77 ijcini 1401 perception

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Intl Journal of Cognitive Informatics and Natural Intelligence, 1(4), 1-13, October-December 2007 1

Copyright 2007, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Globalis prohibited.

ABSTRACT

An interactive motivation-attitude theory is developed based on the Layered Reference Model of the Brain(LRMB) and the Object-Attribute-Relation (OAR) model. This paper presents a rigorous model of human

perceptual processes such as emotions, motivations, and attitudes. A set of mathematical models and formalcognitive processes of perception is developed. Interactions and relationships between motivation andattitude are formally described in Real-Time Process Algebra (RTPA). Applications of the mathematicalmodels of motivations and attitudes in software engineering are demonstrated. This work is a part of the

formalization of LRMB, which provides a comprehensive model for explaining the fundamental cognitive processes of the brain and their interactions. This work demonstrates that the complicated human emotionaland perceptual phenomena can be rigorously modeled and formally treated based on cognitive informaticstheories and denotational mathematics.

Keywords: attitude; cognitive informatics; cognitive model; emotion; LRMB; mathematical model;motivation; PAR; perceptional processes; RTPA; the brain

INTRODUCTIONA variety of life functions and cognitive pro-cesses has been identi ed in cognitive informat -ics (Wang, 2002a, 2003a, 2007b) and cognitive

psychology (Payne & Wenger, 1998; Pinel,1997; Smith, 1993; Westen, 1999; Wilson &Keil, 1999). In order to formally and rigorouslydescribe a comprehensive and coherent set ofmental processes and their relationships, anLRMB has been developed (Wang & Wang,

2006; Wang, Wang, Patel, & Patel, 2006) thatexplains the functional mechanisms and cog-nitive processes of the brain and the naturalintelligence. LRMB encompasses 39 cognitive

processes at six layers known as the sensation,memory, perception, action, meta and highercognitive layers from the bottom up.

De nition 1: Perception is a set of internal sensational cognitive processes of the brain at

On the Cognitive Processes ofHuman Perception with

Emotions, Motivations, and

AttitudesYingxu Wang, University of Calgary, Canada


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2 Intl Journal of Cognitive Informatics and Natural Intelligence, 1(4), 1-13, October-December 2007


the subconscious cognitive function layer thatdetects, relates, interprets, and searches internalcognitive information in the mind.

Perception may be considered as the sixthsense of human beings since almost all cognitivelife functions rely on it. Perception is also animportant cognitive function at the subconsciouslayers that determines personality. In otherwords, personality is a faculty of all subcon-scious life functions and experience cumulatedvia conscious life functions. It is recognized thata crucial component of the future generationcomputers known as the cognitive computers is the perceptual engine that mimic the naturalintelligence (Wang, 2006, 2007c).

The main cognitive processes at the percep-tion layer of LRMB are emotion, motivation,and attitude (Wang et al., 2006). This article

presents a formal treatment of the three per-ceptual processes, their interrelationships, andinteractions. It demonstrates that complicated

psychological and cognitive mental processesmay be formally modeled and rigorously de-scribed. Mathematical models of the psycho-logical and cognitive processes of emotions,motivations, and attitudes are developed in thefollowing three sections. Then, interactions andrelationships between emotions, motivations,and attitudes are analyzed. Based on the inte-grated models of the three perception processes,the formal description of the cognitive processesof motivations and attitudes will be presentedusing RTPA (Wang, 2002b, 2003c, 2006b,2007a). Applications of the formal models ofemotions, motivations, and attitudes will be

demonstrated in a case study on maximizingstrengths of individual motivations in softwareengineering.

THE HIERARCHICAL MODELOF EMOTIONSEmotions are a set of states or results of per-ception that interprets the feelings of human

beings on external stimuli or events in the binarycategories of pleasant or unpleasant.

De nition 2: An emotion is a personal feelingderived from ones current internal status,mood, circumstances, historical context, and

external stimuli.

