Writing Increases Precision-Weighted Inference of Conceptual Organization: A Bayesian-Brain Active-Inference Model of...

The 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.

ATSs 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.

Written 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.

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