Instrumentalizing Cognitive Dissonance Emotions

doi:10.4236/psych.2012.312153

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Psychology

2012. Vol.3, No.12, 1018-1026

Published Online December 2012 in SciRes (http://www.SciRP.org/journal/psych) http://dx.doi.org/10.4236/psych.2012.312153

1018

Instrumentalizing Cognitive Dissonance Emotions

Marie-Claude Bonniot-Cabanac1, Michel Cabanac1, José F. Fontanari2,

Leonid I. Perlovsky3

1Department of Psychiatry & Neurosciences, Faculty of Medicine, Laval University, Quebec, Canada

2Instituto de F´ısica de São Carlos, Universidade de São Paulo, São Carlos SP, Brazil

3Athinoula A. Martinos Center for Biomedical Imaging, Harvard University, Charlestown, USA

Email: lperl@rcn.com

Received September 22nd, 2012; revised October 18th, 2012; accepted November 16th, 2012

Many psychologists think that there are few basic emotions, and most emotions are combinations of these

few. Here we advance a hypothesis that the number of principally different emotions is near infinite. We

consider emotions as mental states with hedonic content, indicating satisfaction and dissatisfaction. Basic

emotions correspond to bodily signals, and there are relatively few of them. Our hypothesis is that a large

number of emotions are related to the knowledge instinct (KI, or a need for knowledge). KI drives the

mind to fit mental representations to cognitive experiences and to resolve mental contradictions. Discom-

fort due to holding contradictory knowledge elements are known as cognitive dissonances. We emphasize

that cognitive dissonances involve specific emotions. The number of cognitive dissonances is combinato-

rial in terms of elements of knowledge. Correspondingly, the number of these knowledge-related emo-

tions is very large. We report experimental results on measuring these emotions and indicating that emo-

tions of cognitive dissonance exist. We also make a step toward proving that these emotions are different

from basic emotions in principle, and outline future research directions toward proving that their number

is large.

Keywords: Cognitive Dissonance Emotions; Basic Emotions; Instrumentalizing; Basic Emotions

Introduction

Emotions are topics of much discussion in psychological lit-

erature, their mechanisms and functions are subjects of contro-

versy (Ekman, 1999; Juslin & Västfjäll, 2008). Psychological

functions of emotions have been related to social functioning

and associated with facial expressions (Ekman, 1957), emotions

have been related to survival (Plutchik, 1962), and to mental

states (Ortony et al., 1987). Belief-desire theory of emotions

has been developed in (Gratch et al., 2009; Reisenzein, 2009).

Emotions have been argued to perform appraisals of concepts

and events (Bechara et al., 2004; Damasio et al., 1994; Scherer

et al., 2001), although the question of phylogenic emergence of

emotions vs. mental representations of events has not been ade-

quately addressed in literature (Cabanac et al., 2009). Emotions

have been associated with creativity (Perlovsky, 2010g;

Festinger, 1957). We consider emotions as mental states with

hedonic content (Cabanac, 2002) that indicates satisfaction and

dissatisfaction of instinctual drives (Grossberg & Levine, 1987).

Usually psychologists study basic emotions (Ekman, 1999);

there are several proposals as to the nature of basic emotions,

and their number varies among authors from two to a dozen

(Plutchik, 1980; Tooby & Cosmides, 1990; Lazarus, 1991,

Johnson-Laird & Oatley, 1992; Ekman, 1999; Ortony & Turner,

1990; Izard, 1992). We consider basic emotions as related to

bodily instincts and named by specific words. There are few

different basic emotions, much fewer than emotional words

(Petrov et al., 2012). Most psychologists consider other emo-

tions as combinations of the basic emotions (Plutchik, 1962;

Ekman, 1999; Ortony & Turner, 1990; Izard, 1992).

We would emphasize that basic emotions unify human and

animal worlds. But what is the origin of the wealth of specifi-

cally human emotional experience, such as emotions of the

beautiful and sublime, musical emotions, and emotions we

perceive in songs and language prosody (Perlovsky, 2000, 2001,

2010a, 2010b, 2011c, 2011e, 2011g)? We disagree that they are

just mixtures of few basic emotions. We suggest that the num-

ber of fundamentally different human emotions is much larger

than the number of emotional words.

Most of human emotions are related to knowledge; they are

called aesthetic emotions since (Kant, 1790). We suggest that

they are related to satisfaction or dissatisfaction of the knowl-

edge instinct (KI) (Perlovsky, 1997, 2001, 2006a, 2006b, 2007a,

2007b, 2007c, 2008, 2010c, 2010d, 2010e, Perlovsky, Bon-

niot-Cabanac et al., 2012; Levine & Perlovsky, 2008, 2010),

and are inseparable from learning and cognition (Perlovsky &

Ilin, 2012a, 2012b; Tikhanoff et al., 2006; Kovalerchuk, 2012;

Vityaev et al., 2011). Existence of these emotions have experi-

mentally been demonstrated in (Bonniot-Cabanac & Cabanac,

2009; Bonniot-Cabanac et al., 2012, Perlovsky & Ilin, 2010).

They are related to the arts, the beautiful and sublime (Per-

lovsky, 2000, 2001, 2002a, 2002b, 2010a, 2010f, 2010g, 2011a,

2011b, 2011f, 2012a, 2012b, 2012c, 2012d, 2012e, 2012i,

2012j, 2012k).

