🤖 AI Summary
This work addresses the susceptibility of human judgment and annotator disagreement in discerning false, misleading, and malicious information by proposing the Bounded Pragmatic Listener (BPL) model. Integrating Rational Speech Act theory with three cognitive constraints—working memory limitations, information bottlenecks, and salience-based sampling—within a Bayesian framework, BPL formally characterizes the “depth-mismatch paradox,” elucidating distinct mechanisms underlying cognitive vulnerability across types of informational disorder. By incorporating cognitive heuristics such as bounded recursion depth, prior compression, and availability-based sample size, the model achieves competitive performance on truthfulness classification benchmarks LIAR and MultiFC, while providing empirical support for the proposed paradox.
📝 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.