Affordable Access

deepdyve-link
Publisher Website

Nonmonotonic Plasticity: How Memory Retrieval Drives Learning.

Authors
  • Ritvo, Victoria J H1
  • Turk-Browne, Nicholas B2
  • Norman, Kenneth A3
  • 1 Department of Psychology, Princeton University, Princeton, NJ 08540, USA.
  • 2 Department of Psychology, Yale University, New Haven, CT 06520-8205, USA.
  • 3 Department of Psychology, Princeton University, Princeton, NJ 08540, USA; Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08544, USA. Electronic address: [email protected]
Type
Published Article
Journal
Trends in cognitive sciences
Publication Date
Jul 26, 2019
Identifiers
DOI: 10.1016/j.tics.2019.06.007
PMID: 31358438
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

What are the principles that govern whether neural representations move apart (differentiate) or together (integrate) as a function of learning? According to supervised learning models that are trained to predict outcomes in the world, integration should occur when two stimuli predict the same outcome. Numerous findings support this, but - paradoxically - some recent fMRI studies have found that pairing different stimuli with the same associate causes differentiation, not integration. To explain these and related findings, we argue that supervised learning needs to be supplemented with unsupervised learning that is driven by spreading activation in a U-shaped way, such that inactive memories are not modified, moderate activation of memories causes weakening (leading to differentiation), and higher activation causes strengthening (leading to integration). Copyright © 2019 Elsevier Ltd. All rights reserved.

Report this publication

Statistics

Seen <100 times