Active vision is linked to category selectivity in the individual brain.

Code ↔ Paper

3 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.

The 3 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Results › Replication of stable individual gaze tendencies towards faces and text ↔ src/dataset/computeFixationMaps.m, the whole file · a weak match · score 0.59 · NUS VIP Visual, MIT license, Information Processing Lab, durations, fixations, gaze
[2] § Results › Replication of stable individual gaze tendencies towards faces and text ↔ src/dataset/showEyeData.m, the whole file · a weak match · score 0.59 · NUS VIP Visual, MIT license, Information Processing Lab, durations, fixations, gaze
[3] § Methods › Eye-tracking ↔ lib/gbvs/makeGBVSParams.m, the whole file · a weak match · score 0.51 · Eye movements, angle, resolution, scene

Paper

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The authors' code

MATLAB · 50 lines · 1.6 KB · MIT · 1 match

function computeFixationMaps(params)
% computeFixationMaps(params)
%
% ----------------------------------------------------------------------
% Matlab tools for "Predicting human gaze beyond pixels," Journal of Vision, 2014
% Juan Xu, Ming Jiang, Shuo Wang, Mohan Kankanhalli, Qi Zhao
%
% Copyright (c) 2014 NUS VIP - Visual Information Processing Lab
%
% Distributed under the MIT License
% See LICENSE file in the distribution folder.
% -----------------------------------------------------------------------
load(fullfile(params.path.eye, 'fixations.mat'));
outputPath = params.path.maps.fixation;
if ~exist(outputPath, 'dir')
mkdir(outputPath);
end
for i = 1 : params.nStimuli
filename = params.stimuli{i};
shortname = filename(1 : end - length(params.ext));
img = im2double(imread(fullfile(params.path.stimuli, filename)));
[h w ~] = size(img);
map = zeros([h w]);
fix_x = [];
fix_y = [];
fix_duration = [];
for j = 1:length(fixations{i}.subjects)
sub = fixations{i}.subjects{j};
fix_x = [fix_x max(1, min(round(sub.fix_x), w))];
fix_y = [fix_y max(1, min(round(sub.fix_y), h))];
fix_duration = [fix_duration sub.fix_duration];
end
fix_x = floor(fix_x);
fix_y = floor(fix_y);
for k = 1 : length(fix_x)
map(fix_y(k), fix_x(k)) = 1;
end
fixationPts = map;
save(fullfile(outputPath, [shortname '.mat']), 'fixationPts', 'fix_x', 'fix_y', 'fix_duration');
map = imfilter(map, params.eye.gaussian, 0);
map = normalise(map);
imwrite(map, fullfile(outputPath, filename));
end
end

computeFixationMaps.m at commit 4a46789, under MIT · at the source

Overview

Authors: Diana Kollenda^1,2, Elaheh Akbari^1,2, Maximilian D Broda^1,2, Benjamin de Haas^1,2

ORCID iDs: Diana Kollenda, Maximilian D Broda, Benjamin de Haas

Experimental Psychology, Justus-Liebig University Giessen, Giessen, Germany
Center for Mind, Brain and Behaviour, Marburg and Giessen, Germany

Institutions: Justus-Liebig-Universität Gießen (Germany)

Journal: Nature human behaviour, volume 10, issue 9, pages 1808-1820

Dates: received 21 August 2025; accepted 6 May 2026; published online 7 July 2026; in print 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41562-026-02494-5 · PMID 42414593 · PMCID PMC13590408 · OpenAlex W7167618931

Open access: hybrid, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), cognitive (subfield)

Methods: Statistics, Connectivity, fMRI & imaging, Physiology & signal measures, Machine learning

Keywords: Human behaviour, Visual system

MeSH: Brain*, Facial Recognition*, Fixation, Ocular*, Pattern Recognition, Visual*, Adult, Brain Mapping, Eye-Tracking Technology, Female, Humans, Magnetic Resonance Imaging, Male, Reading, Young Adult (* major topic)

