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Updated: Jul 11, 2026

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A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
Published on: January 5, 2018
A neural network model of retrieval-induced forgetting.
Kenneth A Norman1, Ehren L Newman, Greg Detre
1Department of Psychology, Princeton University, Princeton, NJ 08540, USA. knorman@princeton.edu
Psychological Review
|October 3, 2007
Summary
Retrieval-induced forgetting (RIF) impairs memory recall. A new neural network model explains how RIF strengthens target memories and weakens competing ones in the brain.
Area of Science:
- Cognitive Neuroscience
- Computational Neuroscience
Background:
- Retrieval-induced forgetting (RIF) is a phenomenon where recalling a memory impairs recall of related memories.
- Existing models do not fully explain the mechanisms underlying RIF in semantic and episodic memory.
Purpose of the Study:
- To present a novel neural network model explaining the brain mechanisms of retrieval-induced forgetting.
- To account for puzzling findings related to RIF and generate new predictions.
Main Methods:
- Developed a neural network learning algorithm utilizing feedback inhibition oscillations.
- Applied the model to explain RIF effects based on memory strength and retrieval practice.
Main Results:
- The model successfully explains why retrieval practice causes more forgetting than item presentation.
- The model accounts for the influence of competing memory strength and target memory strength on RIF.
- The model addresses the variable generalization of RIF to independent cues.
Conclusions:
- The proposed neural network model provides a unified explanation for various RIF phenomena.
- The model offers novel predictions regarding the boundary conditions of RIF effects.
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