Categorization by Humans and Machines by Glenn V. Nakamura, Douglas L. Medin and Roman Taraban (Eds.)

By Glenn V. Nakamura, Douglas L. Medin and Roman Taraban (Eds.)

The target of the sequence has consistently been to supply a discussion board within which best participants to a space can write approximately major our bodies of analysis during which they're concerned. The working approach has been to ask contributions from attention-grabbing, energetic investigators, after which let them primarily loose rein to give their views on vital study difficulties. the results of such invites during the last 20 years has been collections of papers which encompass considerate integrations supplying an summary of a specific medical challenge. The sequence has an outstanding culture of top of the range papers and is broadly learn by way of researchers in cognitive and experimental psychology.

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Categories andconcepts. Cambridge, MA: Harvard University Press. , & Bookman, L. ). (1993). Computational architectures integrating neural and symbolic processes. Boston: Kluwer. , & Medin, D. (1987). Relationships between similaritybased and explanation-based categorization. In D. ), Contemporary science and natural explanation: Common sense conception of causality (pp. 204-240). Sussex: Harvester Press. Williams, R. (1986). The logic of activation functions. In D. Rumelhart, J. ), Parallel distributed processing: Explorations in the microstructure of cognition: Vol.

I. The Core Exemplar Model In a typical category learning experiment, subjects have the task of assigning members of a set of stimulus patterns to alternative categories (for example, images on a radar screen to types of aircraft or symptom patterns to alternative diseases). On each of a sequence of trials, a subject is shown a pattern (a category exemplar), chooses a category, and receives informative feedback, usually the display of the correct category label. , a list of symptoms characterizing a patient together with a disease category).

Suppose that, on the first few trials of an experiment with this design, a learner saw the pattern-category combinations lllA211B221B112A then was tested with the previously unexperienced pattern 122. Referring to Table 11, it is apparent that the categories can be defined in terms of a simple rule-patterns with the first feature having value 1 belong to Category A, those with the first feature having value 2 to Category B. A learner who had already induced this rule after the four learning trials would classify the new pattern 122 correctly.

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