The DynamicalPerspective in Personality and Social Psychologypsy2.fau.edu/~vallacher/pdfs/essays/Vallacher_et_al_(2002)_The... · Inthisarticle, weintroducebasicconceptsand ... Indeed,

Personality and Social Psychology Review2002, Vol. 6, No. 4, 264-273

Copyright © 2002 byLawrence Erlbaum Associates, Inc.

The Dynamical Perspective in Personality and Social Psychology

Robin R. VallacherDepartment ofPsychologyFlorida Atlantic University

Stephen J. ReadDepartment ofPsychology

University ofSouthern California

Andrzej NowakDepartment ofPsychology

University of Warsaw

Human experience reflects the interplay ofmultiple forces operating on various timescales to promote constantly evolving patterns of thought, emotion, and action. Thecomplexity and dynamism ofpersonal and social phenomena have long been recog-nized, but capturing thesefeatures ofpsychologicalprocess represents a serious chal-lengefor traditional research methods. In this article, we introduce basic concepts andmethodsfrom the study ofnonlinear dynamical systems, andwe outline the relevance ofthese ideas and approachesfor investigating phenomena at different levels ofpsycho-logical reality. We suggest that the dynamicalperspective is ideally suited to capture theemergence and maintenance ofglobalproperties in apsychological system, andfor in-vestigating the time-dependent relation between external influences and a system's in-ternally generatedforces. Althoughfairly new to personality and socialpsychology, the

dynamicalperspective has been implemented with respect to a wide variety ofphenom-ena, utilizing both empirical methods and computer simulations. This diversity oftop-ics and methods is reflected in the articles comprising the special issue.

The subject matter of personality and social psy-chology is inherently dynamic. Actions are com-prised of movement, judgments are grounded in theflow of thought, emotions rise and fall in intensityover time, social interactions unfold with a particularrhythm of words and gestures, and relationships aredefined in terms of the evolution of roles and mutualsentiment. The dynamism inherent in personal andinterpersonal experience has not been lost on ourfield. Indeed, the nature of human dynamism pro-vided a focal point in the earliest attempts to charac-terize intrapersonal and interpersonal processes, asreflected in the seminal work of such pioneers asJames (1890), Mead (1934), Cooley (1902), Lewin(1936), and Asch (1946). The focus on dynamics isapparent today in the coupling of the word dynamicwith the various literatures that define the field. Thus,we speak of personality dynamics, dynamics of atti-tude change, interpersonal dynamics, and group dy-

We thank Eliot Smith for his constructive comments on an earlierversion of this article.

Requests for reprints should be sent to Robin R. Vallacher, De-partment of Psychology, Florida Atlantic University, Boca Raton,FL 33431. E-mail: [email protected]

namics, as if these topics each represented a particu-lar manifestation of an underlying proclivity for evo-lution and change on the part of people.

In this basic sense, the theme of the special issue-the dynamical perspective in personality and social psy-chology-is hardly controversial. Recent years, how-ever, have witnessed the ascendance of a new way toconceptualize and investigate the nature ofdynamism atdifferent levels of psychological reality. Areas of in-quiry as diverse as cognitive neuroscience (cf. Port &van Gelder, 1995), developmental psychology (e.g.,Fischer & Bidell, 1997; Levine & Fitzgerald, 1992;Thelen& Smith, 1994; van Geert, 1991), organizationalbehavior (Axelrod & Cohen, 2000; Guastello, 1995),and political sociology (e.g., Axelrod, 1984; Nowak &Vallacher, 2001; Weidlich, 1991) are being reframed interms that allow rigorous and precise insight into basicdynamic processes that heretofore could only be in-ferred, and were often overlooked for want of appropri-ate tools. There are signs that this new approach to dy-namics is emerging as apotentially integrative paradigmfor personality and socialpsychology as well (cf. Carver& Scheier, 1999; Cervone & Mischel, 2002; Lewis,1997; Nowak& Vallacher, 1998a; Read& Miller, 1998;Smith, 1996; Vallacher & Nowak, 1994a, 1997). The

264 at FLORIDA ATLANTIC UNIV on March 1, 2010 http://psr.sagepub.comDownloaded from

http://psr.sagepub.com

THE DYNAMICAL PERSPECTIVE

aim ofthe special issue is to highlight this new paradigmand illustrate its relevance to a broad spectrum of topicsin personality and social psychology. Accordingly, wehave assembled a group of researchers, each of whomhas charted new directions for theory and researchwithin the dynamical perspective.

To set the stage for their contributions to the spe-cial issue, we outline in broad form the approach todynamics and complexity that has transformed thenatural sciences in recent years. We suggest that thisperspective resonates especially well with enduringissues in personality and social psychology, and thusserves as a valuable heuristic for research and a po-tential integrative vehicle for theory construction. Thedynamical perspective should not be looked upon assimply a metaphor, however, but rather as a set ofprinciples, methods, and tools that impose rigor andprecision on topics that are often opaque whenviewed through traditional lenses. The set of contri-butors we have assembled has been entrusted with thechallenge to make this case.

Dynamism and Complexity in Personaland Interpersonal Experience

As noted earlier, the centrality of dynamics to hu-man experience was recognized in early treatments ofpersonal and interpersonal processes. James (1890)theorized about the dynamic nature of human thoughtand action, with special emphasis on the continuousand ever-changing stream of thought. Cooley (1902)emphasized people's constant press for action, evenin the absence of incentives and other external forces.Mead (1934) discussed people's capacity for sym-bolic representation and the enormous range of inter-pretation to which this capacity gives rise. Lewin(1936) suggested that stability and variability in overtbehavior reflect a persistent struggle to resolve con-flicting motivational forces, including those withinthe person as well as those arising from outside influ-ences. Psychodynamic theories (e.g., Freud, 1937), ofcourse, shared this emphasis on conflict-induced dy-namism, with particular importance assigned to mo-tives and fears that are opaque to consciousness. Asch(1946) suggested that social judgment reflects the in-terplay of thoughts and feelings, with this interplaypromoting the emergence of a unique Gestalt that isnot reducible to the additive components of the indi-vidual cognitive elements themselves. Krech andCrutchfield (1948), in one of the earliest attempts tosystematize social psychology in a textbook, framedinterpersonal thought and behavior in Gestalt terms,with an explicit emphasis on people's constant recon-figuration of their experience in response to conflict-ing fields of psychological forces.