Emotions are closely related to desiresand willingness. A desire is a personal feelingor willingness to possess an object, to conductan interaction with the external world, or to

prepare for an event to happen. A willingnessis the faculty of conscious, deliberate, andvoluntary choice of actions.

According to the study by Fischer, Shaver,and Carnochan (1990) and Wilson and Keil

(1999), the taxonomy of emotions can bedescribed at three levels known as the sub-category, basic, and super levels as shown inTable 1.

It is interesting that human emotions at the perceptual layer may be classi ed into only twoopposite categories: pleasant and unpleasant .Various emotions in the two categories can beclassi ed at ve levels according to its strengthsof subjective feelings as shown in Table 2(Wang, 2005), where each level encompasses

Level Description

Super level Positive (pleasant) Negative (unpleasant)

Basic level Joy Love Anger Sadness Fear

Sub-category levelBliss, pride,contentment

Fondness,infatuation

Annoyance, hostility,contempt, jealousy

Agony, grief, guilt,loneliness

Horror, worry

Table 1. Taxonomy of emotions


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a pair of positive/negative or pleasant/unpleas-ant emotions.

De nition 3: The strength of emotion |E m| is anormalized measure of how strong a personsemotion on a ve-level scale identi ed from 0through 4, that is:

0 | E m| 4 (1)

where | E m| represents the absolute strength ofan emotion regardless whether it is positive(pleasant) or negative (unpleasant), and thescope of | E m| is corresponding to the de nitionsof Table 2.

It is observed that an organ known as hy- pothalamus in the brain is supposed to interpretthe properties or types of emotions in terms of

pleasant or unpleasant (Payne & Wenger, 1998;Pinel, 1997; Smith, 1993; Wang et al., 2006;Westen, 1999).

De nition 4: Let T e be a type of emotion, ES theexternal stimulus, IS the internal perceptual

status, and BL the Boolean values true or false.The perceptual mechanism of the hypothalamuscan be described as a function, that is:

T e : ES IS BL (2)

It is interesting that the same event or stimu-lus ES may be explained in different types, interms of pleasant or unpleasant, due to the dif-ference of the real-time context of the perceptual

status IS of the brain. For instance, walking fromhome to the of ce may be interpreted as a pleas -ant activity for one who likes physical exercise,

but the same walk due to car breakdown will be interpreted as unpleasant. This observationand the taxonomy provided in Tables 1 and 2leads to the following Theorem.

Theorem 1: The human emotional system isa binary system that interprets or perceivesan external stimulus and/or internal status as

pleasant or unpleasant.

Although there are various emotionalcategories in different levels, the binary emo-tional system of the brain provides a set of

pairwise universal solutions to express humanfeelings. For example, angry may be explainedas a default solution or generic reaction for anemotional event when there is no better solutionavailable; otherwise, delight will be the defaultemotional reaction.

Level(Positive/Negative) Description

0 No emotion -

1 Weak emotionComfort Safeness, contentment, ful llment, trust

Fear Worry, horror, jealousy, frightening, threatening

2 Moderate emotionJoy Delight, fun, interest, pride

Sadness Anxiety, loneliness, regret, guilt, grief, sorrow, agony

3 Strong emotionPleasure Happiness, bliss, excitement, ecstasy

Anger Annoyance, hostility, contempt, infuriated, enraged

4 Strongest emotionLove Intimacy, passion, amorousness, fondness, infatuation

Hate Disgust, detestation, abhorrence, bitterness

Table 2. The hierarchy of emotions


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THE MATHEMATICAL MODELOF MOTIVATIONSMotivation is an innate potential power of human

beings that energizes behavior. It is motivationthat triggers the transformation from thought(information) into action (energy). In otherwords, human behaviors are the embodiment ofmotivations. Therefore, any cognitive behavioris driven by an individual motivation.

