A Computational Implementation of a Goal-Directed Theory of Affect

• URL: http://arxiv.org/abs/2609.06654v1
• Date: Sun, 06 Sep 2026 15:08:47 GMT
• Title: A Computational Implementation of a Goal-Directed Theory of Affect
• Abstract summary: This paper presents the first high-fidelity computational implementation of the Goal-Directed Theory (GDT) of affect.<n>In this framework, affect is not a post-hoc label but a functional byproduct emerging from the interplay between discrepancy detection and action selection.<n>Results demonstrate that complex affective profiles, like an anticipatory "lift" and a failure "crash", emerge naturally from simple interactions between goal-discrepancy and action-selection expectancies.
• Score: 1.8499314936771558
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational modeling of emotion has long faced a tension between descriptive, "snapshot-based" appraisal models and granular, signal-driven architectures that often lack appropriate psychological grounding. This paper addresses this gap by presenting the first high-fidelity computational implementation of the Goal-Directed Theory (GDT) of affect. In this framework, affect is not a post-hoc label but a functional byproduct emerging from the continuous interplay between discrepancy detection and action selection within an agent's internal processing cycles. We evaluate the model through a series of principled simulations (Dice/Corridor tasks) designed to isolate affective signatures and dynamics during multi-step goal pursuit. Results demonstrate that complex affective profiles, like an anticipatory "lift" and a failure "crash", emerge naturally from simple interactions between goal-discrepancy and action-selection expectancies without requiring additional dedicated modules. By ensuring every computational component maps directly to components of the psychological theory, this work establishes a transparent, testable framework that enables a continuous "simulation-empiry" research loop. Our work contributes to moving the field beyond "black-box" heuristics toward a granular, mechanistic understanding of affect, integrated into the core of agent behavior.

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