Brooks Lee R, Squire-Graydon Rosemary, Wood Timothy J
Department of Psychology, McMaster University, Hamilton, Ontario, Canada.
Mem Cognit. 2007 Jan;35(1):1-14. doi: 10.3758/bf03195937.
Many people tend to believe that natural categories have perfectly predictive defining features. They do not easily accept the family resemblance view that the features characteristic of a category are not individually sufficient to predict the category. However, common category-learning tasks do not produce this simpler-than-it-is belief. If there is no simple classification principle in a task, the participants know that fact and can report it. We argue that most category-learning tasks in which family resemblance categories are used fail to produce the everyday simpler-than-it-is belief because they encourage analysis of identification criteria during training. To simulate the learning occurring in many natural circumstances, we developed a procedure in which participants' analytic activity is diverted from the way in which the stimuli are identified to the use to which the stimuli will be put. Finally, we discuss the prevalence of this diverted analysis in everyday categorization.
许多人倾向于认为自然范畴具有完全可预测的定义特征。他们不容易接受家族相似性观点,即一个范畴的特征并非单独足以预测该范畴。然而,常见的范畴学习任务并不会产生这种比实际情况更简单的信念。如果一项任务中不存在简单的分类原则,参与者会知道这一事实并能够报告出来。我们认为,大多数使用家族相似性范畴的范畴学习任务不会产生日常那种比实际情况更简单的信念,因为它们在训练过程中鼓励对识别标准进行分析。为了模拟在许多自然情境中发生的学习,我们开发了一种程序,在该程序中,参与者的分析活动从刺激的识别方式转移到刺激的使用方式上。最后,我们讨论这种转移分析在日常分类中的普遍性。