judge.

2.3. T H E M O D E L

By means of the following example we will explain the mathematical model used to describe the complex of relationships between the meanings of the terms for personality traits. Let us suppose that the use which a certain judge makes of the following four terms: A. B. C. D.

intelligent resourceful sensitive egocentric

FORMULATION OF THE PROBLEM

15

has been studied. Now the relationships between the traits are represented by means of the following spatial model:

It is shown in this diagram that in the case of this judge there is a relatively large measure of agreement between the traits intelligent (A) and resourceful (B). This is apparent from the short distance between the points which represent these traits. On the other hand the measure of agreement between the traits sensitive (C) and egocentric (D) is small. This is represented in the diagram by the large distance between the points C and D. The space in the diagram represents the totality of meanings and shades of meaning in the passing of a judgment; the points in this space denote definite meanings, i.e. the meanings of the terms used by the judge. The greater the measure of agreement in meaning the closer are the points corresponding to the meanings in the diagram. Thus the distance between the points is used as a measure for the difference in meaning. The model described in the above example is only a theoretical construction. In reality only the behavior of the subject (judge) is given to us. The advantage of a suitable model is that in addition to clearly summing up and describing the observed behavior of the subject, it also provides an opportunity of extrapolating to behavior not previously observed, i.e. of predicting what the subject will do under given circumstances. In principle our model is identical with Hays' model (34). However, with Hays' model only qualitative descriptions of the behavior of the subject could be obtained. By quantifying the model we were able to achieve farther-reaching results. Recently, Restle (21) has shown that a model based on measures of sets and of symmetric set differences may be isomorphic with a distance model based on points and distances respectively. Thus dissimilarity between

16

RELATIONSHIPS BETWEEN MEANINGS

concepts may be conceived either as a function of the 'distance' of the concepts in a meaning space or as a function of the number of elements they have in common. For a general discussion of the role of mathematical models in science we refer to the chapter by Coombs, Raiffa and Thrall in (35).

2.4. T H E C O N N E C T I O N B E T W E E N B E H A V I O R A N D M O D E L

The model represents the state of the relationships between meanings, but not how the subject manipulates the relationships, and we can only measure these relationships by referring to the behavior of the subject. In order to be able to interpret the behavior of a judge in terms of the model, it is necessary to postulate how the subject is supposed to manipulate the relationships when making a judgment. Let us suppose that the subject is asked to make a choice between two pairs of terms, for example intelligent-resourceful and sensitive-egocentric. If he is asked to state which pair of meanings are most closely related he will probably choose the pair intelligent-resourceful. However, if he is presented with the pairs intelligent-egocentric and resourceful-egocentric, very likely the choice will be more difficult. The subject will probably hesitate at first, vacillating between the two pairs before making his choice. That is to say he will choose with a greater or lesser degree of certainty. The probability of a definite choice will increase according to his certainty. 1 Apparently there is a connection between the probabilities of choice and the distances in the model. This connection is intuitively evident but has not yet been demonstrated. It is therefore presented as a postulate at the beginning of this investigation (see also Appendix A). At a later stage its validity will be tested. The behavior dealt with here is a restricted choice behavior. This type of behavior was needed in order to construct the model and to test the validity of the postulate. However, the model also appeared to be relevant in explaining certain aspects of another form of behavior, namely the forming of judgments on others. 1 For a further discussion of the relationship between certainty and probability as used here, see Chapter 6.

17

FORMULATION OF THE P R O B L E M

2.5. T H E POSTULATE

The postulate is to establish a connection between, on the one hand, the psychological event, i.e. the making of a choice, with greater or lesser degree of certainty (and probability) between two pairs of terms, and, on the other hand, the distances which correspond to the same pairs of terms in the psychological model. If the probability of choice is . 5, it is obvious that the choice of one pair of terms is as probable as the choice of the other. Now the postulate is as follows: The probability of choosing one of the two pairs deviates from. 5 according as: a. the absolute difference between the two corresponding greater, and/or b. the sum of the distances is smaller.

distances

is

The postulate has been formulated as an analogue to the laws governing certain psychophysical experiments in which straight lines are compared with each other. For example:

a

b

c

d

e f

— Fig. 2.2

It is easier to indicate which line of the pair a and b or of the pair e and / is longer than it is in the case of the pair c and d. In the case of a and b the difference is greater than between c and d, while the sums are equal (first part of the postulate). In the case of e and / their sum is smaller than that of c and d, while the differences are equal (second part of the postulate).

18

RELATIONSHIPS BETWEEN MEANINGS

2.6. THE CALCULATION OF DISTANCES A N D TESTING OF THE POSTULATE

In the previous section it was proposed that the relationships between the traits could be expressed by means of a spatial model, and the manipulation of these relationships described by means of a postulate. Both are unproved propositions. In our investigation we have tried to examine the adequacy of these propositions. This was done in the following way. The subject always had to make a choice from two pairs of terms. The pairs presented can be distinguished into two types: Type I, adjoint pairs; the two pairs have one term in common, for example in the pairs intelligent-resourceful and intelligent-sensitive; Type II, disjoint pairs; the two pairs have no common term, for example the pairs intelligent-resourceful and sensitive-egocentric. Now the distances in the model were calculated from the choices from adjoint pairs, by making use of the postulate. The resulting distance values, however, can only be considered correct if both the model and the postulate are valid. The validity of the postulate was then tested by means of choices from disjoint pairs, which choices were predicted with help of the already calculated distances. Since the adjoint pairs (I) are different from the disjoint ones (II) the test of the validity of the model and the postulate is based on two experimentally independent sets of data. In diagrammatic form: Choices from adjoint pairs + postulate -> distances, and choices from disjoint pairs + distances postulate. 2.7. THE WIDER IMPLICATION OF THE INVESTIGATION

At the conclusion of the investigation the results were examined in the light of Luce's theory concerning individual choice behavior (16). It appeared that, as far as the personality characteristics were concerned, the subject's behavior in making a choice may be interpreted as a specific case of a type of behavior described in Luce's theory. Since on the one hand our model was also applicable to another kind of concepts (perceptual concepts), and on the other hand Luce's theory provides a satisfactory explanation of the results of quite different kinds of psychological experiments (psychophysical measurements, learning), it was felt that this investigation could contribute to a general theory on the working of the human mind.

3. S O M E P R E V I O U S I N V E S T I G A T I O N S

3.1. INTRODUCTION

For the purposes of this investigation it is assumed that the subjective meaning of a personality trait serves as a functional unity for the subject. It is further assumed that fixed relationships exist between such functional unities for each subject. In our opinion these assumptions can be extended beyond personality traits to other meaningful concepts, and to other functional unities than those appertaining to meanings. Thus the word expressing an idea may equally well be able to serve as a functional unity for the subject, and fixed relationships may exist between words and the meanings appertaining to these words. In our opinion this view corresponds with the one maintained by Osgood, and Staats and Staats, among others. In diagrammatic form: words

A

B

meanings

a

b

C

d

The diagram shows that relationships may exist between words, between meanings, and between words and meanings. It also appears from the diagram that it is logically possible for words to occur without meaning (C = e.g. a nonsense syllable), and meanings without words (d = e.g. a melody). Several investigations concerning certain aspects of the diagram were found in the literature. Certain of them have been selected for their illustrative qualities, so the list is by no means complete.

3.2

Hays (34) used a model similar to the one used in our investigation, in order to establish the mutual relationships between various personality traits as conceived by his subjects.

20

RELATIONSHIPS BETWEEN MEANINGS

The test material was composed of eight traits, consisting of four pairs of opposite traits. Each subject was given one of these eight traits as datum and asked to state on a probability scale how probable he regarded each of the remaining seven traits to be on the basis of the datum. For example: A person is intelligent. How likely is it that he is also: l_ 1. dominant? impossible I 2. stingy? impossible etc.

I certain I certain

A qualitative analysis led to the conclusion that the traits and the relationships between them could be described by a two-dimensional structure. Hays then repeated the experiment in another form. From the eight traits sixteen different combinations could be formed, each consisting of four traits not opposite to each other. Each combination represented a fictitious 'person'. These 'persons' were presented in triads. The subject was asked to state which two persons in each triad resembled each other most and which had least resemblance. By means of a qualitative analysis a model could be constructed for the relationships between these 'persons'. In the model the 'persons' were represented by points and the measure of difference between them was shown by the distances between the points. It appeared that the model could be described by a structure in a four-dimensional space. One of the dimensions described the over-all good-bad impression given by the respective stimuli (the 'persons'). Another dimension was defined by the relative order of the traits in each combination of four, from which it appeared that of the four traits given to the subject the one which was given first had a marked influence on the subject's judgment. The importance of these two experiments to us was the evidence that a model could be constructed to describe this kind of judgments.

