September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Neural bases of automaticity
Author Affiliations
  • Mathieu Servant
    Department of Psychology, Vanderbilt University
  • Peter Cassey
    Department of Psychology, Vanderbilt University
  • Geoffrey Woodman
    Department of Psychology, Vanderbilt University
  • Gordon Logan
    Department of Psychology, Vanderbilt University
Journal of Vision August 2017, Vol.17, 861. doi:
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      Mathieu Servant, Peter Cassey, Geoffrey Woodman, Gordon Logan; Neural bases of automaticity. Journal of Vision 2017;17(10):861. doi:

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      © ARVO (1962-2015); The Authors (2016-present)

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Automaticity allows us to perform tasks in a fast, efficient, and effortless manner after sufficient practice. Theories of automaticity propose that across practice processing transitions from being controlled by working memory to being controlled by long-term memory retrieval. Recent event-related potential (ERP) studies have sought to test this prediction, however, these experiments did not use the canonical paradigms used to study automaticity. Specifically, automaticity is typically studied using practice regimes with consistent mapping between targets and distractors and spaced practice with individual targets, features that these previous studies lacked. The aim of the present work was to examine whether the practice-induced shift from working memory to long-term memory inferred from subjects' ERPs is observed under the conditions in which automaticity is traditionally studied. We found that to be the case in 3 experiments, firmly supporting the predictions of theories. In addition, we found that the temporal distribution of practice (massed versus spaced) modulates the shape of learning curves. The ERP data revealed that the switch to long-term memory is slower for spaced than massed practice, suggesting that memory systems are used in a strategic manner. This finding provides new constraints for theories of learning and automaticity.

Meeting abstract presented at VSS 2017


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