These classic statements have found support inempirical research conducted in the interveningyears, and they resonate well with lay intuitions re-garding mental and behavioral processes. The sheernumber and variety of factors identified as relevant tohuman experience guarantee that everything peoplethink and do is constantly subject to change.Thoughts, feelings, and actions are influenced by amyriad of social stimuli that run the gamut fromthose that are momentary and trivial (e.g., a stranger'sglance) to those that are persistent and significant(e.g., criticism from a loved one). This influence iscentral to everyday social interaction, with each per-son responding to the real or imagined thoughts, feel-ings, and actions of the other person. Even in the ab-sence of interpersonal contact, an individual's mentalstate and predisposition for action can take on a vari-ety of different forms as he or she reflects on past ex-periences or imagines those yet to take place. Patternsof thought, feeling, and action are generated as wellby features of the larger social context, including theperson's relationship with various groups, his or herposition in society as a whole, the nature of varioussocial institutions, and the assortment of beliefs, val-ues, and expectations that collectively define culture.

The potential for complexity and constant change isenhanced by several orders of magnitude when oneconsiders the possible ways in which these factors caninteract to influence an individual. The norms and be-liefs in a particular social context, for instance, mayrun contrary to personal beliefs, societal norms, orstandards of achievement. Which factor or blend offactors predominates, in turn, may depend upon yetother social influences and their interaction with priorexperiences reaching back to childhood. Complex in-teractions of this type hold potential for generating di-verse patterns of thought and behavior across individu-als and for establishing different patterns within agiven individual over time.

Even if we somehow managed to identify all rele-vant factors and specified how they interact to influ-ence thought and behavior, we may still be at a loss toexplain or predict a person's beliefs, decisions, desires,or courses of action. Indeed, often the only explanationavailable for someone's action centers on the person'sinternal state-his or her goals, feelings, personalitytraits, motives, self-defined principles and values, sud-den impulses, and so on. The human potential for inter-nal causation not only confers upon people the capac-ity to resist external influences, but also an inclinationto act in opposition to them. Unlike lower organisms,humans can disregard promises of reward, threats ofpunishment, social pressure from peers and authorityfigures, and other external inducements to action. In ef-fect, then, the complex edifice of interacting causalforces permeating social life can collapse in the face ofpersonal desires, values, and momentary whims.



VALLACHER, READ, & NOWAK

The Relevance of NonlinearDynamical Systems

Because of its inherent dynamism and complexity,the subject matter of personality and social psychologyrepresents a serious challenge for the methods andtools developed within the traditional natural scienceparadigm. Indeed, one could argue that the structure ofhuman social experience is simply too intricate andmultifaceted to admit to complete description, let aloneprecise prediction. Although this assessment has ledsome to question the goal of framing interpersonalphenomena in scientific terms (e.g., Gergen, 1994;Harre, 1987; Parker & Shotter, 1990), the nature of thefield's subject matter actually puts social psychologyin a strong position to lead developments in science aswe enter the 21 st century. This is because the physicalsciences have undergone a profound transformationsince the 1980s, a transformation that makes these ar-eas of inquiry more in tune with what personality andsocial psychologists have been talking about all along.The basis for this transformation was the realizationthat many phenomena in nature do not conform to cer-tain long-standing assumptions regarding causalityand reduction, but rather are more appropriately con-ceptualized as nonlinear dynamical systems (cf.Davies, 1988; Eckmann & Ruelle, 1985; Glass &Mackey, 1988; Gleick, 1987; Haken, 1978; Schuster,1984; Thompson & Stewart, 1986; Weisbuch, 1992).

Broadly defined, a dynamical system is simply a setof elements that undergoes change over time by virtueof interactions among the elements. The primary taskof dynamical systems theory is to describe the connec-tions among a system's elements and the changes in thesystem's behavior that these connections promote.Prior to the advent of the mathematical theory of non-linear dynamical systems, the physical sciences as-sumed that the relations among elements could be ap-proximated as linear. A linear relation simply meansthat a change in one element (represented as a variable)is directly proportional to changes in another element(variable) the greater the change in magnitude of onevariable, the greater the resulting change in magnitudeof the other variable. Expressed in causal terms, linear-ity means that the magnitude of the effect is propor-tional to the magnitude of the cause. In a linear system,moreover, the relations among variables are additive,so that a description of the system can be decomposedinto separate influences, each of which can be analyzedindependently. From this perspective, the complexityof a system's behavior is a direct reflection of the num-ber of interacting elements and the complexity of theirmutual influences.

In a nonlinear system, the effects of changes inone variable are not reflected in a proportional man-ner in other variables. A variable may increase dra-matically in magnitude, for example, with no corre-

sponding change in magnitude of another variableuntil a threshold is reached, beyond which evenminiscule changes in the first variable can promotevery large changes in the second variable. The behav-ior of a nonlinear system, moreover, often cannot bedecomposed into separate additive influences. In-stead, the relations among variables typically dependon the values of other variables in the system andthus are interactive in nature. This means that onecannot ignore the effects of other variables when de-scribing the relation between one's variables of inter-est. These features of nonlinear systems provide a dif-ferent perspective on the source of complexity in asystem's behavior. Even a system consisting of a fewelements can exhibit behavior of enormous complex-ity when the interactions among the elements arenonlinear rather than linear (cf. Schuster, 1984).