Topic: Face Recognition and Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Deutsche Forschungsgemeinschaft (German Research Foundation) (222641018-SFB/TRR 135 TP C9, 222541018-SFB/TRR 135 TP C9); European Research Council (852885)

Citations: not cited yet (Europe PMC); 82 references in the paper

Abstract

Individuals reliably differ in how they look at complex visual scenes, with the most prominent variation in their propensity to fixate faces and text. Here we tested the hypothesis that these differences in gaze are linked to representational properties of the individual visual system in 61 adults. Eye-tracking captured each observer’s characteristic gaze tendencies during naturalistic scene viewing, and independent functional magnetic resonance imaging recorded category-selective responses to faces, words and other stimuli when participants were instructed to fixate centrally. We find that the propensity to fixate faces or text goes along with enhanced distinctiveness and enlarged functional regions of corresponding categorical representations in the ventral stream. These in turn predicted performance on reading and face recognition tasks. Thus, active vision appears linked to the precision of category-selective encoding and corresponding neural resources in the individual brain.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

NUS-VIP/predicting-human-gaze-beyond-pixels

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 4a467890fcb40a2fec04cdded552760942bac7a4, 1 April 2015

Languages: MATLAB (73), C++ (8), C (5), C/C++ (1)

Size: 2,289 files, 87 scripts

Software Heritage: not archived

Found in: the text, “Replication of stable individual gaze tendencies”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Image Processing Toolbox (16 files), Statistics and Machine Learning Toolbox (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

