An Inference-Based Architecture for Intent and Affordance Saturation in Decision-Making
This addresses decision-making challenges, particularly in autism research, by offering a novel computational model to explain paralysis, but it is incremental as it builds on existing inference-based frameworks.
The paper tackled decision paralysis by proposing a computational account where paralysis arises from convergence failure in hierarchical decision processes, separating intent and affordance selection and using a mixture of reverse- and forward-KL objectives. Simulations reproduced key features like slow response times and failure modes, treating autism as an extreme regime of this continuum.
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.