This paper introduces the Bayesian Construal Model (BCM), a unifying psychological framework that conceptualises human behaviour as a process of dynamic Bayesian inference. Initially developed as the Bayesian Situational Construal Model (BSCM) in alignment with Funder’s (2016) Situational Construal Model (SCM), the framework is here renamed the BCM to reflect its broader theoretical scope, mechanistic orientation, and applicability beyond the SCM triad. Building on the SCM, the BCM integrates insights from Bayesian brain theory and the Free Energy Principle (FEP) to formalise how personality traits (priors) and situational cues (likelihoods) combine to generate subjective construals (posterior beliefs), which in turn guide behaviour. This recursive cycle of expectation, interpretation, and updating allows individuals to navigate uncertainty and maintain psychological coherence over time. While first applied to understanding healthcare non-attendance, the BCM generalises to a wide range of behavioural domains. It functions as a cognitive operating system, offering a theoretically integrative, neurocognitively grounded, and computationally tractable scaffold for understanding adaptive, context-sensitive human action.
Dane Halliwell (Fri,) studied this question.