Modeling Human Learning as Context Dependent Knowledge Utility Optimization

Abstract. Humans have the ability to flexibly adjust their information process-ing strategy according to situational characteristics. However, such ability has been largely overlooked in computational modeling research in high-order hu-man cognition, particularly in learning. The present work introduces frameworks of cognitive models of human learning that take contextual factors into account. The framework assumes that human learning processes are not strictly error min-imization, but optimization of knowledge. A simulation study was conducted and showed that the present framework successfully replicated observed psychological phenomena.

Coincidence of Agreement between Probabilistic and Algebraic Choosers

individual choice, probabilistic chooser, algebraic chooser, May’s model, transitivity, rationality,