English

A Cognitively Grounded Bayesian Framework for Misinformation Susceptibility

Computation and Language 2026-05-12 v1 Artificial Intelligence Machine Learning

Abstract

In this (work in progress) paper, we present Bounded Pragmatic Listener (or BPL), a cognitively grounded Bayesian framework for modelling susceptibility to information disorder. BPL extends Rational Speech Act theory with three cognitively motivated bounds derived from the bounded rationality literature with a) a recursion depth bound (that emphasises working memory limits);b) a prior compression parameter (which is oriented at capturing information bottleneck); and c) an availability sample size (that operationalises importance sampling with saliency-weighted proposals). This allows us to test predictions about misinformation susceptibility, annotator disagreement, and the differential vulnerability to mis-, dis-, and mal-information as defined in the Information Disorder framework. We validate BPL on the LIAR and MultiFC benchmarks showcasing competitive veracity classification and experimental support for the depth-mismatch paradox.

Keywords

Cite

@article{arxiv.2605.09483,
  title  = {A Cognitively Grounded Bayesian Framework for Misinformation Susceptibility},
  author = {Pranava Madhyastha},
  journal= {arXiv preprint arXiv:2605.09483},
  year   = {2026}
}

Comments

work in progress

R2 v1 2026-07-01T13:01:39.949Z