Evidence map›Paper›PMID 29362808›Full record

ArticleJournal of vision2018

Stimulus conflation and tuning selectivity in V4 neurons: a model of visual crowding.

Brad C Motter

Abstract read
In one paragraph

Article in Journal of vision, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Human V4 size predicts crowding distance.Nature communications · 2025
    Article
  2. Neural Correlates of Crowding in Macaque Area V4.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024
    Article
  3. Neural correlates of crowding in macaque area V4.bioRxiv : the preprint server for biology · 2023
    Article
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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

1 author.

Brad C MotterVeterans Affairs Medical Center, Syracuse, NY, USA.

Funding

Neural Mechanisms of Visual CrowdingR01EY018693 · NEI · VETERANS HEALTH RESEARCH INSTITUTE OF CENTRAL NEW YORK, INC. · PI MOTTER, BRAD C · 2008 to 2011
$1.5M
NEI NIH HHS R01 EY018693
6 · The paper itself

Abstract

Visual crowding is a fundamental constraint on our ability to identify peripheral objects in cluttered environments. This study proposes a descriptive model for understanding crowding based on the tuning selectivity for stimuli within the receptive field (RF) and examines potential neural correlates in cortical area V4. For V4 neurons, optimally sized, letter-like stimuli are much smaller than the RF. This permits stimulus conflation, the fusing of separate objects into a single identity, to occur within the RF of single neurons. Flanking interactions between such stimuli were found to be limited to the RF. The response to an optimal stimulus centered in the neuron's RF, is suppressed by the simultaneous presentation of flanking stimuli within the RF. The degree of suppression is a function of the neuron's stimulus tuning properties and the position of the flanker within the RF. A single neuron may show suppression or facilitation depending on the detailed stimulus conditions and the relationship to tuning selectivity. Loss of activity in the set of neurons tuned to a particular stimulus alters its overall representation and potential identification, thus forming a basis for visual crowding effects. The mechanisms that determine the outcome of conflation are associated with object identification, and are not some other independent visual phenomena.

Indexed as

CrowdingAnimalsMacaca mulattaNeuronsPattern Recognition, VisualPhotic StimulationVisual CortexVisual Fields

Identifiers

PMID29362808
PMCPMC5783327

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.