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Neural computations in the foveal and peripheral visual fields during active search.

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2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

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  1. [1] § Methods › Data analysis: selection of category-selective units ↔ code/data preprocessing/category_selectivity_foveal.m, the whole file · a weak match · score 0.62 · 50–200 ms, category selective, SI, cues, 150 ms, window
  2. [2] § Methods › Data analysis: selection of category-selective units ↔ code/data preprocessing/category_selectivity_peri.m, lines 1–39 · score 0.58 · 50–200 ms, category selective, peripheral units, 150 ms, window, saccade

Paper

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The authors' code

MATLAB · 136 lines · 5 KB · no license · 1 match

  1. % foveal unit
  2. % selectivity calculated between face / house category during category task (ranksum)
  3. % baseline time window: -150 to 0 ms
  4. % response time window: 50 to 200 ms for V4, IT, and VPA; 100 to 200 ms for OFC
  6. clear;clc;close all;
  7. disk_name = '/Users/OSF dataset/';
  8. % TrlInfoMatrix
  9. tri_num = 1;
  10. bgn_time = 2;
  11. end_time = 3;
  12. task_name = 4;
  13. sam_ind = 5;
  14. tar1_ind = 6;
  15. tar1_loc_ind = 7;
  16. tar1_loc_x = 8;
  17. tar1_loc_y = 9;
  18. tar2_ind = 10;
  19. tar2_loc_ind = 11;
  20. tar2_loc_x = 12;
  21. tar2_loc_y = 13;
  22. chosen_tar_ind = 14;
  23. chosen_tar_loc_ind= 15;
  24. chosen_tar_loc_x = 16;
  25. chosen_tar_loc_y = 17;
  26. FP_on_time = 18;
  27. fix_FP_time = 19;
  28. sam_on_time = 20;
  29. sam_off_time = 21;
  30. FP_back_time = 22;
  31. tar_on_time = 23;
  32. search_onset_time= 24; % the first saccade onset after array onset
  33. search_times = 25;
  34. tar_off_time= 26;
  35. rew_time = 27;
  36. break_fix_time = 28;
  37. correct_or_not = 29;
  38. err_type = 30; %correct-36,noresponse-37,lateresponse-38,breakfixcenter-39,nofixcenter-40,earlyresponse-41,choosewrongpicture-42
  40. % SearchSti
  41. pic_ind = 1;
  42. loc_ind = 2;
  43. loc_x = 3;
  44. loc_y = 4;
  45. obj_num = 5; %tar1/distr1-10/tar2:1/2-11/12
  47. % SearchEye
  48. fix_ind= 1;
  49. sac_off_time = 2;% fixate stimulus time
  50. tar_on_timeE = [1,2];% first row is target on time
  51. sac_onset_time = 3;% eye leave stimulus time
  52. search_onset_timeE = [1,3];%first row is eye leave center point time
  53. sti_loc_ind = 4;% first row is 0
  54. sti_loc_x = 5;% first row is 0
  55. sti_loc_y = 6;% first row is 0
  56. sti_ind = 7;% first row is nan
  57. obj_numE = 8; %tar1/distr/tar2:1/2-11/12, first row is nan
  59. % parameter
  60. cat_task = 5;
  61. det_task = 4;
  62. corr_ = 36;
  63. old_house = [2001:2040,2049:2051,2053:2057];
  64. old_face = 3001:3040;
  65. flower = 1001:1040;
  66. hand = 4001:4040;
  68. FR_time = -2*1000:2*1000;
  69. baseline_rgX = find(FR_time>=-150&FR_time<=0);
