APPLICATION OF FOUR STOCHASTIC MODELS TO LEARNING OF NUMERICAL SEQUENCES

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概要

• 論文の詳細を見る

• The task is to learn numerical sequences, 8 in total, by anticipating each new member of the sequences. With the data of 50 university students, four stochastic models were fitted to the decomposed subprocesses, each under a subrule. The subprocesses were 24 in all. The single-operator model of Bush and Sternberg accounts for the easier subprocesses where as the subprocesses of intermediate difficulty were best fitted by the two-phase model of Norman. The difficult subprocesses were accounted for by the all-or-none model of Bower and also by the two-phase model. None of the types of subprocess seemed to be in accordance with the random-trial-increment model of Norman. Concerning with the difficulty of the subprocess reflected into values of the parameters, some interesting trends were described.

• 公益社団法人 日本心理学会の論文

著者

• SUZUKI SAYOKO

  Department of Psychology, Keio University

• INDOW TAROW

  Department of Psychology, Keio University

関連論文

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• STRATEGIES IN CONCEPT IDENTIFICATION:STOCHASTIC MODEL AND COMPUTER SIMULATION I
• APPLICATION OF FOUR STOCHASTIC MODELS TO LEARNING OF NUMERICAL SEQUENCES

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