Ihara & Tamura Vol.2, No.3

課題構造の問題解決に及ぼす効果のベイズ的分析 ---確率更新課題における「データによる仮説の否定」の効果
A Bayesian Analysis of Effects of Task Structures on Problem Solving : Effects of "Undermining Hypotheses by Data" in Probability Updating Tasks

井原二郎 IHARA Jiro・田村佳彦 TAMURA Yoshihiko

Vol.2, No.3 (August 1995), pp.25-47. Received 1994/7/4, accepted 1995/2/9.

ABSTRACT: As a framework for analyzing the effects of task structures in problem solving, a probabilistic model of problem solving is formulated by introducing ``probabilities of using problem representations.'' The effects of ``undermining hypotheses by data (or evidence)'' in probability updating tasks are experimentally examined by measuring the probabilities of using problem representations. ``Undermining'' here means both ``direct undermining by data'' and ``indirect undermining via the likelihood function of which value is zero.'' The experimental analysis shows that (1) undermining is a strong obstacle to the Bayesian solutions of the probability updating tasks, and (2) there exist differences between the direct and the indirect undermining effects. A mathematical model, named ``Probability Flow Model,'' is made which expresses how the probabilities of using problem representations depend upon the general tendencies of human information use. This Probability Flow Model is experimentally validated. The differences between the direct and the indirect undermining effects are examined on the basis of the Probability Flow Model. The analysis shows that the differences are due to the differences in the degree of realization of the general tendencies of human information use. An interpretation of the differences in the degree of realization of the general tendencies is given from the viewpoint of how to relate a datum to hypotheses in solving the probability updating tasks. A new approach to human inductive reasoning, in which there has been no theoretical progress during the last two decades, is also suggested from the viewpoint of belief fixation and belief perseverance. It is an old custom that the classical statistics in Neyman-Pearson school is used in psychological data analyses, although its application to them is unreasonable. In this paper, Bayesian statistics is adopted because of its appropriateness to psychological data.

keywords：問題解決 roblem solving，確率更新 probability updating，帰納推論 inductive reasoning，人間の情報使用の一般的傾向 general tendencies of human informationuse，ベイズ統計学 Bayesian statistics

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