Digit-tracking reveals curiosity-driven visual attention in macaque monkeys.
The 6 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
- [1] § Methods › The convolutional neural network model ↔ 03_CNN_Prediction/PredictSaliency.m, lines 2–41 · score 0.76 · Convolutional Neural Network, AlexNet, saliency map, layer, prediction, CNN
- [2] § Methods › Digit-tracking method ↔ 01_Task/DIGITRACK_SetB.m, lines 10–24 · score 0.57 · aperture window, Gaussian blur, picture, resolution, distance, exploration
- [3] § Methods › Digit-tracking method ↔ 01_Task/DIGITRACK_DEMO.m, lines 10–24 · score 0.56 · aperture window, Gaussian blur, picture, resolution, distance, exploration
- [4] § Results › Digit-tracking captures features predicted by models ↔ 03_CNN_Prediction/PredictSaliency.m, lines 2–41 · score 0.55 · Convolutional Neural Network, CNN predictions, saliency, human, maps
- [5] § Methods › Attention maps ↔ 02_Attention_Map/filter_2d_heat_map.m, the whole file · a weak match · score 0.51 · heat maps, density, kernel, Attention maps, Gaussian
- [6] § Methods › Attention maps ↔ 02_Attention_Map/filter_3d_heat_map.m, the whole file · a weak match · score 0.51 · heat maps, density, kernel, Attention maps, Gaussian
Paper
Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC
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The authors' code
MATLAB · 228 lines · 9.9 KB · no license · 2 matches
- function [SALIENCY_MAP,SALIENCY_MAP_H,SALIENCY_MAP_M,SALIENCY_MAP_Mat,SALIENCY_MAP_Mav]=PredictSaliency(I,Levels,HFpenalty,prct_features)
- % PredictSaliency() - Makes a saliency map from an input image (I) using the
- % relu5 layer of the AlexNet Convolutional Neural Network (CNN) and weights
- % learned using a digit-tracking task and the wCORR method (Lio et al.
- % 2019) and weights estimated with the same method in an alternative dataset and with human and monkey explorations.
- % The input image is resized at the resolution of the CNN, or
- % sampled in a set of sub-images at the resolution of the CNN. Then
- % predicted saliency for each sub-images are assembled to produce a
- % saliency map for the whole image.
- %
- %
- % USAGES :
- % e.g.
- % [SALIENCY_MAP, SALIENCY_MAP_H, SALIENCY_MAP_M, SALIENCY_MAP_Mat, SALIENCY_MAP_Mav] = PredictSaliency(I,3,0.75,10)
- % % 3 pyramid levels, high frequency penalty=0.75, 10 percent for feature selection
- %
- %
- %
- % INPUTS:
- % -------
- % I = RGB image.
- % Levels (integer) = Number of levels in the pyramid. Default: 3. More levels = more detailed saliency prediction.
- % HFpenalty (0<HFpenalty<=1) = Default: 0.75. If 1 (no penalty). If <1, reduce the weight of the higher levels of the pyramid to obtain saliency prediction less biased on highly salient small details.
- % prct_features (percent) = Percentile threshold for attractive and avoided feature selection
- %
- %
- % OUTPUTS:
- % --------
- % SALIENCY_MAP (matrix) = Main saliency map with the dimensions of the input picture I.
- % SALIENCY_MAP_H (matrix) = Human-based weights saliency map.
- % SALIENCY_MAP_M (matrix) = Monkey-based weights saliency map.
- % SALIENCY_MAP_Mat (matrix) = Monkey attractive features saliency map.
- % SALIENCY_MAP_Mav (matrix) = Monkey avoided features saliency map.
- %
- % % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
- % Author : G. Lio
- % Centre de Neurosciences Cognitives, CNRS UMR 5229, Lyon, France
- % v1.0 2019
- % This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
- %
- % Legal code :
- % https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
- net = alexnet; % CNN used for features extraction
- load Wpredict.mat % Weights learned using the digit-tracking experiment described in the Lio, Duhamel, Sirigu, 2019 (Nat. Com.) paper.
- load WpredictDatabase2Z.mat % Weights learned using Human and Monkey digit-tracking data on a second picture database. Estimates where intra-picture Z-transformed to focus on weight relative activation in place of global weight activation (focus on feature choice than features absolute attraction and compensate for global less CNN activation in noiser attention-map estimates).
- switch nargin
- case 3
- FlagDisplay=1;
- case 2
- FlagDisplay=1;
- HFpenalty=0.75;
- case 1
- FlagDisplay=1;
- HFpenalty=0.75; % 1=no penalty <1= Reduce the saliency level of highly salient details.