Dynamical systems are characterized by global sys-tem-level properties. When the relations among systemelements are nonlinear in nature, neither the system'smacrolevel properties nor the patterns of change inthese properties are inherent in the system. Rather,these properties and their patterns of change emergefrom rules specifying how the system's elements inter-act. Emergence is reminiscent of pattern formation inGestalt psychology (cf. K6hler, 1947) and is capturedby the well-known phrase, "the whole is more than thesum of its parts." In less evocative terms, the propertiesand patterns of behavior characterizing a system mayarise in a fashion that cannot be predicted solely fromknowledge of the individual elements in isolation. De-spite the holistic (i.e., non-reductionistic) connotationof this feature of nonlinear systems, emergence is actu-ally a well-specified process and can be understood interms of a tendency toward self-organization among asystem's elements. The basic idea is that the interactionamong system elements, where each element adjusts toother elements, can promote the emergence of highlycoherent structures that provide coordination for thesystem elements (cf. Haken, 1978; Kelso, 1995). Asystem's macrolevel properties derive from the internalworkings of the system, in other words, without theneed for a higher order control mechanism. This capac-ity for emergence is a defining feature of nonlinear sys-tems in many areas of science, having been demon-strated in fields as diverse as hydrodynamics (Ruelle &Takens, 1971), meteorology (Lorenz, 1963), laserphysics (Haken, 1982), and biology (e.g., Amit, 1989;Glass & Mackey, 1988).

The emergence of system-level properties by meansof self-organization is apparent in a variety of other-wise distinct personal and interpersonal phenomena.Classic accounts of group and societal dynamics, forexample, noted how group norms often developthrough the spontaneous coordination of members' im-pulses and actions, without the need for a higher-levelauthority to impose rules and standards (cf. Durkheim,




1938; Turner & Killian, 1957). In recent years, this ob-servation has been verified in computer simulationsand empirical research on social influence and interde-pendence (e.g., Axelrod, 1984; Messick & Liebrand,1995; Nowak, Szamrej, & Latane, 1990; Nowak &Vallacher, 2001). Thus, simple social interactions overtime tend to promote the emergence of public opinion,altruistic values, and other group level properties. Atan intrapersonal level, meanwhile, the spontaneousself-organization of cognitive and affective elementsinto higher order structures has been revealed in exper-imental work on social judgment (e.g., Vallacher,Nowak, & Kaufman, 1994) and action identification(Vallacher, Nowak, Markus, & Strauss, 1998), and incomputer simulations of self-reflection processes(Nowak, Vallacher, Tesser, & Borkowski, 2000). Thiswork suggests that organized patterns of social think-ing can emerge without the need for a higher-level cog-nitive mechanism or homunculus.

The specific trajectory of self-organization in a sys-tem reflects the system's attempt to satisfy multipleconstraints embedded in the initial state of the system.These constraints include the initial states of the ele-ments, the nature of the interactions among elements,and the external influences on the system. The sys-tem's evolution represents its attempt to reach a statethat does the best possible job of satisfying such con-straints. The evolution of a group norm, for example,depends on the initial dispositions and attitudes of eachgroup member, the nature of the relationships amonggroup members, and the exposure of group members toideas and information from sources outside of thegroup. The norm that ultimately emerges representsthe group's attempt to find a balance among these po-tentially conflicting constraints. Constraint satisfac-tion also underlies the self-organization of specificthoughts and feelings into higher order cognitive struc-tures within the individual. Thus, an individual's atti-tudes and values presumably arise from the attempt toreconcile his or her preexisting judgments, diversepieces of old and new information, and conflicting so-cial pressures and expectations.

Dynamical systems are rarely self-contained, butrather are open to influence from external factors byvirtue of being embedded in a larger context. Thus, aperson's attitude may change in response to persuasivecommunication, a group may reverse a decision basedon new information, and a society can undergo trans-formations due to changing international conditions.The result of such influences, however, is dependent onthe internal state of the system in question. This is be-cause external factors do not cause changes directly inan otherwise passive system, but rather exert their in-fluence by modifying the course of whatever internallygenerated dynamics are operative for the person,group, or society. Lacking insight into the ongoing pro-cesses within a person or social group, it is difficult to

know what effect a given external influence is likely tohave. When external influences are present, the sys-tem's macrolevel properties may change in a mannerthat is non-proportional to the magnitude of the influ-ences. Sometimes an external factor produces only re-sistance, with little or no change in the ongoing pro-cesses of the person or group. At other times, theperson or group may show an exaggerated response toa lesser value of the same external factor. At yet othertimes, an external influence may initiate a process thatunfolds according to its own pattern of changes, the ef-fects of which may not be apparent for days, minutes,or years, depending on the phenomenon in question.

Research strategies that reduce dynamics to a singlepass and focus on a stable outcome are clearly inade-quate to capture these propensities. Rather than focus-ing on a snapshot of a specific phenomenon, insight isoften better served by exploring how the system inquestion evolves in time. A system may eventually sta-bilize at a given value, but knowing this value may beless informative than knowing the sequence of statesthrough which the system evolved in route to this state.Two people may ultimately be swayed by a persuasivemessage, for example, but they may have experiencedqualitatively different routes to their shared stance,with one person incrementally adjusting his or her ini-tial position and the other person demonstrating siz-able swings in opinion before reaching a new attitudeequilibrium. The evolution of each person's attitudemay provide insight into the nature of his or her under-lying cognitive structure and personality traits thatwould not be forthcoming from knowing only the ulti-mate effect of the persuasive appeal.

Beyond that, some systems may fail to reach a sin-gle stable state that satisfies all constraints, displayinginstead a sustained pattern of changes among differ-ent states. For phenomena characterized by such dy-namic as opposed to static equilibrium tendencies,the attempt to identify a single state as most represen-tative may provide an impoverished and a potentiallymisleading depiction. There may be greater informa-tion value, for example, in knowing the temporal pat-tern of someone's mood variability than in knowingthe central tendency of his or her mood state a statehe or she might never experience. At an interpersonallevel, identifying the temporal pattern of a couple'smutual affect may provide greater insight into the na-ture of the relationship than simply collapsing overtime to compute the mean level of affect in the rela-tionship. A husband and wife may oscillate betweenperiods of deep passion and bitter resentment, for ex-ample, and never experience the central tendency ofthese opposing sentiments.

