An Inference-Based Architecture for Intent and Affordance Saturation in Decision-Making

• URL: http://arxiv.org/abs/2512.23144v1
• Date: Mon, 29 Dec 2025 02:13:34 GMT
• Title: An Inference-Based Architecture for Intent and Affordance Saturation in Decision-Making
• Authors: Wendyam Eric Lionel Ilboudo, Saori C Tanaka,
• Abstract summary: Decision paralysis, i.e. hesitation, freezing, or failure to act despite full knowledge and motivation, poses a challenge for choice models.<n>We propose a computational account in which paralysis arises from convergence failure in a hierarchical decision process.
• Score: 0.7734726150561086
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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