Prior knowledge and functionally relevant features in concept learning.

Wisniewski, Edward J.

doi:10.1037/0278-7393.21.2.449

Cited by 129 publications

(115 citation statements)

References 45 publications

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“…One common explanation of knowledge effects is that knowledge directs attention toward certain features at the expense of others (Murphy & Medin, 1985;Pazzani, 1991;Wisniewski, 1995). This suggestion has been taken up in more formal models as well.…”

Section: Mechanisms Of Knowledge Effectsmentioning

confidence: 99%

Category learning with minimal prior knowledge.

Kaplan¹,

Murphy²

2000

Journal of Experimental Psychology: Learning, Memory, and Cogni

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In 6 experiments, the authors examined the use of prior knowledge in category learning. Previous studies of the effects of knowledge on category learning have used categories in which knowledge was related to all of the category's features. However, people's knowledge of real-world categories often consists of many "rote" features that are not related to their prior knowledge. Five experiments found that even minimal prior knowledge (1 knowledge-relevant feature and 5 rote features per exemplar) can facilitate category learning. Posttests revealed that although the knowledge aided learning, subjects also acquired the rote features that were not related to knowledge, contradicting predictions of an attentional explanation of the knowledge effect. The results of Experiment 6 suggested that subjects attempt to link even rote features to their knowledge. When people learn a new category, they are influenced by both the properties of the observed category exemplars and their knowledge about the category's domain. Learning is easier when prior knowledge is consistent with a category that is to be learned than when knowledge is inconsistent or simply absent (Murphy & Allopenna, 1994; Pazzani, 1991; Wattenmaker, Dewey, T. Murphy, & Medin, 1986). By prior knowledge we mean knowledge about a broad domain, such as animals, birds, vehicles, or computers, that exists before learning a new category. This knowledge connects features of a new category, perhaps through causal relations, and provides some sort of explanation of why the category has the properties it does (see Carey, 1985; Keil, 1989; Murphy, 1993, in press; Murphy & Medin, 1985). To give a very simple example: People have some idea about how animals fly that provides a possible explanation for why something that has wings can live high on a cliff-the wings explain flying, which in turn explains why this habitat is possible. Such prior knowledge contrasts with category structure: empirical or statistical information about a category of the sort "most of the category members have wings" or "one of the exemplars has a red back, wings, and three legs." Both sources of information are used in much category learning, as one cannot rely solely on prior knowledge in learning something new, and yet such knowledge can greatly speed learning when it is correct. One of the main issues facing the psychology of concepts is to understand how prior knowledge influences category learning. Although it seems intuitively obvious that such knowledge can aid

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Section: Mechanisms Of Knowledge Effectsmentioning

confidence: 99%

Category learning with minimal prior knowledge.

Kaplan¹,

Murphy²

2000

Journal of Experimental Psychology: Learning, Memory, and Cogni

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“…Murphy and Wisniewski (1989) found that whether categorization judgments were affected by feature combinations depended on whether those combinations were expected on the basis of prior knowledge rather than on whether they had been previously observed in category exemplars (also see Chapman & Chapman, 1967, 1969. Wisniewski (1995) found that certain objects were better examples of the category captures animals when they possessed novel combinations of features that were useful (e.g., contains peanuts and caught a squirrel) versus when they did not (e.g., contains acorns and caught an elephant; also see Malt & Smith, 1984;Rehder & Ross, 2001).…”

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confidence: 99%

A Causal-Model Theory of Conceptual Representation and Categorization.

Rehder¹

2003

Journal of Experimental Psychology: Learning, Memory, and Cogni

155

213

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This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches.

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“…It also influences how one infers the presence of unobserved features in category members (Rehder & Burnett, 2005). Finally, theoretical knowledge affects how objects are classified into their correct category (Ahn, 1998;Rehder, 2003aRehder, , 2003bRehder & Hastie, 2001;Sloman, Love, & Ahn, 1998;Wisniewski, 1995). This article addresses how one type of theoretical knowledge-causal knowledge-influences the classification of objects.…”

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confidence: 99%

How causal knowledge affects classification: A generative theory of categorization.

Rehder¹,

Kim²

2006

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Several theories have been proposed regarding how causal relations among features of objects affect how those objects are classified. The assumptions of these theories were tested in 3 experiments that manipulated the causal knowledge associated with novel categories. There were 3 results. The 1st was a multiple cause effect in which a feature's importance increases with its number of causes. The 2nd was a coherence effect in which good category members are those whose features jointly corroborate the category's causal knowledge. These 2 effects can be accounted for by assuming that good category members are those likely to be generated by a category's causal laws. The 3rd result was a primary cause effect, in which primary causes are more important to category membership. This effect can also be explained by a generative account with an additional assumption: that categories often are perceived to have hidden generative causes.

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Prior knowledge and functionally relevant features in concept learning.

Cited by 129 publications

References 45 publications

Category learning with minimal prior knowledge.

Category learning with minimal prior knowledge.

A Causal-Model Theory of Conceptual Representation and Categorization.

How causal knowledge affects classification: A generative theory of categorization.

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