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Sensorimotor transformation of number in the primate parietal cortex.

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
  1. [1] § Results › Behavioral relevance of motor planning activity for the motor counting process ↔ visualization_ErrorTrials.m, lines 17–71 · score 0.76 · incorrect trials, numerical distance, tuning curves, normalized activity, firing rate, boxplots
  2. [2] § Methods › Error trial analysis ↔ visualization_ErrorTrials.m, lines 17–71 · score 0.59 · incorrect trials, error trials, firing rates, activity, position
  3. [3] § Methods › Time-resolved population analyses ↔ visualization_TempDecoding.m, lines 212–241 · score 0.53 · cross phase, motor preparation, decoding, trained, instruction, accuracy

Paper

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The authors' code

MATLAB · 170 lines · 6.7 KB · CC-BY-4.0 · 2 matches

  1. clear, clc
  3. load('data_ErrorActivity')
  4. load('data_SVMDecodingError.mat')
  6. fig = figure('Units','centimeters', 'Position',[2 2 20 22], 'Color','w');
  8. %% Define variables
  9. dist = -4:4;
  11. bx_col =[0.5 0.5 0.5; 0 0 0];
  12. m_col = { '-k','-w'};
  13. dirbx_col = [.7 .7 .7;.7 .7 .7;1 1 1;0 0 0;1 1 1];
  14. colbar = [1 1 1;.6 .6 .6;.6 .6 .6;1 1 1];
  15. colPL = [0.55 0.728 0.106; 0.3271 0.569 0.7819; 0.79 0 0];
  17. %% Error Analysis
  18. % Tuning curves correct and incorrect trials
  20. axDistErrTun = axes('Units','centimeters', 'Position',[1.5 13.5 8 6]);
  21. hold on
  23. % mean error tuning
  24. plot(dist, DistErrorTuning.meanErrTun, '-','Linewidth', 2, 'Color',[.5 .5 .5])
  25. errorbar(dist, DistErrorTuning.meanErrTun, DistErrorTuning.semErrTun,'Color',[.5 .5 .5],'LineStyle',...
  26. 'none','CapSize',0, 'LineWidth',1.5)
  27. % mean correct tuning
  28. plot(dist, DistErrorTuning.meanCorrTun,'k-','Linewidth',2)
  29. errorbar(dist, DistErrorTuning.meanCorrTun, DistErrorTuning.semCorrTun,'k','LineStyle','none',...
  30. 'CapSize',0,'LineWidth',1.5)
  32. % axis properties
  33. set(axDistErrTun, 'Box','off','TickDir','out', 'XLim',[-4.25 4.25],...
  34. 'YTick',.4:.2:1,'YLim',[.2 1.1 ], 'LineWidth', 2, 'XColor', [0 0 0], 'YColor', [0 0 0])
  36. xlabel('Numerical Distance', 'FontSize', 12)
  37. ylabel('Normalized Activity', 'FontSize', 12)
  39. % Create legend
  40. legend('Error', '', 'Correct', 'FontSize', 12)
  42. % Add average firing rate during correct and incorrect trials
  43. axAvAct = axes('Units', 'centimeters', 'Position',[5 14 1.3 2.6]);
  44. hold on
  46. boxplot(DistErrorTuning.avFR, 'PlotStyle','traditional','Symbol','','Widths',.5, 'Positions',[1,2])
  48. % get object handles and modify them
  49. boxes = findobj(gcf, 'type', 'line', 'Tag', 'Box');
  50. medians = findobj(gcf, 'type', 'line', 'Tag', 'Median');
  51. outliers = findobj(gcf, 'type', 'line', 'Tag', 'Outliers');
  52. whiskers_up = findobj(gcf, 'type', 'line', 'Tag', 'Upper Whisker');
  53. whiskers_dn = findobj(gcf, 'type', 'line', 'Tag', 'Lower Whisker');
  54. for box=1:length(boxes)
  55. patch(get(boxes(box),'XData'),get(boxes(box),'YData'),...
  56. bx_col(box,:),'FaceAlpha',.9, 'linew', 2);
  57. plot(medians(box).XData,medians(box).YData, m_col{box},'LineWidth',2);
  58. end
  59. set([whiskers_up;whiskers_dn], 'LineStyle','-', 'lineWidth', 2)
  60. delete(outliers)
  61. delete(medians)
  63. % Show significance
  64. line(axAvAct, [1 2], [31 31], 'LineWidth', 1.5, 'Color','k')
  65. text(axAvAct, 0.35, 0.95, '***', 'Units', 'normalized', 'FontSize', 12, 'FontWeight', 'bold')
  67. % axis properties
  68. set(axAvAct, 'Box','off','TickDir','out','XLim',[.5 2.5],...
  69. 'XTick',1:2,'XTickLabel',{'Correct','Error'},'YTick',0:5:30,...
  70. 'YLim',[0 35],'LineWidth', 2, 'XColor', [0 0 0], 'YColor', [0 0 0])
  71. ylabel(axAvAct, 'FR [Hz]')
  74. %% Error analysis based on direction of error made
