Non-invasive neurophysiological measures of learning: A meta-analysis.

Neurosci Biobehav Rev

Department of Cognitive Science & Artificial Intelligence, Tilburg University, Dante Building, Room D 330, Warandelaan 2, 5037 AB Tilburg, The Netherlands.

Published: April 2019

AI Article Synopsis

  • A meta-analysis of 113 experiments revealed that neurophysiological measures, primarily using electroencephalography and eye-tracking, showed large effect sizes in the context of learning.
  • While neurophysiological outcomes were influenced by factors such as the sensory system, learning duration, feedback, and participant age, behavioral outcomes were not, leading to smaller effect sizes for neurophysiology when these factors were controlled.
  • The study suggests that neurophysiology is a valid measure for assessing learning and calls for further research to explore the complex interactions between learning, neurophysiology, behavior, and contextual factors.

Article Abstract

In a meta-analysis of 113 experiments we examined neurophysiological outcomes of learning, and the relationship between neurophysiological and behavioral outcomes of learning. Findings showed neurophysiology yielding large effect sizes, with the majority of studies examining electroencephalography and eye-related outcome measures. Effect sizes on neurophysiological outcomes were smaller than effect sizes on behavioral outcomes, however. Neurophysiological outcomes were, but behavioral outcomes were not, influenced by several modulating factors. These factors included the sensory system in which learning took place, number of learning days, whether feedback on performance was provided, and age of participants. Controlling for these factors resulted in the effect size differences between behavior and neurophysiology to disappear. The findings of the current meta-analysis demonstrate that neurophysiology is an appropriate measure in assessing learning, particularly when taking into account factors that could have an influence on neurophysiology. We propose a first model to aid further studies that are needed to examine the exact interplay between learning, neurophysiology, behavior, individual differences, and task-related aspects.

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Source
http://dx.doi.org/10.1016/j.neubiorev.2019.02.001DOI Listing

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