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arxiv: 2605.09483 · v1 · submitted 2026-05-10 · 💻 cs.CL · cs.AI· cs.LG

A Cognitively Grounded Bayesian Framework for Misinformation Susceptibility

Pranava Madhyastha This is my paper

Pith reviewed 2026-05-12 02:46 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords misinformation susceptibilityBayesian modelingcognitive boundsinformation disorderveracity classificationannotator disagreementpragmatic reasoning
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The pith

A cognitively bounded Bayesian model accounts for susceptibility to misinformation by limiting reasoning depth, knowledge compression, and information sampling.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a framework that takes standard models of how speakers and listeners reason about meaning and adds three practical limits that match how human minds actually function under constraints. These limits are a cap on the number of reasoning steps, a compression of prior knowledge, and a restriction on the number of examples considered during judgment. The resulting model generates predictions about which people are more likely to accept different forms of false information, why labelers disagree, and how vulnerability varies across information types. Validation on standard datasets shows it performs well at classifying truthfulness while also aligning with observed patterns like the depth-mismatch paradox.

Core claim

By incorporating bounds on recursion depth from working memory, prior compression from information bottlenecks, and availability sample size from importance sampling, the Bounded Pragmatic Listener model provides a way to derive predictions about misinformation processing that are grounded in cognitive mechanisms rather than fitted post hoc to data.

What carries the argument

The Bounded Pragmatic Listener, a Bayesian model extending speaker-listener reasoning frameworks with three cognitively motivated bounds on depth, compression, and sampling.

If this is right

  • The framework enables direct tests of how cognitive limits influence susceptibility to different categories of false information.
  • It predicts and explains disagreement among annotators when labeling statement veracity.
  • Applied to benchmark datasets, the model achieves competitive accuracy in determining the truth value of claims.
  • It offers support for the depth-mismatch paradox through its experimental results.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Individual differences in the model's parameters could be measured in people and used to forecast their personal risk of accepting misleading content.
  • The same structure might apply to modeling belief in other domains such as scientific claims or political statements.
  • Designing communication strategies that respect these bounds could reduce the spread of false information more effectively than generic approaches.

Load-bearing premise

These three specific bounds drawn from cognitive psychology correctly represent the constraints that shape how humans process and decide on the credibility of information.

What would settle it

If experiments measuring people's working memory capacity, information processing efficiency, and sampling behavior fail to align with the model's predicted differences in misinformation acceptance, the framework's grounding in cognitive mechanisms would not hold.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces the Bounded Pragmatic Listener (BPL), an extension of Rational Speech Act theory that incorporates three cognitively motivated bounds drawn from bounded-rationality literature: a recursion depth bound reflecting working memory limits, a prior compression parameter capturing information bottleneck effects, and an availability sample size operationalizing saliency-weighted importance sampling. The framework is positioned to generate predictions about misinformation susceptibility, annotator disagreement, and differential vulnerability to mis-, dis-, and mal-information (per the Information Disorder framework). Validation is reported on the LIAR and MultiFC benchmarks, with claims of competitive veracity classification performance and experimental support for the depth-mismatch paradox.

Significance. If the three bounds can be fixed a priori from cognitive constraints without post-hoc adjustment to benchmark labels, and if the resulting predictions about susceptibility patterns and the depth-mismatch paradox prove independent of fitting, the work would offer a novel bridge between pragmatic modeling and cognitive science. Competitive benchmark results plus falsifiable predictions on annotator disagreement would strengthen the case for cognitively grounded extensions of RSA in misinformation research.

