計算機モデルによるＯｐｅｎ‐ｅｎｄな状況での認知の分析

Abstract

In this paper, we propose a novel usage for computational cognitive models. In cognitive science, computational models have played a critical role of theories for human cognitions. Many computational models have simulated results of controlled psychological experiments successfully. However, there have been only a few attempts to apply the models to complex realistic phenomena. We call such a situation ``open-ended situation''. In this study, MAC/FAC (``many are called, but few are chosen''), proposed by [Forbus 95], that models two stages of analogical reasoning was applied to our open-ended psychological experiment. In our experiment, subjects were presented a cue story, and retrieved cases that had been learned in their everyday life. Following this, they rated inferential soundness (goodness as analogy) of each retrieved case. For each retrieved case, we computed two kinds of similarity scores (content vectors/structural evaluation scores) using the algorithms of the MAC/FAC. As a result, the computed content vectors explained the overall retrieval of cases well, whereas the structural evaluation scores had a strong relation to the rated scores. These results support the MAC/FAC's theoretical assumption - different similarities are involved on the two stages of analogical reasoning. Our study is an attempt to use a computational model as an analysis device for open-ended human cognitions.