Here we concentrate on aesthetic emotions related to cogni-

tive dissonances (CD) (Festinger, 1957). In the CD theory a

discomfort due to contradictions in knowledge has been con-

sidered as a non-differentiated state. Emotions related to CD

have not been studied. Here we emphasize that a contradiction

between any two elements of knowledge is a separate mental

state with its own CD emotion. This is a reason for a large

number of CD and the corresponding emotions. Every combi-

M.-C. BONNIOT-CABANAC ET AL.

nation of two or more elements of knowledge contains some

degree of contra- diction among these elements (Perlovsky,

2004, 2009a, 2009b, 2009c, 2010a, 2012b, 2012d, 2012e, 2012f,

2012g, 2012h, 2012i).

It is discussed in these references that cognitive dissonances

are uniquely human phenomena. Evolution of knowledge abili-

ties in animals was slow, and no contradiction in the system of

knowledge evolved. In animals’ mind, conceptual understand-

ing of a situation, its emotional evaluation, and appropriate

behavior are a single unified psychic state. In the human mind,

with evolution of language, differentiation of human knowledge

accelerated (Fontanari et al., 2007, 2008a, 2008b, 2009; Tikha-

nov et al., 2006). This differentiation lead to cognitive disso-

nances, a loss of the unity of self, and potential lost of concen-

tration of will. Those of our ancestors, who could differentiate

the knowledge about the world while maintaining concentration

of will, received evolutionary advantage. Therefore emotions

unifying contradictions in knowledge had to evolve along with

language. As language and culture were evolving into a power-

ful system with tremendous differentiation of knowledge about

the world and self, the number of contradictions grew combi-

natorially (Perlovsky, 2004, 2006c, 2010a, 2012e). Every com-

bination of conceptual pieces of knowledge led to its own

shades of contradictions. Therefore, maintaining motivation for

this diversified knowledge required virtually infinite number of

shades of motivations. Knowledge elements acquired from

surrounding language contradict to bodily instincts and to each

other. Continuing evolution of language and knowledge re-

quired resolution of these contradictions. The resolution of CD

required emotions, potentially a unique emotion for each dis-

sonance (Perlovsky, Cabanac et al., 2012; Perlovsky & Levine,

2012). Existence and nature of these emotions is explored in

this paper, both experimentally and theoretically.

Aesthetic emotions related to cognitive dissonances can be

illustrated in the following examples. Consider an emotion

related to a mental effort of making a choice if one receives

offers of positions at Harvard and Stanford. Each offer would

be considered great by many people, and would result in highly

positive emotions; these emotions would be mostly basic emo-

tions related to satisfaction of basic needs and aspirations. But

making the choice between the two offers could be difficult and

evoke negative emotions. For many people it could be an ex-

cruciatingly painful decision. Making important life choices,

concerning place of living or job, even between wonderful al-

ternatives, could be difficult. Thus basic emotions associated

with each alternative have nothing to do with mental effort of

choice. This proves the main hypothesis of this paper: Emo-

tions of CD are different from basic emotions. A first step to-

ward exploring this possibility is a hypothesis that the number

of CD emotions is much larger than the number of basic emo-

tions.

However, there are no methods of measuring CD emotions.

Whereas basic emotions are named with specific words, there

are no words for CD emotions. Therefore the main goal of this

paper is to instrumentalize CD emotions, in other words to

develop experimental methods for their exploration. The fol-

lowing parts of the paper investigate properties of aesthetic

emotional spaces. Even minute decisions, seemingly effortless,

still require some emotional effort. To make a choice, any

choice, one has to have a motivation, therefore, an emotion.

Even most simple everyday choices between “tea or coffee”,

“red or white wine”, create minute cognitive dissonances, and

related emotions. These kinds of emotions are considered be-

low. The primary goal of the following parts of this paper is to

explore experimental methods “instrumentalizing” aesthetic

emotions of cognitive dissonances, demonstrating that these

emotions exist, can be experimentally measured, and to initiate

scientific research of their properties.

Experimental Method

This study followed a classical psychologic method that ex-

plored mental experience in interviews where participants an-

swered printed questionnaires (Bonniot-Cabanac & Cabanac,

2009, 2010; Balasko & Cabanac, 1998; Cabanac et al., 1997,

2002; Cabanac & Bonniot-Cabanac, 2007; Ramirez et al., 2005,

2009; Perlovsky, Bonniot-Cabanac et al., 2012). Thirty four

anonymous participants (who were referred to by numbers

only), 17 men (age 12 - 70) and 17 women (age 16 - 72) were

presented two questionnaires each containing ten items. Both

questionnaires presented the same items, but the participant was

asked to rate hedonicity from one and intensity from the other.

All items described a decision to be made among two conflict-

ing motivations and the participant was to rate analogically the

magnitude of her/his experience.

Hedonicity was explored by the first questionnaire—Ques-

tionnaire H: a horizontal line was present below the item with a

zero on its middle, a minus (−) sign at the left end and a plus (+)

sign at the right end. The participant was to pencil a small ver-

tical mark on the right side of that line if the feeling was pleas-

ant, or on the left side if unpleasant. The experienced hedonic

feeling would be indicated by the distance from the middle

(zero mark) of the line.

The second questionnaire—Questionnaire E—explored emo-

tion: as before, a horizontal line was present below the item but

with the zero mark on its left end. The participant was to pencil

a small vertical mark at that line rating the intensity of the ex-

perienced feeling. The distance from the zero mark would indi-

cate the magnitude of the experience. After rating the magni-

tude of the emotion the participant wrote one word describing

the nature of the experienced emotion, e.g., curiosity, surprise,

joy, indifference, anger, etc. 33 of the 34 participants responded

with the emotion word.