The approach of nonlinear dynamical systems isideally suited to investigate internally generated dy-namics, self-organization, the emergence of globalproperties from the interaction of basic elements, and




the time-dependent relation between external influ-ences and a system's intrinsic dynamics. And becausethis approach is defined in formal terms, it holds poten-tial for identifying invariant properties of dynamicsthat transcend topical boundaries and levels of analy-sis. Research on nonlinear dynamical systems has es-tablished, in fact, that the dynamics of highly diversesystems in areas as distinct as physics, chemistry, biol-ogy, and economics conform to a handful of basic pat-terns or attractors. Rather than simply evolving towarda stable equilibrium (i.e., fixed-point attractor), sys-tems can display self-sustaining temporal patterns(e.g., periodic, quasi-periodic, or chaotic attractors) asa result of repeated iterations of the mutual influencesamong variables internal to the system.

To identify and investigate patterns of intrinsic dy-namics, mathematicians and scientists in various fieldshave developed a variety of methods and tools, many ofwhich are readily adaptable to basic concerns in per-sonality and social psychology. This suggests the po-tential for developing general laws of psychologicaldynamics that apply to all levels of social reality, fromthe flow of individual thoughts to societal transforma-tions. Beyond providing coherence to an admittedlyfragmented discipline (cf. Kenrick, 2001; Vallacher &Nowak, 1994b), the discovery of such laws in socialpsychology may foster new levels of integration withother areas of psychology that have already embracedthe dynamical perspective (e.g., developmental andcognitive psychology) and with other areas of scienceas well.

Dynamical Research: FromMeta-Theory to Implementation

No one would argue with the suggestion that humansocial experience is complex and dynamic, nor wouldmost observers deny the potential relevance and utilityof nonlinear dynamical systems to personal and inter-personal phenomena. Indeed, the past decade has wit-nessed the emergence of considerable interest and curi-osity regarding the promise of the dynamical approach(cf. Barton, 1994; Carver & Scheier, 1999; Eiser, 1994;Goldstein, 1996; Guastello, 1995; Holland, 1995;Kenrick, 2001; Nowak & Vallacher, 1998a; Vallacher& Nowak, 1994a, 1997). What is less clear to many re-searchers, though, is whether it is necessary-or possi-ble, for that matter-to implement this approach intheir own research agendas. Many of the methods andtools developed to investigate the dynamic propertiesof complex systems are foreign to the experience ofpersonality and social psychologists, and it isn'tself-evident that going to the trouble to adapt suchtools will have a ready pay-off in advancing theoreticalunderstanding or real-world application.

This concern has diminished somewhat in recentyears with the advent of several research programs thathave established a track record in implementing dy-namical concepts and methods. Some of these pro-grams have used experimental methods to track thetemporal trajectories of diverse processes, includingsocial interaction (e.g., Beek & Hopkins, 1992; Buder,1991; Newtson, 1994), personality expression (e.g.,Brown & Moskowitz, 1998; Mischel & Shoda, 1995),mood (e.g., Schuldberg & Gottlieb, 2002), group dy-namics (e.g., Arrow, 1997; Arrow, McGrath, &Berdahl, 2000; Losada & Markovitch, 1990), close re-lationships (e.g., Gottman, Murray, Swanson, &Tyson, in press), attitude change (Kaplowitz & Fink,1992; Latane & Nowak, 1994), conformity (Tesser &Achee, 1994), social judgment (e.g., Vallacher et al.,1994), and self-evaluation (e.g., Vallacher & Nowak,2000). In a few instances, the experimental methodshave been supplemented by analytical tools designedto identify the formal properties of the observed dy-namics. The periodic flow of social interaction hasbeen shown to have a fractal (i.e., self-similar) struc-ture (Newtson, 1994), for example, and the intrinsicdynamics of social judgment have been shown to re-flect the operation of a low-dimensional cognitive-af-fective system (Vallacher et al., 1994).

For the most part, however, computer simulationsprovide the tool of choice in investigating personal andinterpersonal dynamics. To date, the most frequentlyemployed simulation platforms in this work are cellu-lar automata and neural networks (cf. Liebrand,Nowak, & Hegselman, 1998; Nowak & Vallacher,1998b; Read & Miller, 1998). These approaches haveproven especially useful in modeling the emergence ofglobal properties from the interactions of individual el-ements. Two levels of social reality are most often in-vestigated in this manner. At the level of intrapersonalprocesses, elements typically correspond to compo-nents of the cognitive system (e.g., specific thoughtsand pieces of information), and the global level refersto such macroscopic properties of cognition as deci-sions, judgments, and self-concepts. Different mani-festations of the tendency toward coherence in socialjudgment (e.g., dissonance reduction, causal reason-ing, impression formation, stereotype formation andchange), for example, have been analyzed as constraintsatisfaction processes within a connectionist or neuralnetwork framework (e.g., Kunda & Thagard, 1996;Read & Montoya, 1999; Read, Vanman, & Miller,1997; Shultz & Lepper, 1996; Smith, 1996). The emer-gence of global properties of self-concept (e.g., self-es-teem, differentiation) from the self-organization ofspecific elements of self-relevant information, mean-while, has been modeled within a cellular automataframework (Nowak et al., 2000). At a higher level ofsocial reality, elements correspond to individuals andthe system-level properties refer to various group-level




phenomena. This line of research has proven success-ful in modeling the emergence of public opinion(Nowak et al., 1990), the development of social net-works (Nowak, Vallacher, & Burnstein, 1998), theemergence of cooperation in social dilemma situations(e.g., Messick & Liebrand, 1995), and the nature ofeconomic and political transitions in society (Nowak &Vallacher, 2001).

