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. 2025 Jul 24.
doi: 10.1037/abn0001021. Online ahead of print.

The virtual personality model: Toward a dynamic structured motivational systems framework for understanding personality disorders

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The virtual personality model: Toward a dynamic structured motivational systems framework for understanding personality disorders

Stephen J Read et al. J Psychopathol Clin Sci. .

Abstract

Dissatisfaction with the Diagnostic and Statistical Manual of Mental Disorders (2022) spurred attempts to reconceptualize personality disorders (PDs): But reconciling reconceptualizations is challenging. The Virtual Personality Model (VPM) may fill this gap, affording an integration of more static Hierarchical Taxonomy of Psychopathology and maladaptive trait models with a dynamic, interpersonal approach. VPM is implemented as a "runnable" computational model that treats personality as emergent from dynamic, structured motivational systems with person-specific parameter values (e.g., sensitivities) that, in interaction with situational affordances, can produce individual within-subject variability over time. Viewing dysfunctional motivational systems in social interaction as key, VPM argues that (a) at the broadest level, motivational systems consist of an approach system (governing sensitivity to reward) and an avoidance system (governing sensitivity to punishment/threat). Differential sensitivities may link to particular PDs and traits within Hierarchical Taxonomy of Psychopathology superspectrum (i.e., externalizing, emotional dysregulation). The broad systems each govern specific motives (e.g., dominance), many with epigenetic underpinnings/dysfunctions already tied to PDs. (b) Various social concepts (e.g., traits, situations, events, and emotions) can be viewed as motive/goal-based structures. Analyzing externalizing traits, we illustrate how VPM can illuminate within-person variations in the behavioral expression of these systems. (c) VPM's explicit neurobiological assumptions help address comorbidities, including between depression and anxiety. (d) We discuss implications for theory and how new methods (e.g., systematic representative design [SRD]; Miller et al., 2019) and techniques/technologies (e.g., artificial intelligence, virtual reality, and sensors) for capturing the dynamics of PDs may advance diagnosis and therapy using VPM to "model to the case." (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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