发表机构
Leiden University; KU Leuven; Leiden Institute of Advanced Computer Science; Research Group of Quantitative Psychology and Individual Differences（莱顿大学; 鲁汶大学; 莱顿高级计算机科学研究所; 定量心理学与个体差异研究组）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文首次高保真实现情感目标导向理论，通过模拟实验证明复杂情感特征可自然涌现，建立透明可测试框架，推动情感计算从黑箱走向机制性理解。
AI 中文摘要
情感的计算建模长期以来一直面临描述性、“基于快照”的评估模型与缺乏适当心理学基础的细粒度信号驱动架构之间的张力。本文通过呈现情感目标导向理论（GDT）的首个高保真计算实现来解决这一差距。在该框架中，情感不是事后标签，而是智能体内部处理周期中差异检测与动作选择之间持续相互作用所产生的功能性副产品。我们通过一系列原理性模拟（骰子/走廊任务）来评估该模型，这些模拟旨在隔离多步骤目标追求过程中的情感特征和动态。结果表明，复杂的情感特征，如预期性“提升”和失败“崩溃”，自然地从目标差异与动作选择期望之间的简单相互作用中产生，无需额外的专用模块。通过确保每个计算组件直接映射到心理学理论的组成部分，这项工作建立了一个透明、可测试的框架，实现了持续的“模拟-经验”研究循环。我们的工作有助于推动该领域超越“黑箱”启发式方法，走向对情感的细粒度、机制性理解，并将其整合到智能体行为的核心中。
英文摘要
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.
