用推理机制解释决策瘫痪，揭示动机充足却不动的原因。
An Inference-Based Architecture for Intent and Affordance Saturation in Decision-Making
- 将目标选择与行动方式分离，通过混合反向与正向KL散度建模决策过程。
- 当选项价值相近时，出现意图饱和和动作饱和两种瘫痪模式，反应时间呈长尾分布。
- 该模型可模拟自闭症等极端情况，适用于研究决策障碍与神经多样性。
决策瘫痪，即在充分知情和有动机的情况下仍出现犹豫、停滞或无法行动，对传统假设选项已明确且可直接比较的决策模型构成挑战。基于自闭症研究中的定性报告，我们提出一种计算模型：瘫痪源于层级决策过程中收敛失败。将意图选择（追求什么）与可用性选择（如何实现）分离，并将承诺形式化为在反向与正向Kullback-Leibler（KL）目标混合下的推理。反向KL具有寻模特性，促进快速承诺；正向KL具有覆盖特性，保留多个可能的目标或行为。在静态和动态（漂移扩散）模型中，正向KL偏向的推理导致缓慢、长尾分布的反应时间，并在价值相近时表现出两种明显失效模式：意图饱和与可用性饱和。多选项任务的模拟重现了决策惯性与系统停机的关键特征，将自闭症视为一个普遍、基于推理的决策连续体中的极端状态。
原文摘要 · Abstract (English)
Decision paralysis, i.e. hesitation, freezing, or failure to act despite full knowledge and motivation, poses a challenge for choice models that assume options are already specified and readily comparable. Drawing on qualitative reports in autism research that are especially salient, we propose a computational account in which paralysis arises from convergence failure in a hierarchical decision process. We separate intent selection (what to pursue) from affordance selection (how to pursue the goal) and formalize commitment as inference under a mixture of reverse- and forward-Kullback-Leibler (KL) objectives. Reverse KL is mode-seeking and promotes rapid commitment, whereas forward KL is mode-covering and preserves multiple plausible goals or actions. In static and dynamic (drift-diffusion) models, forward-KL-biased inference yields slow, heavy-tailed response times and two distinct failure modes, intent saturation and affordance saturation, when values are similar. Simulations in multi-option tasks reproduce key features of decision inertia and shutdown, treating autism as an extreme regime of a general, inference-based, decision-making continuum.
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