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.

翻译：决策瘫痪，即在拥有充分认知与动机的情况下仍出现犹豫、僵滞或行动失败的现象，对传统选择模型提出了挑战，因为这类模型默认选项已被明确指定且可直接比较。借鉴自闭症研究中尤为突出的质性报告，我们提出一种计算性解释：瘫痪源于层级决策过程中的收敛失败。我们将意图选择（追求什么）与可供性选择（如何达成目标）相分离，并将决策承诺形式化为在反向与正向Kullback-Leibler（KL）目标混合下的推理过程。反向KL具有模态聚焦特性，能促进快速承诺；而正向KL具有模态覆盖特性，可保留多个可能的目标或行动。在静态与动态（漂移扩散）模型中，当选项价值相近时，偏向正向KL的推理会产生缓慢、重尾的响应时间，并引发两种不同的失效模式：意图饱和与可供性饱和。在多选项任务中的仿真实验复现了决策惰性与行为中止的关键特征，从而将自闭症视为一种基于推理的通用决策连续统的极端状态。