89 files

demo.m — MATLAB, 70 lines
lib/gbvs/algsrc/connectMatrix.m — MATLAB, 75 lines
lib/gbvs/algsrc/distanceMatrix.m — MATLAB, 74 lines
lib/gbvs/algsrc/formMapPyramid.m — MATLAB, 51 lines
lib/gbvs/algsrc/getDims.m — MATLAB, 5 lines
lib/gbvs/algsrc/graphsalapply.m — MATLAB, 61 lines
lib/gbvs/algsrc/graphsalinit.m — MATLAB, 34 lines
lib/gbvs/algsrc/indexmatrix.m — MATLAB, 8 lines
lib/gbvs/algsrc/initGBVS.m — MATLAB, 57 lines
lib/gbvs/algsrc/makeLocationMap.m — MATLAB, 38 lines
lib/gbvs/algsrc/mexArrangeLinear.cc — C++, 56 lines
lib/gbvs/algsrc/mexAssignWeights.cc — C++, 70 lines
lib/gbvs/algsrc/mexColumnNormalize.cc — C++, 34 lines
lib/gbvs/algsrc/mexSumOverScales.cc — C++, 60 lines
lib/gbvs/algsrc/mexVectorToMap.cc — C++, 45 lines
lib/gbvs/algsrc/namenodes.m — MATLAB, 19 lines
lib/gbvs/algsrc/partitionindex.m — MATLAB, 17 lines
lib/gbvs/algsrc/principalEigenvectorRaw.m — MATLAB, 39 lines
lib/gbvs/algsrc/simpledistance.m — MATLAB, 38 lines
lib/gbvs/algsrc/sparseness.m — MATLAB, 2 lines
lib/gbvs/compile/cleanmex.m — MATLAB, 1 line
lib/gbvs/compile/gbvs_compile.m — MATLAB, 19 lines
lib/gbvs/compile/gbvs_compile2.m — MATLAB, 19 lines
lib/gbvs/demo/demonstration.m — MATLAB, 73 lines
lib/gbvs/demo/flicker_motion_demo.m — MATLAB, 29 lines
lib/gbvs/demo/simplest_demonstration.m — MATLAB, 29 lines
lib/gbvs/gbvs.m — MATLAB, 221 lines
lib/gbvs/gbvs_fast.m — MATLAB, 16 lines
lib/gbvs/gbvs_install.m — MATLAB, 5 lines
lib/gbvs/ittikochmap.m — MATLAB, 21 lines
lib/gbvs/makeGBVSParams.m — MATLAB, 141 lines, 1 match
lib/gbvs/saltoolbox/attenuateBordersGBVS.m — MATLAB, 35 lines
lib/gbvs/saltoolbox/makeGaborFilterGBVS.m — MATLAB, 78 lines
lib/gbvs/saltoolbox/maxNormalizeStdGBVS.m — MATLAB, 39 lines
lib/gbvs/saltoolbox/mexLocalMaximaGBVS.cc — C++, 68 lines
lib/gbvs/saltoolbox/mySubsample.cc — C++, 192 lines
lib/gbvs/saltoolbox/safeDivideGBVS.m — MATLAB, 17 lines
lib/gbvs/util/areaROC.m — MATLAB, 13 lines
lib/gbvs/util/featureChannels/C_color.m — MATLAB, 17 lines
lib/gbvs/util/featureChannels/D_dklcolor.m — MATLAB, 33 lines
lib/gbvs/util/featureChannels/F_flicker.m — MATLAB, 9 lines
lib/gbvs/util/featureChannels/I_intensity.m — MATLAB, 9 lines
lib/gbvs/util/featureChannels/M_motion.m — MATLAB, 18 lines
lib/gbvs/util/featureChannels/O_orientation.m — MATLAB, 16 lines
lib/gbvs/util/featureChannels/R_contrast.m — MATLAB, 13 lines
lib/gbvs/util/getBestRows.m — MATLAB, 11 lines
lib/gbvs/util/getFeatureMaps.m — MATLAB, 135 lines
lib/gbvs/util/getIntelligentThresholds.m — MATLAB, 8 lines
lib/gbvs/util/heatmap_overlay.m — MATLAB, 29 lines
lib/gbvs/util/linearmap.m — MATLAB, 4 lines
lib/gbvs/util/makeFixationMask.m — MATLAB, 31 lines
lib/gbvs/util/myContrast.cc — C++, 82 lines
lib/gbvs/util/mycombnk.m — MATLAB, 30 lines
lib/gbvs/util/myconv2.m — MATLAB, 12 lines
lib/gbvs/util/mygausskernel.m — MATLAB, 11 lines
lib/gbvs/util/mygetrgb.m — MATLAB, 6 lines
lib/gbvs/util/mymessage.m — MATLAB, 19 lines
lib/gbvs/util/padImage.m — MATLAB, 20 lines
lib/gbvs/util/padImageOld.m — MATLAB, 33 lines
lib/gbvs/util/rankimg.m — MATLAB, 11 lines
lib/gbvs/util/rgb2dkl.m — MATLAB, 312 lines
lib/gbvs/util/rocSal.m — MATLAB, 70 lines
lib/gbvs/util/rocScoreSaliencyVsFixations.m — MATLAB, 13 lines
lib/gbvs/util/shiftImage.m — MATLAB, 18 lines
lib/gbvs/util/show_imgnmap.m — MATLAB, 3 lines
lib/gbvs/util/show_imgnmap2.m — MATLAB, 3 lines
lib/liblinear/libsvmread.c — C, 212 lines
lib/liblinear/libsvmwrite.c — C, 106 lines
lib/liblinear/linear_model_matlab.c — C, 176 lines
lib/liblinear/linear_model_matlab.h — C/C++, 2 lines
lib/liblinear/make.m — MATLAB, 21 lines
lib/liblinear/predict.c — C, 331 lines
lib/liblinear/train.c — C, 418 lines
src/common/config.m — MATLAB, 51 lines
src/common/normalise.m — MATLAB, 22 lines
src/dataset/computeFixationMaps.m — MATLAB, 50 lines, 1 match
src/dataset/computeMouseFixationMaps.m — MATLAB, 39 lines
src/dataset/showEyeData.m — MATLAB, 50 lines, 1 match
src/metric/computeInterSubjectAUC.m — MATLAB, 57 lines
src/metric/normalizedAUC.m — MATLAB, 53 lines
src/model/computeIttiMaps.m — MATLAB, 46 lines
src/model/computeObjectMaps.m — MATLAB, 60 lines
src/model/computeSaliencyMaps.m — MATLAB, 43 lines
src/model/computeSemanticMaps.m — MATLAB, 57 lines
src/model/extractObjectFeatures.m — MATLAB, 63 lines
src/model/splitData.m — MATLAB, 33 lines
src/model/trainModel.m — MATLAB, 76 lines
LICENSE — License, 21 lines
README.md — Text, 55 lines