- Levels=3; % pyramid levels / more levels=more detailed analysis of the picture
- end
- Output_resolution=[227 227]; % Images resolution = input resolution of the CNN
- Stride=75; % Overlap between windows.
- LevelCoeff=ones(1,Levels);
- if(Levels>1)
- for l=2:Levels
- LevelCoeff(l)=LevelCoeff(l-1).*HFpenalty;
- end
- end
- sPICT=size(I);
- %) superresolution images generation
- [OUT,COORD,W] = PyramidExp(I,Output_resolution,Levels,Stride);
- for LEV=1:Levels
- fprintf('>> Process Layer %d...\n',LEV);
- s=size(OUT{LEV});
- if(length(s)==3)
- nb_pict=1;
- else
- nb_pict=s(4);
- end
- RES{LEV}=zeros(Output_resolution(1).*LEV,Output_resolution(1).*LEV);
- RES_H{LEV}=zeros(Output_resolution(1).*LEV,Output_resolution(1).*LEV);
- RES_M{LEV}=zeros(Output_resolution(1).*LEV,Output_resolution(1).*LEV);
- RES_D{LEV}=zeros(Output_resolution(1).*LEV,Output_resolution(1).*LEV);
- RES_Mat{LEV}=zeros(Output_resolution(1).*LEV,Output_resolution(1).*LEV);
- RES_Mav{LEV}=zeros(Output_resolution(1).*LEV,Output_resolution(1).*LEV);
- for j=1:nb_pict
- %) Conv-Layer + RELU outputs
- act5 = activations(net,OUT{LEV}(:,:,:,j),'relu5','OutputAs','channels');
- sz = size(act5);
- act5 = reshape(act5,[sz(1) sz(2) 1 sz(3)]);
- %) Interp to the CNN input resolution
- act=squeeze(imresize(act5,[227 227]));
- %) Weighted combination of the channels
- idx1av=(WpredictM1<prctile(WpredictM1,prct_features));
- idx2av=(WpredictM2<prctile(WpredictM2,prct_features));
- idx3av=(WpredictM3<prctile(WpredictM3,prct_features));
- idx1at=(WpredictM1>prctile(WpredictM1,100-prct_features));
- idx2at=(WpredictM2>prctile(WpredictM2,100-prct_features));
- idx3at=(WpredictM3>prctile(WpredictM3,100-prct_features));
- WpredictM1av=abs(WpredictM1.*idx1av);
- WpredictM2av=abs(WpredictM2.*idx2av);
- WpredictM3av=abs(WpredictM3.*idx3av);
- WpredictM1at=WpredictM1.*idx1at;
- WpredictM2at=WpredictM2.*idx2at;
- WpredictM3at=WpredictM3.*idx3at;
- s=size(act);
- for i=1:s(3)
- if(i==1)
- learnedMapH1=act(:,:,i).*Wpredict(i);
- learnedMapH2=act(:,:,i).*WpredictH1(i);
- learnedMapM=act(:,:,i).*WpredictM1(i);
- learnedMapD=act(:,:,i).*WpredictDIFF(i);
- learnedMapMat=act(:,:,i).*WpredictM1at(i);
- learnedMapMav=act(:,:,i).*WpredictM1av(i);
- else
- learnedMapH1=learnedMapH1+act(:,:,i).*Wpredict(i);
- learnedMapH2=learnedMapH2+act(:,:,i).*WpredictH1(i);
- learnedMapM=learnedMapM+act(:,:,i).*WpredictM1(i);
- learnedMapD=learnedMapD+act(:,:,i).*WpredictDIFF(i);
- learnedMapMat=learnedMapMat+act(:,:,i).*WpredictM1at(i);
- learnedMapMav=learnedMapMav+act(:,:,i).*WpredictM1av(i);
- end
- end
- learnedMapH1=imresize(learnedMapH1,Output_resolution);
- learnedMapH2=imresize(learnedMapH2,Output_resolution);
- learnedMapM=imresize(learnedMapM,Output_resolution);
- learnedMapD=imresize(learnedMapD,Output_resolution);
- learnedMapMat=imresize(learnedMapMat,Output_resolution);
- learnedMapMav=imresize(learnedMapMav,Output_resolution);
- RES{LEV}(COORD{LEV}.x1(j):COORD{LEV}.x2(j),COORD{LEV}.y1(j):COORD{LEV}.y2(j))=RES{LEV}(COORD{LEV}.x1(j):COORD{LEV}.x2(j),COORD{LEV}.y1(j):COORD{LEV}.y2(j))+double(learnedMapH1);
- RES_H{LEV}(COORD{LEV}.x1(j):COORD{LEV}.x2(j),COORD{LEV}.y1(j):COORD{LEV}.y2(j))=RES_H{LEV}(COORD{LEV}.x1(j):COORD{LEV}.x2(j),COORD{LEV}.y1(j):COORD{LEV}.y2(j))+double(learnedMapH2);
- RES_M{LEV}(COORD{LEV}.x1(j):COORD{LEV}.x2(j),COORD{LEV}.y1(j):COORD{LEV}.y2(j))=RES_M{LEV}(COORD{LEV}.x1(j):COORD{LEV}.x2(j),COORD{LEV}.y1(j):COORD{LEV}.y2(j))+double(learnedMapM);
- RES_D{LEV}(COORD{LEV}.x1(j):COORD{LEV}.x2(j),COORD{LEV}.y1(j):COORD{LEV}.y2(j))=RES_D{LEV}(COORD{LEV}.x1(j):COORD{LEV}.x2(j),COORD{LEV}.y1(j):COORD{LEV}.y2(j))+double(learnedMapD);