The hedonic and magnitude feelings were measured quanti-

tatively in millimeters as well as recorded semantically. Ques-

tionnaires E and H were presented separately over time varied

from about one hour to half a day, depending on the availabil-

ity of the participant. This was done in order to minimize a

possible influence of answering one questionnaire on the re-

sponse to the other questionnaire. The first questionnaire was

presented in the morning period while keeping the gender of the

participants balanced, and the second questionnaire was pre-

sented in the afternoon.

Decisions to be made were described by ten items covering a

broad range of motivations, from minor decisions in the daily

life (e.g., how about movie or theatre for tonight?) to clear but

non-vital problems (e.g., would you go for a high-gain but risky

investment or for a low-gain but secure one?) and finally to

vital problems (e.g., would you go for radical surgery or for

life-long therapy to treat a severe illness?). This ten items are

presented in the appendix).

Analysis of the Data

The degree of arousal E takes on positive values only

M.-C. BONNIOT-CABANAC ET AL.

whereas the degree of pleasure H can take on positive as well as

negative values,





E0,1 and





H1,1 .

Characterizing emotions by two-dimensional space of

arousal and pleasure corresponds to model (Russell, 1980,

1989). The data are presented in Figure 1 where the symbols

indicate the values of the arousal E and pleasure H for the 340

points corresponding to the 10 choice questions of the 34 sub-

jects.

A different characterization of the data in Figures 2 and 3

shows distributions and of the arousal and



H





pleasure degrees. The normalization is such that

340 

 . One can see that the subjects’ answers

are well balanced. The mean values of these distributions yield

<E> = 0.417 and <H> = 0.058. The mean value <E> can be

used to separate the regime high arousal (E > <E>) from low

arousal (E < <E>). For the degree of pleasure we can average

the absolute value of H. We find <|H|> = 0.423. This can be

used to define the regimes of high pleasure, H > <|H|> and high

displeasure, H < −<|H|>.

These figures show the raw data, which now are interpreted

and analyzed.

Figure 1.

Scatter plot of the arousal and pleasure degrees. For each one of the 34

subjects and for each question we represent the measured degrees of

arousal and pleasure as the coordinates (E, H) of a point in a two-di-

mensional space. The Roman numerals indicate the location of the

emotion classes defined according to Table 1).

Figure 2.

Histogram of the distribution of values of the arousal measure E shown

in the scatter plot of Figure 1 using a bin of size 0.02. The mean of this

distribution is indicated by the vertical green line.

Figure 3.

Histogram of the distribution of values of the arousal measure H shown

in the scatter plot of Figure 1 using a bin of size 0.02. The mean of this

distribution is 0.058. Here the symmetric vertical green lines indicate

the mean and the negative mean of the absolute value of H.

The Arousal-Hedonicity Correlation

Our previous analysis, indicate a strong correlation between

the degrees of arousal (E) and pleasure (H). Figure 4 presents

these correlations for the 34 subjects that have completed the

questionnaires (red crosses). We also show correlations ob-

tained in the case E and H are chosen randomly and uniformly

in the ranges [0,1] and [−1.1], respectively (green x). The hori-

zontal lines located at 13



delimit region bounded by one

standard deviation in the case E and H are independent random

variables. We expect that on average about 32% of the correla-

tion values would lay outside this region. For the random sam-

ple illustrated in the figure, only 21% of the values are outside

those boundaries whereas 47% of the correlation values associ-

ated to the subjects’ choices lay outside the one-standard devia-

tion region. This indicates that the actual data are significantly

more correlated than the random ones. We have studied the

correlation between E and the absolute value of H, i.e., Covα(E,

|H|), for each participant α, and the results were qualitatively

similar to those exhibited in Figure 4.

An interesting result revealed by Figure 4 is the enormous

variation of the arousal-pleasure correlation among the subjects.

For example, for the subject , arousal and pleasure are

maximally correlated whereas for subject these quan-

tities as maximally uncorrelated. This result casts doubts on any

conclusion based on the averaging over different subjects. Ac-

tually, if one averages the correlations over the 34 subjects we

find

α7



α19







αE,H 340Cor .132



which is within the one-

standard deviation zone of the random model.

A positive correlation indicates that the deviations of the

pleasure and arousal degrees from the correspondent subject’s

means are in the same direction. It is interesting that the sub-

ject-dependent mean degrees of arousal and pleasure, α

and

α, respectively, vary wildly among subjects, reflect-

ing perhaps the personality and life-experience of them. These

quantities are show in Figure 5.

The Emotion Words

As mentioned, 33 participants described the emotions they

felt by a single emotion word. They used a total of 78 different

1020

M.-C. BONNIOT-CABANAC ET AL.

Figure 4.

Correlations between arousal and pleasure as defined in Equations (1)

and (2) for each one of the 34 subjects (red crosses). The green x sym-

bols illustrate an outcome when the arousal and pleasure degrees are

chosen randomly. The dashed horizontal lines, which are located at

13, indicate one standard deviation of the correlation for the ran-

dom case.

Figure 5.

Mean degree of arousal (red crosses) and mean degree of pleasure

(green x’s) for each subject. These means are calculated averaging over

the measured values of E’s and H’s for the ten choice questions. The

dashed horizontal lines indicate the average of these mean values over

the 34 subjects.

emotion words for the 330 choice questions, which means that

on the average each word was used 4 times. Considering that

many of these words are synonymous or have very close

meanings which could not be tell apart unless by an ex-

perienced psychologist, which was not the case of any of the

participants, we decided to group the emotion words in 18

classes as described in Table 1. The numbers in the column

Frequency indicate how many times a word in the cor-

responding class was used by the participants.

