A Computational Implementation of a Goal-Directed Theory of Affect

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.