Virtual Personalities: Using Computational Modeling to Understand Within-Person Variability

A longitudinal approach to disentangle how conscientiousness creates happy people: The mediating role of self-leadership and the moderating role of perceived leadership effectiveness

This study relied on the conservation of resources model to explore the interaction between individual differences (conscientiousness and behavior-focused self-leadership) and contextual factors (perceived leadership effectiveness) to predict well-being. Using results from a three-wave longitudinal study of working adults (N = 107*3 = 321, mean age = 46.05 years, 54% male), we examined: (1) the indirect effect of conscientiousness on well-being via behavior-focused self-leadership; and (2) the moderating role of perceived leadership effectiveness on the indirect effect. The multilevel results showed that conscientiousness influenced well-being through behavior-focused self-leadership over time. The results also showed that the indirect effect was moderated by perceived leadership effectiveness, in such a way that it became stronger when individuals had leaders perceived as less effective (versus more effective). Thus, behavior-focused self-leadership seems to be a process through which conscientiousness influences well-being; when conscientiousness was lower there was an increase behavior-focused self-leadership when the leader was perceived as effective; this contextual need decreased as conscientiousness increased. That is, it seems that when there is something external regulating the individual, s/he feels less need to self-regulate. The results highlight the role of personal (conscientiousness), cognitive (behavior-focused self-leadership) and contextual resources (perceived leadership effectiveness) for well-being.

Behavioral regulation relies on interacting forces and predictive models

OBJECTIVE

We discuss how our recent neural network model of personality and motivation can explain many aspects of the regulation of behavior.

METHOD

Contrary to approaches that focus on a goal-corrected, set-point, and discrepancy-reducing mechanism, we argue that many aspects of regulation can be understood in terms of two other mechanisms. First, many aspects of the stability and coherence of personality, as well as the dynamics of personality, can be understood in terms of the interaction of forces within organized motivational systems, and their interaction with features of the environment and interoceptive states, that identify an individual's current needs. This has been described as a settling point or equilibrium of forces model, rather than a set-point architecture. Second, regulation has been shown to depend also on the use of predictive models of the world, either learned or innate. Such models can be thought of as feedforward models, in contrast to the feedback models characteristic of set-point, goal-corrected systems.

RESULTS AND CONCLUSIONS

We describe a neural network model of these processes that simulates the behavior over time and situations of an individual and shows how important regulatory processes can operate through a process of interactive forces and predictive models of the world.

A general motivational architecture for human and animal personality

To achieve integration in the study of personality, researchers need to model the motivational processes that give rise to stable individual differences in behavior, cognition, and emotion. The missing link in current approaches is a motivational architecture-a description of the core set of mechanisms that underlie motivation, plus a functional account of their operating logic and inter-relations. This paper presents the initial version of such an architecture, the General Architecture of Motivation (GAM). The GAM offers a common language for individual differences in humans and other animals, and a conceptual toolkit for building species-specific models of personality. The paper describes the main components of the GAM and their interplay, and examines the contribution of these components to the emergence of individual differences. The final section discusses how the GAM can be used to construct explicit functional models of personality, and presents a roadmap for future research.

Dynamics of personality: The Zurich model of motivation revived, extended, and applied to personality

Personality researchers are increasingly interested in the dynamics of personality, that is, the proximal causal mechanisms underlying personality and behavior. Here, we review the Zurich Model of Social Motivation concerning its potential to explain central aspects of personality. It is a cybernetic model that provides a nomothetic structure of the causal relationships among needs for security, arousal, and power, and uses them to explain an individual's approach-avoidance or "proximity-distance" behavior. We review core features of the model and extend them by adding features based on recent behavioral and neuroscientific evidence. We close by discussing the model considering contemporary issues in personality science such as the dynamics of personality, five-factor personality traits and states, and personality growth.

Psychometric Evaluation of a Big Five Personality State Scale for Intensive Longitudinal Studies

Despite enthusiasm for using intensive longitudinal designs to measure day-to-day manifestations of personality underlying differences between people, the validity of personality state scales has yet to be established. In this study, we evaluated the psychometrics of 20-item and 10-item daily, Big Five personality state scales in three independent samples (N = 1,041). We used multilevel models to separately examine the validity of the scales for assessing personality variation at the between- and within-person levels. Results showed that a five-factor structure at both levels fits the data well, the scales had good convergent and discriminative associations with external variables, and personality states captured similar nomological nets as established global, self-report personality inventories. Limitations of the scales were identified (e.g., low reliability, low correlations with external criterion) that point to a need for more, systematic psychometric work. Our findings provide initial support for the use of personality state scales in intensive longitudinal designs to study between-person traits, within-person processes, and their interrelationship.