SOME PREVIOUS INVESTIGATIONS

21

3.3

Staats and Staats (30) changed the meaning of a word by linking it systematically with a meaningful, non-verbal aspect of the subject's environment. The subject received an annoying stimulus (electric shock or a loud noise) at the same time as the presentation of the word, e.g. LARGE. It was later established by means of the Semantic Differential that the subject clearly had a more negative conception of the word LARGE. Moreover the stronger the stimulus the greater the change in meaning for the subject. The object of this experiment was to demonstrate one of the ways in which a word can be given a meaning or made to change its meaning. Another way is by forming associations with other meaningful words (33).

3.4

Staats, Staats and Biggs (28) wished to determine whether the meaning of a word could be changed by a learning process, and to what extent the change in meaning was connected with the original meaning. A word A was projected on a screen before a group of subjects; at the same time the conductor of the experiment read out another word B, which the subjects had to repeat aloud. The subjects thought the aim of the experiment was to investigate whether two series of words given simultaneously could be learned simultaneously. There were six A words and one hundred and eight B words. Each word A was given once with each of eighteen other words B. In the first part of the experiment the ^4-words did not have extreme values on Osgood's evaluative dimension, which was verified with the help of the Semantic Differential. Of two of the ^4-words (RED and YELLOW) one was systematically combined with 18 5-words which had a high positive value on the evaluative dimension, while the other was combined with 18 B-words with a high negative value on the same dimension. In another group of subjects the two /1-words were interchanged. In other respects the experiment remained unchanged. In the second part of the experiment the former two yi-words were replaced by two y4-words with a high negative value on the evaluation dimension (UNFAIR and AWFUL). In this case too, two groups of sub-

22

RELATIONSHIPS BETWEEN MEANINGS

jects were used. Those subjects who had some idea of the real aim of the experiment were excluded when the data were analysed. After the subjects had been given the A- and the 5-words they were asked to rate the ^4-words on a seven-point scale running from 'pleasant' ( = 1) to 'unpleasant' ( = 7). TABLE 3.1 Mean scale values after conditioning A - words Experiment

Group

1st Part

1 2

2nd Part

1 2

Red

Yellow

2.38 3.85

4.23 2.92

Unfair

Awful

5.82 6.06

6.41 5.41

Thus, although it appeared that the meaning of a word could be changed by conditioning it, it was not evident from the results to what extent the change in meaning was related to the intensity of meaning of the word in question on the evaluative dimension. In other investigations Staats and Staats imparted a certain meaning to meaningless syllables (29,31), names of countries and proper names by conditioning. A further investigation indicated that the size of the change in meaning increased with the number of presentations of the unconditioned stimulus words (32).

3.5

King-Ellison and Jenkins (14) were interested in the connection between the frequency with which a word occurs in the language and the exposure time necessary in order to recognize the word when it is presented tachistoscopically. This effect has already been mentioned by Howes (8) and Solomon and Postman (25) and others. Howes found correlation coefficients of the order of -.65 to -.75 for the relationship between exposure time and the logarithm of the frequency

23

SOME P R E V I O U S I N V E S T I G A T I O N S

with which the word occurs in the language. Solomon and Postman found a correlation coefficient of -.96 for the connection. Five-letter artificial words were used in the present investigation. The frequency with which these words were presented varied : 2 words were each presented 25 times 2 10 ?» >» »? ?? »? 5^ 2 2 „ „ „ „ twice and 2 „ „ „ „ once A v r

?»

»?

??

»?

?»

The series was completed with fourteen other words to bring it up to 100. Each word was typed out on a card. Each word was presented to the subject who had to spell and read the word aloud. After a time interval ten words were presented tachistoscopically. A note was made of the time required to recognize each word. TABLE 3.2 Amount of Information

Frequency class Information (=—log 2 Mean threshold-value of recognition (in msec)

)

and Threshold

Values

n = 25

n = 10

n = 5

n = 2

n = 1

2.000

3.322

4.322

5.644

6.644

168

203

221

271

289

It appears from this that the minimum exposure time necessary to recognize a word increases linearly with the information value of the word expressed in the logarithm of its proportion of the total number of presentations.

3.6

Van de Geer and Croon (5) studied the connection between the facility with which a stimulus can be named and its recognizability. They used twenty-four different colors. Their subjects were first asked to give names to four of these colors. They were then given a table of 120 colors (which included the previously given four colors) and told to pick out the four

24

RELATIONSHIPS BETWEEN MEANINGS

colors. Altogether six groups of four colors were used. It appeared from the results that the colors which were named most quickly, with the greatest measure of agreement, and were most easily described by the subjects, were also recognized most quickly, most easily and with the least number of mistakes.

3.7

Penfield and Roberts (20) have reviewed in their highly interesting book 'Speech and Brain-mechanisms' the results they obtained from brainoperations carried out on a large number of people (more than 500) with epileptic symptoms. To this end they removed parts of the cortex. Before doing this they established (by means of direct electric stimuli) the position of the parts of the cortex which are important in speaking and thinking. One of the phenomena which occurred during this work was the inability of the patient to find the word for the object he was shown (e.g. the picture of a shoe), although he was perfectly well aware of the meaning of the object ('that is what you put your feet in'). When the electrode was removed from the cortex the patient was immediately able to find the elusive word. Accordingly, Penfield and Roberts are of the opinion that other brain mechanisms are necessary for the evocation of a concept than for finding the word indicating this concept.

3.8

Howes and Osgood (9) gave their subjects series of four words and told them to listen carefully to each word, but to give an association only to the fourth word. The effect of the first three words on the association given was important to the investigators. Their results show that: a. The probability of the occurrence of an association belonging to one of the first three words increased with the rank number of the word in question within the series. If the association for word A was a, then the probability that a would be given by a subject was greater for the series W1AW2W3 than for the series Aw\w2wi, and greatest of all for the series W1W2AW3.

SOME PREVIOUS INVESTIGATIONS

25

b. The probability that a certain association would be given appeared to be greater if it was relevant to more words in the series. Thus, if the association for A\, A2 and A3 was always the same word a, then the probability P(a) was greatest for the series A1A2A3W, lower for the series A\Azww and lowest for the series A ¡www. c. When the association for the synonyms A and A' was the same word a, then the probability P(a) was greatest for the synonym which occurred most frequently in the language.

3.9

Mink (18) first gave his subjects a list of words to read through once. They were then given a second list and asked to state which words they recognized from the first list. The second list contained the original words, words associated with them, and words not associated with them. It appeared from the results that the subjects reacted more to the associated than to the non-associated words. Moreover the stronger the association of the associated words with the original words the more pronounced the effect seemed to be. The strength of the associations was determined by means of the Minnesota norms (22).

3.10

Wertheimer (37) investigated the relationships between the sound of a word and its meaning. In a preliminary investigation he took two groups of five words. The first group (A) consisted of words, the sounds of which corresponded very well with their meanings. In the second group of words (B) this was not the case. The strength of the sound-meaning connection was also determined in another way: the subjects stared at the words until they ceased to have any meaning for them. The time required for this to happen was longer in the case of the ^-words than the ¿-words.

26

RELATIONSHIPS BETWEEN MEANINGS

3.11

Bugelski and Scharlock (2) gave their subjects three lists to learn, consisting of pairs of nonsense syllables. The first part of each pair formed the stimulus, the second part the response required. The investigation showed that learning the associations A-B and B-C made it easier to learn the association A-C. None of the subjects made conscious use of the nonsense syllables B in learning the pairs A-C. 3.12

Russell and Storms (23) also investigated to what extent word-associations facilitated the learning of new word-connections. The Minnesota norms concerning word-associations were used to draw up the pairs of words to be learned. The subjects first had to learn a number of pairs, each consisting of a nonsense syllable A and a meaningful word B. With the help of the Minnesota norms the word C, which was most strongly associated to B, and the word D, which was most strongly associated to C, could be determined. The subjects then had to learn the pairs of words A-D. For the control group words X were chosen which appeared from the norms to have no associations with the words B, C, or D. The results showed that after learning the pairs A-B the pairs A-D could be learned much better than the pairs A-X. In other words, the existence of word-associations facilitates the learning of new associations. 3.13

Ryan (24) investigated whether learning a list of pairs of words was facilitated to a greater extent by word-word-associations than by meaningmeaning associations. From a basic list of nine words (B) he drew up three lists A, S and C as follows: A had strong word- and slight meaning-associations with B S had slight word- and strong meaning-associations with B C had slight word- and slight meaning-associations with B

SOME PREVIOUS INVESTIGATIONS

27

The word- and meaning-associations were determined with the help of the Minnesota norms (11, 12, 22). There was also a response list R. The subjects first learned for example A-R, then one of the other lists together with R, and then again A-R. The primary lists and the transfer lists were systematically interchanged. The transfer-effect of the intermediate list could thus be determined. Ryan found that word-word associations had the strongest transfereffect, followed by the meaning-meaning associations. The words with generally weak associations showed the smallest transfer-effect.

