TY - GEN
T1 - A cognitive model of multi-objective multi-concept formation
AU - Matsuka, Toshihiko
AU - Sakamoto, Yasuaki
AU - Nickerson, Jeffrey V.
AU - Chouchourelou, Arieta
PY - 2006
Y1 - 2006
N2 - 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 over-looked in computational modeling research. Simulation studies show that our new modeling framework successfully replicated observed phenomena.
AB - 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 over-looked in computational modeling research. Simulation studies show that our new modeling framework successfully replicated observed phenomena.
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U2 - 10.1007/11840817_59
DO - 10.1007/11840817_59
M3 - Conference contribution
AN - SCOPUS:33749835533
SN - 3540386254
SN - 9783540386254
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 563
EP - 572
BT - Artificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings
T2 - 16th International Conference on Artificial Neural Networks, ICANN 2006
Y2 - 10 September 2006 through 14 September 2006
ER -