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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 10, 2022
- Source ID
- 10.7554/elife.74445
Entities
People
- Kenneth A. Norman
- Qihong Lu
- Uri Hasson
Organizations
- Office of Naval Research
- Princeton University