Modeling incentive salience in Pavlovian learning more parsimoniously using a multiple attribute model

We present a multi-attribute incentive salience (MAIS) model as a computational account of incentive salience in model-based Pavlovian learning. A model of incentive salience as a joint function of reward value and physiological state has been previously proposed by Zhang et al. (2009). In that model, the function takes additive or multiplicative forms depending on whether a preference shifts from positive to negative or vice versa. We demonstrate that arbitrarily varying this function is unnecessary to explain observed data. A multiplicative function is sufficient if one takes into account empirical data suggesting the incentive salience function for an incentive is comprised of multiple physiological signals. We compare our model to the previously proposed model on two datasets. We find the MAIS model predicts the outcomes equally well, fits empirical data describing multiple sensory representations of a single stimulus, better approximates the dual-structure appetitive-aversive nature of the reward system, is compatible with existing knowledge about incentive salience in Pavlovian learning, and better describes revaluation in Pavlovian learning. This model addresses a call (Dayan & Berridge, 2014) for algorithmic and computational models of model-based Pavlovian learning that consistently and fully explain empirical observations. Because a multi-attribute model is relevant even for simple Pavlovian associations, it should be useful in a wide variety of decision-making contexts, including agent modeling and addiction research.

Psychometric properties of the Polish version of the Self-Pluralism Scale (SPS)

BACKGROUND

The Self-Pluralism Scale (SPS) measures the declared degree of self-pluralism, visible already in William James's works. Self-pluralism refers to the degree to which one perceives oneself as typically feeling, behaving, and being different, in different situations, and at different times. The purpose of the current study was to evaluate the psychometric properties of the Polish version of the SPS.

PARTICIPANTS AND PROCEDURE

A total of 1747 participants (67% were women) between the ages of 15 and 70 years completed the SPS along with measures of self-concept inconsistency, self-concept differentiation, dissociative experiences, internal dialogical activity, personality, and social desirability.

RESULTS

Internal reliability and test-retest reliability were high. The full version has too low indices of fit whereas the brief, 10-item version fits the data well. As indicators of the convergent validity, a positive correlation of SPS with self-concept inconsistency, self-concept differentiation, dissociative experiences, internal dialogical activity and neuroticism and a negative correlation with agreeableness and social desirability were found.

CONCLUSIONS

The results suggest that the brief, 10-item version is more valid than the full, 30-item version. The tool may be used for scientific research concerning self-pluralism. After collecting data from a sample that would allow norms to be constructed, the tool may also be useful for individual diagnosis.

The dynamics, processes, mechanisms, and functioning of personality: An overview of the field

Personality psychology has long focused on structural trait models, but it can also offer a rich understanding of the dynamics, processes, mechanisms, and functioning of individual differences or entire persons. The field of personality dynamics, which works towards such an understanding, has experienced a renaissance in the last two decades. This review article seeks to act as a primer of that field. It covers its historical roots, summarizes current research strands - along with their theoretical backbones and methodologies - in an accessible way, and sketches some considerations for the future. In doing so, we introduce relevant concepts, give an overview of different topics and phenomena subsumed under the broad umbrella term 'dynamics', and highlight the interdisciplinarity as well as applied relevance of the field. We hope this article can serve as a useful overview for scholars within and outside of personality psychology who are interested in the dynamic nature of human behaviour and experience.

The Dynamics of Personality Approach (DPA): 20 Tenets for Uncovering the Causal Mechanisms of Personality

Over the last few decades, most personality psychology research has been focused on assessing personality via scores on a few broad traits and investigating how these scores predict various behaviours and outcomes. This approach does not seek to explain the causal mechanisms underlying human personality and thus falls short of explaining the proximal sources of traits as well as the variation of individuals’ behaviour over time and across situations. On the basis of the commonalities shared by influential process–oriented personality theories and models, we describe a general dynamics of personality approach (DPA). The DPA relies heavily on theoretical principles applicable to complex adaptive systems that self–regulate via feedback mechanisms, and it parses the sources of personality in terms of various psychological functions relevant in different phases of self–regulation. Thus, we consider personality to be rooted in individual differences in various cognitive, emotional–motivational, and volitional functions, as well as their causal interactions. In this article, we lay out 20 tenets for the DPA that may serve as a guideline for integrative research in personality science. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology

Performance anxiety and the plasticity of emotional responses

Current psychological theories of performance anxiety focus heavily on relating performers' physiological and mental states to their abilities to maintain focus and execute learned skills. How task-specific expertise and past experiences moderate the degree to which individuals become anxious in a given performance context are not well accounted for within these theories. This review considers how individual differences arising from learning may shape the psychobiological, emotional, and cognitive processes that modulate anxious states associated with the performance of highly trained skills. Current approaches to understanding performance anxiety are presented, followed by a critique of these approaches. A connectionist model is proposed as an alternative approach to characterising performance anxiety by viewing performers' anxious states at a specific time point as jointly determined by experience-dependent plasticity, competition between motivational systems, and ongoing cognitive and somatic states. Clarifying how experience-dependent plasticity contributes to the emergence of socio-evaluative anxiety in challenging situations can not only help performers avoid developing maladaptive emotional responses, but may also provide new clues about how memories of past events and imagined future states interact with motivational processes to drive changes in emotional states and cognitive processing.

