A Model of Human Category Learning with Dynamic Multi-Objective Hypotheses Testing with Retrospective Verifications

This paper introduces a new cognitive model of human learning, specifically applied for category learning. Our new model, called SCODI, assumes that human learning is driven by heuristically controlled optimization processes of subjectively and contextually defined utility of knowledge being acquired, and offers hypothesis-testing-like interpretations with emphasis on stochastic processes. SCODI is built on an algorithm that (a) allows the utilization of past experience to retrospectively evaluating the current hypotheses set in order to revise knowledge and concepts, (b) is capable of generating and testing more than one set of hypotheses for a given corrective feedback datum, and (c) adapts to dynamically fluctuating contextual factors in learning. SCODIs effectiveness in replicating observed human data was established by two simulation studies.