- RES_Mat{LEV}(COORD{LEV}.x1(j):COORD{LEV}.x2(j),COORD{LEV}.y1(j):COORD{LEV}.y2(j))=RES_Mat{LEV}(COORD{LEV}.x1(j):COORD{LEV}.x2(j),COORD{LEV}.y1(j):COORD{LEV}.y2(j))+double(learnedMapMat);
- RES_Mav{LEV}(COORD{LEV}.x1(j):COORD{LEV}.x2(j),COORD{LEV}.y1(j):COORD{LEV}.y2(j))=RES_Mav{LEV}(COORD{LEV}.x1(j):COORD{LEV}.x2(j),COORD{LEV}.y1(j):COORD{LEV}.y2(j))+double(learnedMapMav);
- end
- RES{LEV}=RES{LEV}./double(W{LEV});
- RES_H{LEV}=RES_H{LEV}./double(W{LEV});
- RES_M{LEV}=RES_M{LEV}./double(W{LEV});
- RES_D{LEV}=RES_D{LEV}./double(W{LEV});
- RES_Mat{LEV}=RES_Mat{LEV}./double(W{LEV});
- RES_Mav{LEV}=RES_Mav{LEV}./double(W{LEV});
- if(LEV==1)
- SALIENCY_MAP=imresize(RES{LEV},sPICT(1:2));
- SALIENCY_MAP_H=imresize(RES_H{LEV},sPICT(1:2));
- SALIENCY_MAP_M=imresize(RES_M{LEV},sPICT(1:2));
- SALIENCY_MAP_DIFF=imresize(RES_D{LEV},sPICT(1:2));
- SALIENCY_MAP_Mat=imresize(RES_Mat{LEV},sPICT(1:2));
- SALIENCY_MAP_Mav=imresize(RES_Mav{LEV},sPICT(1:2));
- else
- SALIENCY_MAP=SALIENCY_MAP+LevelCoeff(LEV).*imresize(RES{LEV},sPICT(1:2));
- SALIENCY_MAP_H=SALIENCY_MAP_H+LevelCoeff(LEV).*imresize(RES_H{LEV},sPICT(1:2));
- SALIENCY_MAP_M=SALIENCY_MAP_M+LevelCoeff(LEV).*imresize(RES_M{LEV},sPICT(1:2));
- SALIENCY_MAP_DIFF=SALIENCY_MAP_DIFF+LevelCoeff(LEV).*imresize(RES_D{LEV},sPICT(1:2));
- SALIENCY_MAP_Mat=SALIENCY_MAP_Mat+LevelCoeff(LEV).*imresize(RES_Mat{LEV},sPICT(1:2));
- SALIENCY_MAP_Mav=SALIENCY_MAP_Mav+LevelCoeff(LEV).*imresize(RES_Mav{LEV},sPICT(1:2));
- end
- end
- SALIENCY_MAP=SALIENCY_MAP./sum(LevelCoeff);
- SALIENCY_MAP_H=SALIENCY_MAP_H./sum(LevelCoeff);
- SALIENCY_MAP_M=SALIENCY_MAP_M./sum(LevelCoeff);
- SALIENCY_MAP_Mat=SALIENCY_MAP_Mat./sum(LevelCoeff);
- SALIENCY_MAP_Mav=SALIENCY_MAP_Mav./sum(LevelCoeff);
- end %// END FCT
PredictSaliency.m, no license · at the source
Overview
- Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR 5229, Université Claude Bernard Lyon, 67 Bd Pinel, 69675 Bron Cedex, France
- IMind Center of Excellence for Autism, Le Vinatier Hospital, Bron, France
- Institute of Neuroscience la Timone, UMR7289, CNRS, Aix-Marseille University, Marseille, France
Abstract
We used digit-tracking, a touch-based method for assessing visual attention, to investigate spontaneous exploration in macaque monkeys. By engaging with degraded images on a touch-sensitive display, monkeys could uncover high-resolution portions through finger movements, allowing for natural and unrestricted interaction. Monkeys received juice rewards after touching a predetermined number of pixels, but no specific regions were targeted. Attention maps were generated from their interactions, along with data from human digit-tracking and monkey eye-tracking experiments. Direct comparisons across recording methods revealed that monkey digit-tracking attention maps were significantly correlated with both monkey eye-tracking and human digit-tracking maps, indicating shared patterns of visual exploration across species and devices. We further applied a saliency model and a Convolutional Neural Network (CNN) model to predict the empirical explorations. The correlation between model prediction maps and empirical attention maps indicated that monkeys focused non-randomly on information-rich regions, with the CNN model providing the most accurate predictions. These findings suggest that exploration was driven by intrinsic curiosity, beyond the extrinsic rewards for interaction. Digit-tracking offers a minimally invasive, portable alternative to eye-tracking, expanding research opportunities in visual cognition within ecologically valid settings.
