Evidence map›Paper›PMID 40355416›Full record

ArticleScientific data2025

A large neuronal dataset for natural category-based free-gaze visual search in macaques.

Jie Zhang, Xiaocang Zhu, Huihui Zhou, Shuo Wang

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. 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

4 authors.

Jie Zhang *Department of Radiology, Washington University in St. Louis, St. Louis, MO, 63110, USA. zjie@wustl.edu.ORCID http://orcid.org/0000-0002-1464-8639
Xiaocang Zhu *Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Huihui ZhouPeng Cheng Laboratory, Shenzhen, 518000, China. zhouhh@pcl.ac.cn.
Shuo WangDepartment of Radiology, Washington University in St. Louis, St. Louis, MO, 63110, USA. shuowang@wustl.edu.

Funding

Model-Based Investigation of Aberrant Neural Face Representation in AutismR01MH129426 · NIMH · WASHINGTON UNIVERSITY · PI Shuo Wang · 2022 to 2026
$1.9M
Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) R01MH129426National Science Foundation (NSF) BCS-1945230NIMH NIH HHS R01 MH129426United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (AF Office of Scientific Research) FA9550-21-1-0088
6 · The paper itself

Abstract

Goal-directed visual attention is a fundamental cognitive process that demonstrates the brain's remarkable ability to prioritize visual information relevant to specific tasks or objectives. Despite its importance, there is a lack of comprehensive datasets to investigate the underlying neural mechanisms. Here, we present a large naturalistic visual search dataset in which rhesus macaques searched for targets among natural stimuli based on object categories using voluntary eye movements. The stimulus set included 40 images per category across four categories: faces, houses, flowers, and hands. We recorded activity from 6871 units in area V4, 8641 units in the inferior temporal cortex (IT), 5622 units in the orbital frontal cortex (OFC), and 9916 units in the lateral prefrontal cortex (LPFC). These units exhibited diverse receptive fields and selectivity for visual categories. Together, our extensive dataset provides a rich neuronal population across multiple brain areas, enabling a comprehensive analysis of the neural processes underlying goal-directed visual attention.

Indexed as

AttentionMacaca mulattaNeuronsVisual PerceptionAnimalsEye Movements

Identifiers

PMID40355416
PMCPMC12069534

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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.