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Mapping across domains without feedback: A neural network model of transfer of implicit knowledge

  • Zoltán Dienes
  • Gerry T. M. Altmann
  • Shi-Ji Gao
Conference paper
Part of the Workshops in Computing book series (WORKSHOPS COMP.)

Abstract

Exposure to exemplars of an artificial grammar allows subjects to decide subsequently whether a novel sequence does or does not belong to the same grammar [1], If subjects are exposed to exemplars of an artificial grammar in one domain (e.g. sequences of tones differing in pitch), subjects can later classify novel sequences in another domain (e.g. sequences of letters). This paper introduces a version of the Simple Recurrent Network (SRN) that can also transfer its knowledge of artificial grammars across domains without feedback. The performance of the model is sensitive to at least some of the same variables that affect subjects’ performance in ways not predicted by other theories.

Keywords

Letter String Hide Unit Test String Implicit Knowledge Input Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 1995

Authors and Affiliations

  • Zoltán Dienes
    • 1
  • Gerry T. M. Altmann
    • 1
  • Shi-Ji Gao
    • 1
  1. 1.Lab. of Experimental PsychologyUniversity of SussexFalmer, BrightonUK

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