3.14

Dicken (4) was interested in the part played by meanings and meaningrelationships in stimulus-generalizations. In other words, suppose that a group of words have approximately the same meaning, and that the group is split in two and a certain manner of responding to the words in the first group is learned. The question then is whether the same reponse can be expected when the words from the second group are given. The investigation showed that this was in fact the case.

3.15

Osgood and Ferguson (19) demonstrated that the meaning of a concept depends to some extent on the context in which it is found. Eight nouns denoting careers and eight adjectives were used in the experiment. Each of the nouns and adjectives was given a scale value by a group of subjects for each of the three factors found by Osgood. This was also done with each of the sixty-four combinations of nouns and adjectives. By means of the congruity principle predicted scale values of the combinations were computed from the scale values of the nouns and the adjectives, obtained by having them rated separately. Of one hundred ninety-two scale values of the combinations one hundred thirty were predicted correctly, twenty-four not so well, while thirty-eight predictions appeared to be 'clear failures'.

28

RELATIONSHIPS BETWEEN MEANINGS 3.16

Bruner, Shapiro and Tagiuri (34) carried out an investigation into the way in which conclusions concerning someone's personality traits are reached. This investigation is described more fully in section 6.3. Briefly it was as follows: A subject was given a certain personality trait or a combination of traits. He then had to indicate for each of fifty-nine other traits how probable he thought them on the basis of the datum. It appeared from the results that, knowing what judgments, averaged, to expect on the basis of data which consisted of only one trait, it was possible to predict in 98 % of the cases what judgment, averaged, would be made when the data consisted of combinations of the same traits. The experiment was very carefully designed and all sorts of disturbing effects could be nullified by correct balancing. 3.17

Jones and Kohler (13) gave their subjects a series of statements about segregation to learn. Each of these statements was typed on a separate card. The subject had to read through the series (twelve statements) and then reproduce aloud what he could remember, read the series again and reproduce the content, five times in all. The reproduced statements were scored according to their correctness. The statements could be divided into four types according to the two criteria of the degree of plausibility and the attitude towards segregation, for or against:

Pro segregation And segragation

Plausible

Not plausible

a c

b d

From a preliminary investigation the subjects could be divided into a pro- and an anti-segregation group. The results showed that the pro-segregationists found it more difficult to learn the type of statements b and c than the types a and d, while the reverse was true for the anti-segregationists. The subjects who were neither pro- nor anti-segregationists learned all types of statements equally well.

SOME PREVIOUS INVESTIGATIONS

29

In view of the above experiment and conceiving a statement as a connection between two or more ideas it would appear to be easier to learn the statements which correspond to one's own idea-associations than those which do not.

3.18

In their final report (10) Jenkins and Russell refer to the investigation carried out by Gilkinson, Paulson and Sikkink concerning the effect of political speeches. 'Biased listening', i.e. the tendency to remember better and recognize more easily political statements which correspond to the original views of the listener, could clearly be demonstrated. Moreover the strength of this effect appeared to vary with the different political groups. Thus it seems that the degree of associatedness of politically important matters varies with the different political groups.

3.19

Bruner, Goodnow and Austin (1) have studied thinking in a series of experiments. One of the experiments was as follows: Three attributes were taken, each of which showed two variations. Eight different combinations of the three attributes could thus be drawn up. When the attributes are A, B and C, with the variations A\, A2, B\, Bi, Ci and C2, the possible combinations are as follows:

AiBiCi A1B1C2 A1B2C1 A1B2C2

AZB1C1 A2BiC2 A2B2C! A2B2C2

One of the attribute variations (e.g. Az) had to be traced by the subject. For this purpose he could always indicate a combination and ask whether the attribute variation he was looking for appeared in it. The conductor of the experiment answered 'yes' or 'no'. By means of deduction and elimination the subject could then in principle find the correct solution. An

30

RELATIONSHIPS BETWEEN MEANINGS

essential stage in the thought process was the forming of a hypothesis concerning the nature of the solution which he had to put to himself after each answer from the conductor of the experiment, in order to be able to indicate the next combination. It appeared that, in the case of 'abstract' attributes, e.g. A = form with A\ — rectangle and A2 = triangle; B = color of the figure with J?i = yellow and Bz = black; C = border of the combination with C\ = border present and C2 = no border present; in contrast to 'thematic' attributes, e.g. A = sex with A\ = husband and Az = wife; B = clothing with B\ = night clothing and B2 = day clothing; C = affectation with Ci = smiling and Cz = frowning, the subject behaved differently when forming hypotheses concerning the correct solution. In the case of 'thematic' attributes the thought process was less strictly logical than in the case of'abstract' attributes, and the subjects seemed to be guided more by their daily expectations concerning the attribute-combinations presented to them. They tended to cling longer to certain hypotheses than the available data justified, e.g. the hypothesis that the sex attribute was important for the solution.

3.20. S U M M A R Y

From the investigations referred to the following observations can be made: a. Words and word-meanings can be regarded as separate functional unities. These functional unities may result in strong or less strong effects, probably as a result of being to a greater or lesser extent organized as a functional unity .We refer also to the effect of frequent occurrence in the language and of repeated presentations (sections 5, 6, 7, 8, 9, 10, 13). b. The relationships between these functional unities can in principle be described in a quantitative manner (sections 2, 8, 9, 10, 12, 13, 15, 16). c. The strength of these relationships is connected with the relative frequency with which they occurred in the past (sections 3, 4, 8, 11, 12). d. The selectivity which occurs in thinking is connected with the existence of these relationships (sections 16, 17, 18, 19). e. The learning of certain connections is influenced by the existence of other connections (sections 11, 12, 13, 14)

SOME PREVIOUS INVESTIGATIONS

31

f. The interaction of these relationships can in principle be predicted (section 8, 15, 16). In the present investigation an attempt was made to give a quantitative description of the relationships between certain word-meanings. The use subjects made of these relationships in making judgments was also examined. As a result of these investigations - together with Luce's theory (16) - a theory on the interaction of the relationships between wordmeanings was formulated. On the basis of this theory certain results in the literature (sections 3.15 and 3.16) could be explained. Furthermore this theory appeared to be a suitable starting point for a more thorough psychological investigation of thinking.

4. T H E INVESTIGATION

4.1. SURVEY

The following stages were followed: A. Calculation of distances. 1. Collection of the initial data (see 4.3). 2. Investigation of the reliability of these data (see 4.4). 3. Correction of the initial data (see 4.5). 4. Explanation of the occurrence of intransitivities (see 4.6). 5. Actual calculation of the distances (see 4.7). B. Testing of the postulate (see 4.8 and 4.9). 1. The factor 'difference'. 2. The factor 'sum'. 3. Second testing of the factor 'sum'. C. Investigation of the relevance of the model for judgments (see 4.10). D. Investigation of the communality of the meaning structures (see 4.11). 4.2. THE SUBJECTS A N D THE PERSONALITY TRAITS

The subjects consisted of seventeen graduates in psychology who took part in this investigation as part of a compulsory practical course. They will be referred to by the letters A to Q. Their participation in the different stages of the investigation is shown in the next table. TABLE 4.1 The Participation

of the Subjects in the Different Parts of the

Part A1 A2 A3 A4 A5 B1 B2 B3 C D

Subjects All D, J, All D, J, All A, B, A, B, E, G, A, B, All

K, M, N, O, K, M, N, O C, C, I, C,

D, D, J, E,

F, F, K, F,

H H L, M, N, O, Q G, H, L, P, Q

Investigation

33

THE INVESTIGATION

The subjects were split up for the various stages of the investigation not because by so doing better results were expected, but for reasons of convenience and to effect a fair division of the various activities. The trait concepts (categories) used, are as follows: TABLE 4 . 2 The Utilised Trait Concepts Trait Index

Trait (Category)

Ki k2 k3 k4 k5 k5 k7 Kg

intelligent sociable practical aggressive dominating critical humorous secondary

(Categories)

Trait Index

Trait (Category)

k9 Kio Kn Ki2 K,3 Kl4 Kl5

unsure tenacious egocentric resourceful sensitive introvert congenial

These terms were chosen by a number of subjects on the basis of the following criteria: a. they had to be terms which occur frequently in psychology; b. they had to be terms with as varied meanings as possible. N.B. The trait 'secondary', short for 'secondary function', was taken from the typology of temperaments of Heymans.