Computer simulations have two noteworthy advan-tages in investigating the dynamics of complex personaland interpersonal processes. First, they allow one to in-vestigate the relationship between micro- andmacrolevels of social reality. An investigator can equipindividual elements with established rules of behaviorand observe how these rules give rise to global proper-ties for the set of elements as a whole. Thus, for exam-ple, simple rules of social interaction can promote theevolution of shared norms and attitudes in a group. In areversal of this procedure, one can start with knownglobal phenomena and trace backwards to discoverwhat rules on the level of individual elements are neces-sary to produce the system-level phenomena. The sec-ond advantage of computer simulations is their capacityto reveal temporal patterns. For many phenomena, it isunreasonable to expect the effects of a given cause to berevealed immediately. An insult may produce hate, forexample, but the development of such a feeling maytake a relatively long time to develop. And although loveat first sight is a frequent subject of novels and movies,in reality many interactions and prolonged contact maybe necessary for a romantic attraction to develop. Thevery nature of computer simulations is ideal for study-ing the effects of multiple iterations of a given process.Decades of real time, and thousands of real interactions,may be compressed into seconds of computer time, re-vealing delayed consequences that simply cannot be ob-served in real time. It is not surprising, then, that com-puter simulations have proven to be central to thedevelopment of dynamical models, both in the naturalsciences and in the recent applications to personal andsocial psychological phenomena.

The use of computers should not be viewed as an al-ternative to experimental research. To the contrary,these two approaches are complementary, providingcross-validation for one another and working togetherin theory construction and testing. Computers, first ofall, provide a tool for the visualization of both experi-mental and simulation data. Through computer visual-ization, an investigator can discover patterns that arepredicted by theory or that exist in reality. Thus, onecan literally see the emergence of temporal and spatialpatterns in a social psychological process, whether thespread of public opinion through social influence(Nowak et al., 1990) or the progressive differentiationof self-concept through socially provided feedback onone's qualities (Nowak et al., 2000). Beyond that, thecomparison of patterns and outcomes identified in ex-

perimental data and the patterns and outcomes pro-duced by computer simulation of a model provides anew means of verifying a theory. The results of an ex-periment or a set of experiments can be implemented ina computer program to assess feasibility and long-termconsequences of the process in question. The results ofa computer simulation, in turn, may suggest a particu-lar configuration of influences that can be validated insubsequent experimental work. Through repeated iter-ations of the reciprocal feedback between simulationand experiment, one is in a position to gain greater pre-cision in theoretical understanding.We wish to emphasize that the dynamical approach

does not represent a challenge to well-established intu-itions regarding human experience. To the contrary, theapplication of dynamical concepts and methods islikely to enhance rather than diminish the uniquenessof personality and social psychology. Unlike the appli-cation of traditional natural science assumptions, thedynamical approach provides full expression to thecomplexity and malleability of human experience, en-abling researchers to be more explicit than ever aboutthe issues that defined our field in its infancy. Only re-cently have the physical sciences matured to the pointthat they can appreciate what personality and socialpsychologists have known all along. In their attempt tocapture the complex and dynamic nature of basic phys-ical processes, scientists in many disciplines have de-veloped a wide variety of algorithms, formal tools, andnew empirical approaches. By adapting these methodsto the special nature of human experience, the field ofpersonality and social psychology is in a position toimpose precision and rigor on the early insights thatdefined this area of inquiry.

Overview of Articles in theSpecial Issue

The dynamical perspective is in its infancy and thusstill largely unfamiliar to the majority of personalityand social psychologists. Despite the emerging lines ofresearch described previously, then, it may not be clearhow the various methods and tools we have describedcan be implemented to shed new light on the widerange of topics that define the field. By bringing to-gether prominent researchers who are on the cuttingedge of this approach, we hope to bridge the gap be-tween metaphor and practice, to translate promise intoreality. Each of the following articles outlines a uniqueapproach and does so in the context of an importantintrapersonal or interpersonal phenomenon. To high-light the range of phenomena addressed, we havesorted these articles into a tripartite structure thatshould prove familiar to everyone in our field: Cogni-tive and Affective Dynamics, Interpersonal and GroupDynamics, and Personality Dynamics. We hope that




the specific configuration of basic strategies and topicsrepresented in this set of articles will promote theemergence of a deeper appreciation of what the dy-namical perspective has to offer the field.

Cognitive and Affective Dynamics

Thagard and Nerb (this issue) examine how parallelconstraint satisfaction processes operating in a dynamicneural network can capture important aspects of emo-tional dynamics. One model, HOTCO, shows how an in-dividual's emotions may shift as a result of constraintsatisfaction processes operating on a set of both cogni-tive and evaluative components. A second model,ITERA, integrates work from appraisal theories ofemo-tion within a constraint satisfaction model and demon-strates how appraisals can be integrated with other typesof information to generate an emotional response.

Simon and Holyoak (this issue) argue that earlywork on consistency theories, although quite promis-ing, failed to provide an account of how attitudes areformed and decisions are made. However, they suggestthat recent connectionist models of constraint satisfac-tion provide a general and testable account of the rolethat consistency principles play in decision making andattitude formation. They then discuss their recent bodyof work on complex legal decision making that demon-strates how constraint-satisfaction mechanisms cantransform initially ambiguous legal evidence into co-herent decisions.

Queller (this issue) presents a recurrent distributednetwork model that simulates two seemingly distinctmodels of stereotype change-book keeping andsubtyping-that have been presented as reflecting dif-ferent cognitive processes. She shows that the apparentdifference between book keeping and subtyping is dueto differences in the degree of covariation among fea-tures induced by the different stimulus sets used. Hermodel thus explains, and thereby integrates, these twotypes of stereotype change.

Carver and Scheier (this issue) are well known fortheir control systems theory of self-regulation, whichportrays an individual as guiding his or her behavior bymonitoring the discrepancy between a current state anda goal state. They note that this "top down" view seemsto conflict with the more "bottom up" view ofdynamicalsystems models, in which attractors and repellors for asystem develop by means of self-organization pro-cesses. However, they argue that these principles some-times reflect different ways oflooking at the same issue,and sometimes represent complementary principlesthat address differentlevels ofthemore general process.

Interpersonal and Group Dynamics

Shoda, Tiernan, and Mischel (this issue) extendtheir neural network model of Cognitive Affective Per-

sonality Systems theory to dyadic interactions. Theyfirst identify the major attractors of individual net-works operating in isolation and then they show thatwhen two networks are coupled, the dyadic networkfrequently develops attractors that are not found in ei-ther of the individual networks. Their work shows hownew patterns of thought and behavior can emerge fromthe interaction of individuals with different personali-ties, and it also has implications for how we mightthink about influence in relationships.

