Evidence map›Paper›PMID 42651082›Full record

ArticleBrain sciences2026

Writing Increases Precision-Weighted Inference of Conceptual Organization: A Bayesian-Brain Active-Inference Model of the Writing-over-Speaking Advantage in Analytic Thinking.

Angelica Maria Silva, Renata Melanie Truelove, Anthony Millán De Lange, Roberto Limongi

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Article in Brain sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Angelica Maria SilvaDepartment of Francophone Studies and Languages, Faculty of Arts, Brandon University, 270-18th Street, Brandon, MB R7A 6A9, Canada.
Renata Melanie TrueloveSt. Stephen's College, University of Alberta, Edmonton, AB T6G 2J6, Canada.
Anthony Millán De LangeDepartment of Psychology, School of Psychology, Faculty of Humanities, Arts, and Social Sciences, Universidad del Norte, Km. 5 vía Puerto Colombia, Barranquilla 081007, Colombia.ORCID 0000-0002-4187-8835
Roberto LimongiDepartment of Psychology, Faculty of Science, Brandon University, 270-18th Street, Brandon, MB R7A 6A9, Canada.

Funding

Social Sciences and Humanities Research Council 430-2024-00727
6 · The paper itself

Abstract

backgroundThe analytic thinking score (ATS) has been interpreted as a linguistic marker of organized thought. Written language typically shows a higher ATS than spoken language. From the perspective of active inference under the free-energy principle, we proposed a preliminary neurocomputational model of this writing-over-speaking advantage. We propose that ATS is an externally computed linguistic measure reflecting analytic-thinking active states that arise from precision-weighted inference over internal conceptual-organization (CO) states. We hypothesize that written production shows a higher ATS when the active-inference agent increases posterior confidence in high-CO states.

methodsATSs were extracted from written and spoken samples produced by university students who described thematic apperception test images. Participants were modeled as active-inference agents using a two-timestep Markov decision process (MDP) in which speaking and writing sensory cues updated beliefs about internal CO states which then drove analytic thinking active states. Belief updating was formalized through marginal message-passing and theoretically interpreted in terms of prediction-error signaling and precision-weighted neuronal synaptic gain. An attention-related parameter (AP) controlled the precision of the CO-sensory state mapping. Bayesian model selection was used to assess the model's preliminary construct validity.

resultsWritten responses showed higher ATSs than spoken responses. The AP estimate indicated that writing cues supported posterior inference toward high-CO states stronger than speaking cues. Bayesian model selection favored the active-inference MDP over a Variational Laplace linear model.

conclusionsThe current preliminary evidence speaks to a candidate active-inference model in which writing ascribes higher precision-weighted inference of CO, reflected in higher ATSs.

Indexed as

active inferenceanalytic thinkingnatural language processingwriting

Identifiers

PMID42651082
PMCPMC13511085

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