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2 results

  • 14 - Computational Models of Implicit Learning

  • from Part III - Computational Modeling of Various Cognitive Functionalities and Domains
  • Edited by Ron Sun
  • Book:
    The Cambridge Handbook of Computational Psychology
    Published online:
    05 June 2012
    Print publication:
    28 April 2008, pp 396-421

  • Summary

    This chapter surveys a variety of formal models of categorization, with emphasis on exemplar models. The chapter reviews exemplar models' similarity functions, learning algorithms, mechanisms for exemplar recruitment, formalizations of response probability, and response dynamics. A mutual goal of different formal models is to account for detailed quantitative data from laboratory experiments in categorization. Although a variety of representational formats have been formalized, exemplar models have been especially richly explored by researchers. The chapter discriminates numerous exemplar models to excise their functional components and to examine those components side by side. The main functional components include the computation of similarity, the learning of associations and attention, the recruitment of exemplars, the determination of response probability, and the generation of response times. This dissection revealed a variety of formalizations available for expressing any given psychological process.

  • The usefulness of partial information: Effects of go probability in the choice/nogo task

  • KATHY A. LOW, JEFF MILLER
  • Journal:
    Psychophysiology / Volume 36 / Issue 3 / May 1999
    Published online by Cambridge University Press:
    25 September 2001, pp. 288-297
    Print publication:
    May 1999

  • In the present study, we examined whether the use of partial information for response preparation depends on the utility of that partial information for meeting task demands. Using a choice/Nogo task, the utility of the partial information was varied by manipulating the proportion of Go relative to Nogo signals, and preliminary response preparation was indexed by the degree of lateralized readiness potential (LRP) activity on Nogo trials. The Nogo LRP was clearly present when Go probability was high (67% Go) and, to a lesser extent, when Go and Nogo signals were equiprobable. However, the Nogo LRP disappeared when the probability of a Go signal was low (25% Go). This pattern of results supports the hypothesis that response preparation will be based on partial stimulus information only when the information has significant utility in the context of the overall task requirements.