Hypothesis Generation, Sparse Categories, and the Positive Test Strategy
We consider the situation in which a learner must induce the rule that explains an observed set of data but the hypothesis space of possible rules is not explicitly enumerated or identified. The first part of the article demonstrates that as long as hypotheses are sparse (i.e., index less than half...
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| Veröffentlicht in: | Psychological review 2011-01, Vol.118 (1), p.120-134 |
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| creator | Navarro, Daniel J Perfors, Amy F |
| description | We consider the situation in which a learner must induce the rule that explains an observed set of data but the hypothesis space of possible rules is not explicitly enumerated or identified. The first part of the article demonstrates that as long as hypotheses are sparse (i.e., index less than half of the possible entities in the domain) then a positive test strategy is near optimal. The second part of this article then demonstrates that a preference for sparse hypotheses (a sparsity bias) emerges as a natural consequence of the family resemblance principle; that is, it arises from the requirement that good rules index entities that are more similar to one another than they are to entities that do not satisfy the rule. |
| doi_str_mv | 10.1037/a0021110 |
| format | Article |
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| ispartof | Psychological review, 2011-01, Vol.118 (1), p.120-134 |
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| subjects | Active Learning Bayes Theorem Bias Biological and medical sciences Classification Cognition & reasoning Cognition. Intelligence Cognitive Hypothesis Testing Cognitive psychology Critical thinking Fundamental and applied biological sciences. Psychology Human Humans Hypothesis Hypothesis Testing Learning Learning Strategies Memory Methodology Models, Psychological Preferences Problem-Based Learning Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Reasoning. Problem solving Research methods |
| title | Hypothesis Generation, Sparse Categories, and the Positive Test Strategy |
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