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  1. (1 other version)Implicit learning and tacit knowledge.Arthur S. Reber - 1989 - Journal of Experimental Psychology: General 118 (3):219-235.
    I examine the phenomenon of implicit learning, the process by which knowledge about the rule-governed complexities of the stimulus environment is acquired independently of conscious attempts to do so. Our research with the two seemingly disparate experimental paradigms of synthetic grammar learning and probability learning, is reviewed and integrated with other approaches to the general problem of unconscious cognition. The conclusions reached are as follows: Implicit learning produces a tacit knowledge base that is abstract and representative of the structure of (...)
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  • (1 other version)Implicit learning and tacit knowledge.Arthur S. Reber - 1989 - Journal of Experimental Psychology 118:219-35.
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  • (1 other version)Finding Structure in Time.Jeffrey L. Elman - 1990 - Cognitive Science 14 (2):179-211.
    Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves: (...)
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  • Two modes of learning for interactive tasks.Neil A. Hayes & Donald E. Broadbent - 1988 - Cognition 28 (3):249-276.
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  • (1 other version){Finding structure in time}.J. Elman - 1993 - {Cognitive Science} 48:71-99.
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  • Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing.Axel Cleeremans - 1993 - MIT Press.
    What do people learn when they do not know that they are learning? Until recently, all of the work in the area of implicit learning focused on empirical questions and methods. In this book, Axel Cleeremans explores unintentional learning from an information-processing perspective. He introduces a theoretical framework that unifies existing data and models on implicit learning, along with a detailed computational model of human performance in sequence-learning situations.
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