First, we address the issue of whether the subjects used these

emotion words or, more correctly, classes, in a coherent way, in

the sense that we expect that many participants use words

within the same class to describe their emotions for a given

choice question. To quantify this expectation, we calculate the

probability that one selects two participants at random and they

describe their emotions by words belonging to the same class

Table 1.

Basic emotion words and frequency of their use by participants to de-

scribe their CD emotions.

ClassWords Frequency

I Joy, pleasure, satisfaction, delight, enthusiasm,

elation, excitement, luck, fun, well-being 67

II Desire, greed 12

III Indifference, minor interest, boredom 57

IV Discomfort, embarrassment, uneasiness, distress,

impatience, guilt, anguish, displeasure, disarray 33

V Interest, curiosity, motivation, purpose, puzzling29

VI Surprise 9

VII Hope, anticipation, expectation, waiting 32

VIII Rejection, repulsion 3

IX Furor, anger, wrath, irritation, indignation 9

X Serenity, relaxation, relief, safety, comfort 11

XI Sadness, nostalgia, fatalism, patience, weariness 7

XII Concern, anxiety, fear, stress, nervousness, think-

ing, exasperated, doh! 31

XIII Hesitation, incertitude, indecision, uncertainty,

unbelief, swindle 10

XIV Disappointment, frustration, despair, disgust, dis-

couragement, perplexity 9

XV Challenge, difficulty 2

XVI Contempt, disdain 6

XVIISolidarity, commitment 2

XVIIICourage 1

for the same choice question k = 1, ···, 10. We recall that in this

part of the experiment there are a total of 33 participants who

can form 528 different pairs. So the desired probability is esti-

mated by counting the number of pairs of subjects who choose

words in the same class for each question k = 1, ···, 10 and di-

viding by 528. In Table 2 we show the results together with a

realization of the random situation in which the subjects pick

the classes I, ···, XVIII with probability proportional to the

frequencies exhibited in Table 1.

Our goal has been to demonstrate that basic emotion words

selected by participants do not adequately characterize their

feelings of choice. If the probabilities in each column would be

comparable this would be sufficient for our purpose. However,

for 8 out of 10 cases real data result in a higher probability than

the random model. Does it mean that basic emotions selected

by subjects reflect the real difference in their feeling of choice?

This psychological question, however cannot be answered from

existing data. We have to acknowledge that if non-random

probabilities from actual data are higher than random one, there

could be emotional or other reasons affecting the choice non-

randomly. Therefore this test works only one way: if there is no

statistically significant difference between the columns, that

means that there is no emotional difference either. However, if

M.-C. BONNIOT-CABANAC ET AL.

the difference is statistically significant, we could not infer the

psychological reason, and could only conclude that the test

requires improvement.

The next step is to analyze statistical significance of the dif-

ferences. We averaged columns 2 and 3 in Table 2. The aver-

aged probabilities for real data (column 2) and for the random

model (column 3) are Pd = 0.1670 and Pr = 0.1062. The data

probability is larger than the random one. Is this difference

statistically significant? We approach this by generating 105

random realizations similar to shown in the third column of

Table 2 and computed their mean and standard deviation (of

the probability distribution of Pr). The results are = 0.1108 and

σr = 0.004. Hence Pd is 14 standard deviations away from Pr,

which means we can safely discard the possibility that the as-

signment of the emotion words to the choice questions were

random. We would repeat again that psychological interpreta-

tions of these results so far remain uncertain.

As the next step we verify whether and how the emotion-

word classes presented in Table 1 are related to the degrees of

arousal and pleasure. A simple way to approach this issue is to

draw the histograms of the degrees of arousal and pleasure

associated to a fixed emotion-word class. This is done in Fig-

ures 6-8 for emotion words in classes I, V and XII, respectively.

(These figures can be compared with Figures 2 and 3, for

which all classes are included.) The means and the standard

deviations for each one of the 18 classes are presented in Table

This table illustrates the inadequacy of characterizing emo-

tions solely through the degrees of arousal and pleasure. In fact,

in Figure 1 we show where the “centers of mass” of each class

are located in the (E, H) space. We have omitted class XVIII

because it overlaps with class VI. In addition, classes XI, XII,

XIII and XIV are also too close to each other. The usefulness of

such a representation is to get some insight about the similarity

between emotions. Negative emotions represented by words in

classes IV, VIII, XI-XIV, and XVI are close to each other. It is

interesting that among these negative emotions contempt (XVI)

is the closest to repulsion (VIII). It is also a welcome surprise

that emotions such as curiosity (V), surprise (VI) and courage

Table 2.

Estimated probabilities of participants picking emotional word classes

for each question, averaged over participants. Actual data are in column

2 and simulated random data are in column 3.

k Data Random

1 0.1648 0.0739

2 0.2102 0.1382

3 0.1534 0.0909

4 0.2898 0.0682

5 0.1326 0.1345

6 0.1837 0.0966

7 0.1061 0.1439

8 0.1932 0.1136

9 0.1439 0.0947

10 0.0928 0.1079

(XVIII) are also overlapping in the (E, H) space. There is defi-

nitely much to learn from such a representation but our statis-

tics is still too poor to make any conclusive claim about the

similarity between choice emotions. It is known from other

studies specifically concentrated on basic emotions, that among

approximately 150 emotional words there are between 5 and 30

different basic emotions (Ekman, 1999; Ortony & Turner, 1990;

Izard, 1992; Petrov et al., 2012).

