Evidence map›Paper›PMID 37503133›Full record

ArticlebioRxiv : the preprint server for biology2023

NEURAL CORRELATES OF PERCEPTUAL SIMILARITY MASKING IN PRIMATE V1.

Spencer C Chen, Yuzhi Chen, Wilson S Geisler, Eyal Seidemann

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Spencer C ChenCenter for Perceptual Systems, University of Texas at Austin.
Yuzhi ChenCenter for Perceptual Systems, University of Texas at Austin.
Wilson S GeislerCenter for Perceptual Systems, University of Texas at Austin.
Eyal SeidemannCenter for Perceptual Systems, University of Texas at Austin.ORCID 0000-0003-2841-5948
The University of Texas at Austin · USRutgers Sexual and Reproductive Health and Rights · NL

Funding

Linking Neural Population Activity and Visual PerceptionR01EY016454 · NEI · UNIVERSITY OF TEXAS AUSTIN · PI SEIDEMANN, EYAL J · 2005 to 2024
$6.6M
Visual Search and Detection in Natural ScenesR01EY024662 · NEI · UNIVERSITY OF TEXAS AT AUSTIN · PI GEISLER, WILSON S · 2015 to 2022
$2.9M
An optical-genetic toolbox for reading and writing neural population codes in functional mapsU01NS099720 · NINDS · UNIVERSITY OF TEXAS AT AUSTIN · PI GEISLER, WILSON S, SEIDEMANN, EYAL J · 2016 to 2018
$1.8M
NEI NIH HHS R01 EY016454NEI NIH HHS R01 EY024662NINDS NIH HHS U01 NS099720
6 · The paper itself

Abstract

Visual detection is a fundamental natural task. Detection becomes more challenging as the similarity between the target and the background in which it is embedded increases, a phenomenon termed "similarity masking". To test the hypothesis that V1 contributes to similarity masking, we used voltage sensitive dye imaging (VSDI) to measure V1 population responses while macaque monkeys performed a detection task under varying levels of target-background similarity. Paradoxically, we find that during an initial transient phase, V1 responses to the target are enhanced, rather than suppressed, by target-background similarity. This effect reverses in the second phase of the response, so that in this phase V1 signals are positively correlated with the behavioral effect of similarity. Finally, we show that a simple model with delayed divisive normalization can qualitatively account for our findings. Overall, our results support the hypothesis that a nonlinear gain control mechanism in V1 contributes to perceptual similarity masking.

Identifiers

PMID37503133
PMCPMC10369882
OpenAlexW4384070684

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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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 OpenQuestion graph.