Mechanisms of skill acquisition and the law of practice
The Soar papers (vol. 1)
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
On the learning algorithms of descriptive models of high-order human cognition
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
On the Knowledge Organization in Concept Formation: An Exploratory Cognitive Modeling Study
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Modeling high-order human intelligence with intelligence of swarm
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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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 important observed psychological phenomena.