4.3. T H E C O L L E C T I O N O F T H E I N I T I A L D A T A

The terms were presented in all possible different combinations of three, so that altogether there were four hundred fifty-five combinations. Each combination of three was typed on a separate card. Below is an example of one such card: intelligent sensitive

unsure

The instruction given to the subjects was: State from each triad of terms which pair is the least and which pair the most alike in meaning.'' 1

34

RELATIONSHIPS BETWEEN MEANINGS

The subjects gave their choice by putting a + ( = most alike) or a — ( = least alike) on an answer sheet. The terms, symbolized by letters were given on the answer sheet in the form of a triangle in the same way as on the card. The plus and minus signs were put between the chosen pairs. For example: a

+ b

c

In this example the subject has shown that he finds the greatest similarity between 'intelligent' and 'sensitive' and the least between 'intelligent' and 'unsure'. The interpretation of this choice is that the subject has made the following choices from the three adjoint pairs of terms:

Possibilities of choice ab and ac ba and be ca and cb

Similarity Greatest

Least

ab ab be

ac be ac

A considerable amount of time was saved by giving the terms in triads, moreover the task was not much more difficult than if two pairs of terms had been given. In principle no time limit was placed on the exposure time for each triad. In practice the mean was 15 seconds per card. In the series of triads each term occurred ninety-one times and each pair of terms thirteen times. In deciding in what sequence to give the triads an attempt was made to spread the terms and pairs of terms in the triads as evenly as possible over the series. Moreover each term had to occur the same number of times in each of the three possible positions in the triangle. At the same time care was taken to see that, as far as possible, the same term did not occur in two consecutive triads. By applying a system of cyclic permutations it was felt that this was achieved fairly successfully. The choices were scored for each subject (cf. Appendix B) and the frequencies obtained were noted in matrices. Table 4.3 gives an example

35

THE INVESTIGATION

of such a frequency matrix. In this matrix the number 6, for example, found in the first row and fourth column showed the following: The combination of terms intelligent-aggressive (K1K4) occurred thirteen times, each time with a different third term. Of these thirteen combinations the subject chose intelligent-aggressive six times as most similar instead of the combination intelligent-with-another-term. Thus each row of the matrix shows how often the subject chose the term in the column as most similar to the term in the row. These matrices are the initial data. TABLE 4.3 Frequency Matrix of Subject A

Categories (leading) Ki K2 Kj K4 K5 k6 k7 Kg k9

Km Kn K,2 Ku K14 K,5

Categories (to be compared) k4

Ki

k2

14 5 11 9 6 13 12 8 4 4 4 13 6 5 10

7 12 6 5 14 9 7 8 11 14 4 8 5 7 14 13 9 8 13 14 5 10 8 10 9 8 3 5 4 3 0 2 9 2 9 0 5 6 10 12 2 6 10 13 6 12 7 7 10 6 7 2 4 3 2 1 10 6 5 1

k3

k5

k7

k8

Kg

11 11 6 10 10 6 9 3 10 5 14 4 7 14

2 4 2 2 3

0

k6

6

5 6 0

7 11 9 7 3

2 9 11 4 13

6

3

Kio K n

Kl2 Kl3 Kl4

2 3 13 4 2 4 7 5 12 12 10 7 1 11 12 6 1 1 6 5 11 0 2 7 5 5 14 9 10 9 5 8 14 6 7 3 7 2 14 13 8 7 4 11 14 7 1 0 6 7 14 7 5 3 4 1 13 10 7 10 8 5 7 3 1 9 8 2 5

Kl5

8 11 1 8 0 3 3 0 4 4 4 13 13 10 11 10 8 4 6 7 11 0 4 2 6 14 7 13 9 14 8 12 6 14

6 10 4 6 3 8 11 7 13

5 3

4.4. THE INVESTIGATION OF THE RELIABILITY OF THE DATA

It is important that the initial data are reliable, i.e. that the subjects always make the same choice each time the same triad of terms is given. In order to examine this six subjects were asked to state their choice again from the 455 triads, two weeks after they had originally been given the triads. The results were summarized in matrices in the same way as the first time. The matrices were compared with each other row by row for each subject.

36

RELATIONSHIPS BETWEEN MEANINGS

By correlating the corresponding rows of the two matrices according to the product-moment correlation method the degree of reliability of the selections made with regard to the term corresponding to each row was obtained. For example (subject D, third row of his frequency matrix) : First time: 13 8 14 10 8 8 5 3 1 10 2 8 Second time: 12 8 14 13 10 11 6 1 1 7 3 7

5 5

1 1

7 6

r = -92 Fifteen product-moment correlation coefficients were calculated for each subject, i.e. one for each row. The mean of this gives the mean reliability of the subject's initial data.

4.5. T H E C O R R E C T I O N OF T H E I N I T I A L D A T A

As outlined in 4.3 the initial data consisted of the frequency matrices. The next stage was to calculate the distances in the model from these data. The distances in the model had to comply with the following condition: it must be possible to order every three distances in a transitive way, i.e. if the three distances are for example d\, d^ and di, with d\ > d2 and di > dj, then d\ > di should obtain. Thus the frequency matrix must present an ordering of the distances which satisfies the condition of transitivity. The empirically found frequency matrices did not entirely satisfy this condition. Corrections had therefore to be made. In a fictitious example concerning five terms a, b, c, d and e the following matrix is given: TABLE 4 . 4 Frequency Matrix (Fictitious Categories (leading) a b c d e

Data)

Categories (to be compared) a

b

c

d

e

4 0 1 2 0

0 4 2 1 1

3 3 4 3 2

1 2 3 4 3

2 1 0 0 4

THE INVESTIGATION

37

It can be seen from the row of this matrix how many times the 'subject' chose the terms in the columns as most similar to the terms in the rows. For example: The term b was never chosen from the terms b, c, d and e as being most similar to the term a; c three times, d once and e twice. It can be proved by means of the postulate that the rank-order of mathematical expectations of the frequencies in a row of the frequency matrix corresponds with the rank-order of the distances in the model.The greatest distance always corresponds with the smallest frequency and the smallest distance with the greatest frequency (cf. Appendix A and B). Thus, from the first row of the above frequency matrix, the rank-order of the corresponding distances is: , , , , da6 > «ad > "ae > "ac If the rank-order of the distances belonging to the three terms a, d and e is examined, then it follows that: daa > dae dad < dde

(first row) (fourth row)

dae > dde

(fifth TOW)

If the three partial rank-orders are combined, then: dad > dae > dde > dad This rank-order is intransitive, for from dad > dae (1st row) and dae > dde (5th row) it does not follow that dai > dae, but dad < dde (4th row). By making certain changes in the frequencies such intransitivities can be made to disappear. The method of doing this and the methodological admissibility of it are discussed in Appendix C. For the extent of the alterations consult section 5.3. 4.6. THE EXPLANATION OF HOW INTRANSITIVIES ARISE

The appearance of intransitivities can in our opinion be explained in two ways: a. the model is in principle inadequate to describe the choice behavior in question;

38

RELATIONSHIPS BETWEEN MEANINGS

b. the intransitivities are a result of the fact that the frequencies obtained were sample values and as such are susceptible to sampling errors. In our opinion the second possibility is the most likely. The fact that the model appeared to be an adequate representation of the choice behavior of the subject argues against the first explanation (the inadequacy of the model). Moreover the intransitivities could be eliminated by slight adjustments in the initial data. An important argument in favour of accepting the second supposition (sampling errors) is the close relationship which appeared to exist between the reliability of the initial data, expressed in the reliability coefficients (cf. 4.4), and the number of corrections necessary in connection with the intransitivities; that is to say, the higher the reliability coefficient the smaller the number of corrections. For each subject participating in stage A2 of the investigation two sets of initial data were obtained. These two sets are regarded as independent samples from the same sampling distribution. The reliability coefficient can be regarded as an indication of the degree of variation of a sampling distribution, because two independent samples from a sampling distribution with marked variation will in general bear less resemblance than two samples from a sampling distribution with a small variation.

4.7. THE ACTUAL CALCULATION OF THE DISTANCES

It is now possible to calculate for each subject the distances between each pair of term points in the model with the help of the transformation method given in Appendix D. The matrix with the modified initial data is the starting point for this method. It now appears that it is not always possible to construct a geometrical model from the calculated distances between three points, for example: If the calculated distances between the points a, b and c are 1, 2 and 10, then the triangle abc can never be constructed with these data. In a triangle the sum of two of the sides is always greater than the third side, and in this case the distances do not satisfy this condition. However, by adding the same constant value to each of the distances, e.g. 10, the condition will be fulfilled. The distances will then be 11, 12 and 20, and a

THE INVESTIGATION

39

triangle (i.e. a two-dimensional model) can be constructed from these data: C

If 7 is used instead of 10 as the additive constant for each of the distances the values will then become 8,9 and 17, giving a unidimensional and therefore more parsimonious model. 8

a

9

c 17

b

Fig. 4 . 2

It was not necessary to introduce an additive constant to test the postulate (see 4.8), but its use was required to explain certain results (see 5.8).