De nition 5: A motivation is a willingness ordesire triggered by an emotion or external

stimulus to pursue a goal or a reason for trig- gering an action.

As described in LRMB (Wang et al., 2006),motivation is a cognitive process of the brain atthe perception layer that explains the initiation,

persistence, and intensity of personal emotionsand desires, which are the faculty of conscious,deliberate, and voluntary choices of actions.

Motivation is a psychological and socialmodulating and coordinating in uence on thedirection, vigor, and composition of behavior.This in uence arises from a wide variety of

internal, environmental, and social sources, andis manifested at many levels of behavioral andneural organizations.

The taxonomy of motives can be classi edinto two categories known as learned and un-learned (Wittig, 2001). The latter is the primarymotives such as the survival motives (hunger,thirst, breath, shelter, sleep, and eliminating

pain). The former are the secondary motivessuch as the need for achievement, friendship,af liation, dominance of power, and relief

anxiety, which are acquired and extended basedon the primary motives.

De nition 6: The strength of motivation M is anormalized measure of how strong a personsmotivation on a scale of 0 through 100, thatis:

0 M 100 (3)

where M = 100 is the strongest motivation and M = 0 is the weakest motivation.

It is observed that the strength of a motiva-tion is determined by multiple factors (Westen,1999; Wilson & Keil, 1999) such as:

a. The absolute motivation |E m| : The strengthof the emotion.

b. The relative motivation E - S : A relativedifference or inequity between the ex-

pectancy of a person E for an object oran action towards a certain goal and thecurrent status S of the person.

c. The cost to ful ll the motivation C : Asubjective assessment of the effort neededto accomplish the expected goal.

Therefore, the strength of a motivation can be quantitatively analyzed and estimated by thesubjective and objective motivations and theircost as described in the following theorem.

Theorem 2: The strength of a motivation M is proportional to both the strength of emotion |E m|and the difference between the expectancy ofdesire E and the current status S, of a person,and is inversely proportional to the cost to ac-complish the expected motivation C, that is:

2.5 | | ( - )m E E S M C

(4)

where 0 |E m| 4, 0 ( E,S ) 10, 1 C 10,and the coef cient 2.5 makes the value of Mnormalized in the scope of [0 .. 100].

In Theorem 2, the strength of a motivationis measured in the scope 0 M 100. When M

> 1, the motivation is considered being a desiredmotivation, because it indicates both an existingemotion and a positive expectancy. The higherthe value of M, the stronger the motivation.

According to Theorem 2, in a softwareengineering context, the rational action of amanager of a group is to encourage individualemotional desire, and the expectancy of eachsoftware engineer and to decrease the requiredeffort for the employees by providing additionalresources or adopting certain tools.


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Corollary 1: There are super strong motiva -tions toward a resolute goal by a determinedexpectancy of a person at any cost.

It is noteworthy that a motivation is onlya potential mental power of human beings,and a strong motivation will not necessarilyresult in a behavior or action. The condition fortransforming a motivation into a real behavioror action is dependent on multiple factors, suchas values, social norms, expected dif culties,availability of resources, and the existence ofalternative goals.

The motivation of a person is constrained by the attitude and decision-making strate-gies of the person. The former is the internal(subjective) judgment of the feasibility of themotivation, and the latter is the external (social)

judgment of the feasibility of the motivation.Attitude and decision-making mechanisms will

be analyzed in the following subsections.

THE MATHEMATICAL MODELOF ATTITUDESAs described in the previous section, motivation

is the potential power that may trigger an observ-able behavior or action. Before the behavior is

performed, it is judged by an internal regulationsystem known as the attitude.

Psychologists perceive attitude in variousways. Fazio (1986) describes an attitude as anassociation between an act or object and anevaluation. Eagly and Chaiken (1992) de nethat an attitude is a tendency of a human toevaluate a person, concept, or group positivelyor negatively in a given context. More recently,

Wittig (2001) describes attitude as a learnedevaluative reaction to people, objects, events,and other stimuli. Attitudes may be formallyde ned as follows.