Conclusion

Cognitive dissonance (CD) is a field of psychology intensely

studied for more than 50 years. Nevertheless emotions of CD to

our knowledge have not been studied. Experimental demonstra-

tion of CD emotions is possibly the main contribution of this

paper. Choice decisions appear to be made in the hedonic di-

mension of consciousness (Cabanac et al., 1997, 2002, 2011);

the hedonic experience takes place as an actual or an expected

reward. In this paper we made a step toward exploring a new

type of emotions, aesthetic emotions related to dissatisfaction

of the knowledge instinct, or more specifically, emotions of CD

related to contradictions between two pieces of knowledge.

These emotions are different in principle from basic emotions.

Whereas specific words exist to name basic emotions, there are

no specific words for most emotions of cognitive dissonance.

This creates difficulty to studying these emotions. This diffi-

culty might be a reason that these emotions have not been sys-

tematically studied in psychological literature. Although the

words “cognitive dissonance” have been used for long time, we

emphasize again, emotions of cognitive dissonance have not

been recognized as a special type of emotions different in prin-

ciple from basic emotions.

Table 3.

Average and standard deviation of arousal and pleasure for each class.

Class Arousal Pleasure

I 0.501  0.218 0.417  0.324

II 0.400  0.210 0.374  0.243

III 0.143  0.204 −0.067  0.348

IV 0.472  0.225 −0.213  0.543

V 0.530  0.190 0.151  0.455

VI 0.439  0.286 0.308  0.348

VII 0.410  0.273 0.274  0.402

VIII 0.307  0.327 −0.514  0.364

IX 0.550  0.250 −0.630  0.505

X 0.481  0.248 0.274  0.505

XI 0.512  0.315 −0.308  0.683

XII 0.456  0.241 −0.241  0.547

XIII 0.455  0.220 −0.317  0.407

XIV 0.522  0.186 −0.348  0.578

XV 0.300  0.233 0.171  0.428

XVI 0.400  0.262 −0.209  0.544

XVII 0.597  0.097 0.050  0.378

XVIII 0.406  0.000 0.328  0.000

1022

M.-C. BONNIOT-CABANAC ET AL.

Figure 6.

Histograms of the degrees of arousal (red lines) and pleasure (green

lines) restricted to choices associated to emotion words belonging to

class I, e.g., joy, pleasure, enthusiasm, etc. For this class, the mean

degree of arousal is 0.501 and the mean degree of pleasure is 0.417.

Figure 7.

Histograms of the degrees of arousal (red lines) and pleasure (green

lines) restricted to choices associated to emotion words belonging to

class V, e.g., curiosity, interest, purpose etc. For this class, the mean

degree of arousal is 0.530 and the mean degree of pleasure is 0.151.

Figure 8.

Histograms of the degrees of arousal (red lines) and pleasure (green

lines) restricted to choices associated to emotion words belonging to

class XII, e.g., fear, anxiety, concern etc. For this class, the mean de-

gree of arousal is 0.456 and the mean degree of pleasure is −0.241.

In this paper, following (Grossberg & Levine, 1987), we

consider emotions as feelings and mental states related to neu-

ral signals, which indicate to various brain regions satisfaction

or dissatisfaction of fundamental organism needs. Mechanisms

measuring these needs we call instincts. Basic emotions are

mostly related to bodily needs, whereas aesthetic emotions are

related to need for knowledge or knowledge instinct (KI). This

is a fundamental theoretical difference between basic and aes-

thetic emotions. Also in (Perlovsky, 2006c, 2010a, 2012c,

2012d) the arguments were presented that emotions of cogni-

tive dissonance could be in some way similar to musical emo-

tions. Steps to demonstrating this experimentally have been

made in (Masataka et al., 2012a, 2012b; Perlovsky, Cabanac et

al., 2012). It has been demonstrated that music helps resolve

CD emotions of discomfort, so that contradictions in knowl-

edge can be tolerated (Masataka et al., 2012a, 2012b; Perlovsky,

Cabanac et al., 2012). This made possible accumulation of

knowledge and evolution of cultures. The “Mozart effect” has

been demonstrated to be caused by resolving CD. Still many

questions remain challenges for future research.

The main experiential argument separating basic and aes-

thetic emotions of cognitive dissonances in this paper are ex-

amples of the following type of choices. A choice between

different types of work could be difficult and experienced as

negative CD emotions, even if each alternative separately is

experienced as positive basic emotions. In this paper our results

are inconclusive with regard to qualitative difference between

basic and aesthetic emotions. It is possible that standard meth-

ods of exploring emotions, arousal and pleasure plots, charac-

terizing each aesthetic emotion of cognitive dissonance by a

single basic emotion—these methods may not be sensitive

enough for fine distinctions required to differentiate details of

aesthetic emotions. One has to keep in mind that mechanisms

of basic emotions are hundreds of millions or even billions

years old, whereas CD emotions are due to recently evolved

mechanisms, probably less than millions years old.

The main experimental result of this paper is “instrumental-

izing” aesthetic emotions of cognitive dissonance, by present-

ing subject questions as mental choices between two alterna-

tives. In future we suggest more refined measures, such as 1)

using more refined splitting of basic emotions in classes than

the relatively subjective groupings in Table 2; 2) characterizing

each emotion of choice by several basic emotions; 3) measuring

distances-similarities between various choice emotions and then

use these distances for analyzing properties of the aesthetic

emotional space.

REFERENCES

Balaskó, M., & Cabanac, M. (1998). Grammatical choice and affective

experience in a second-language test. Neuropsychobiology, 37, 205-

210. doi:10.1159/000026504

Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994).

