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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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The computational nature of memory modification.

Samuel J Gershman1, Marie-H Monfils2, Kenneth A Norman3

  • 1Department of Psychology and Center for Brain Science, Harvard University, Cambridge, United States.

Elife
|March 16, 2017
PubMed
Summary
This summary is machine-generated.

Memory retrieval can alter future behavior by modifying memory traces. Our computational model explains this using associative learning and structure learning to identify experience clusters, successfully simulating Pavlovian conditioning findings.

Keywords:
Bayesian inferencePavlovian conditioningextinctionmemoryneurosciencenonereconsolidation

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Area of Science:

  • Computational neuroscience
  • Cognitive psychology
  • Machine learning

Background:

  • Memory retrieval is known to alter subsequent memory influence on behavior.
  • Existing models do not fully capture the dynamic modification of memory traces.

Purpose of the Study:

  • To develop a computational theory of memory modification.
  • To explain how retrieved memories are updated based on new experiences.
  • To link memory modification to structure learning and latent cause discovery.

Main Methods:

  • Developed a computational framework based on probabilistic principles.
  • Incorporated a structure learning mechanism to segment experience into latent causes.
  • Modeled memory trace modification via classical associative learning.
  • Used computational simulations to test the model.

Main Results:

  • The model explains how new memories form when new latent causes are inferred.
  • The model demonstrates how old memories are modified when experiences are linked to existing latent causes.
  • Simulations successfully reproduced key findings from Pavlovian conditioning studies on memory modification.

Conclusions:

  • The proposed computational theory provides a unified account of memory modification.
  • Structure learning plays a crucial role in determining memory plasticity.
  • The model offers a testable framework for understanding memory dynamics and behavior.