基于推断的意图和可能性饱和决策架构
An Inference-Based Architecture for Intent and Affordance Saturation in Decision-Making
AI总结：
本文提出了一种基于推断的决策架构，通过分离意图和可能性选择，解释了决策瘫痪的机制，并展示了其在自闭症研究中的应用。
摘要：
决策瘫痪，即犹豫、冻结或在充分了解和动机下仍无法采取行动，对假设选项已指定且易于比较的选择模型构成挑战。基于自闭症研究中的定性报告，我们提出了一种计算模型，其中瘫痪源于分层决策过程中的收敛失败。我们将意图选择（追求什么）与可能性选择（如何追求目标）分开，并将承诺形式化为在混合的反向和正向Kullback-Leibler（KL）目标下的推断。反向KL是模式寻求的，促进快速承诺，而正向KL是模式覆盖的，保持多个可能的目标或行动。在静态和动态（漂移扩散）模型中，正向KL偏见的推断会导致缓慢、重尾响应时间以及两种不同的失败模式，即意图饱和和可能性饱和，当值相似时。在多选项任务中的模拟再现了决策惰性与关闭的关键特征，将自闭症视为一般、基于推断的决策连续体的极端情况。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