De nition 7: An attitude is a subjective tendencytowards a motivation, an object, a goal, or anaction based on an intuitive evaluation of its

feasibility.

The modes of attitudes can be positive ornegative, which can be quantitatively analyzedusing the following model.

De nition 8. The mode of an attitude A isdetermined by both an objective judgment ofits conformance to the social norm N and a

subjective judgment of its empirical feasibility F, that is:

1, 0,

N F A

N F

T T

F F (5)

where A = 1 indicates a positive attitude; oth-

erwise, it indicates a negative attitude.

INTERACTIONS BETWEENMOTIVATION AND ATTITUDEThis section discusses the relationship betweenthe set of interlinked perceptual psychological

processes such as emotions, motivations, at-titudes, decisions, and behaviors as formallymodeled in the preceeding sections. A motiva-tion/attitude-driven behavioral model will bedeveloped for formally describing the cognitive

processes of motivations and attitudes.It is observed that motivation and attitude

have considerable impact on behavior andin uence the ways a person thinks and feels(Westen, 1999). A reasoned action model is

proposed by Fishbein and Ajzen (1975) thatsuggests human behavior is directly generated

by behavioral intensions, which are controlled by the attitude and social norms. An initialmotivation before the judgment by an attitudeis only a temporal idea; with the judgment ofthe attitude, it becomes a rational motivation(Wang et al., 2006), also known as the behav-ioral intention .

The relationship between an emotion, mo-tivation, attitude, and behavior can be formallyand quantitatively described by the Motiva-tion/Attitude-Driven Behavior (MADB) modelas illustrated in Figure 1. In the MADB model,motivation and attitude have been de ned in


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Equations 4 and 5. The rational motivation,decision, and behavior can be quantitativelyanalyzed according to the following de ni -tions. It is noteworthy that, as shown in Figure1, a motivation is triggered by an emotion ordesire.

De nition 9: A rational motivation M r is amotivation regulated by an attitude A with a

positive or negative judgment, that is:

2.5 | | ( - )

r

m

M M A

E E S A

C

(6)

De nition 10: A decision D for con rming anattitude for executing a motivated behavior is abinary choice on the basis of the availability of

time T, resources R, and energy P, that is:

1, =

0, =

T R P D

T R P

T

F (7)

De nition 11. A behavior B driven by a moti -vation M r and an attitude is a realized actioninitiated by a motivation M and supported bya positive attitude A and a positive decision Dtoward the action, that is:

2.5 | | ( - ), 1

,

m r

E E S M D A D

C B otherwise

T

F

(8)

FORMAL DESCRIPTION OFCOGNITIVE PROCESSES OFMOTIVATION AND ATTITUDEThe formal models of emotion, motivation,and attitude have been developed in previ-ous sections. This section extends the modelsand their relationship into detailed cognitive

processes based on the OAR model (Wang,2007d) and using RTPA (Wang, 2002b, 2003c,2006b, 2007a), which enable more rigoroustreatment and computer simulations of theMADB model.

The Cognitive Process ofMotivationsThe mathematical model of rational motivationis described in Equation 6. Based on Equation6, the cognitive process of motivation is pre-sented in Figure 2. The motivation process isdivided into four major sub-processes knownas: (1) to form motivation goal; (2) to estimatestrength of motivation; (3) to form rationalmotivation; and (4) to stimulate behavior for

the motivation.

