TY - JOUR
T1 - Toward a descriptive cognitive model of human learning
AU - Matsuka, Toshihiko
AU - Sakamoto, Yasuaki
AU - Chouchourelou, Arieta
AU - Nickerson, Jeffrey V.
PY - 2008/8
Y1 - 2008/8
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 overlooked in computational modeling research. Simulation studies show that our new modeling framework successfully replicated important observed psychological 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 overlooked in computational modeling research. Simulation studies show that our new modeling framework successfully replicated important observed psychological phenomena.
KW - Cognitive modeling
KW - Evolutionary computation
KW - Human learning
UR - http://www.scopus.com/inward/record.url?scp=56449095120&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=56449095120&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2007.12.039
DO - 10.1016/j.neucom.2007.12.039
M3 - Article
AN - SCOPUS:56449095120
SN - 0925-2312
VL - 71
SP - 2446
EP - 2455
JO - Neurocomputing
JF - Neurocomputing
IS - 13-15
ER -