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  1. Generative Entrenchment and Evolution.Jeffrey C. Schank & William C. Wimsatt - 1986 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986:33 - 60.
    The generative entrenchment of an entity is a measure of how much of the generated structure or activity of a complex system depends upon the presence or activity of that entity. It is argued that entities with higher degrees of generative entrenchment are more conservative in evolutionary changes of such systems. A variety of models of complex structures incorporating the effects of generative entrenchment are presented and we demonstrate their relevance in analyzing and explaining a variety of developmental and evolutionary (...)
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  • The cognitive unconscious: An evolutionary perspective.Arthur S. Reber - 1992 - Consciousness and Cognition 1 (2):93-133.
    In recent decades it has become increasingly clear that a substantial amount of cognitive work goes on independent of consciousness. The research has been carried out largely under two rubrics, implicit learning and implicit memory. The former has been concerned primarily with the acquisition of knowledge independent of awareness and the latter with the manner in which memories not readily available to conscious recall or recognition play a role in behavior; collectively these operations comprise the essential functions of the cognitive (...)
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  • Passing thoughts on the evolutionary stability of implicit motor behaviour: Performance retention under physiological fatigue.J. M. Poolton, R. S. W. Masters & J. P. Maxwell - 2007 - Consciousness and Cognition 16 (2):456-468.
    Heuristics of evolutionary biology dictate that phylogenetically older processes are inherently more stable and resilient to disruption than younger processes. On the grounds that non-declarative behaviour emerged long before declarative behaviour, Reber argues that implicit learning is supported by neural processes that are evolutionarily older than those supporting explicit learning. Reber suggested that implicit learning thus leads to performance that is more robust than explicit learning. Applying this evolutionary framework to motor performance, we examined whether implicit motor learning, relative to (...)
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