Strengthen/weaken

MotivationBehavior

Rational

motivation Outcome

Attitude(Perceptualfeasibility )

Values/socialnorms

Experience

Availabilityof time,

resources,and energy

Decision( physical

feasibility )

Stimuli

Internal process External process

Satisfy/dissatisfy

M M r

A D

B

N FT/R/P

Emotion

Figure 1. The motivation/attitude-driven behavior (MADB) model


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The Motivation Process

Motivation (I :: o S; O :: OAR ST){ I. Form motivation goal(s)

ObjectIdentification (o, A , R )

II. Estimate strength of motivation M(o) N Quantify (E m(o)N) // The strength of emotion

Quantify (S(o) N) // The current status

Quantify (E(o) N) // The expectancy of desire

Quantify (C(o) N) // The cost to accomplish

2.5 (o) ( (o) - (o) )(o)(o)

m E E S M C

N N NN

N

( M(o) N > 1 M(o) BL = T // Positive motivation

| ~

M(o) BL = F // Negative motivation )

III. Check the mode of attitude A(o) N // Refer to the Attitude process

IV. Form rational motivation M r (o)M r (o) N := M(o) N A(o) N

( M r (o)N > 1

M r (o)BL = T // Rational motivation| ~

M r (o)BL = F // Irrational motivation )

V. Determine physical availability D(o) N // Refer to the Attitude process

VI. Stimulate behavior for M r (o)( D(o) N = 1 // Implement motivation o

GenerateAction (M r (o))

ExecuteAction (M r (o))

R := R | ~ // Give up motivation o

D(o) N := 0o :=R :=

)OAR ST = // Form new OAR model

Memorization (OAR ST)}

Figure 2. The cognitive process of motivation


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The MADB model provides a formal ex- planation of the mechanism and relationship between motivation, attitude, and behavior.The model can be used to describe how themotivation process drives human behaviorsand actions, and how the attitude as well asthe decision-making process help to regulatethe motivation and determines whether themotivation should be implemented.

The Cognitive Process of Attitudes The mathematical model of attitude has beendescribed in Equation 5. Based on Equation 5,the cognitive process of attitude is presented inFigure 3. The attitude process is divided intothree major sub-processes known as: (1) to checkthe mode of attitude; (2) to determine physicalavailability; and (3) to stimulate behavior forthe motivation.

The Integrated Process ofMotivation and AttitudesAccording to the MADB model and the for-mal description of the motivation and attitude

processes as shown in Figures 1 through 3, the

cognitive processes of motivation and attitudeare interleaved. An integrated process that com- bines both motivation and attitude is given inFigure 4 via the following sub-processes: (1) toform motivation goals; (2) to estimate strengthof motivation; (3) to check the mode of attitude;(4) to form rational motivation; (5) to determine

physical availability; and (6) to stimulate be-havior for the rational motivation.

MAXIMIZING STRENGTHS OFINDIVIDUAL MOTIVATIONSStudies in sociopsychology provide a richtheoretical basis for perceiving new insightsinto the organization of software engineering.It is noteworthy that in a software organiza-tion, according to Theorem 2, the strength of amotivation of individuals M is proportional to

both the strength of emotion and the difference between the expectancy and the current statusof a person. At the same time, it is inversely

proportional to the cost to accomplish the ex-

pected motivation C . The job of managementat different levels of an organization tree is toencourage and improve E m and E , and to helpemployees to reduce C .

Example 1: In software engineering projectorganization, the manager and program-mers may be motivated to the improvement of

software quality to a different extent. Assumethe following factors as shown in Table 3 arecollected from a project on the strengths ofmotivations to improve the quality of a software

system, analyze how the factors in uence the strengths of motivations of the manager andthe programmers.

According to Theorem 2, the strengths ofmotivations of the manager M 1 and the pro-grammers M 2 can be estimated using Equation4, respectively:

12.5 | | ( - )( )

2.5 4 (8 - 5)3

10.0

m E E S M manager C

and

22.5 3.6 (8 - 6)

( )8

2.3

M programer

The results show that the manager has muchstronger motivation to improve the quality of the

software system than that of the programmers inthe given project. Therefore, the rational actionfor the manager is to encourage the expectancyof the programmers or to decrease the requiredeffort for the programmers by providing addi-tional resources or adopting certain tools.

