Pruning of memories by context-based prediction error

The capacity of long-term memory is thought to be virtually unlimited. However, our memory bank may need to be pruned regularly to ensure that the information most important for behavior can be stored and accessed efficiently. Using functional magnetic resonance imaging of the human brain, we report...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS Vol. 111; no. 24; pp. 8997 - 9002
Main Authors: Kim, Ghootae, Lewis-Peacock, Jarrod A., Norman, Kenneth A., Turk-Browne, Nicholas B.
Format: Journal Article
Language:English
Published: United States National Academy of Sciences 17-06-2014
National Acad Sciences
Subjects:
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The capacity of long-term memory is thought to be virtually unlimited. However, our memory bank may need to be pruned regularly to ensure that the information most important for behavior can be stored and accessed efficiently. Using functional magnetic resonance imaging of the human brain, we report the discovery of a context-based mechanism for determining which memories to prune. Specifically, when a previously experienced context is reencountered, the brain automatically generates predictions about which items should appear in that context. If an item fails to appear when strongly expected, its representation in memory is weakened, and it is more likely to be forgotten. We find robust support for this mechanism using multivariate pattern classification and pattern similarity analyses. The results are explained by a model in which context-based predictions activate item representations just enough for them to be weakened during a misprediction. These findings reveal an ongoing and adaptive process for pruning unreliable memories.
Bibliography:http://dx.doi.org/10.1073/pnas.1319438111
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Edited by Daniel L. Schacter, Harvard University, Cambridge, MA, and approved May 8, 2014 (received for review October 16, 2013)
Author contributions: G.K., J.A.L.-P., K.A.N., and N.B.T.-B. designed research; G.K. performed research; G.K. and J.A.L.-P. analyzed data; and G.K., J.A.L.-P., K.A.N., and N.B.T.-B. wrote the paper.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1319438111