Supplementary Information: The online version contains supplementary material available at https://
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 6 matches between paragraphs and lines of code.
DirkBWalther/SaliencyToolbox
097daa42b51a4c7c8c1253408e260e9c94e6fa0f, 16 September 2023Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
75 files
- LTUsegmentMap.m, MATLAB, 77 lines
- LTUsimulate.m, MATLAB, 40 lines
- STBgenerateDoc.m, MATLAB, 34 lines
- STBlicense.m, MATLAB, 18 lines
- applyIOR.m, MATLAB, 38 lines
- attenuateBorders.m, MATLAB, 35 lines
- batchSaliency.m, MATLAB, 114 lines
- callingFunctionName.m, MATLAB, 17 lines
- centerSurround.m, MATLAB, 77 lines
- centerSurroundTopDown.m, MATLAB, 49 lines
- checkImageSize.m, MATLAB, 69 lines
- clamp.m, MATLAB, 27 lines
- combineMaps.m, MATLAB, 26 lines
- computeAllSaliencyMaps.m
, MATLAB, 40 lines - contrastModulate.m, MATLAB, 63 lines
- conv2PreserveEnergy.m, MATLAB, 82 lines
- dataStructures.m, MATLAB, 186 lines
- debugMsg.m, MATLAB, 79 lines
- declareGlobal.m, MATLAB, 20 lines
- defaultGaborParams.m, MATLAB, 28 lines
- defaultLeakyIntFire.m, MATLAB, 33 lines
- defaultLevelParams.m, MATLAB, 49 lines
- defaultSaliencyParams.m, MATLAB, 98 lines
- diskIOR.m, MATLAB, 41 lines
- displayImage.m, MATLAB, 37 lines
- displayMap.m, MATLAB, 46 lines
- displayMaps.m, MATLAB, 65 lines
- displayPyramid.m, MATLAB, 42 lines
- drawDisk.m, MATLAB, 71 lines
- emptyMap.m, MATLAB, 35 lines
- ensureDirExists.m, MATLAB, 54 lines
- estimateShape.m, MATLAB, 207 lines
- evolveLeakyIntFire.m, MATLAB, 43 lines
- evolveWTA.m, MATLAB, 52 lines
- fastSegmentMap.m, MATLAB, 56 lines
- gaborFilterMap.m, MATLAB, 35 lines
- gaussian.m, MATLAB, 44 lines
- gaussianSubsample.m, MATLAB, 68 lines
- getLocalMaxima.m, MATLAB, 27 lines
- getRGB.m, MATLAB, 38 lines
- guiLevelParams.m, MATLAB, 191 lines
- guiSaliency.m, MATLAB, 981 lines
- hueDistance.m, MATLAB, 54 lines
- initializeGlobal.m, MATLAB, 38 lines
- initializeImage.m, MATLAB, 104 lines
- initializeWTA.m, MATLAB, 39 lines
- loadImage.m, MATLAB, 26 lines
- makeBlueYellowPyramid.m, MATLAB, 60 lines
- makeDyadicPyramid.m, MATLAB, 48 lines
- makeFeaturePyramids.m, MATLAB, 103 lines
- makeGaborFilter.m, MATLAB, 80 lines
- makeGaussianPyramid.m, MATLAB, 32 lines
- makeHuePyramid.m, MATLAB, 39 lines
- makeIntensityPyramid.m, MATLAB, 34 lines
- makeLTUsegmentNetwork.m, MATLAB, 67 lines
- makeOrientationPyramid.m
, MATLAB, 38 lines - makeRedGreenPyramid.m, MATLAB, 58 lines
- makeSaliencyMap.m, MATLAB, 108 lines
- makeSqrt2Pyramid.m, MATLAB, 65 lines
- maxNormalize.m, MATLAB, 50 lines
- maxNormalizeIterative.m, MATLAB, 61 lines
- maxNormalizeLocalMax.m, MATLAB, 47 lines
- normalizeImage.m, MATLAB, 34 lines
- plotSalientLocation.m, MATLAB, 111 lines
- removeColorFeatures.m, MATLAB, 41 lines
- runSaliency.m, MATLAB, 141 lines
- safeDivide.m, MATLAB, 17 lines
- sepConv2PreserveEnergy.m
, MATLAB, 83 lines - shapeIOR.m, MATLAB, 33 lines
- showImage.m, MATLAB, 55 lines
- skinHueParams.m, MATLAB, 40 lines
- winnerToImgCoords.m, MATLAB, 31 lines
- LICENSE.TXT, License, 82 lines
- License.md, License, 84 lines
- README.md, Text, 49 lines
OSF na4mv
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
- 27 September 2026: the link answers (HTTP 200)
