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Interfering with a memory without erasing its trace.

Gesa Lange1, Mario Senden1, Alexandra Radermacher2

  • 1Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands.

Neural Networks : the Official Journal of the International Neural Network Society
|October 9, 2019
PubMed
Summary
This summary is machine-generated.

Even highly trained skills are vulnerable to interference from new learning. This study shows that skill interference in experts is regulated by reweighting neural connections, not erasing them.

Keywords:
Behavioral interferenceEarly visual cortexExpert skillPerceptual learningRecurrent neural networkTuning curves

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Novice skill performance is susceptible to interference from subsequent training.
  • It remains unclear if extensively trained skills exhibit similar vulnerability.
  • The memory mechanisms regulating interference between expert skills require investigation.

Purpose of the Study:

  • To investigate interference in extensively trained skills.
  • To identify memory mechanisms underlying skill interference in experts.
  • To compare neural network model predictions with human perceptual learning.

Main Methods:

  • Developed a recurrent neural network model of V1 for orientation discrimination.
  • Trained the model on one task, then a second similar task, assessing interference.
  • Conducted a human perceptual learning experiment mirroring the model's training protocol.

Main Results:

  • Extensively trained skills showed significant interference from subsequent training.
  • Interference magnitude correlated with the relative training amounts for each task.
  • Empirical human data closely matched model predictions for interference patterns.

Conclusions:

  • Behavioral interference in expert skills can be explained by antagonistic changes in neuronal tuning.
  • Interference arises from reweighting of lateral inhibition, not erasure of learned connections.
  • Neural network models effectively predict human perceptual learning and skill interference.