کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10452465 | 919059 | 2005 | 51 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Feature inference and the causal structure of categories
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موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
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چکیده انگلیسی
The purpose of this article was to establish how theoretical category knowledge-specifically, knowledge of the causal relations that link the features of categories-supports the ability to infer the presence of unobserved features. Our experiments were designed to test proposals that causal knowledge is represented psychologically as Bayesian networks. In five experiments we found that Bayes' nets generally predicted participants' feature inferences quite well. However, we also observed a pervasive violation of one of the defining principles of Bayes' nets-the causal Markov condition-because the presence of characteristic features invariably led participants to infer yet another characteristic feature. We argue that this effect arises from a domain-general bias to assume the presence of underlying mechanisms associated with the category. Specifically, people take an exemplar to be a “well functioning” category member when it has most or all of the category's characteristic features, and thus are likely to infer a characteristic value on an unobserved dimension.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Cognitive Psychology - Volume 50, Issue 3, May 2005, Pages 264-314
Journal: Cognitive Psychology - Volume 50, Issue 3, May 2005, Pages 264-314
نویسندگان
Bob Rehder, Russell C. Burnett,