Nonmonotonic Plasticity: How Memory Retrieval Drives Learning

Victoria J.H. Ritvo, Nicholas B. Turk-Browne, Kenneth A. Norman

Research output: Contribution to journalReview articlepeer-review

8 Scopus citations

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).

Original languageEnglish (US)
Pages (from-to)726-742
Number of pages17
JournalTrends in Cognitive Sciences
Volume23
Issue number9
DOIs
StatePublished - Sep 2019

All Science Journal Classification (ASJC) codes

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience

Keywords

  • differentiation
  • fMRI
  • integration
  • neural networks
  • unsupervised learning

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