Computational Social Psychology

Robin R. Vallacher, Stephen J. Read, Andrzej Nowak, Robin R. Vallacher, Stephen J. Read, Andrzej Nowak

• Publisher: Routledge
• Year: 2017
• Print ISBN: 9781138951648
• eBook ISBN: 9781351701679
• Topic: Psychology
• Subtopic: Computer Science General
• Index: Psychology

Computational Social Psychology showcases a new approach to social psychology that enables theorists and researchers to specify social psychological processes in terms of formal rules that can be implemented and tested using the power of high speed computing technology and sophisticated software. This approach allows for previously infeasible investigations of the multi-dimensional nature of human experience as it unfolds in accordance with different temporal patterns on different timescales. In effect, the computational approach represents a rediscovery of the themes and ambitions that launched the field over a century ago.

The book brings together social psychologists with varying topical interests who are taking the lead in this redirection of the field. Many present formal models that are implemented in computer simulations to test basic assumptions and investigate the emergence of higher-order properties; others develop models to fit the real-time evolution of people's inner states, overt behavior, and social interactions. Collectively, the contributions illustrate how the methods and tools of the computational approach can investigate, and transform, the diverse landscape of social psychology.

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PART I

Intrapersonal Dynamics

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2

VIRTUAL PERSONALITIES

A Neural Network Model of the Structure and Dynamics of Personality

Stephen J. Read, Vita Droutman, and Lynn C. Miller

There is a long-standing split in personality between structural and dynamic approaches to understanding personality (for recent discussions, see Funder, 2001; Mischel & Shoda, 1998). These two approaches typically rely on different theories and constructs. The structural approach typically focuses on determining the psychometric structure of personality tests (e.g., Lee & Ashton, 2004; McCrae & Costa, 1999) or the everyday language used to describe personality (Digman, 1997; Goldberg, 1981; John & Srivastava, 1999), and a major (although not the only) focus is on understanding stable personality dispositions. In contrast, dynamic approaches, such as Mischel’s Cognitive-Affective Systems theory (Mischel & Shoda, 1995, 2008), Atkinson’s Dynamics of Action model (Atkinson & Birch, 1970) or McAdams’ narrative approach to personality (McAdams, 2008; McAdams, Josselson, & Lieblich, 2006) typically focus on uncovering and understanding the psychological mechanisms that underlie individual differences and changes in behavior across situations and time, and are less interested in understanding stable, broad dispositions. Although some personality researchers have suggested that there is no need to try to bring these two approaches together, many researchers have become increasingly concerned with doing so.

In the current chapter we argue that modeling personality in terms of the behavior of organized motivational systems can help unify the two approaches. We present a neural network model of personality in terms of structured motivational systems (Read et al., 2010; Read & Miller, 2002) that both demonstrates how the structure of personality, such as the Big Five, can arise from these motivational systems and how the dynamics of everyday behavior can arise from the interaction between these structured motivational systems and the changing affordances of situations over time and the bodily states of individuals. Our goal is to show how personality structure and personality dynamics can arise from the same psychological architecture in interactions with the social and physical environment.

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People differ considerably in their behavior and how they respond to the same situations. Personality psychologists who take the psychometric approach to personality often try to understand these individual differences by asking people to respond to items on a personality test, which typically ask people to characterize themselves in terms of typical behaviors, beliefs, emotions, and sometimes motivation. The response to these items across a large sample of individuals is factor analyzed, and the result is typically something like the Big Five (John, Naumann, & Soto, 2008; John & Srivastava, 1999) or similar structures such as the six-factor HEXACO model (Ashton & Lee, 2007; Lee & Ashton, 2004), which essentially adds an Honesty/Humility factor to the Big Five. The Big Five (often referred to by the acronym OCEAN) consists of five major dimensions of human personality: Openness to experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism.

Differences between individuals can then be considered in terms of different individuals’ relative standing on each of the Big Five dimensions. Each individual can be thought of as having a relative profile across each dimension that describes their standing relative to others on the various dimensions.

But something is typically missing in the various structural approaches to personality: an explanation of why this particular structure is found and what are the underlying mechanisms or processes. We have argued that this particular structure is the result of underlying structured motivational systems.

Our neural network model of personality (Read et al., 2010; see also Read & Miller, 1989) argues that personality traits, and specifically the Big Five, arise from the interaction between structured motivational systems and the goal affordances of situations. We have argued that a number of relatively specific brain systems manage a variety of different motivational domains and their related behavior, and that these specific systems are then organized into two higher-level Approach and Avoidance systems that integrate over the lower-level systems. The structure of these motivational systems is responsible for the structure of human personality. But additionally, the dynamics of personality can be understood in terms of the ways in which these structured motivational systems interact with the affordances of different situations that the individual encounters and with the individual’s current bodily state.

As noted above, personality researchers have largely addressed the structure and dynamics of personality independently. However, researchers have become increasingly interested in how those two important approaches can be related. Here we use our neural network model of personality to show how the structure and dynamics of personality can arise from the dynamics of structured motivational systems.

In the first section of the chapter, we demonstrate how such structured motivational systems can transform variability in chronic motivations across individuals into patterns of behavior from which we can recover the Big Five. We will do this by constructing a neural network model of a set of structured motivational systems, and then training the network to associate different clusters of behavior with different underlying motives. This network will then be used to simulate the behavior of a large number of different individuals as they respond to a variety of different situations. We will then factor analyze the resulting behavior. The result of this factor analysis will map onto the underlying motivational structures in the model.