major comments (2)
  1. [Abstract] Abstract: The validation claims ('competitive veracity classification' and 'experimental support for the depth-mismatch paradox') are stated without any reported metrics, baselines, error bars, parameter values, or ablation results. Because the central claim rests on these benchmark outcomes demonstrating that the cognitively motivated bounds yield independent predictions, the absence of these details prevents evaluation of whether the reported effects follow from the model or from parameter tuning.
  2. [Abstract] Abstract: The recursion depth bound, prior compression parameter, and availability sample size are described as 'cognitively motivated' and 'derived from the bounded rationality literature,' yet the abstract provides no explicit mapping from cognitive constraints (working memory limits, information bottleneck, saliency-weighted sampling) to fixed numerical values or sampling procedures that are independent of the LIAR/MultiFC label distributions. This leaves open whether the depth-mismatch support and differential vulnerability predictions are genuine consequences of the bounds or artifacts of fitting the free parameters listed in the axiom ledger.
minor comments (1)
  1. [Abstract] The manuscript is explicitly labeled 'work in progress,' which is appropriate given the missing implementation details, but this status should be reflected in the title or a dedicated limitations subsection to set reader expectations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our work-in-progress manuscript. We address each major comment below and outline planned revisions to improve clarity and evaluability.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The validation claims ('competitive veracity classification' and 'experimental support for the depth-mismatch paradox') are stated without any reported metrics, baselines, error bars, parameter values, or ablation results. Because the central claim rests on these benchmark outcomes demonstrating that the cognitively motivated bounds yield independent predictions, the absence of these details prevents evaluation of whether the reported effects follow from the model or from parameter tuning.

    Authors: We agree that the abstract, as a concise summary, omits the specific numerical details needed for immediate evaluation. The full manuscript already contains the benchmark results on LIAR and MultiFC (including accuracies, F1 scores, baseline comparisons, error bars, and ablations) in the Experiments section, along with the parameter values used. To address the concern directly, we will revise the abstract to incorporate key metrics, baseline references, and a brief note on the parameter settings. This change will make the validation claims self-contained and allow readers to assess whether the effects stem from the model structure. revision: yes

  2. Referee: [Abstract] Abstract: The recursion depth bound, prior compression parameter, and availability sample size are described as 'cognitively motivated' and 'derived from the bounded rationality literature,' yet the abstract provides no explicit mapping from cognitive constraints (working memory limits, information bottleneck, saliency-weighted sampling) to fixed numerical values or sampling procedures that are independent of the LIAR/MultiFC label distributions. This leaves open whether the depth-mismatch support and differential vulnerability predictions are genuine consequences of the bounds or artifacts of fitting the free parameters listed in the axiom ledger.

    Authors: The manuscript derives the bounds from cognitive literature as stated (recursion depth from working-memory limits on pragmatic recursion, prior compression from information-bottleneck effects, and availability sampling from saliency-weighted importance sampling). While some free parameters are estimated on the benchmarks, the core bound values are fixed a priori from the cited cognitive constraints rather than tuned solely to label distributions. We will revise the abstract and add an explicit mapping subsection (with numerical values and literature sources) to clarify this independence. This revision will directly respond to the concern about potential fitting artifacts. revision: partial

Circularity Check

0 steps flagged

No significant circularity; derivation self-contained

full rationale

The provided abstract and context present BPL as an extension of RSA theory incorporating three bounds explicitly derived from bounded-rationality literature (working memory, information bottleneck, saliency-weighted sampling). These are positioned as enabling independent testable predictions about susceptibility and information disorder distinctions, with validation on LIAR/MultiFC reported as competitive classification plus support for the depth-mismatch paradox. No equations, parameter-fitting steps, or self-citation chains are exhibited that would reduce the predictions or bounds to post-hoc adjustments of the same inputs by construction. The derivation therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

3 free parameters · 2 axioms · 1 invented entities

The claim rests on three introduced bounds whose specific numerical values and independence from benchmark fitting are not derived in the abstract, plus standard Bayesian assumptions.

free parameters (3)
  • recursion depth bound
    Limits recursion levels to model working memory constraints; value not specified as derived
  • prior compression parameter
    Compresses priors to capture information bottleneck; value not specified as derived
  • availability sample size
    Sets sample size for saliency-weighted importance sampling; value not specified as derived
axioms (2)
  • standard math Bayesian updating governs pragmatic inference in language understanding
    Core assumption inherited from Rational Speech Act theory
  • domain assumption Bounds from bounded rationality literature directly apply to misinformation processing
    Assumed without additional justification in the abstract
invented entities (1)
  • Bounded Pragmatic Listener no independent evidence
    purpose: Framework for modeling susceptibility to information disorder
    New named model introduced by the paper

pith-pipeline@v0.9.0 · 5431 in / 1630 out tokens · 105197 ms · 2026-05-12T02:46:52.626918+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages · 1 internal anchor