At the source: github.com/NUS-VIP/predicting-human-gaze-beyond-pixels

OSF 9fhxk

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 2 files

Software Heritage: not checked

Found in: “Code availability”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

1 file, to read at the source

This repository has no license: its authors keep all rights. Read it at the source.

readme.txt — Text, 27 lines, not shown here

At the source: osf.io/9fhxk/

Code availability

MATLAB code and stimuli used to generate the figures and results are freely available via OSF at https://osf.io/9fhxk/.

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
87 scripts, each with its path and the digest of its content;
3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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

Data availability

The anonymized data used to generate the results in this study and generate Figs. 1–5 are freely available via OSF at https://osf.io/9fhxk/. The raw fMRI data generated in this study are protected by privacy laws and not available. However, raw or processed data may be shared with accredited researchers upon request.

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

Versions

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Version 2, 28 September 2026

Publisher: — → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 2 keywords, 13 MeSH terms, 2 funders, 75 references.

Cite

This paper

Kollenda, D., Akbari, E., Broda, M. D., & de Haas, B. (2026). Active vision is linked to category selectivity in the individual brain. Nature human behaviour, 10(9), 1808-1820. https://doi.org/10.1038/s41562-026-02494-5

BibTeX

@article{kollenda2026active, author = {Kollenda, Diana and Akbari, Elaheh and Broda, Maximilian D and de Haas, Benjamin}, title = {{Active vision is linked to category selectivity in the individual brain}}, journal = {Nature human behaviour}, year = {2026}, month = jul, volume = {10}, number = {9}, pages = {1808--1820}, publisher = {Nature Portfolio}, issn = {2397-3374}, doi = {10.1038/s41562-026-02494-5}, url = {https://doi.org/10.1038/s41562-026-02494-5}, pmid = {42414593}, pmcid = {PMC13590408} }

RIS

TY - JOUR AU - Kollenda, Diana AU - Akbari, Elaheh AU - Broda, Maximilian D AU - de Haas, Benjamin TI - Active vision is linked to category selectivity in the individual brain T2 - Nature human behaviour J2 - Nat Hum Behav PY - 2026 DA - 2026/07/07 VL - 10 IS - 9 SP - 1808 EP - 1820 SN - 2397-3374 PB - Nature Portfolio DO - 10.1038/s41562-026-02494-5 UR - https://doi.org/10.1038/s41562-026-02494-5 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41562-026-02494-5", "type": "article-journal", "title": "Active vision is linked to category selectivity in the individual brain", "container-title": "Nature human behaviour", "author": [ { "family": "Kollenda", "given": "Diana" }, { "family": "Akbari", "given": "Elaheh" }, { "family": "Broda", "given": "Maximilian D" }, { "family": "de Haas", "given": "Benjamin" } ], "container-title-short": "Nat Hum Behav", "volume": "10", "issue": "9", "page": "1808-1820", "DOI": "10.1038/s41562-026-02494-5", "PMID": "42414593", "PMCID": "PMC13590408", "ISSN": "2397-3374", "publisher": "Nature Portfolio", "URL": "https://doi.org/10.1038/s41562-026-02494-5", "language": "en", "issued": { "date-parts": [ [ 2026, 7, 7 ] ] } }

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Active vision is linked to category selectivity in the individual brain.

Code ↔ Paper

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NUS-VIP/predicting-human-gaze-beyond-pixels

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