Insensitivity to future consequences following dam-age to human

prefrontal cortex. Cognition, 50, 7-15.

doi:10.1016/0010-0277(94)90018-3

Bonniot-Cabanac, M.-C., & Cabanac, M. (2009). Pleasure in decision

making situations: Politics and gambling. Journal of Risk Research,

12, 619-645. doi:10.1080/13669870802579798

Bonniot-Cabanac, M.-C., & Cabanac, M. (2010). Do government offi-

cials decide more rationally than the rest of us? A study using par-

ticipants from the legislature, the executive, and the judiciary. Social

Behavior & Personality, 38, 1147-1152.

M.-C. BONNIOT-CABANAC ET AL.

doi:10.2224/sbp.2010.38.8.1147

Bonniot-Cabanac, M.-C., Cabanac, M., Fontanari, J. F., & Perlovsky,

L.I. (2012). A structural model of emotions of cognitive dissonances.

Neural Networks, 32, 57-64.

Cabanac, М. (2002). What is emotion? Behavioural Processes, 60,

69-83.

Cabanac, M., & Bonniot-Cabanac, M. C. (2007). Decision making:

Rational or hedonic? Behavioral and Brain Functions, 3, 1-45.

doi:10.1186/1744-9081-3-45

Cabanac, M., Cabanac, A. J., & André Parent, A. (2009). When in phy-

logeny did consciousness emerge? Behavioral and Brain Research,

198, 267-272. doi:10.1016/j.bbr.2008.11.028

Cabanac, M., & Bonniot-Cabanac, M.-C. (2011). Hedonicity and

memory of odors. International Journal of Psychological Studies, 3,

178-185.

Cabanac, M., Guillaume, J., Balaskó, M., & Fleury, A. (2002). Pleasure

in decision making situations. Biomed Central, 2, 7.

Cabanac, M., Pouliot, C., & J. Everett. (1997). Pleasure as a sign of

efficacy of mental activity. European Psychologi st, 2, 226-234.

doi:10.1027/1016-9040.2.3.226

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., & Damasio,

A. R. (1994). The return of phineas gage: Clues about the brain from

the skull of a famous patient. Science, 264, 1102-1105.

doi:10.1126/science.8178168

Ekman, P. (1957). A methodological discussion of nonverbal behavior.

Journal of Psychology, 43, 141-149.

doi:10.1080/00223980.1957.9713059

Ekman, P. (1999). Basic emotions. In T. Dalgleish and M. Power (Eds.),

Handbook of Cognition and Emotion. Sussex: John Wiley & Sons.

Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA:

Stanford University Press.

Gratch, J., Marsella, S., & Petta, P. (2009). Modeling the cognitive

antecedents and consequences of emotion. Cognitive Systems Re-

search, 10, 1-5. doi:10.1016/j.cogsys.2008.06.001

Grossberg, S., & Levine, D. (1987). Neural dynamics of attentionally

modulated Pavlovian conditioning: Blocking, interstimulus interval,

and secondary reinforcement. Applied Optics, 26, 5015-5030.

doi:10.1364/AO.26.005015

Kant, I. (1790). The critique of judgment. Amherst, MA: Prometheus

Books.

Izard, C. E. (1992). Basic emotions, relations among emotions, and

emotion-cognition relations. Psychological Review, 99, 561-565.

doi:10.1037/0033-295X.99.3.561

Johnson-Laird, P. N., & Oatley, K. (1992). Basic emotions: a cognitive

science approach to function, folk theory and empirical study. Cog-

nition and Emotion, 6, 201-223.

doi:10.1080/02699939208411069

Juslin, P. N., & Västfjäll, D. (2008) Emotional responses to music: The

need to consider underlying mechanisms. Behavioral and Brain Sci-

ences, 31, 559-575.

Kovalerchuk, B., Perlovsky, L., & Wheeler, G. (2012). Modeling of

phenomena and dynamic logic of phenomena. Journal of Applied

Non-classical Logics, 22, 51-82. doi:10.1080/11663081.2012.682439

Lazarus, R. S. (1991). Emotion and adaptation. New York: Oxford

University Press.

Levine, D. S., & Perlovsky, L. I. (2008). Neuroscientific insights on

biblical myths: Simplifying Heuristics versus Careful Thinking: Sci-

entific analysis of millennial spiritual issues. Zygon, Journal of Sci-

ence and Religion, 43, 797-821.

Levine, D. S., & Perlovsky, L. I. (2010). Emotion in the pursuit of

understanding. International Journal o f S ynt het ic Emotions, 1, 1-11.

doi:10.4018/jse.2010070101

Masataka, N., & Perlovsky, L. I. (2012a). Music can reduce cognitive

dissonance. Nature Precedings.

Masataka, N., & Perlovsky, L. I. (2012b). The efficacy of musical

emotions provoked by Mozart’s music for the reconciliation of cog-

nitive dissonance. Scientific Reports (in press).

doi:10.1038/srep00694

Ortony, A., Clore, G. L., & Foss, M. A. (1987). The referential struc-

ture of the affective lexicon. Cognitive Science, 11, 341-364.

doi:10.1207/s15516709cog1103_4

Ortony, A., & Turner, T. J. (1990). What’s basic about basic emotions?

Psychological Review, 97, 315-331.

doi:10.1037/0033-295X.97.3.315

Perlovsky, L. I. (1997). Physical concepts of intellect. Proceedings of

Russian Academy of Sciences, 354, 320-323.

