A neural network model of when to retrieve and encode episodic memories

Abstract
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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

The following data sets were generated

1. Lu Q
(2021) A neural network model of when to retrieve and encode episodic memories
GitHub.

https://github.com/qihongl/learn-hippo

Article and author information

Author details

Qihong Lu

Department of Psychology, Princeton University, Princeton, United States

For correspondence
qlu@princeton.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-0730-5240
Uri Hasson

Princeton Neuroscience Institute, Princeton University, Princeton, United States

Competing interests
The authors declare that no competing interests exist.
Kenneth A Norman

Princeton Neuroscience Institute, Princeton University, Princeton, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-5887-9682

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.

Copyright

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.