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New computational model implements Goal-Directed Theory of affect

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a computational model that implements the Goal-Directed Theory (GDT) of affect, aiming to bridge the gap between descriptive appraisal models and signal-driven architectures in emotion modeling. This new framework posits that affect arises from the continuous interaction between discrepancy detection and action selection within an agent's processing. The model was evaluated using Dice/Corridor tasks, demonstrating the emergence of affective signatures like anticipatory "lift" and failure "crash" from simple goal-discrepancy and action-selection expectancies. AI

IMPACT Provides a mechanistic understanding of affect integrated into agent behavior, potentially influencing future AI development in emotional intelligence.

RANK_REASON Academic paper detailing a new computational model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New computational model implements Goal-Directed Theory of affect

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Academic paper detailing a new computational model. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Bernhard Hilpert, Tam\'as Sz\H{u}cs, Joost Broekens, Agnes Moors ·

    A Computational Implementation of a Goal-Directed Theory of Affect

    arXiv:2609.06654v1 Announce Type: new 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 th…

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