12 files
- 01_Task/
DIGITRACK_DEMO.m , MATLAB, 188 lines, 1 match - 01_Task/
DIGITRACK_SetA.m , MATLAB, 181 lines - 01_Task/
DIGITRACK_SetB.m , MATLAB, 181 lines, 1 match - 01_Task/
DIGITRACK_TEST1new.m , MATLAB, 193 lines - 01_Task/
DIGITRACK_TEST2new.m , MATLAB, 222 lines - 02_Attention_Map/
apply_map.m , MATLAB, 11 lines - 02_Attention_Map/
attention_map_demo.m , MATLAB, 57 lines - 02_Attention_Map/
fastheatmap.m , MATLAB, 133 lines - 02_Attention_Map/
filter_2d_heat_map.m , MATLAB, 32 lines, 1 match - 02_Attention_Map/
filter_3d_heat_map.m , MATLAB, 38 lines, 1 match - 03_CNN_Prediction/
PredictSaliency.m , MATLAB, 228 lines, 2 matches - 03_CNN_Prediction/
PyramidExp.m , MATLAB, 96 lines
The paper's code and data availability statement is in the Data section.
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:
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- 84 scripts, each with its path and the digest of its content;
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Data availability
The example code and data are available on the Open Science Framework (https://
Reproduced under the paper's license (CC BY), from the paper cited above.
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 2 keywords, 11 MeSH terms, 2 funders, 24 references.
Cite
This paper
Yang, Y., Ameloot, A., Lio, G., Sirigu, A., & Duhamel, J.-R. (2026). Digit-tracking reveals curiosity-driven visual attention in macaque monkeys. Scientific reports, 16(1), 27117. https://
BibTeX
@article{yang2026digit,
author = {Yang, Yidong and Ameloot, Antoine and Lio, Guillaume and Sirigu, Angela and Duhamel, Jean-René},
title = {{Digit-tracking reveals curiosity-driven visual attention in macaque monkeys}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {27117},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/
url = {https://
pmid = {42288666},
pmcid = {PMC13527078}
}
RIS
TY - JOUR
AU - Yang, Yidong
AU - Ameloot, Antoine
AU - Lio, Guillaume
AU - Sirigu, Angela
AU - Duhamel, Jean-René
TI - Digit-tracking reveals curiosity-driven visual attention in macaque monkeys
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/
VL - 16
IS - 1
SP - 27117
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/
UR - https://
LA - en
ER -
CSL-JSON
{
"id": "10.1038/
"type": "article-journal",
"title": "Digit-tracking reveals curiosity-driven visual attention in macaque monkeys",
"container-title": "Scientific reports",
"author": [
{
"family": "Yang",
"given": "Yidong"
},
{
"family": "Ameloot",
"given": "Antoine"
},
{
"family": "Lio",
"given": "Guillaume"
},
{
"family": "Sirigu",
"given": "Angela"
},
{
"family": "Duhamel",
"given": "Jean-René"
}
],
"container-title-short":
"volume": "16",
"issue": "1",
"page": "27117",
"DOI": "10.1038/
"PMID": "42288666",
"PMCID": "PMC13527078",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
14
]
]
}
}
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The map's fingerprint: sha256:45435ab597b7c47f…
Add the badge to its README
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Markdown
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Request its removal
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Discussion, reproductions, activity
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