Gottman, C. Swanson, and K. Swanson (this issue)provide a step by step account ofhow marital interactioncan be modeled as a set of difference equations that cap-ture important aspects of a couple's dynamics. Theypresent empirical data demonstrating that that thismodel can effectively predict long-term marital stabilityand divorce. As they note, this technique is quite generaland can be used to model any kind of dyadic interactionfor which time series data are available.

Axelrod, Riolo, and Cohen (this issue) use interact-ing adaptive agents, playing a Prisoner's Dilemmagame, to examine the impact of different kinds of com-munication patterns on the development of coopera-tion among individuals. They find that even when pat-terns of communication are randomly chosen and theagents are geographically distant from one another, co-operation will still develop as long as the interactionsamong the agents persist over time. They discuss theimplications of this finding for the development andmaintenance of cooperation in an age in which somuch interaction is electronically mediated.

Kenrick, Maner, Butner, Li, Becker and Schaller(this issue) note that evolutionary approaches and dy-namical systems approaches are becoming increas-ingly important in social psychology and they discusshow the insights from these two approaches can beintegrated. They focus on six basic adaptive problemsof life in social groups and, through the use of bothconceptual analysis and computer simulations, theyexplore how a dynamical systems approach can pro-vide insight into the dynamics that evolve in each ofthose domains.

Personality Dynamics

Read and Miller (this issue), like Shoda et al. (this is-sue), present a neural network model of personality.Whereas Shoda et al. focus on the abstract characteris-tics of their model, such as its dynamics and the numberand types of attractors, Read and Miller present a modelthat captures important aspects of what is currentlyknown about personality structure. Relying heavily onwork in temperament, personality structure, and theneuroscience ofmotivation and emotion, they constructa neural network model that attempts to simulate someofthe major distinctions in personality, such as extrover-sion, neuroticism, and conscientiousness.




Vallacher, Nowak, Froehlich, and Rockloff (this is-sue) investigate the implications of conceptualizing theself-concept as a self-organizing dynamical system.They suggest that self-evaluative thought evolves to-ward regions of maximal evaluative coherence inself-structure, and that the valence of these fixed-pointattractors dictates a person's level of self-esteem. Theypresent preliminary research on the flow ofself-evaluative thought showing that whereas self-es-teem is related to overall self-evaluation, self-conceptcoherence determines the dynamic properties of suchthinking (e.g., movement between differentself-evaluative states).

Johnson and Nowak (this issue) examine dynamicalpatterns in the emotions and symptomatology of indi-viduals with bipolar depression. To do so, they use anewly developed technique that identifies the numberand nature of attractors in time series data. In applyingthis method to the monthly self-reports of their partici-pants, they identify several distinct types of attractors,each associated with a set of outcome criteria. Theyshow that the lack of stable attractors is especially dys-functional in that it predicts frequency of hospitaliza-tion and suicidality.

References

Amit, D. J. (1989). Modeling brainfunction: The world ofattractorneural networks. Cambridge, England: Cambridge UniversityPress.

Arrow, H. (1997). Stability, bistability, and instability in small groupinfluence patterns. Journal of Personality and Social Psychol-ogy, 72, 75-85.

Arrow, H., McGrath, J. E., & Berdahl, J. L. (2000). Small groups ascomplex systems. Thousand Oaks, CA: Sage.

Asch, S. E. (1946). Forming impressions of personalities. Journal ofAbnormal and Social Psychology, 41, 258-290.

Axelrod, R. (1984). The evolution of cooperation. New York: BasicBooks.

Axelrod, R., & Cohen, M. D. (2000). Harnessing complexity: Orga-nizational implications ofa scientificfrontier New York: BasicBooks.

Axelrod, R., Riolo, R. L., & Cohen, M. D. (2002). Beyond geogra-phy: Cooperation with persistent links in the absence of clus-tered neighborhoods. Personality and Social Psychology Re-view, 6, 341-346.

Barton, S. (1994). Chaos, self-organization, and psychology. Ameri-can Psychologist, 49, 5-14.

Beek, P. J., & Hopkins, B. (1992). Four requirements for a dynamicalsystems approach to the development of social coordination.Human Movement Science, 11, 425-442.

Brown, K. W., & Moskowitz, D. S. (1998). Dynamic stability of be-havior: The rhythms of our interpersonal lives. Journal ofPer-sonality, 66, 105-134.

Buder, E. H. (1991). A nonlinear dynamic model of social interac-tion. Communication Research, 18, 174-198.

Carver, C. S., & Scheier, M. F. (1999). Themes and issues in theself-regulation of behavior. In R. S. Wyer, Jr. (Ed.), Advances insocial cognition (Vol. 12, pp. 1-105). Mahwah, NJ: LawrenceErlbaum Associates, Inc.

Carver, C. S., & Scheier, M. F. (2002). Control processes and self-or-ganization as complementary principles underlying behavior.Personality and Social Psychology Review, 6, 304-315.

Cervone, D., & Mischel, W. (Eds.) (2002). Advances in personalityscience (Vol. 1). New York: Guilford.

Cooley, C. H. (1902). Human nature and the social order. New York:Scribner.

Davies, P. (1988). The cosmic blueprint: New discoveries in nature'screative ability to order the universe. New York: Simon &Schuster.

Durkheim, E. (1938). The rules of sociological method. Chicago:University of Chicago Press.

Eckmann, J. P., & Ruelle, D. (1985). Ergodic theory of chaos andstrange attractors. Review ofModern Physics, 57, 617-656.

Eiser, J. R. (1994). Attitudes, chaos and the connectionist mind. Ox-ford, England: Blackwell.

Fischer, K. W., & Bidell, T. R. (1997). Dynamic development of psy-chological structures in action and thought. In W. Damon (Se-ries Ed.) & R. Lerner (Volume Ed.), Handbook ofchildpsychol-ogy: Vol. 1. Theoretical models of human development (pp.467-561). New York: Wiley.