4.8. T E S T I N G T H E P O S T U L A T E

At this stage of the investigation the distances for each subject and for each pair of terms are known. They are calculated from the subject's choices from adjoint pairs (cf. 2.6 and 4.3), i.e. pairs of terms with one term in common. The postulate may now be tested. Choices from disjoint pairs, i.e. pairs with no term in common, are used for this purpose. These are new choice possibilities for the subject. The length of time between the first part of the investigation (collection of initial data) and the second part (testing the postulate) was from two to twelve months. Six of the subjects were used in this stage of the investigation (see 4.2) The postulate is as follows (see 2.5):

40

RELATIONSHIPS BETWEEN MEANINGS

The probability of choosing one of the two pairs deviates further from • 5 according as: a. the absolute difference between the two corresponding distances is greater, and/or b. the sum of the distances is smaller. In testing the postulate the effect of each of these two factors must be examined independently of the other. It will be seen from the following example how this can be achieved. Let us suppose that the calculated distances for a certain subject are as follows : TABLE 4 . 5 Pairs of Categories and their Distances Pair a b c d e f g h i j k I m n o P

Distance 50 -, 48 45 40. 38 36 30 28 27 25 24 18 io ì 7 5

3

J

Remarks = largest distances (upper quartile; see 4.11 and 5.10) The data in this table are fictitous. With 15 categories the number of pairs would be 105, so this table is a shortened version of the tables we found. The table has to be read as follows: Pair a (e.g. sensitive-egocentric) has a distance equal to 50. Pair n (e.g. intelligent-resourceful) has a distance equal to 7. Etc. = smallest distances (lower quartile; see 4.11 and 5.10)

From the above table it is now possible to make up a number of pairs of alternatives where only one of the two factors ('sum' or 'difference') varies. For example for the pair of alternatives with the term-pairs a and h we find: difference = 22; sum = 78 while for the pair of alternatives with the term-pairs o and i we find: difference = 22; sum = 32. Thus the differences from these two pairs of alternatives are constant, the sums vary. A possible effect with regard to the probability of choice will be dependent on the factor 'sum' alone.

41

THE INVESTIGATION

A few more examples each consisting of two pairs of alternatives: b ~g difference = sum = j - n difference = sum =

18 78 18 32

difference is constant sum is not constant

b ~g difference sum a -h difference sum

sum is constant difference is not constant

=

18 78 22 78

j - n difference = sum = i - 0 difference = sum =

18 32 22 32

sum is constant difference is not constant

= =

=

Difference 18 22

Sum 32 78

j -n b-g

i-o a-h

In this scheme the factor 'difference' can be tested by examining the column totals. If the row totals are examined the factor 'sum' is tested. From one hundred and five pairs of terms forty pairs of alternatives, each consisting of disjoint term-pairs, were made up for each of the subjects separately. Thirty-six of these pairs of alternatives were grouped in levels as follows: for the factor 'difference': four levels (very large, large, medium, small); for the factor 'sum': three levels (large, medium, small). The four remaining pairs of alternatives each had a difference equal to 0. In tabular form: TABLE 4 . 6 Cell-frequencies in the First Analysis of Variance Design Sum Small Medium Large

Difference Very large

Large

Medium

Small

0 9 0

3 3 3

3 3 3

3 3 3

42

RELATIONSHIPS BETWEEN MEANINGS

The figures show the number of pairs of alternatives for each particular combination of a level of the factor 'difference' and a level of the factor 'sum'. The combinations small sum - very large difference and large sum very large difference could in principle not be constructed (see 5.8). In order to give an equal weight to the columns in the scheme when testing the factor 'difference', nine pairs of alternatives which could be constructed were drawn up for the combination very large difference medium sum. In testing the factor 'difference' it was then assumed that the mean effect of the levels small sum and large sum is approximately equal to the effect of the level medium sum. The presentation of the pairs of alternatives was as follows: a. the pairs of alternatives with difference 0 and the nine pairs of alternatives with very large difference were given alternately. This was done to give the subjects an impression of all the varying degrees of difficulty of the work: thus choices from pairs of alternatives with difference 0 should be very difficult, choices from pairs of alternatives with a very large difference should be relatively easy; b. the twenty-seven remaining pairs of alternatives were then given in such an order that the mean rank number of the pairs of alternatives for each cell in the above table was constant. The following instruction was given: 'From the given pairs of terms choose that pair with terms most alike in meaning, and show on the scale the degree of certainty with which you have made the choice.'' Example of an pair of alternatives given: intelligent resourceful

very certain

sensitive egocentric

very uncertain

THE INVESTIGATION

43

The predictions of each subject's choice were made beforehand. In order to eliminate the tendency of always choosing the left pair of terms or the right (i.e. response set) the predicted choice was sometimes put on the left and sometimes on the right of the card. Two aspects were noted for each choice made: a. the probability aspect: a check was made to see whether the actual choice agreed with the prediction; b. the certainty aspect: the indications on the six-point certainty scale were turned into scores running from 1 (very certain) to 6 (very uncertain).

4.9. T H E S E C O N D TEST O F T H E F A C T O R 'SUM'

The investigation described above showed that the factor 'difference' had a significant effect on both aspects, but the factor 'sum' only on the certainty aspect. TABLE 4 . 7 Probability

Factors Difference Sum

Certainty significant significant

significant not significant

The factor 'sum' was therefore tested again using ten of the eleven remaining subjects (the 11th subject (P) could not take part in this stage of the investigation due to unforeseen circumstances). Pairs of alternatives consisting of disjoint pairs, which this time had one level of the factor 'difference' but two levels of the factor 'sum' were again given. TABLE 4 . 8 Cell-frequencies in the Second Analysis of Variance Design Sum

Difference Medium

Small Large

9 9

From the foregoing test the difference necessary for the maximum effect of the factor 'sum' and the sum values are calculated. At the same time

44

RELATIONSHIPS BETWEEN MEANINGS

the number of observations required for a significant sum effect could be estimated. In other respects the investigation was carried out in a similar manner to the previous one.

4.10. THE INVESTIGATION OF T H E RELEVANCE OF T H E MODEL FOR J U D G M E N T S

In a third stage of the investigation ten subjects judged each other (and themselves) in terms of each of the fifteen traits previously mentioned. From ten persons forty-five different combinations of two can be formed. Each pair had to be judged with respect to each of the fifteen traits. The judging process consisted of assigning a rank-order to each pair with regard to the applicability of the trait concept as conceived by the judge. Each member of the group had to function as a judge. In this way for each judge and each trait the frequencies were obtained with which a particular person was assigned first place. Thus a range of frequencies from large to small was obtained, according to the extent to which the trait was considered to be applicable to the person being judged. Example from one of the judges: TABLE 4.9 Choice Frequencies (Judge A) Categories Intelligent Resourceful etc.

Judged Persons a

b

c

d

e etc.

0 0

3 2

7 8

3 6

8 5

The number 8 under e in the first row (i.e. intelligent) shows that the judge, having compared the subject e pairwise with the other nine, chose him eight times as the most intelligent person. From these data measures indicating the difference between trait concepts as used by this judge were computed in the following way: For each pair of trait concepts the difference between the two frequencies pertaining to the same judged subject was squared and summed over

THE INVESTIGATION

45

all judged subjects. Thus in the case of two series of frequencies showing little agreement the measure indicating the difference between these two series (trait concepts) should be relatively large. The distances between the terms were also known for this judge. Next the relationship between the distances and the sum of squared differences was established by computing a product-moment correlation coefficient. If the model is relevant for this kind of actual judgments the value of this coefficient should be positive.

4.11. THE INVESTIGATION OF THE COMMUNALITY OF THE SEVENTEEN MEANING STRUCTURES

In the three previous stages of the investigation the data were worked out for each subject separately. Finally in the fourth stage an attempt was made to establish the degree of similarity between the individual meaning structures of the whole group of subjects. It was possible to establish in the case of each subject whether a particular distance fel in the upper or lower quartile (see the example in 4.8). For each pair of terms it could be established how often the corresponding seventeen distances fell in the upper or lower quartile. If a distance was found more frequently in the upper than in the lower quartile this was indicated with a + . If the reverse were true it was indicated with a —. In this way it was possible to establish the measure of agreement between the seventeen subjects and to find out whether this agreement demonstrated a certain communal structure (see 5.12).