Perlovsky, L. I. (2000). Beauty and mathematical intellect. Zvezda, 9,

190-201.Perlovsky, L. I. (2001). Mystery of sublime and mathemat-

ics of intelli- gence. Zvezda, 8, 174-190.

Perlovsky, L. I. (2002a). Physical theory of information processing in

the mind: Concepts and emotions. SEED on Line Journal, 2,

36-54Perlovsky, L. I. (2002b). Aesthetics and mathematical theories

of intellect. Iskusstvoznanie, 2, 558-594.Perlovsky, L. I. (2004). In-

tegrating language and cognition. IEEE Connections, Feature Article,

2, 8-12.

Perlovsky, L. I. (2006a). Toward physics of the mind: Concepts, emo-

tions, consciousness, and symbols. Physics of Life Reviews, 3, 22-55.

doi:10.1016/j.plrev.2005.11.003

Perlovsky, L. I. (2006b). Fuzzy dynamic logic. New Mathematics and

Natural Computation, 2, 43-55. doi:10.1142/S1793005706000300

Perlovsky, L. I. (2006c). Music—The first priciple. URL (last checked

3 December 2011). http://www.ceo.spb.ru/libretto/kon_lan/ogl.shtml

Perlovsky, L. I. (2007a). Cognitive high level information fusion. In-

formation Sciences, 177, 2099-2118. doi:10.1016/j.ins.2006.12.026

Perlovsky, L. I. (2007b). Evolution of Languages, Consciousness, and

Cultures. IEEE Computational Intelligence Magazine, 2, 25-39.

doi:10.1109/MCI.2007.385364

Perlovsky, L. I. (2007c). The Mind vs. logic: Aristotle and zadeh. Soci-

ety for Mathematics of Uncertainty, Critical Review, 1, 30-33.

Perlovsky, L. I. (2008). Music and consciousness. Journal of Arts,

Sciences and Technology, 41, 420-421.

Perlovsky, L. I. (2009a). Language and cognition. Neural Networks, 22,

247-257. doi:10.1016/j.neunet.2009.03.007

Perlovsky, L. I. (2009b). Language and emotions: Emotional Sapir-

Whorf hypothesis. Neural Networks, 2 2, 518-526.

doi:10.1016/j.neunet.2009.06.034

Perlovsky, L. I. (2009c). “Vague-to-crisp” neural mechanism of per-

ception. IEEE Transactions of Neural Networks, 20, 1363-1367.

doi:10.1109/TNN.2009.2025501

Perlovsky, L. I. (2010a). Musical emotions: Functions, origin, evolution.

Physics of Life Reviews, 7, 2-27. doi:10.1016/j.plrev.2009.11.001

Perlovsky, L. I. (2010b). Neural mechanisms of the mind, aristotle,

zadeh, & fMRI. IEEE Transactions of Neural Networks, 21, 718-733.

doi:10.1109/TNN.2010.2041250

Perlovsky, L. I. (2010c). Intersections of mathematical, cognitive, and

aesthetic theories of mind. Psychology of Aesthetics, Creativity, and

the Arts, 4, 11-17. doi:10.1037/a0018147

Perlovsky, L. I. (2010d). The Mind is not a kludge. Skeptic, 15, 51-55

Perlovsky, L. I. (2010e). Joint acquisition of language and cognition;

WebmedCentralBRAIN, 1, WMC00994.

http://www.webmedcentral.com/article_view/994

Perlovsky L. I. (2010f). Physics of the mind: Concepts, emotions, lan-

guage, cognition, consciousness, beauty, music, and symbolic culture.

WebmedCentral Psychology 2010, 1, WMC001374.

Perlovsky, L. I. (2010g). Beauty and art, Cognitive function, evolution,

and mathematical models of the mind. WebmedCentral Psychology

2010, 1, WMC001322.

Perlovsky, L. I. (2011a). Music, cognitive function, origin, and evolu-

tion of musical emotions. WebmedCentral Psychology 2011, 2,

WMC001494.

Perlovsky, L. I. (2011b). Consciousness and free will: A scientific

possibility due to advances in cognitive science. WebmedCentral

Psychology 2011, 2, WMC001539

Perlovsky, L. I. (2011c). Language, emotions, and cultures: Emotional

Sapir-Whorf hypothesis. WebmedCentral Psychology 2011, 2,

WMC001580.

Perlovsky, L. I. (2011d). Computational intelligence applications for

defense. IEEE Computational Intelligence Magazine, 6, 20-28.

doi:10.1109/MCI.2010.939581

Perlovsky L. I. (2011e). Language and cognition interaction neural

mechanisms. Computational Intelligence and Neuroscience, 3,

454587. doi:10.1155/2011/454587

1024

M.-C. BONNIOT-CABANAC ET AL.

Perlovsky, L. I. (2011f). High cognitive emotions in language prosody.

Physics of Life Reviews, 8, 408-409.

doi:10.1016/j.plrev.2011.10.007

Perlovsky, L. I. (2011g). Abstract concepts in language and cognition,

Physics of Life Reviews, 8, 375-376. doi:10.1016/j.plrev.2011.10.006

Perlovsky, L. I. (2012a). Free will and advances in cognitive science.

Open Journal of Philosophy (OJPP), 2, 32-37.

doi:10.4236/ojpp.2012.21005

Perlovsky, L. I. (2012b). The cognitive function of emotions of spiritu-

ally sublime. Review of Psychology Frontier, 1, 1-10.

