Common Sense and the Limits of Inferential-Role Intuitive Theories in the Advent of AI

Abstract Common-sense learning and reasoning is a landmark of human-like intelligence. While classical-AI expert systems could convince at it in only narrow domains, contemporary deep-neural network models surprise with rapid performance improvements across many domains. At the same time, Bayesian Intuitive Theories have been influential in cognitive science as formal accounts of rational learning and reasoning. This paper targets Bayesian Intuitive Theories insofar as they rely on inferential-role semantics for conceptual content, to critically evaluate the promises and limits of this influential approach at mediating a theory of human-like common sense. I argue that both insights from deep-learning models and from Bayesian intuitive theories (insofar as they rely on inferential-role semantics for conceptual content) are insufficient to capture what seems to be centrally important in human-like common-sense learning and reasoning: Not just its internal consistency, but also its inherent relationship to the outside world. To address this challenge, I propose a situated revision of Bayesian Intuitive Theories that preserves the epistemic standards of rational reasoning while grounding domain-structured semantic content in ecological perception. By focusing on the role of semantic structure in embodied intervention, this proposal shows why common-sense reasoning inherently relates to the outside world. Consequently, machine-learning models mediate a theory of human-like common-sense only if the inferences that they implement satisfy both semantic and (inter)active constraints.