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The primary focus in computational modeling research in high order human cognition is to compare how realistically embedded algorithm describes human cognitive processes. However, several current models incorporated learning algorithms that apparently have questionable descriptive validity or qualitative plausibleness. The present research attempts to bridge this gap by identifying five critical issues overlooked by previous modeling research and then introducing a modeling framework that addresses the issues and offers better qualitative plausibleness. A simulation study utilizing the present framework with two distinctive implementation approaches shows their descriptive validity. [ABSTRACT FROM AUTHOR]

The majority of previous computational models of high-order human cognition incorporate gradient descent algorithms for their learning mechanisms and strict error minimization as the sole objective of learning. Recently, however, the validity of gradient descent as a descriptive model of real human cognitive processes has been criticized. In the present paper, we introduce a new framework for descriptive models of human learning that offers qualitatively plausible interpretations of cognitive behaviors. Specifically, we apply a simple multi-objective evolutionary algorithm as a learning method for modeling human category learning, where the definition of the learning objective is not based solely on the accuracy of knowledge, but also on the subjectively and contextually determined utility of knowledge being acquired. In addition, unlike gradient descent, our model assumes that humans entertain multiple hypotheses and learn not only by modifying a single existing hypothesis but also by combining a set of hypotheses. This learning-by-combination has been empirically supported, but largely overlooked in computational modeling research. Simulation studies show that our new modeling framework successfully replicated observed phenomena. [ABSTRACT FROM AUTHOR]