Causal Inference in Generalizable Environments: Systematic Representative Design

Causal inference and generalizability both matter. Historically, systematic designs emphasize causal inference, while representative designs focus on generalizability. Here, we suggest a transformative synthesis - Systematic Representative Design (SRD) - concurrently enhancing both causal inference and "built-in" generalizability by leveraging today's intelligent agent, virtual environments, and other technologies. In SRD, a "default control group" (DCG) can be created in a virtual environment by representatively sampling from real-world situations. Experimental groups can be built with systematic manipulations onto the DCG base. Applying systematic design features (e.g., random assignment to DCG versus experimental groups) in SRD affords valid causal inferences. After explicating the proposed SRD synthesis, we delineate how the approach concurrently advances generalizability and robustness, cause-effect inference and precision science, a computationally-enabled cumulative psychological science supporting both "bigger theory" and concrete implementations grappling with tough questions (e.g., what is context?) and affording rapidly-scalable interventions for real-world problems.

Using Neural Networks as Models of Personality Process: A Tutorial

This paper presents a tutorial for creating neural network models of personality processes. Such models enable researchers to create explicit models of both personality structure and personality dynamics, and to address issues of recent concern in personality, such as, "If personality is stable, then how is it possible that within subject variability in personality states can be as large as or larger than between subject variability in personality?" or "Is it possible to understand personality dynamics and personality structure within a common framework?" We discuss why one should want to use neural networks, review what a neural network model is, review a previous model we have constructed, discuss how to conceptualize issues in such a way that they can be computationally modeled, show how that conceptualization can be translated into a model, and discuss the utility of such models for understanding personality structure and personality dynamics. To build our model we use a neural network modeling package called emergent that is freely available, and a specific architecture called Leabra to build a runnable model that addresses one of the questions posed above: How can within subject variability in personality related states be as large as between subject variability in personality?

The Big Five, Everyday Contexts and Activities, and Affective Experience

Prior research shows that personality traits predict time spent with different people and frequency of engagement in different activities. Further, personality traits, company, and activity are related to the experience of affect. However, little research has examined personality, context, and affect together in the same study. In the current study, 78 people described their Big Five traits and took part in a 1-week experience sampling study using mobile phones as a means for data collection. Participants indicated their current company, activity, and momentary affect along the dimensions of energetic arousal (EA), tense arousal (TA), and hedonic tone (HT). Poisson regressions revealed that traits predicted higher frequencies of trait-consistent contexts: for example, extraversion was related to more frequently being with various types of company. Results predicting contexts from multilevel logistic regressions were sparser. Multilevel models revealed that traits and contexts had main effects on affect, yet there were relatively few interactions of traits X contexts predicting affect. We discuss more specific implications of these findings.

Personality Dynamics, Motivation, and the Logic of Explanation

Dynamic personality approaches provide an important step forward for twenty-first century personality theories because they promise greater explanatory power compared with latent trait approaches. Nevertheless, whether dynamic personality theories satisfactorily address motivated action remains unclear. To address this, this article discusses the logic of explanation and problems with latent trait approaches in terms of circularity and reification. The article then assesses explanation within dynamic personality accounts and the putative role of motivation. While dynamic personality approaches avoid many of the problems associated with latent trait accounts, a satisfactory account of motivational systems and “human nature” is currently missing. Suggestions for addressing the dynamics of human nature in terms of criteria for motivational systems are discussed. Attachment theory is offered as one possible foundation for addressing the motivational dynamics of personality.

The Virtual Personalities Neural Network Model: Neurobiological Underpinnings

The Virtual Personalities Model is a motive-based neural network model that provides both a psychological model and a computational implementation that explicates the dynamics and often large within-person variability in behavior that arises over time. At the same time the same model can produce -- across many virtual personalities - between subject variability in behavior that when factor analyzed yields familiar personality structure (e.g., the Big-5). First, we describe our personality model and its implementation as a neural network model. Second, we focus on detailing the neurobiological underpinnings of this model. Third, we examine the learning mechanisms, and their biological substrates, as ways that the model gets "wired up", discussing Pavlovian and instrumental conditioning, Pavlovian to instrumental transfer (PIT), and habits. Finally, we describe the dynamics of how initial differences in propensities (e.g., dopamine functioning), wiring differences due to experience, and other factors could operate together to develop and change personality over time, and how this might be empirically examined. Thus, our goal is to contribute to the rising chorus of voices seeking a more precise neurobiologically-based science of the complex dynamics underlying personality.