5. R E S U L T S

5.1. THE INITIAL DATA

For each subject a square matrix of the order of fifteen was constructed. The terms were set along two of the sides of this matrix. For each cell of the matrix a frequency was noted, for example in the cell pertaining to row i and colum j the frequency «y was noted, this representing the number of times the term K,- was chosen as being most alike in meaning to Ki, in comparison to the remaining thirteen terms. This process was repeated for each cell of the matrix. This matrix was called the frequency matrix of the subject (see 4.3). 5.2. THE RELIABILITY OF THE DATA

After two weeks the experiment was repeated with six of the subjects. Accordingly there are now two matrices for each of these six subjects. These matrices were correlated row by row. The results were the following product-moment correlation coefficients: TABLE 5.1 Reliability Coefficients Category K, K2 K3 K4 K5 K6 K7 KG K9 KIO KM

K12 KlJ KJ4 K,5 Mean (column)

Subject

Mean (row)

D

J

K

M

N

O

.90 .93 .92 .96 .80 .88 .57 .73 .77 .78 .71 .88 .42 .83 .67

.94 .94 .72 .88 .84 .94 .90 .96 .85 .85 .74 .89 .88 .93 .96

.86 .96 .93 .90 .91 .71 .89 .85 .95 .90 .88 .94 .97 .92 .96

.94 .90 .82 .89 .90 .90 .85 .90 .86 .85 .90 .84 .94 .85 .92

.92 .67 .73 .92 .89 .59 .86 .74 .78 .64 .65 .83 .93 .87 .70

.91 .92 .85 .80 .87 .92 .91 .71 .95 .72 .89 .84 .92 .47 .92

.91 .89 .83 .89 .87 .82 .83 .82 .86 .79 .79 .87 .84 .81 .85

.78

.88

.90 .88

.78

.84

.84

RESULTS

47

In the opinion of the writer these values are satisfactorily high, especially in view of the fact that the subjects were asked to perform a task of an extremely monotonous nature: They were presented with combinations made up out of such a limited number of terms, that the same words occurred over and over again, and hour after hour they had to respond by setting down plus and minus signs. 5.3. T H E I N T R A N S I T I V I T I E S

The frequency matrices were corrected for possible intransitivities. Small changes of the frequencies were made where necessary (see Appendix C for the way this was done). For each subject the absolute amount of these deviations together with their number were tabulated. TABLE 5 . 2 Total Number of Changes in Frequency Size of Change (absolute)

Subject

A B C D E F G H I J K L M N O P Q

V2

0

1

2

3

133 126 114 139 147 121 179 133 175 172 156 177 152 99 145 148 162

61 64 78 56 52 79 30 67 33 34 44 23 47 80 58 59 42

15 16 18 15 11 7 1 10 1 2 9 9 11 27 6 1 6

1 4 0 0 0 3 0 0 1 2 1 1 0 4 1 2 0

25%

5%

1%

Mean (%) 69%

Matrices

.619 .781 .714 .552 .457 .638 .162 .510 .219 .286 .424 .324 .433 1.067 .433 .386 .314

Ninety-four percent of the cases showed deviations of not more than one point. Thus the total effect of the changes is very small. It is possible to

48

RELATIONSHIPS BETWEEN MEANINGS

evaluate the effect of the changes in another way. For each of the subjects the mean squared difference between the original and the changed frequencies was computed. These means are set forth in the column below v2. Again the total effect of the changes is very small. The relatively large differences between subjects with respect to the changes which had to be applied, are noticeable. An explanation of this phenomenon was not attempted. An example of a frequency matrix corrected for intransitivities is the following: TABLE 5.3

Frequency Matrix Corrected* for Intransitivities

Categories (leading) KI K2 K3 K4 K5

KI K 2 K 3 K 4 K 5 K 6 K 7 K 8 K , KIO KN KI2 KN KU K15 14

7

7 14 (5) 12 11 (11) 9 4 (5) 6 9 13

K7

12 10 (9) 7 4 (8) 4 9

K9

KIO

KL5

10

KI3

5

10

6

4 (5)

* In parentheses the original frequencies.

6

0

8 (7) 8 (7) 1

2

14

13

6

9 (8) 12 2 13 (10) (3)(11) 4 1 8

5

7 0 (6) 4 2 (3) (3) 8 4

3 0

3 (4) 11 3 13 (4) 8 13 11 (10) (7) 13 11 10

9 6 (8) 4 11 14 9 6 5 2 (7) (7) (7) 11 9 0 5 8 14 3 (4) (6) (7) 10 12 6 8 3 4 2 14 (9) (11) (7) (5) (1) 6 4 13 10 8 12 7 11 (7) (10) (8) (9) (7) 1 9 12 1 3 13 5 7 3

KM

K12

4

5

8 5 11 11 3 0 1 2 13 (6) (2) (2) (3) 11 8 10 6 12 3 9 4 1 5 (10) (4) (8) (9) (7) (8) (2) (4) 14 4 8 10 6 2 2 7 5 13 (12) 1 5 12 10 8 6 14 13 9 2 (7) (3) (2) (7) 8 13 14 10 5 3 1 11 12 7 (6) 10 8 10 14 3 2 1 6 5 11 (4) (1) 8 2 5 7 14 4 6 0 1 9 (2) (7) (3) (5) (5) 2 6 5 14 9 11 10 5 3 0 (10) (9) 2 9 0 3 6 8 14 6 7 3 12

7 10 12 (6) 4 2 7 10 13 (6) 13 4 12 8 6 (7) (7) (6) 1 5 11 7 9 (6) (10) (6) (7) (2) 1 5 4 3 2

KN

A)

Categories (to be compared)

K6

KG

(Subject

9 (8) 11 2 9 (7) 14 6

4 0 7 (6) 13 9 (8) 14

49

RESULTS 5.4. TEST OF T H E H Y P O T H E S I S C O N C E R N I N G T H E O C C U R R E N C E OF I N T R A N S I T I V I T I E S

In 4 . 6 it was explained that the existence of a relationship between, on the one hand the reliability of the initial data, expressed by the correlation coefficients in section 5.2, and on the other the effect of the changes, expressed by the mean squared differences in section 5.3, would be an important argument to support the hypothesis that intransitivities occur as a consequence of sampling errors. For the six subjects, mentioned in section 5.2, these two series of coefficients are: TABLE 5 . 4 Comparison of Coefficients Coefficients Subjects D J K M N O

Section 5 . 2 .78 .88 .90 .88 .78 .84

Section 5 . 3 .552 .286 .424 .433 1.067 .433

The rank-order correlation coefficient of Spearman between the two columns is r = - . 86. This value is significant at the .05 level, one-tailed. Because the value of r is high, as well as deviating from 0 in an improbable way, it is considered that the above relationship is demonstrated. Thus it would seem that these findings support the implicit assumption of the model, i.e. that in principle the subject's choices are based on a transitive ordering of the pairs of terms.

5.5. T H E COMPUTATION OF THE DISTANCES

By means of a method of transformation (see Appendix D) a matrix with distances between the pairs of points corresponding with the terms could be computed from the corrected frequency matrix. In this way a distance matrix was obtained for each of the subjects. This matrix is also

50

RELATIONSHIPS BETWEEN

MEANINGS

of the order of fifteen, and has along the sides the fifteen terms. The distance dy is written down for each pair of terms K^Kj. TABLE 5.5 Distance Matrix (Subject A) Categories

Categories KI K2

K3 K4 K5 K6 K

7

K„ K,

Kio K,,

Kl2 K,3 Kl4 K,5

KI

K

0

28 0

2

K7

K3

K4

K5

K6

3 8 0

11 27 25 0

37 10 18 1 0

4 4 32 7 9 23 11 48 9 44 0 41 0

KG

K9

39 45 46 26 47 47 50 26 48 53 42 46 45 43 0 27 0

KIC K J I K l 2 K 1 3 K l 4 K I 5

41 34 22 7 5 31 50 21 43 0

40 49 24 9 2 32 49 22 40 4 0

1 33 2 12 19 6 8 45 46 20 12 0

29 7 25 13 45 11 5 28 14 31 30 34 0

37 47 48 51 52 36 47

6 6 18 28 54 41 1

21 16 17 20 41 4 54 35 13 13 2 0 18 0 1

5.6. TEST OF THE POSTULATE: THE FACTOR 'DIFFERENCE'

Six of the subjects received forty pairs of alternatives each. They had to choose the pair of terms which were most alike in meaning. These pairs of alternatives were made up for each of the subjects separately, according to the scheme in 4.8. If the greatest distance belonging to each of the subjects is made equal to one, the levels of the factors 'difference' and 'sum' of the pairs of alternatives had the following mean values1 for each of the subjects. 1. No account was taken of the additive constant. The value of it was estimated to be approximately . 70 (after scrutenizing the distance matrices). The value 1.40 would thus have to be added to the levels of the factor 'sum' (see 5.9).