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    A Cognitively Grounded Bayesian Framework for Misinformation Susceptibility

    Introduction Every day, millions of people encounter claims on- line that turn out to be false. Some of these, for e.g., afabricatedstatisticaboutunemployment, are shared in good faith by people who simply got it wrong. Others, like a fabricated quote attributed to a politician, are crafted with deliberate intent to deceive. And others still, such as the ...

    work page internal anchor Pith review Pith/arXiv arXiv 2017

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    Computational Approaches to Misinformation Automated fact-checking and misinformation de- tection have been extensively surveyed (Guo et al., 2022; Thorne and Vlachos, 2018)

    Background and Related Work 2.1. Computational Approaches to Misinformation Automated fact-checking and misinformation de- tection have been extensively surveyed (Guo et al., 2022; Thorne and Vlachos, 2018). The dominant paradigm treats veracity prediction as a classifi- cation task over claims, evidence, and metadata features. Early work used surface fea...

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    says”, “claims

    The Bounded Pragmatic Listener Bounded Pragmatic Listener is a formal model of belief updating in agents subject to three cognitively-motivated resource constraints.BPL is an instance of the Rational Speech Acts frame- work (Frank and Goodman, 2012; Goodman and Frank, 2016) in which the idealised pragmatic lis- tener is replaced by an agent whose inferenc...

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    Datasets We use two datasets to empirically validateBPL

    Experimental Setup 4.1. Datasets We use two datasets to empirically validateBPL. Liar(Wang, 2017).12,836 labelled statements from PolitiFact with six fine-grained veracity labels (pants-fire, false, barely-true, half-true, mostly-true, true) and five speaker history count columns. We use the binary mapping (false/barely-true/pants- fire → 0, half-true/mos...

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    Veracity Classification Table 2 reports 5-fold CV classification perfor- mance across both datasets

    Results 5.1. Veracity Classification Table 2 reports 5-fold CV classification perfor- mance across both datasets. Liarresults.The surface baseline achieves AUC = 1.000, an artefact of speaker history counts encoding prior falsity rate, which is effectively the test label derived from the same fact-checking cor- pus (Wang, 2017). We note thatBPLFull (AUC =...

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    We want to also high- light that utilising an interpretable Bayesian infer- ence framework helps in seeing the exact compo- nents that modulate the system

    Discussion Our results suggest that our computational pipeline yields highly competitive classification capabilities compared to systems relying on external evidence retrieval or model fine tuning. We want to also high- light that utilising an interpretable Bayesian infer- ence framework helps in seeing the exact compo- nents that modulate the system. We ...

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    Our experiments show thatBPLis a good veracity classifier driven primarily by prior compression

    Conclusion We introducedBPLframework, a formal model of misinformation susceptibility that extends RSA with three cognitively-motivated resource constraints: recursion depth, prior compression, and availability sample size. Our experiments show thatBPLis a good veracity classifier driven primarily by prior compression. Additions of LLM substantially im- p...

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    PP00029):Robustinferencewithprob- abilistic answer set programs scaffolds for large language models

    Acknowledgements This work was supported in part by the Alan Tur- ing Institute Fundamental Research programme (ProjectNo. PP00029):Robustinferencewithprob- abilistic answer set programs scaffolds for large language models

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    Bibliographical References Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, and Jakob Grue Simonsen

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    MultiFC:Areal-worldmulti-domaindataset for evidence-based fact checking of claims. In Proceedings of the 2019 Conference on Em- pirical Methods in Natural Language Process- ing, pages 4685–4697. Association for Compu- tational Linguistics. RamyBaly,GiovanniDaSanMartino,JamesGlass, and Preslav Nakov. 2020. We can detect your bias: Predicting the political ...

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    Agreeing to disagree: Annotating offen- sivelanguagedatasetswithannotators’disagree- ment. InProceedings of the 2021 Conference on Empirical Methods in Natural Language Pro- cessing, pages 10528–10539. Association for Computational Linguistics. Falk Lieder and Thomas L. Griffiths. 2020. Resource-rational analysis: Understanding hu- man cognition as the op...

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