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Prediction error and memory across the lifespan.

Yee Lee Shing1, Garvin Brod2, Andrea Greve3

  • 1Department of Psychology, Goethe University Frankfurt, Germany; IDeA-Center for Individual Development and Adaptive Education of Children at Risk, Frankfurt am Main, Germany.

Neuroscience and Biobehavioral Reviews
|November 11, 2023
PubMed
Summary
This summary is machine-generated.

Prediction Errors (PEs) enhance episodic memory encoding and consolidation by promoting synaptic changes. This review examines how PEs influence memory across the lifespan, highlighting neuroscientific mechanisms and age-related differences.

Keywords:
Lifespan developmentMemoryPrediction error

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Area of Science:

  • Cognitive Neuroscience
  • Neurobiology of Memory
  • Lifespan Psychology

Background:

  • Prediction Errors (PEs) are increasingly recognized for their role in memory.
  • The predictive processing framework and PIMMS model offer insights into memory systems.
  • Understanding PE-PEs influence on memory across the lifespan is critical.

Purpose of the Study:

  • To review the influence of Prediction Errors (PEs) on episodic memory throughout the lifespan.
  • To explore the neuroscientific mechanisms underlying PE-driven memory enhancement.
  • To highlight age-related heterogeneity in memory processes and neurocognitive mechanisms.

Main Methods:

  • Literature review drawing upon the predictive processing framework and PIMMS model.
  • Discussion of neuroscientific evidence, including hippocampal and noradrenergic system involvement.
  • Analysis of divergent trajectories of episodic and semantic memory across age.

Main Results:

  • PEs are proposed to promote synaptic change, enhancing memory encoding and consolidation.
  • Key neuroscientific mechanisms involve the hippocampus, entorhinal cortex-hippocampus pathway, and the noradrenergic system.
  • Memory processes and neurocognitive mechanisms for PE-driven enhancement show heterogeneity across age.

Conclusions:

  • PEs play a significant role in memory enhancement, with age-dependent variations.
  • Further research comparing neural networks involved in PE learning across age groups is warranted.
  • A deeper understanding of PE-driven learning across the lifespan is crucial.