Inductive Reasoning: Experimental, Developmental, and Computational Approaches

Inductive Reasoning: Experimental, Developmental, and Computational Approaches

Language: English

Pages: 376

ISBN: 0521672449

Format: PDF / Kindle (mobi) / ePub

Inductive reasoning is everyday, intuitive reasoning; it contrasts with deductive or logical reasoning. Inductive reasoning is much more prevalent than deductive reasoning, yet there has been much less research on inductive reasoning. Using contributions from the leading researchers in the field, the interdisciplinary approach of this book is relevant to those interested in psychology (including cognitive and developmental psychology), decision-making, philosophy, computer science, and education.

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sustained effort by psychologists to document how well the diversity 14:24 P1: JZP 0521672443c01 CUFX144-Feeney 0 521 85648 5 16 July 19, 2007 Evan Heit principle serves as a descriptive account of how people carry out informal, inductive reasoning. Osherson et al. (1990) documented diversity effects in adults by using written arguments like the following: Hippos require Vitamin K for the liver to function. (11) Rhinos require Vitamin K for the liver to function.

being similar to other members of the same category and dissimilar to members of contrasting categories. 14:30 P1: JZP 0521672443c03 CUFX144-Feeney 0 521 85648 5 Interpreting Asymmetries of Projection July 19, 2007 59 On the surface it does not appear that humans look more like other animals than, say, a dog does. But if we allow frequency of exposure to examples of the concept to bias typicality (see Barsalou, 1985, for evidence that frequency of instantiation as a member of the category

about these effects in terms of availability? 6:51 P1: KNQ 0521672443c05 CUFX144-Feeney 0 521 85648 5 130 July 20, 2007 P. Shafto, J. D. Coley, and A. Vitkin    Traditional models of inductive reasoning have focused on a single kind of knowledge in a domain, eschewing the effects of context. For example, the similarity-coverage model (Osherson et al., 1990) focused on taxonomic knowledge in the domain of biology, using similarity between premises and conclusions as

Verbal Learning and Verbal Behavior, 5, 381–391. Proffitt, J. B., Coley, J. D., & Medin, D. L. (2000). Expertise and category-based induction. Journal of Experimental Psychology: Learning, Memory & Cognition, 26, 811–828. Ross, B. H., & Murphy, G. L. (1999). Food for thought: Cross-classification and category organization in a complex real-world domain. Cognitive Psychology, 38, 495–553. Shafto, P., & Coley, J. D. (2003). Development of categorization and reasoning in the natural world: Novices

categories in X and excludes all negatively labeled categories in X. Then Equation 4 is equivalent to p(h) p(y has Q|X) = h∈H:y∈h,h consistent with X (5) p(h) h∈H:h consistent with X which is the proportion of hypotheses consistent with X that also include y, where each hypothesis is weighted by its prior probability p(h). The probability of generalizing to y will thus be high to the extent that it is included in most of the high-prior-probability hypotheses that also include the observed

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