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In the second section of the chapter, we will show how both within- and between-person variability in personality-related behavior can be understood in terms of the dynamics of the interaction between individual’s motives, the affordances of situations, and current bodily states. In a series of recent experience-sampling studies of everyday behavior, Fleeson has shown that while there are clear individual differences, consistent with the Big Five, in trends in personality related behavior over time, at the same time the within person variability over situations is at least as large as the between person differences (Fleeson, 2012; Fleeson & Jayawickreme, 2015).

In the final section of the chapter, we will discuss the implications of our computational model for thinking about person–situation interactions.

General Theoretical Background

The Virtual Personalities model draws upon a wide range of research from personality, neuroscience, developmental psychology, and evolutionary approaches to human behavior that have helped us identify the basic motivational structures that underlie human behavior. Here we briefly review some of the relevant research.

The Big Five Structure

A large body of research examining the structure of trait scales (e.g., Lee & Ashton, 2004; McCrae & Costa, 1999; Wiggins & Trapnell, 1996) and another body of research that has examined the structure of trait language used in everyday language (e.g., Digman, 1997; Goldberg, 1981; Peabody & De Raad, 2002; Saucier & Ostendorf, 1999) has demonstrated that the overall structure of human personality can be described in terms of five broad factors, typically referred to as the Big Five, or OCEAN, as explained above. Other research, such as HEXACO (Ashton & Lee, 2007; Lee & Ashton, 2004), has argued for six factors, most of which largely overlap with the Big Five.

Traits as Goal-Based Structures

We and others have argued for a Motive/Goal-based model of traits: the central idea is that personality traits can be viewed as configurations of chronic goals, plans, resources, beliefs, and styles (e.g., Fleeson, 2012; Fleeson & Jayawickreme, 2015; Miller & Read, 1987; Read & Miller, 1989; Yang et al., 2014). Probably most central is the role of motives and goals: at the center of most traits is something people want or something they want to avoid.

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Evolutionary Tasks

A number of researchers (e.g., Bugental, 2000; Kenrick & Trost, 1997) have noted that there are a wide range of problems and tasks that all humans must solve if they are to survive and successfully reproduce, and they have identified a number of motivational systems that have evolved to handle these tasks (e.g., Maslow, 1943; Murray, 1938). Among the various tasks that must be solved and the relevant system are mating, nurturance of young, affiliation and bonding with peers, establishing dominance hierarchies, insuring attachment to caregivers, avoidance of social rejection, and avoidance of physical harm. Over the last 15 years we have developed two extensive motive taxonomies from which we draw in our work (Chulef, Read, & Walsh, 2001; Talevich, Read, Walsh, Chopra, & Iyer, 2015).

Biological Temperament

In addition, work from neuroscience and work on biological temperament has demonstrated that there are at least three major biologically based dimensions of personality or temperament that map onto three dimensions of the Big Five: Extraversion, Neuroticism, and Conscientiousness. Each of these three dimensions corresponds to a major aspect of motivation. A number of researchers have argued that there is a general Approach (Clark & Watson, 1999; Rothbart & Bates, 1998) or Behavioral Approach System (BAS) (Gray, 1987; Gray & McNaughton, 2000) that governs sensitivity to cues signaling rewards and, when activated, results in an active approach. This maps onto Extraversion. There is considerable evidence that the neurotransmitter dopamine plays a major role in this Approach or Reward system.

In addition, there is a general Avoidance or Behavioral Inhibition System (BIS) (Carver & White, 1994; Clark & Watson, 1999; Gray, 1987; Gray & McNaughton, 2000) that governs sensitivity to cues of punishment and loss and manages avoidance of threatening situations. This maps onto Neuroticism. A third broad dimension concerns a General Disinhibition/Constraint System (Derryberry & Rothbart, 1997; Rothbart & Bates, 1998) which is related to executive function and inhibitory control. This probably maps onto Conscientiousness.

Overview of the Simulations and Their Implications

The section below on “The Dynamics of Motivation” details how we go from a structured motivational model to how resulting motivational dynamics across hundreds of virtual personalities produce emergent outcomes resembling the Big Five.

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The section on “Within-Person versus Between-Person Variability” describes a simulation of how our neural network model captures motivational dynamics and changes in behavior over time.

The section on “Person–Situation Interactions” discusses the implications of this computational model for thinking about person–situation interactions.

Overview of Neural Networks

A neural network model consists of nodes and weighted links between them. Processing in the network proceeds by the passing of activation among nodes along these weighted links. The activation of a node is a function of the inputs it receives from the other nodes with which it is linked. Typically, the input is a weighted average (or sum) of the activations from other nodes, where the activations are weighted by the strength of the link:

• Nodes—These represent various features or concepts, analogous to neurons or systems of neurons. Their level of activation represents their importance and their readiness to fire and send activation to other nodes with which they are linked.

• Layers—No...

Table of contents

• Cover Page
• Computational Social Psychology
• Series Page
• Title Page
• Copyright Page
• Table of Contents
• List of Contributors
• Preface
• Acknowledgments
• Overview
• Part I Intrapersonal Dynamics
• Part II Interpersonal Dynamics
• Part III Collective Dynamics
• Part IV Transforming Social Psychology
• Index