Evidence map›Paper›PMID 41279213›Full record

ArticlebioRxiv : the preprint server for biology2025

A compressed code for memory discrimination.

Dale Zhou, Sharon M Noh, Nora C Harhen, Nidhi V Banavar, C Brock Kirwan, Michael A Yassa, Aaron M Bornstein

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Dale ZhouUniversity of California, Irvine, Neurobiology and Behavior, 519 Biological Sciences Quad, Irvine, 92697, United States.ORCID 0000-0001-9240-1327
Sharon M NohUniversity of California, Irvine, Department of Cognitive Sciences, Social Science Lab 334, Irvine, 92697, United States.
Nora C HarhenNew York University, Department of Psychology, 6 Washington Place, New York, NY 10003, United States.
Nidhi V BanavarUniversity of California, Berkeley, Department of Political Science, 210 Social Sciences Building, Berkeley, CA 94720, United States.
C Brock KirwanUniversity of Pennsylvania, MindCORE Neuroimaging Facility, 3401 Grays Ferry Ave, Philadelphia, 19146, United States.
Michael A YassaUniversity of California, Irvine, Neurobiology and Behavior, 519 Biological Sciences Quad, Irvine, 92697, United States.
Aaron M BornsteinUniversity of California, Irvine, Center for the Neurobiology of Learning and Memory, Qureshey Research Laboratory, Irvine, 92697, United States.

Funding

Testing a Memory-Based Hypothesis for AnhedoniaR01MH128306 · NIMH · UNIVERSITY OF CALIFORNIA-IRVINE · PI JULIAN F. THAYER, Michael A Yassa · 2023 to 2026
$3.2M
Memory guided planning across the lifespanR01AG088306 · NIA · UNIVERSITY OF CALIFORNIA-IRVINE · PI Ilana Jacqueline Bennett, Aaron Michael Bornstein · 2025 to 2026
$1.5M
Improving multi-step planning in aging by overcoming deficits in memory encodingR21AG072673 · NIA · UNIVERSITY OF CALIFORNIA-IRVINE · PI BORNSTEIN, AARON MICHAEL · 2021 to 2022
$801k
NIA NIH HHS R01 AG088306NIA NIH HHS R21 AG072673NIMH NIH HHS R01 MH128306
6 · The paper itself

Abstract

The ability to discriminate similar visual stimuli has been used as an important index of memory function. This ability is widely thought to be supported by expanding the dimensionality of relevant neural codes, such that neural representations for the similar stimuli are maximally distinct, or "separated." An alternative hypothesis is that discrimination is supported by lossy compression of visual inputs, efficiently coding sensory information by discarding seemingly irrelevant details. A benefit of compression, relative to expansion, is that it allows the individual to efficiently retain fewer essential dimensions underlying stimulus variation-a process linked to higher-order visual processing-without hindering discrimination. Under the compression hypothesis, pattern separation is facilitated when more information from similar stimuli can be discarded, rather than preserving more information about distinct stimulus dimensions. We test the compression versus expansion hypotheses by predicting performance on the canonical mnemonic similarity task. First, we train neural networks to compress perceptual and semantic factors of stimuli, and measure lossiness of those representations using the mathematical framework underlying compression. Consistent with the compression hypothesis, and not the expansion hypothesis, we find that greater lossiness predicts the ease and performance of lure discrimination, particularly in later layers of convolutional neural networks shown to predict brain activity in the higher-order visual stream. We then empirically confirm these predictions across two sets of images, four behavioral datasets, and alternative metrics of lossiness. Finally, using task fMRI data, we identify signatures of lossy compression-neural dimensionality reduction and information loss-in the higher-order visual stream regions V4 and IT as well as hippocampal subregions dentate gyrus/CA3 and CA1 associated with lure discrimination performance. These results suggest lossy compression may support mnemonic discrimination behavior by discarding redundant and overlapping information.

Indexed as

efficient codingfalse memorymemory reconstructionnovelty detectionrate-distortion theory

Identifiers

PMID41279213
PMCPMC12632993

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.