  70. response_rgX = find(FR_time>=100&FR_time<=200);
  72. list_name =[disk_name,'RM008/cell list/cell list ofc.txt'];
  73. fp = fopen(list_name,'r');
  74. cell_list = textscan(fp,'%s');
  75. fclose(fp);
  76. cell_list = cell_list{1};
  78. fp = fopen([list_name(1:end-4),'si.txt'], 'w');
  79. for j = 1:length(cell_list)
  80. new_line = cell_list{j};
  81. colons_ind = strfind(new_line,':');
  82. name = new_line(colons_ind(3)+1:colons_ind(4)-1);
  83. sam_response = strcmp(new_line(colons_ind(11)+1:colons_ind(12)-1),'0');
  84. arr_response = strcmp(new_line(colons_ind(13)+1:colons_ind(14)-1),'0');
  85. if sam_response && ~arr_response
  86. clear cal_file_name Eye neuron Photodiode RF_file_name RF_list SearchEye SearchSti TrlInfoMatrix
  87. load([disk_name,'RM0',name(2:3),'/neurons/',name,'.mat'])
  88. % response during cue period
  89. face_trialnum = find(all([TrlInfoMatrix(:,err_type)==corr_,TrlInfoMatrix(:,task_name)==cat_task,ismember(TrlInfoMatrix(:,sam_ind),old_face)],2));
  90. house_trialnum = find(all([TrlInfoMatrix(:,err_type)==corr_,TrlInfoMatrix(:,task_name)==cat_task,ismember(TrlInfoMatrix(:,sam_ind),old_house)],2));
  91. trialnum = face_trialnum;
  92. face_rsp = nan(length(trialnum),length(FR_time));
  93. for i = 1:length(trialnum)
  94. face_rsp(i,:) = ismember(FR_time,ceil((neuron-TrlInfoMatrix(trialnum(i),sam_on_time))*1000));
  95. end
  96. trialnum = house_trialnum;
  97. house_rsp = nan(length(trialnum),length(FR_time));
  98. for i = 1:length(trialnum)
  99. house_rsp(i,:) = ismember(FR_time,ceil((neuron-TrlInfoMatrix(trialnum(i),sam_on_time))*1000));
  100. end
  101. face_rsp = Smooth_Histogram(face_rsp,3);
  102. house_rsp = Smooth_Histogram(house_rsp,3);
  103. %
  104. baseline_house = mean(house_rsp(:,baseline_rgX),2);
  105. baseline_face = mean(face_rsp(:,baseline_rgX),2);
  106. response_house = mean(house_rsp(:,response_rgX),2);
  107. response_face = mean(face_rsp(:,response_rgX),2);
  108. % statistics between house\face
  109. p_cat = nan(2,1);
  110. [~,p_cat(1)] = ranksum(response_house,response_face,'alpha',0.05);
  111. p_cat(2) = mean(response_house) - mean(response_face);
  112. if p_cat(1) == 1 && p_cat(2) > 0
  113. prefer = 'house';
  114. elseif p_cat(1) == 1 && p_cat(2) < 0
  115. prefer = 'face';
  116. else
  117. prefer = num2str(nan);
  118. end
  119. % calculate selectivity index
  120. face_magnitude = mean(response_face) - mean(baseline_face);
  121. house_magnitude = mean(response_house) - mean(baseline_house);
  122. if face_magnitude > 0 && house_magnitude < 0
  123. SI = 1;
  124. elseif face_magnitude < 0 && house_magnitude > 0
  125. SI = -1;
  126. else
  127. SI = (face_magnitude - house_magnitude)/(face_magnitude + house_magnitude);
  128. end
  129. new_line = [new_line(1:colons_ind(15)),prefer,':SI:',num2str(SI),':end'];
  130. else
  131. new_line = [new_line(1:colons_ind(16)),'end'];
  132. end
  133. fprintf(fp,'%s\r\n',new_line);
  134. end
  135. fclose(fp);

