Sampling from Implicit Bayesian Models: Towards a General Framework for Cognitive Modeling

Le résumé fourni par la source

Computational process models are pivotal tools for studying the human mind, offering explicit algorithmic accounts of how human behavior arises. However, they are often siloed across individual tasks and domains, lacking a coherent framework for examining computational principles shared across cognition. The challenges are twofold. First, existing models rely heavily on researcher-defined and task-dependent representations of the stimuli. Second, they typically capture only a limited range of behavioral responses. To address these limitations, we propose a two-component framework. A neural network, trained on ecologically valid ground-truth data, functions as an Implicit Bayesian Model (IBM) that maps complex real-world stimuli directly onto posterior probability distributions. A human-like sampling process, the Autocorrelated Bayesian Sampler (ABS), then samples hypotheses from those posteriors to generate diverse behavioral responses while accounting for systematic deviations from normative predictions. This combination of realistic representations with a general-purpose algorithmic decision process enables cognitive mechanisms to be implemented and transferred across tasks at real-world scale. Through three case studies spanning multiple domains and stimulus modalities, we demonstrate the generality of the IBM-ABS framework in explaining human behavior both qualitatively and quantitatively, and explore its capacity to yield new theoretical insights through incorporating realistic priors and predicting the effects of experimental manipulations.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.