According to sociopsychology (Wiggins,Eiggins, & Zanden, 1994), social environment,such as culture, ethical norms, and attitudegreatly in uences peoples motivation, behav -ior, productivity, and quality towards collabora-

tive work. The chain of individual motivation


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The Attitude Process

Attitude (I :: o S; O :: OAR ST){ I. Form motivation goal(s)


II. Estimate strength of motivation M(o) N // Refer to the Motivation process

III. Check the mode of attitude A(o) N // Perceptual feasibility

Qualify (N(o) BL) // The social norm Qualify (F(o) BL) // The subjective feasibility

( N(o) BL F(o) BL= T

A(o) N := 1| ~

A(o) N := 0)

IV. Form rational motivation M r (o) // Refer to the Motivation process

V. Determine physical availability D(o) N Qualify (T(o) BL) // The time availability Qualify (R(o) BL) // The resource availability Qualify (P(o) BL) // The energy availability

( T(o) BL R(o) BL P(o) BL = T

D(o) N := 1 // Confirmed motivation| ~

D(o) N := 0 // Infeasible motivation )



ExecuteAction (M r (o))

R := R | ~ // Give up motivation o

D(o) N := 0o :=R :=



Figure 3. The cognitive process of attitude

Role E m C E S

The manager 4 3 8 5

Programmers 3.6 8 8 6

Table 3. Motivation factors of a project


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Figure 4 The integrated process of motivation and attitude

The Motivation and Attitude Process

Motivation-Attitude (I :: o S ; O :: OAR ST){ I. Form motivation goal(s)


II. Estimate strength of motivation M(o) N

Quantify (E m(o) N) // The strength of emotion

Quantify (S(o) N) // The current status

Quantify (E(o) N) // The expectancy of desire

Quantify (C(o) N) // The co st to accomplish2.5 (o) ( (o) - (o) )(o)

(o)m E E S M

C

N N NN

N

( M(o) N > 1

M(o) BL = T // Positive motivation

| ~

M(o) BL = F // Negative motivation)

III. Check the mode of attitude A(o) N // Perceptual feasibility

Qualify (N(o) BL) // The social norm

Qualify (F(o) BL) // The subjective feasibility

( N(o) BL F(o) BL = T

A(o) N := 1| ~

A(o) N := 0)

IV. Form rational motivation M r (o)M r (o) N := M(o) N A(o) N

( M r (o)N > 1

M r (o)BL = T // Rational motivation

| ~

M r (o)BL = F // Irrational motivation)

V. Determine physical availability D(o) N

Qualify (T(o) BL) // The time availability

Qualify (R(o) BL) // The resource availability

Qualify (P(o) BL) // The energy availability

( T(o) BL R(o) BL P(o) BL = T

D(o) N := 1 // Confirmed motivation| ~

D(o) N := 0 // Infeasible motivation)



ExecuteAction (M r (o))R := R

| ~ // Give up motivation oD(o) N := 0o :=R :=




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in a software organization can be illustrated asshown in Figure 5.

Cultures and values of a software develop-ment organization helps to establish a set of ethi-cal principles or standards shared by individualsof the organization for judging and normalizingsocial behaviors. The identi cation of a largerset of values and organizational policy towardssocial relations may be helpful to normalize

individual and collective behaviors in a soft-ware development organization that producesinformation products for a global market.

Another condition for supporting creativework of individuals in a software developmentorganization is to encourage diversity in bothways of thinking and work allocation. It is ob-served in social ecology that a great diversityof species and a complex and intricate patternof interactions among the populations of acommunity may confer greater stability on an

ecosystem.

De nition 12: Diversity refers to the socialand technical differences of people in workingorganizations.

Diversity includes a wide range of differ-ences between people such as those of race,ethnicity, age, gender, disability, skills, edu-cations, experience, values, native language,and culture.

