The Virtual Personalities Neural Network Model: Neurobiological Underpinnings

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.

Expressing Personality Through Non-verbal Behaviour in Real-Time Interaction

The attribution of traits plays an important role as a heuristic for how we interact with others. Many psychological models of personality are analytical in that they derive a classification from reported or hypothesised behaviour. In the work presented here, we follow the opposite approach: Our personality model generates behaviour that leads an observer to attribute personality characteristics to the actor. Concretely, the model controls all relevant aspects of non-verbal behaviour such as gaze, facial expression, gesture, and posture. The model, embodied in a virtual human, affords to realistically interact with participants in real-time. Conceptually, our model focuses on the two dimensions of extra/introversion and stability/neuroticism. In the model, personality parameters influence both, the internal affective state as well as the characteristic of the behaviour execution. Importantly, the parameters of the model are based on empirical findings in the behavioural sciences. To evaluate our model, we conducted two types of studies. Firstly, passive experiments where participants rated videos showing variants of behaviour driven by different personality parameter configurations. Secondly, presential experiments where participants interacted with the virtual human, playing rounds of the Rock-Paper-Scissors game. Our results show that the model is effective in conveying the impression of the personality of a virtual character to users. Embodying the model in an artificial social agent capable of real-time interactive behaviour is the only way to move from an analytical to a generative approach to understanding personality, and we believe that this methodology raises a host of novel research questions in the field of personality theory.

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.