51

RESULTS TABLE 5 . 6 First Analysis of Variance Design: Mean Values of Differences Difference Subject

Very large

Large

Medium

Small

A B C D F H

.90 .95 .92 .87 .94 .88

.54 .48 .46 .48 .50 .46

.30 .31 .36 .29 .33 .33

.19 .18 .24 .18 .19 .17

Mean

.91

.48

.32

.19

TABLE 5.7 First Analysis of Variance Design: Mean Values of Sums Sum Subject

Small

Medium

Large

A B C D F H

.58 .52 .52 .56 .52 .54

1.18 1.04 1.04 1.08 1.00 1.00

1.66 1.54 1.56 1.56 1.54 1.58

Mean

.54

1.06

1.56

Accordingly there was very little variation of the mean values among the subjects. The separate pairs of alternatives for each of the subjects also showed only small deviations from these mean values. The choice from each pair of alternatives was scored as follows (see 4.8): a. according to the agreement or disagreement of the choice with its prediction; b. according to the degree of certainty with which the choice was made. To test the factor 'difference' the four levels of this factor, combined for all six subjects, were compared with each other.

52

RELATIONSHIPS BETWEEN MEANINGS

Two aspects of the test had to be considered (this also applies when testing the factor 'sum'): 1. whether the results agree with the predicted trend; 2. the measure of probability (P) that the results may be explained by chance fluctuations. The first aspect is the most important one. The second aspect is indicated by applying statistical tests to the data. The scores according to agreement or disagreement of the choices with the predictions are summarized in the following table: TABLE 5.8 Comparison

Choices Agreeing with Prediction Disagreeing with Prediction Totals

of Choices with Predictions

(Factor

Difference)

Difference

Totals

Very large

Large

Medium

Small

53 (98 %)

45 (83%)

39 (72%)

33 (61 %)

170

1 (2%)

9 (17 %)

15 (28 %)

21 (39 %)

46

54 (100%)

54 (100%)

54 (100%)

54 (100%)

216

The likelihood-ratio test for the hypothesis of independence of, on the one hand the levels of the factor 'difference', and on the other the agreement or disagreement showed : X2 = 29.114; d.f. = 3; P < .001 An analysis of variance with two factors and nine replications per cell was applied to the certainty scores. The results were as follows: TABLE 5.9 Analysis of Variance for Certainty

Degrees of Freedom

Scores

Source of Variation

Sums of Squares

Estimated Variances

Differences Subjects Differences X Subjects Residual

43.426 29.111

3 5

14.475 5.822

5.79 2.33

31.074 480.222

15 192

2.072 2.501

.83

-5)

.30 .37 .37 .43 .43 .50

( > .5)

.50 .57 .63 .57 .63 .70

«

-5)

.7 .7 «

-5)

j ( > .5) .7 .9 .7

( > -5)

•1 « .3

-5)

«

-5)

.3 ( < .5) .3

I ( > .5) .9

.70 .77 .83 .90

.5 •5 ( = .5) .5

.23 .30 .37

.5 ( = .5)

.37 .43

.5

.50

-5)

(=•5)

( > .5)

.43 .50 .50 .57

(=•5)

(=•5)

( > -5)

.57 .63

.5 •5 ( = .5) .5

.7 .7 ( > .5) .9

.7 •9 ( > .5) .9

.63 .70 .77

«

6

( > -5)

Mean

P[X, Y\Af\Bf\

C]

82

RELATIONSHIPS BETWEEN MEANINGS

the 'combined' probability is generally also smaller (or greater) than .5, unless this third probability is very much greater (or smaller) than .5. If the subjects are allowed to give the alternative o f ' n o judgment' when making their choices, we expect - on account of the fact that the 'combined' probability invariably deviates to a greater extent from .5 than the mean of the 'separate' probabilities - that there will be fewer decisions of 'no judgment' in the case of the combined data than there would be on an average in the case of separate data. Bruner, Shapiro and Tagiuri (34) carried out an experiment of which the results confirm the above predictions. They had their subjects decide whether, given one particular trait or combination of traits, some other trait would be likely or not. The following were given: 4 cases of one single trait 5 cases of a combination of two traits 2 cases of a combination of three traits. Starting from each of these cases the judgments had to be given on each of 59 other traits. Thus there were (4 + 5 + 2 ) times 59 = 649 combinations of one or more given traits with one trait to be judged. Each of these combinations was judged by a group of 60 men and a group of 60 women. For each of the 11 ( = 4 + 5 + 2) different kinds of cases a different group of 60 men and 60 women was used, which means that a total of 1320 subjects were involved in the investigation. The judgment of each subject could be classified as follows: likely = positive judgment do not know = no judgment unlikely = negative judgment Supposing that of the judgments the proportion of positive judgments in one group of subjects is equal to p+, and the proportion of negative judgments is equal to p_, and further that p+ > p_, then we call the group judgment a positive inference ( + ) . In the case of p+ < p_ we call the group judgment a negative inference (—). As the group judgments were in part based on traits given separately, and in part on combinations of these traits, it was possible to examine the relationships between the inferences based on the former and those based on the latter. Bruner et al have reported on their results in such a way that a comparison with the predictions deduced from the model is

83

THE S I G N I F I C A N C E OF THE MODEL

possible. For in terms of the theory, a choice has always to be made between two possibilities: the trait is probable (likely) or it is not. The group choice of the former possibility was indicated by a + , that of the latter possibility by a —. A. If the group judgment based on separate data is known, then it is possible to predict with a fair degree of accuracy the group judgment based on the combined data (see also Appendix F). In the following table the possible combinations of group judgments based on separate data are given in the first column. The second column gives the prediction of the group judgments based on the combined data. The extent to which the results confirmed the predictions is given in the third and fourth columns. TABLE 6.5 Sign of Inferences Made from Single Traits and from their Combinations Signs of Inferences made from Single Traits

Signs of Inferences made froj Combined Traits Prediction

In Agreement with Not in Agreement the Prediction with the Prediction

Two Given Traits ++

+

+ - (+ more extreme) -+ ( - more extreme) Three Given Traits

+

+++

+

++—b ++—+

( - not extreme) (+ not extreme) ( - very extreme) (+ very extreme)

}

387

3

}

195

5

}

130

1

J

83

0

)

19

3

814

12

+ +

Totals

In 814 out of 826 cases - more than 98 % - the results confirmed the predictions. B. Furthermore it appeared that in the case of the combined data fewer subjects gave 'no judgment' than on average when the data were given separately.

84

RELATIONSHIPS BETWEEN MEANINGS TABLE 6 . 6 Definite Inferences Made from Single Traits and from their Combinations Sign of Inferences from Single Traits

Totals

Like

Unlike

Combination produces more definite inferences than the the mean of component single traits

479

206

685

Combination produces fewer definite inferences than the mean of component single traits

48

93

141

527

299

826

Totals

From this table it appears that: 1. more subjects give a judgment in the case of combined data than in the case of separate data; 2. in the case of separate data which allow conclusions in the same directions this tendency is stronger than in the case of data which produce conflicting conclusions. The agreement with the predictions deduced from the model together with Luce's theory is striking.

7. S U M M A R Y

Starting from the phenomenon that some pairs of traits are regarded as being more related than other pairs, a model was constructed for which the following assumptions had to be made: 1. For the person who uses a trait concept, this concept has a specific and definite meaning. 2. The degree of similarity of a pair of concept-meanings is definite and independent of other concept-meanings. The model was conceived spatially. It was taken from Coombs' Theory of Data (3). The trait concepts are represented by points in a (meaning-)space. The distances between these points correspond with the degree of similarity of the pairs of trait-meanings, in the sense that a short distance indicates a great similarity. From the investigation it appeared, to begin with, that such a model is of a descriptive as well as a predictive value. Moreover it appeared that Weber's law held good for the handling of the pairs of trait concepts. It could also be demonstrated that certain relationships between the trait concepts, as they appear in judgments about other people, are correlated with the degree of similarity between the trait concepts, as experienced by the person making judgments. With the aid of Luce's theory of choice behavior it was possible to extend our theory to the point where it offered an explanation of the results obtained by Bruner, Shapiro and Tagiuri (34). Some interesting problems of measurement arose.