Freud, S. (1937). The ego and the mechanisms of defense. London:Hogarth.

Gergen, K. J. (1994). Realities and relationships: Soundings in so-cial construction. Cambridge, MA: Harvard University Press.

Glass, L., & Mackey, M. C. (1988). From clocks to chaos: Therhythms of life. Princeton, NJ: Princeton University Press.

Gleick, J. (1987). Chaos: The making of a new science. New York:Viking-Penguin.

Goldstein, J. (1996). Causality and emergence in chaos and com-plexity theories. In W. Sulis & A. Combs (Eds.), Nonlinear dy-namics and human behavior (pp. 161-190). Singapore: WorldScientific.

Gottman, J. M., Murray, J., Swanson, C., Swanson, K., & Tyson, R.(in press). The mathematics of marriage: Nonlinear dynamicmodels. Cambridge, MA: MIT Press.

Gottman, J., Swanson, C., & Swanson, K. (2002). A general systemstheory of marriage: Nonlinear difference equation modeling ofmarital interactions. Personality and Social Psychology Review,6, 326-340.

Guastello, S. J. (1995). Chaos, catastrophe, andhuman affairs: Appli-cations ofnonlineardynamics to work, organizations, and socialevolution. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Haken, H. (1978). Synergetics. Berlin: Springer.Haken, H. (Ed.) (1982). Order and chaos in physics, chemistry, and

biology. Berlin: Springer.Harrb, R. (Ed.) (1986). The social construction of emotion. Oxford:

Blackwell.Holland, J. H. (1995). Emergence: From chaos to order Reading,

MA: Addison-Wesley.James, W. (1890). Principles ofpsychology. New York: Holt.Johnson, S. L., & Nowak, A. (2002). Dynamical patterns in bipolar

depression. Personality and Social Psychology Review, 6,380-387.

Kaplowitz, S. A., & Fink, E. L. (1992). Dynamics of attitude change.In R. L. Levine & H. E. Fitzgerald (Eds.), Analysis ofdynamicpsychological systems (Vol. 2, pp. 341-369).New York: Plenum.

Kelso, J. A. S. (1995). Dynamic patterns: The self-organization ofbrain and behavior Cambridge, MA: MIT Press.

Kenrick, D. T. (2001). Evolutionary psychology, cognitive science,and dynamical systems: Building an integrative paradigm. Cur-rent Directions in Psychological Science, 10, 13-17.

Kenrick, D. T., Maner, J. K., Butner, J., Li, N. P., & Becker, D. V.(2002). Dynamical evolutionary psychology: Mapping the do-mains of the new interactionist paradigm. Personality and So-cia"l Psychology Review, 6, 347-356.

Kdhler, W. (1947). Gestalt psychology: An introduction to new con-cepts in modern psychology (rev. ed.). New York: Liveright.




Krech, D., & Crutchfield, R. S. (1948). Theory and problems of so-cial psychology. New York: McGraw-Hill.

Kunda, Z., & Thagard, P. (1996). Forming impressions from stereo-types, traits, and behaviors: A parallel-constraint-satisfactiontheory. Psychological Review, 103, 284-308.

Latan6, B., & Nowak, A. (1994). Attitudes as catastrophes: From di-mensions to categories with increasing involvement. In R. R.Vallacher & A. Nowak (Eds.), Dynamical systems in social psy-chology (pp. 219-249). San Diego, CA: Academic.

Levine, R. L., & Fitzgerald, H. E. (Eds.) (1992). Analysis ofdynamicpsychological systems (Vol. 1). New York: Plenum.

Lewin, K. (1936). Principles of topological psychology. New York:McGraw-Hill.

Lewis, M. (1997). Personality self-organization: Cascading con-straints on cognition-emotion interactions. In A. Fogel, M. C.D. P. Lyra, & J. Valsiner (Eds.), Dynamics and indeterminism indevelopmental and social processes (pp. 193-216). Mahwah,NJ: Lawrence Erlbaum Associates, Inc.

Liebrand, W., Nowak, A., & Hegselman, R. (Eds.). (1998). Com-puter modeling and the study ofdynamic social processes. NewYork: Sage.

Lorenz, E. (1963). Deterministic nonperiodic flow. Journal ofAtmo-spheric Science, 20, 282-293.

Losada, M., & Markovitch, S. (1990). Group analyzer: A system fordynamic analysis of group interaction. Proceedings of the 23rdAnnual Hawaii International Conference on System Sciences(pp. 101-1 10). Washington, DC: IEEEComputer SocietyPress.

Mead, G. H. (1934). Mind, self, and society. Chicago: University ofChicago Press.

Messick, D. M., & Liebrand, V. B. G. (1995). Individual heuristicsand the dynamics of cooperation in large groups. PsychologicalReview, 102, 131-145.

Mischel, W., & Shoda, Y. (1995). A cognitive-affective system the-ory of personality: Reconceptualizing situations, dispositions,dynamics, and invariance in personality structure. Psychologi-cal Review, 102, 246-268.

Newtson, D. (1994). The perception and coupling of behaviorwaves. In R. R. Vallacher & A. Nowak (Eds.), Dynamical sys-tems in social psychology (pp. 139-167). San Diego, CA: Ac-ademic.

Nowak, A., Szamrej, J., & Latan6, B. (1990). From private attitude topublic opinion: A dynamic theory of social impact. Psychologi-cal Review, 97, 362-376.

Nowak, A., & Vallacher, R. R. (1998a). Dynamical social psychol-ogy. New York: Guilford.

Nowak, A., & Vallacher, R. R. (1998b). Toward computational socialpsychology: Cellular automata and neural network models of inter-personal dynamics. In S. J. Read & L. C. Miller (Eds.),Connectionist models ofsocial reasoning and social behavior (pp.277-311). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Nowak, A., & Vallacher, R. R. (2001). Societal transition: Toward adynamical model of social change. In W. Wosinska, R. B.Cialdini, D. W. Barrett, & J. Reykowski (Eds.), The practice ofsocial influence in multiple cultures (pp. 151-171). Mahwah,NJ: Lawrence Erlbaum Associates, Inc.

