用认知约束的贝叶斯模型预测信息误判易感性
A Cognitively Grounded Bayesian Framework for Misinformation Susceptibility
- 基于理性言语行为理论，引入记忆、信息瓶颈和显著性采样三重认知限制
- 在LIAR和MultiFC数据集上达到竞争性真伪分类效果，验证深度不匹配悖论
- 适合研究信息误导机制或认知偏差的学者，尤其关注虚假信息传播
本文提出认知基础的贝叶斯框架Bounded Pragmatic Listener（BPL），用于建模信息混乱中的易受骗性。BPL在理性言语行为理论基础上，引入三个源自有限理性的认知约束：递归深度上限（反映工作记忆限制）、先验压缩参数（体现信息瓶颈）以及可用样本量（通过显著性加权提案实现重要性采样）。该框架可检验对虚假、错误及恶意信息的差异化易感性、标注者分歧等预测。在LIAR与MultiFC基准上验证了其性能，展现出具有竞争力的真伪分类能力，并支持深度不匹配悖论的实验证据。
原文摘要 · Abstract (English)
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.
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