Theorem 3: The diversity principle states thatthe more diversity of the workforce in an or-

ganization, the higher the opportunity to formnew relations and connections that leads to the

gain of the system fusion effect.

Theorem 3 is particularly useful for soft-ware development organizations where creativework products are engineered. System theory

indicates that if the number of components ofa system reaches a certain levelthe criticalmassthen the functionality of the system may

be dramatically increased (Wang, 2007a). Thatis, the increase of diversity in a system is thecondition to realize the system fusion effect,which results in a more powerful system withnewly created relations and behaviors that only

belong to the system as a whole.

CONCLUSIONThis article has described the perceptual pro-cesses of emotions, motivations, and attitudes

based on which complicated psychological andmental processes may be formally modeled andrigorously explained. Relationships and inter-actions between motivation and attitude have

been formally described in RTPA. It has beenrecognized that the human emotional systemis a binary system that interprets or perceivesan external stimulus and/or internal status asin the categories of pleasant or unpleasant. It

has revealed that the strength of a motivation is

Basichumanneeds

of individuals

Organizational objectives

Behavior

Attitude

Motivation Productivity

The social environment of software engineering

Quality

Figure 5 The chain of motivation in a software organization


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Wang, Y. (2007a). Software engineering foundations:A software science perspective. CRC Software

Engineering Series, 2(1), 580.

Wang, Y. (2007b). The theoretical framework The theoretical frameworkof cognitive informatics. The International

Journal of Cognitive Informatics and Natural Intelligence, 1 (1), 1-27.-27.

Wang, Y. (2007c). Towards theoretical foundationsof autonomic computing. The International

Journal of Cognitive Informatics and Natural Intelligence, 1(3), 1-15.

Wang, Y. (2007d). The OAR model of neural infor-matics for internal knowledge pepresentationin the brain. The International Journal of

Cognitive Informatics and Natural Intelligence, 1(3), 64-75.

Wang, Y., & Wang, Y. (2006). Cognitive informaticsmodels of the brain. IEEE Transactions on Sys-tems, Man, and Cybernetics, 36 (2), 203-207.

Wang, Y., Wang, Y., Patel, S., & Patel, D. (2006). Alayered reference model of the brain (LRMB).

IEEE Transactions on Systems, Man, and Cy-bernetics, 36 (2), 124-133.

Westen, D. (1999). Psychology: Mind, brain, andculture (2nd ed.). New York: John Wiley &Sons.

Wiggins, J. A., Eiggins, B. B., & Zanden, J. V.(1994). Social psychology (5th ed.). New York:McGraw-Hill.

Wilson, R. A., & Keil, F. C. (Eds.). (1999). The MIT encyclopedia of the cognitive sciences. Cambridge, MA: The MIT Press.

Wittig, A. F. (2001). Schaums outlines of theory and problems of introduction to psychology (2nded.). New York: McGraw-Hill.

Yingxu Wang is professor of cognitive informatics and software engineering, director of the InternationalCenter for Cognitive Informatics (ICfCI), and director of the Theoretical and Empirical Software Engineer-ing Research Center (TESERC) at the University of Calgary. He received a PhD in software engineering

from The Nottingham Trent University, UK, in 1997, and a BSc in electrical engineering from ShanghaiTiedao University in 1983. He was a visiting professor in the computing laboratory at Oxford Universityduring 1995, and has been a full professor since 1994. He is editor-in-chief of International Journal ofCognitive Informatics and Natural Intelligence (IJCINI), editor-in-chief of the IGI book series of Advancesin Cognitive Informatics and Natural Intelligence, and editor of CRC book series in Software Engineering.

He has published over 300 papers and 10 books in software engineering and cognitive informatics, andwon dozens of research achievement, best paper, and teaching awards in the last 28 years, particularlythe IBC 21st Century Award for Achievement in recognition of outstanding contribution in the eld ofCognitive Informatics and Software Science.

77 ijcini 1401 perception

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