Nowak, A., Vallacher, R. R., & Burnstein, E. (1998). Computationalsocial psychology: A neural network approach to interpersonaldynamics. In W. Liebrand, A. Nowak, & R. Hegselman (Eds.),Computer modeling and the study of dynamic social processes(pp. 97-125). New York: Sage.

Nowak, A., Vallacher, R. R., Tesser, A., & Borkowski, W. (2000).Society of self: The emergence of collective properties inself-structure. Psychological Review, 39, 39-61.

Parker, I., & Shotter, J. (Eds.). (1990). Deconstructing social psy-chology. London: Routledge Kegan Paul.

Port, R. F., & van Gelder, T. (Eds.). (1995). Mind as motion: Explora-tionsin thedynamics ofcognition. Cambridge, MA: MITPress.

Queller, S. (2002). Stereotype change in a recurrent network. Per-sonality and Social Psychology Review, 6, 295-303.

Read, S. J., & Miller, L. C. (Eds.). (1998). Connectionist models ofsocial reasoning and social behavior Mahwah, NJ: LawrenceErlbaum Associates, Inc.

Read, S. J., & Miller, L. C. (2002). Virtual personalities: A neuralnetwork model of personality. Personality and Social Psychol-ogy Review, 6, 357-369.

Read, S. J., & Montoya, J. A. (1999). An autoassociative model ofcausal reasoning and causal learning: Reply to Van Overvalle's(1998) critique ofRead and Marcus-Newhall (1993). Journal ofPersonality and Social Psychology, 76, 728-742.

Read, S. J., Vanman, E. J., & Miller, L. C. (1997). Connectionism,parallel constraint satisfaction processes, and Gestalt princi-ples: (Re)Introducing cognitive dynamics to social psychology.Personality and Social Psychology Review, 1, 26-53.

Ruelle, D., & Takens, F. (1971). On the nature of turbulence. Com-munications in Mathematical Physics, 20, 167-192.

Schuldberg, D., & Gottlieb, J. (2002). Dynamics and correlates ofmicroscopic changes in affect. Nonlinear Dynamics, Psychol-ogy, and the Life Sciences, 6, 231-257.

Schuster, H. G. (1984). Deterministic chaos. Vienna, Switzerland:Physik Verlag.

Shultz, T. R., & Lepper, M. R. (1996). Cognitive dissonance reductionas constraint satisfaction. PsychologicalReview, 103,219-240.

Shoda, Y., LeeTiernan, S., & Mischel, W. (2002). Personality as adynamical system: Emergence of stability and distinctivenessfrom intra and interpersonal relations. Personality and SocialPsychology Review, 6, 316-325.

Simon, D., & Holyoak, K. J. (2002). Structural dynamics of cogni-tion: From consistency theories to constraint satisfaction. Per-sonality and Social Psychology Review, 6, 283-294.

Smith, E. R. (1996). What do connectionism and social psychologyoffer each other? Journal ofPersonality and Social Psychology,70, 893-912.

Tesser, A., & Achee, J. (1994). Aggression, love, conformity, andother social psychological catastrophes. In R. R. Vallacher & A.Nowak (Eds.), Dynamical systems in social psychology (pp.139-167). San Diego, CA: Academic.

Thagard, P., & Nerb, J. (2002). Emotional gestalts: Appraisal,change, and the dynamics of affect. Personality and Social Psy-chology Review, 6, 274-282.

Thelen, E., & Smith, L. B. (Eds.). (1994). A dynamic systems ap-proach to the development of cognition and action. Cambridge,MA: MIT Press/Bradford Books.

Thompson, J. M. T., & Stewart, H. B. (1986). Nonlinear dynamicsand chaos. New York: Wiley.

Turner, R. H., & Killian, L. M. (1957). Collective behaviorEnglewood Cliffs, NJ: Prentice-Hall.

Vallacher, R. R., & Nowak, A. (Eds.). (1994a). Dynamical systems insocial psychology. San Diego, CA: Academic.

Vallacher, R. R., & Nowak, A. (1994b). The chaos in social psychol-ogy. In R. R. Vallacher & A. Nowak (Eds.), Dynamical systemsin socialpsychology (pp. 1-16). San Diego, CA: Academic.

Vallacher, R. R., & Nowak, A. (1997). The emergence of dynamicalsocial psychology. Psychological Inquiry, 8, 73-99.

Vallacher, R. R., & Nowak, A. (2000). Landscapes of self-reflection:Mapping the peaks and valleys of personal assessment. In A.Tesser, R. Felson, & J. Suls (Eds.), Psychological perspectiveson self and identity (pp. 35-65). Washington, DC: AmericanPsychological Association.

Vallacher, R. R., Nowak, A., Froehlich, M., & Rockloff, M. (2002).The dynamics of self-evaluation. Personality and Social Psy-chology Review, 6, 370-379.

Vallacher, R. R., Nowak, A., & Kaufman, J. (1994). Intrinsic dynam-ics of social judgment. Journal of Personality and Social Psy-chology, 67, 20-34.




Vallacher, R. R., Nowak, A., Markus, J., & Strauss, J. (1998). Dynam-ics in the coordination ofmind and action. In M. Kofta, G. Weary,& G. Sedlek (Eds.), Personal control in action: Cognitive andmotivational mechanisms (pp. 27-59). New York: Plenum.

van Geert, P. (1991). A dynamic systems model of cognitive and lan-guage growth. Psychological Review, 98, 3-53.

Weidlich, W. (1991). Physics and social science: The approach ofsynergetics. Physics Reports, 204, 1-163.

Weisbuch, G. (1992). Complex systems dynamics. Redwood City,CA: Addison-Wesley.



Documents

The DynamicalPerspective in Personality and Social Psychologypsy2.fau.edu/~vallacher/pdfs/essays/Vallacher_et_al_(2002)_The... · Inthisarticle, weintroducebasicconceptsand ... Indeed,