A neural network model of when to retrieve and encode episodic memories
Abstract
Recent human behavioral and neuroimaging results suggest that people are selective in when they encode and retrieve episodic memories. To explain these findings, we trained a memory-augmented neural network to use its episodic memory to support prediction of upcoming states in an environment where past situations sometimes reoccur. We found that the network learned to retrieve selectively as a function of several factors, including its uncertainty about the upcoming state. Additionally, we found that selectively encoding episodic memories at the end of an event (but not mid-event) led to better subsequent prediction performance. In all of these cases, the benefits of selective retrieval and encoding can be explained in terms of reducing the risk of retrieving irrelevant memories. Overall, these modeling results provide a resource-rational account of why episodic retrieval and encoding should be selective and lead to several testable predictions.
Data availability
The code is made publicly available here in a git repo: https://github.com/qihongl/learn-hippoUsers can also use this code ocean capsule to play with one example model to qualitatively replicate some results: https://codeocean.com/capsule/3639589/tree
Article and author information
Author details
Funding
Office of Naval Research (Multi-University Research Initiative Grant,ONR/DoD N00014-17-1-2961)
- Uri Hasson
- Kenneth A Norman
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- David Badre, Brown University, United States
Version history
- Preprint posted: December 16, 2020 (view preprint)
- Received: October 4, 2021
- Accepted: February 9, 2022
- Accepted Manuscript published: February 10, 2022 (version 1)
- Accepted Manuscript updated: February 28, 2022 (version 2)
- Version of Record published: April 11, 2022 (version 3)
Copyright
© 2022, Lu et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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