category_selectivity_foveal.m, no license · at the source

Overview

Authors: Jie Zhang1,2,3, Xiaocang Zhu1,3, Zhengyu Ma2, Shanshan Wang4, Yutian Wang3,5, Hossein Esteky6, Yonghong Tian2, Huihui Zhou1,2
ORCID iDs: Jie Zhang, Huihui Zhou
  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
  2. Network Intelligence Research, Peng Cheng Laboratory, Shenzhen, China
  3. University of Chinese Academy of Sciences, Beijing, China
  4. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
  5. Faculty of Life and Health Sciences, Shenzhen University of Advanced Technology, Shenzhen, China
  6. Research Group for Brain and Cognitive Sciences, School of Medicine, Shahid Beheshti University, Tehran, Iran
Institutions: Chinese Academy of Sciences (China); Peng Cheng Laboratory (China); Shenzhen Institutes of Advanced Technology (China); University of Chinese Academy of Sciences (China); Shenzhen University of Advanced Technology (China); Shahid Beheshti University (Iran)
Journal: eLife, volume 15, article RP109498
Dates: published online 20 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.109498 · PMID 42622516 · PMCID PMC13493125 · OpenAlex W7147707932
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), non-human primate (organism), cognitive (subfield)
Methods: Statistics, Single-unit activity, calcium imaging, Physiology & signal measures, Connectivity
Keywords: Rhesus macaque
MeSH: Attention*, Fovea Centralis*, Visual Cortex*, Visual Fields*, Visual Perception*, Animals, Fixation, Ocular, Macaca mulatta, Male, Photic Stimulation, Saccades (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (62206141, 31671108, 62027804, 62236009); International Partnership Program of the Chinese Academy of Sciences (172644KYSB20160175); Shenzhen Fundamental Research Program (JCYJ20200109114805984)
Citations: not cited yet (Europe PMC); 74 references in the paper

Abstract

Active vision requires coordinated attentional processing across both foveal and peripheral receptive fields (RFs), yet the underlying neural dynamics and computational mechanisms remain poorly understood. Previous research has predominantly focused on attention in the visual periphery, leaving the role of foveal processing in naturalistic tasks largely unexplored. Here, we recorded neural activity from both foveal and peripheral RFs in areas V4 and IT of monkeys during free-gaze visual search among complex stimuli. We found robust feature-based attentional enhancements in foveal units, challenging the prevailing view that such modulation is predominantly peripheral. By integrating data from foveal and peripheral recordings, we revealed a non-uniform, dynamically distributed pattern of feature attention across the visual field. Behaviorally, foveal attentional enhancements promoted sustained or repeated fixations on targets, while peripheral attentional signals facilitated target detection and guidance of future saccades. These findings suggest that foveal and peripheral attention operate in a complementary fashion and highlight the critical role of foveal feature attention in shaping global attention allocation and fixation behavior during active vision. This work advances our understanding of the neural computations that support complex visual search and underscores the need to account for foveal processing in models of attention.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

OSF sdgkr

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (5)
Size: 415 files, 5 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (4 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
5 files
At the source: doi.org/10.17605/OSF.IO/SDGKR

Code availability

The source code for this study is publicly available on OSF (https://doi.org/10.17605/OSF.IO/SDGKR).

Reproduced under the paper's license (CC BY), from the paper cited above.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 5 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

All data that support the findings of this study are publicly available on OSF (https://doi.org/10.17605/OSF.IO/SDGKR).

The following previously published dataset was used:

Zhang J, Zhu X, Zhou H. 2024. Data release for "A large neuronal dataset for natural category-based free-gaze visual search in macaques". Open Science Framework.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 1 keyword, 11 MeSH terms, 3 funders, 74 references.

Cite

This paper

Zhang, J., Zhu, X., Ma, Z., Wang, S., Wang, Y., Esteky, H., Tian, Y., & Zhou, H. (2026). Neural computations in the foveal and peripheral visual fields during active search. eLife, 15, RP109498. https://doi.org/10.7554/elife.109498

BibTeX

@article{zhang2026neural,
author = {Zhang, Jie and Zhu, Xiaocang and Ma, Zhengyu and Wang, Shanshan and Wang, Yutian and Esteky, Hossein and Tian, Yonghong and Zhou, Huihui},
title = {{Neural computations in the foveal and peripheral visual fields during active search}},
journal = {eLife},
year = {2026},
month = aug,
volume = {15},
pages = {RP109498},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.109498},
url = {https://doi.org/10.7554/elife.109498},
pmid = {42622516},
pmcid = {PMC13493125}
}

RIS

TY - JOUR
AU - Zhang, Jie
AU - Zhu, Xiaocang
AU - Ma, Zhengyu
AU - Wang, Shanshan
AU - Wang, Yutian
AU - Esteky, Hossein
AU - Tian, Yonghong
AU - Zhou, Huihui
TI - Neural computations in the foveal and peripheral visual fields during active search
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/08/20
VL - 15
SP - RP109498
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.109498
UR - https://doi.org/10.7554/elife.109498
LA - en
ER -

CSL-JSON

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"family": "Zhang",
"given": "Jie"
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