ISSN 0439-755X
CN 11-1911/B
主办:中国心理学会
   中国科学院心理研究所
出版:科学出版社

心理学报 ›› 2011, Vol. 43 ›› Issue (01): 92-100.

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不同学习方式下归类不确定时的特征推理

刘志雅;莫雷   

  1. 华南师范大学心理应用研究中心, 广州 510631
  • 收稿日期:2009-07-21 修回日期:1900-01-01 发布日期:2011-01-30 出版日期:2011-01-30
  • 通讯作者: 刘志雅

Influence of Category Learning in Feature Predicting When Categories Are Uncertain

LIU Zhi-Ya;MO Lei   

  1. Center for Studies of Psychological Application, South China Normal University, Guangzhou 510631, China
  • Received:2009-07-21 Revised:1900-01-01 Online:2011-01-30 Published:2011-01-30
  • Contact: LIU Zhi-Ya

摘要: 采用学习-迁移模式, 探讨了同时学习和继时学习两种方式下归类不确定时的特征推理。共包括2个实验, 其中实验1探讨了固定学习轮次的情况, 实验2探讨了固定学习正确率的情况。实验结果表明:同时呈现类别要素的同时学习方式下, 被试习得序列式的单类别表征(原型表征), 在归类不确定时的特征推理中按照“单类的Bayesian规则”进行特征推理, 即P(j\F) =P(k\F)·P(j\k); 相继呈现类别要素的继时学习方式下, 被试习得并列式的多类别表征, 在归类不确定时的特征推理中按照“理性模型”进行推理, 即 P(j\F) =Σk P(k\F)·P(j\k)。

关键词: 类别学习, 特征推理, 单类说, 理性模型, 贝叶斯推理

Abstract: This paper studies how feature prediction is influenced by two types of category learning at uncertain classifying circumstance. One type of learning is stimulus by stimulus and another is category by category. Anderson (1991) provided a Bayesian analysis on feather predicting when categories are uncertain. For each object containing features F and each category k, one can predict the presence of a novel feature j by using the formula: P(j\F) =Σk P(k\F)·P(j\k). That is, one calculates for the object how likely it is to be in each category k and how likely that category is to contain the property in question. Then one sums across all the categories in order to make the prediction. In short, this proposal is that people use multiple categories to make predictions when the categorization is uncertain. Murphy & Ross (1994) argued that people make category-based inductions basing on only one category, even when they are not certain that the object is in that category. They found that even if participants give a fairly low rating of their confidence in the category it does not lead them to use multiple categories at making prediction. That is, one can predict the presence of a novel feature j by using the formula: P(j\F) =P(k\F)·P(j\k).
The experiments used the learning-transfer-paradigm which has three phases: learning phase, filler phase and transfer phase. 244 participants took part in two experiments. In experiment 1, participants stopped learning until they completed 4 blocks (64 trials), and in experiment 2 until they reached an accuracy of combination of 80%. In learning phase there were two learning ways: one was stimulus by stimulus (experiment 1b and 2b) and another was categories by categories (experiment 1a and 1b) and participants reacted basing on conditions and received feedback from tester. In transfer phase participants conducted same task as learning phase except that no feedback was given during transfer.
The results in experiment 1a and 2a showed that in category by category learning way the neutral condition was rated 78% and 71.4%, comparing to 83.3% and 76.9% for the adding condition. The difference between two conditions was not significant and “t” equal to -0.43 and 0.424, p>0.05. The results in experiment 1b and 2b showed that in stimulus by stimulus learning way the neutral condition was rated 77.4% and 78.8%, comparing to 68.8% and 81.6% for the adding condition. The difference between these conditions was significant and “t” equal to 2.21 and 1.77, p <0.05.
The study demonstrated that two ways of category learning led to different category representations: in way of learning category by category participants tended to focus on information of single category and their subsequent prediction conformed to the formula: P(j\F) = P(k\F)·P(j\k). Whereas in way of learning stimulus by stimulus participants tended to focus on information of multiple categories, and their subsequent prediction conformed to the formula: P(j\F) =Σk P(k\F)·P(j\k). This observation is part of general trend that is concerned with how category learning influences category representation.

Key words: category learning, feature predicting, single category, multiple categories, Bayesian rule