Perlovsky L. I. (2012c). Emotions of “higher” cognition. Comment to

lindquist at the brain basis of emotion: A meta-analytic review. Brain

and Behavior Sciences, 35, 157-158.

doi:10.1017/S0140525X11001555

Perlovsky, L. I. (2012d). Cognitive function, origin, and evolution of

musical emotions. Musicae Scientiae, 16, 185-199.

doi:10.1177/1029864912448327

Perlovsky, L. I. (2012e). Cognitive function of music: Part I. Interdis-

ciplinary Science Reviews, 37, 129-142.

doi:10.1179/0308018812Z.00000000010

Perlovsky, L. I. (2012f). Nonlinear dynamics and higher cognitive

mental functions. Physics of Life Reviews, 9, 74-75.

doi:10.1016/j.plrev.2011.12.004

Perlovsky, L. I. (2012g). Cognitive function of musical emotions. Psy-

chomusicology (in press).

Perlovsky, L. I. (2012h). Mirror neurons, language, and embodied

cognition. Neural Networks (in press).

Perlovsky, L. I. (2012i). Brain: Conscious and unconscious mecha-

nisms of cognition, emotions, and language. Brain Sciences (in

press).

Perlovsky, L. I. (2012j). The cognitive function of emotions of spiritu-

ally sublime. Review of Psychology Frontier, 1, 1-10.

www.j-psy.org.

Perlovsky, L. I. (2012k). Emotionality of languages affects evolution of

cultures. Review of Psychology Frontier (in press).

Perlovsky, L. I., Bonniot-Cabanac, M., & Cabanac, M. (2010). Curios-

ity and pleasure. WebmedCentral Psychology 2010, 1, WMC001275

Perlovsky, L. I., Cabanac, A., Bonniot-Cabanac, M.-C., & Cabanac, M.

(2012). Mozart effect, cognitive dissonance, and origin of music.

Behavioral Brain Res e a rc h (in press).

Perlovsky, L. I., & Ilin, R. (2010). Neurally and mathematically moti-

vated architecture for language and thought. The Open Neuroimaging

Journal, 4, 70-80. doi:10.2174/1874440001004020070

Perlovsky, L. I., & Ilin, R. (2012a). Mathematical model of grounded

symbols: Perceptual symbol system. Journal of Behavioral and

Brain Science, 2, 195-220. doi:10.4236/jbbs.2012.22024

Perlovsky, L. I., & Ilin, R. (2012b). CWW, language, and thinking.

New Mathematics and Natural Computations (in press).

Perlovsky, L. I., & Levine, D. (2012). The drive for creativity and the

escape from creativity: Neurocognitive mechanisms. Cognitive Com-

putation, 4, 292-305. doi:10.1007/s12559-012-9154-3

Petrov, S., Fontanari, J. F., & Perlovsky, L. I. (2012). Subjective emo-

tions vs. verbalizable emotions in web texts. International Journal of

Psychology and Behavioral Sciences, 2, 173-184.

Plutchik, R. (1962). The emotions: Facts, theories and a new model.

New York: Random House.

Plutchik, R. (1980). A general psychoevolutionary theory of emotion.

In R. Plutchik, & H. Kellerman (Eds.), Emotion: Theory, Research,

and Experience (pp. 3-33). New York: Academic Press.

Ramírez, J. M., Bonniot-Cabanac, M.-C., & Cabanac, M. (2005) Can

aggression provide pleasure? European Psychologist, 10, 136-145.

Ramírez, J. M., Millana, L., Toldos-Romero, M. P., Bonniot-Cabanac,

M.-C., & Cabanac, M. (2009) The pleasure of being aggressive in

incarcerated criminals. The Open Criminology Journal, 2, 1-9.

Reisenzein, R. (2009). Emotions as metarepresentational states of mind:

Naturalizing the belief-desire theory of emotion. Cognitive Systems

Research, 10, 6-20. doi:10.1016/j.cogsys.2008.03.001

Russel, J. A. (1980). A circumplex model of affect. Journal of Person-

ality and Social Psychology, 39, 1161-1178.

Russel, J. A. (1989). Measures of emotion. In R. Plutchik, H. Kelerman

(Eds.), Emotion: The measurement of Emotion (pp. 83-110). New

York: Academic Press. doi:10.1037/h0077714

Scherer, K. R., Schorr, A., & Johnstone, T. (2001). Appraisal processes

in emotion: Theory, methods, research. New York: Oxford Univer-

sity Press.

Tooby, J., & Cosmides, L. (1990). The past explains the present: Emo-

tional adaptations and the structure of ancestral environment. Ethol-

ogy and Sociobiology, 11, 375-424.

doi:10.1016/0162-3095(90)90017-Z

Tikhanoff, V., Fontanari, J. F., Cangelosi, A., & Perlovsky, L. I. (2006).

Language and cognition integration through modeling field theory:

Category formation for symbol grounding. In S. Kollias, A. Stafylo-

patis, W. Duch, & E. Oja (Eds.), Book Series in Computer Science.

Heidelberg: Springer.

Vityaev, E. E., Perlovsky, L. I., Kovalerchuk, B. Y., & Speransky, S. O.

(2011). Probabilistic dynamic logic of the mind and cognition.

Neuroinformatics, 5, 1-20.

M.-C. BONNIOT-CABANAC ET AL.

Appendix

The 10 items of Questionnaire E aiming at measuring the

degree of arousal of the evoked emotion are presented below:

1) Focus on what you feel when you are asked to make a

choice: with the duck with orange what do you prefer of red

wine or white wine? Do you feel an emotion at the idea of this

choice? Indicate its intensity on the line below.