Mapping across domains without feedback: A neural network model of transfer of implicit knowledge
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 , 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.
KeywordsLetter String Hide Unit Test String Implicit Knowledge Input Unit
Unable to display preview. Download preview PDF.
- 3..Dienes Z. & Perner J. Implicit knowledge in people and connectionist networks. In G. Underwood (Ed) Implicit cognition. Oxford University Press, (in press)Google Scholar
- 4..Shanks DR. & St. John MF. Characteristics of dissociable human learning systems. Behavioural and Brain Sciences.(in press)Google Scholar
- 8.Brooks L. Nonanalytic concept formation and memory for instances. In E. Rosch & B.B. Lloyd (Eds.) Cognition and Categorization. Hillsdale, N.J.: Erlbaum, Hillsdale, N.J., 1978, pp.169–211.Google Scholar
- 16.Manza L. & Reber AS. Representation of tacit knowledge: Transfer across stimulus forms and modalities. Unpublished manuscript, 1994.Google Scholar
- 17..Altmann GTM., Dienes Z. & Goode A. On the modality independence of implicitly learned grammatical knowledge. Journal of Experimental Psychology: Learning, Memory, and Cognition (in press).Google Scholar
- 18.Hofstadter DR. Metamagical themas: Questing for the essence of mind and pattern. Middlesex: Penguin, Middlesex, 1985.Google Scholar
- 21.Druhan B. & Mathews R. THIYOS: A classifier system model of implicit knowledge of artificial grammars. Proceedings of the Eleventh Annual Conference of the Cognitive Science Society. NY: Lawrence Erlbaum, New York, 1989.Google Scholar
- 23..Roussel L. & Mathews R. THIYOS: A synthesis of rule-based and exemplar-based models of implicit learning (submitted).Google Scholar
- 24.Dienes Z. Computational models of implicit learning. In DC. Berry & Z. Dienes, Implicit learning: theoretical and empirical issues. Lawrence Erlbaum, Hove, 1993.Google Scholar
- 25.Berry DC. & Dienes Z. Implicit learning: Theoretical and empirical issues. Lawrence Erlbaum, Hove, 1993.Google Scholar
- 27.Cleeremans A. Mechanisms of implicit learning: Connectionist models of sequence processing. MIT Press, Cambridge, 1993.Google Scholar
- 29.McClelland JL. & Elman J. Interactive processes in speech perception: The TRACE model. In JL. McClelland & DE. Rumelhart (eds.), Parallel distributed processing. Explorations in the microstructure of cognition. Cambridge, MA: MIT Press, Cambridge, 1986.Google Scholar