Separate influences in learning: Evidence from artificial grammar learning with traumatic brain injury patients

  • Pothos E
  • Wood R
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Abstract

Artificial grammar learning (AGL) is one of the most extensively employed paradigms for the study of learning. Grammaticality is one of the most common ways to index performance in AGL. However, there is still extensive debate on whether there is a distinct psychological process which can lead to grammaticality knowledge. An application of the COVIS model of categorization in AGL suggests that grammaticality might arise from a hypothesis-testing system (when grammaticality is appropriately balanced with other knowledge influences), so that prefrontal cortex damage should be associated with impaired grammaticality and intact chunk strength performance. This prediction was confirmed in a study of traumatic brain injury (TBI) patients and matched controls. The TBI patient cohort had diffuse prefrontal cortex damage as evidenced by the history of their injury, CT scans, and severe executive functioning problems. Our results allow a novel interpretation of grammaticality and AGL in general. © 2009 Elsevier B.V. All rights reserved.

Author-supplied keywords

  • AGL
  • COVIS
  • Categorization
  • Learning
  • Traumatic brain injury

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Authors

  • Emmanuel M. Pothos

  • Rodger L. Wood

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