APPENDICES

A. THE POSTULATE A N D DERIVATIONS THEREFROM

A subject has to choose from two alternatives. Suppose the alternatives are the following pairs of categories (trait concepts), KiK2

and

K3K4

represented respectively by the elements a\

and

a2,

with their distance-values d\ and d2. The probability of choosing a\ from the pair a\ and a2, i.e. P[a\, a2], is, according to the postulate, dependent on the values of d\ and d2 in the following way: Postulate:

P[au a2] = F[(d2 -

(d2 + di)] = F[v, s]

In this expression F is a continuous, differentiable function of two varia^ bles, v = d2 — d\ (1) and in such a way that

and

(2)

s = d2 + du dF — > 0, dv

condition 1

(3)

dF v—

dF ds

The first condition indicates an increase in F as v becomes larger (5 constant). The second condition indicates either a decrease in F (v constant and > 0) or an increase in F(v constant and < 0), as 5 becomes larger. The third condition indicates that when either v or s changes by the same amount, the resulting change in F will be larger in the case of a change in v than in the case of a change in s.

88

RELATIONSHIPS BETWEEN MEANINGS

Analysis of F[v, s]: For a constant value of P we may rewrite P = F[v, s] as v = f(s) first derivative of v with respect to s is

The

8F dv

ds_

Js~

8F

(6)

dv According to condition 3, , t ds

Ç F

~|J>0

(vF3

89

APPENDICES

From the first two conditions it follows that the functions v = f(s) may be ordered according to their value of P. The limiting functions are v = — s for P = 0, and v = 5 for P = 1. For P = .5 we have v = 0. Furthermore it is apparent from the conditions that the functions v = f(s) do not intersect, except in the point (0, 0). In the case where v = f(s) is a linear function, we have the special case called Weber's law. Setting d = d\ and Ad = dz — d\, then (7)

v = dz-dx=Ad and s = d2 + di = 2d + Ad.

(8)

Now it may be shown that, for a constant k, ^

d

= k (Weber's law) becomes v = — - — s, 2 +k

that is, v is a linear function of s. In a different conception Weber's law may be considered as a first linear approximation to the function v = f(s) at the point (SQ, V0). Then the Weber-proportion has the value

d with

L 1 -f'(so)

(9)

d and

B = V° ~ *°/X?o) l - f ' ( s0)

Here f'(so) is the value of the first derivative of v with respect to s at the point (io, vo). The Weber-proportion will have a relatively constant value for small values of B. A number of lemmas can now be proved using the postulate. Lemma la: P[a\, a2]=\

>• di = d2

Proof: If d\ = d2, then a\ and a2 may be interchanged, from which it follows, that P[au a2] = P[a2, ai] (10) or P[aua2] = 1 -P[aua2] (11)

90

RELATIONSHIPS BETWEEN MEANINGS

so that

P[a 1, a2] = i

(12)

Hence

di = d2 -> P[au a2] = i

(13)

By following the argument in the opposite direction we prove that P[a\, a2] = i

di = d2

Lemma lb: P[a\, a2] > \ Proof:

thus

di < d2

(14)

d2 — d\ > d\ — d2

(15)

d2 + d\ = d\ + d2

(16)

F[(d2 - d{), (d2 + di)} > F[(di

or

d2), (.di + d2)] (17) (condition 1)

P[au a2] > P[a2, aj]

or

P[a\, a2]>

so that Hence

• di < d2

1 — P[au a2]

P[a\, a2] > i di < d2

P]ax, a2[ > 4-

(18) (postulate) (19) (20) (21)

Now we wish to prove the reverse relation P[ai, a2] > i

di < d2

We do this by the argument of reductio ad absurdum. Suppose

di > d2

(22)

di > d2

P[au a2] < i

(23)

di = d2

P[au a2] = Ì

(24) (lemma la)

P[ai, a2] > \

We already proved that and

of which neither consequence is identical to the antecedent of our supposition, and hence this supposition is not valid. Thus the only remaining case P[ai, a2] > i -> di < d2 (25) is valid.

APPENDICES

91

Lemma 2a: P[ai, 03] = P[ai, «3] - P[ai, «3] -±

(32) (lemma lb)

P[ai,a3] P[a2, a3]

(34)

92

RELATIONSHIPS BETWEEN MEANINGS

¿1 < dj < dz P[au a3] > i

(35) (lemmas la and lb)

P[ai, a3] < i (lemma thus

(36) lb)

P[au a3] > P[a2, a3]

(37)

di < d2

(38)

di - di < d2 - dì

(39)

di + dì < d2 + dì

(40)

dì < di < d2

thus or

F[(di - dì), (di + ¿3)] < F[(d2 - dì), (d2 + di)}(condition (41) 3) P[aì, a{\ < P[aì, a2] (42) (postulate)

so that

P[au a3] > P[a2, «3]

(43)

Thus far we have proved the relation di < d2

P[au ad > P[a2, a3]

To prove the reverse relation we make use of the argument of reductio ad absurdum in a way similar to lemma lb. Suppose

P[au a3] > P[a2, a3] -> di > d2

(44)

We have already proved that

and

di>d2^

P[ai, a3] < P[a2, a3]

(45)

di = d2^

P[au a3] = P[a2, a3]

(46) (lemma 2a)

of which neither consequence is identical to the antecedent of our supposition, and hence this supposition is not valid.

APPENDICES

93

Thus the only remaining case P[au

a3] > P[a2,

a3]

(47)

dl < dz

is valid.

Lemma

3a:

Given the set of alternatives A = {a 2P[ah i—1 i#/

at]

—

The proof is analogous to that of lemma 3a.

dh < d}

(53)

94

RELATIONSHIPS BETWEEN MEANINGS

Theorem I: Given 1. a set of alternatives A = {an} with h = 1, 2,..., n; 2. that with each an corresponds a value dh and a value n

Eh = 2iP[ah, at], i¥=h which are elements from the sets D = {dh} and E = {En} respectively; 3. that the rank order of the values dh is RD, and that the rank order of the values En is Re", then

RD and m_ > w+. Since «+ + «_ = m+ + m_ = N, we have n_ = N — n+ and m+ = N — —m-, so n+ — {N — n+) < — (N — mJ), or n+ > «J-, from which it follows that «_ > m+. Thus ^ w_ ^ -- < — < — ti-

m+

or

N

^ n-m< N

That is to say, if one of the group judgments based on the data separately is more extreme positive (or negative) than the other group judgment is negative (or positive), the group judgment based on the combined data will be positive (or negative respectively).

110

RELATIONSHIPS BETWEEN M E A N I N G S

Thus it is demonstrated that, after the introduction of reasonable assumptions, there is a similarity between group judgments and individual judgments. Now it may be seen in the following that the above conclusion will hold regardless of the independency assumption. Suppose a statistical dependency exists between the judgments based on A and those based on B, then deviations from the expected frequencies in the joint distribution will result: A

B

pos. neg.

neg.

pos.

n_m+ T Vr N

n+m+ ,

m+

n+m_ T Vr N

m_

71+

N

From this table it is clear that n+m+ N

>
ingfully similar and associated verbal stimuli. Unpubl. doc. Thesis, Univ. of Minnesota, 1957. 25. Solomon,R.L., and Postman, L., 'Frequency of usage as a determinant of recognition threshold for words', J. exp. Psychol., 1952, 43, 195-201. 26. Stevens,S.S., 'On the psychophysical law', Psychol. Rev. 1957, 64, 153-181. 27. Stevens, S.S., 'Cross-modality validation of subjective scales for Loudness, Vibration and Electric Shock', J. of exp. Psychol., 1959, 57, 201-209. 28. Staats,A.W., Staats,C.K., and Biggs,D.A., 'Meaning of verbal stimuli changed by conditioning', American J. of Psych., 1958, 71, 429-431. 29. Staats.A.W., Staats,C.K., Heard,W.G., and Nims.L.P., 'Replication Report: Meaning established by classical conditioning', J. of exp. Psychol., 1959, 57, 74-80. 30. Staats,A.W., and Staats.C.K., 'First-order conditioning of word meaning', Techn. Report No. 6. Contract No. N onr - 2305 (00) between Office of Naval Research and Arizona State College at Tempe, 1958. 31. Staats,A.W., and Staats.C.K., 'Attitudes established bij classical conditioning', J.abnorm. soc. Psychol., 1958, 57, 37^0. 32. Staats.C.K., and Staats,A.W., 'Effect of number of trials on the language conditioning of meaning', Amer. Psychologist, 1958, 13, 415 (abstract). 33. Staats, A. W., and Staats.C.K., 'Meaning and m: correlated but separate', Psychol. Rev., 1959, 66, 136-144. 34. Tagiuri, R., and Petrullo, L., Person Perception and interpersonal behavior (Stanford, 1958). 35. Thrall, R.M., Coombs, C.H., and Davis, R.L., Decision Processes (New York, 1954). 36. Torgerson, W. S., Theory and Methods of scaling (New York, 1958). 37. Wertheimer,M., 'The relation between the sound of a word and its meaning' American J. of Psych., 1958, 71, 412-415.