  75. axDirErr = axes('Units','centimeters', 'Position',[11.5 13.5 8 6]);
  76. hold on
  78. % direction error
  79. boxplot(DirectionErr.peakActDirErr, [1.5,2.5], 'PlotStyle','traditional', 'Symbol','',...
  80. 'Widths',.3,'Labels',{'error(n-1)','error(n+1)'}, 'Positions',[1.5,2.5])
  81. % correct trials
  82. boxplot(DirectionErr.peakActCorr, [1:3],'PlotStyle','traditional','Symbol','',...
  83. 'Widths',.3,'Labels',{'n-1','n','n+1'},'Positions',1:3)
  85. % get object handles and modify them
  86. boxes = findobj(gca, 'type', 'line', 'Tag', 'Box');
  87. medians = findobj(gca, 'type', 'line', 'Tag', 'Median');
  88. outliers = findobj(gca, 'type', 'line', 'Tag', 'Outliers');
  89. whiskers_up = findobj(gca, 'type', 'line', 'Tag', 'Upper Whisker');
  90. whiskers_dn = findobj(gca, 'type', 'line', 'Tag', 'Lower Whisker');
  92. for box=1:length(boxes)
  93. patch(get(boxes(box),'XData'),get(boxes(box),'YData'),...
  94. dirbx_col((size(dirbx_col,1)+1)-box,:),'FaceAlpha',.9, 'lineWidth', 2);
  95. plot(medians(box).XData,medians(box).YData,'-k','LineWidth',2);
  96. end
  97. set([whiskers_up; whiskers_dn],'LineStyle','-','lineWidth',2)
  98. delete(outliers)
  99. delete(medians)
  101. % Significance minus
  102. line(axDirErr, [1 1.5], [1.3 1.3], 'LineWidth', 1.5, 'Color','k')
  103. text(axDirErr, 0.33, 0.83, '**', 'Units', 'normalized', 'FontSize', 14, 'FontWeight', 'bold')
  105. % Significance plus
  106. line(axDirErr, [2.5 3], [1.3 1.3], 'LineWidth', 1.5, 'Color','k')
  107. text(axDirErr, 0.75, 0.87, 'n.s.', 'Units', 'normalized', 'FontSize', 12, 'FontWeight', 'bold')
  109. % axis properties
  110. y_lim = ceil(max(max([DirectionErr.peakActDirErr'; DirectionErr.peakActCorr']))*10)/10;
  111. set(axDirErr, 'TickDir', 'out', 'Box', 'off', 'YLim', [0 1.6], 'YTick',.25:.25:1.5,...
  112. 'LineWidth', 2, 'XColor', [0 0 0], 'YColor', [0 0 0])
  113. xlabel('Preferred Number', 'FontSize', 12)
  114. ylabel('Normalized Activity', 'FontSize', 12)
  117. %% Decoding of error trials
  119. axErrDec = axes('Units','centimeters', 'Position',[1.5 1.5 5 10]);
  120. hold on
  122. errorbar([1,3],[ErrorDecoding.meanCorr, ErrorDecoding.meanErr],...
  123. [ErrorDecoding.semCorr, ErrorDecoding.semErr],...
  124. 'Marker','none','Color','k','CapSize',8,'LineWidth',1.5,...
  125. 'LineStyle','none')
  126. bar_h2 = bar([1,3],[ErrorDecoding.meanCorr, ErrorDecoding.meanErr],0.6,'FaceColor','flat');
  127. bar_h2.CData = colbar(1:2,:);
  129. plot([0, 4],[ErrorDecoding.ShflCorr, ErrorDecoding.ShflCorr], ...
  130. '--', 'Color',[.3 .3 .3],'LineWidth',2)
  132. % axis properties
  133. set(axErrDec,'YLim',[0,70],'TickDir','out','Box','off','XLim',[-.5;4.5],...
  134. 'XTick',[1,3],'XTickLabel',{'Correct','Error'},'YTick',10:20:70,...
  135. 'LineWidth', 2, 'XColor', [0 0 0], 'YColor', [0 0 0])
  136. ylabel(axErrDec,'Accuracy [%]', 'FontSize',12)
  137. xlabel(axErrDec,sprintf('Trial Prediction'), 'FontSize', 12)
  139. %% Decoding of direction of error
  141. axDirErrDec = polaraxes('Units','centimeters', 'Position',[11 1.5 6 10]);
  142. hold on
  144. % Circular plot
  145. thetas = linspace(0,2*pi,4);
  146. gridlines = 20:20:60;
  147. plt_h = zeros(3,1);
  149. for gl = 1:length(gridlines)
  150. polarplot(axDirErrDec,thetas,repmat(gridlines(gl),[4,1]),...
  151. 'LineWidth',.5,'LineStyle','-','Color',[.75 .75 .75])
  152. end
  153. % results of shuffled labels
  154. plt_h(4) = polarplot(axDirErrDec, thetas,[DirectionErrorDecoding.Shfl DirectionErrorDecoding.Shfl(1)],...
  155. 'LineWidth',1.5,'LineStyle',':','Color','k');
  157. for tt3 = 1:3
  158. plt_h(tt3) = polarplot(axDirErrDec, thetas,[DirectionErrorDecoding.meanConfMat(tt3,:), DirectionErrorDecoding.meanConfMat(tt3,1)],...
  159. 'LineWidth',2,'Color', colPL(tt3,:),'Marker','none');
  160. end
  162. % axis properties
  163. set(axDirErrDec,'ThetaZeroLocation','top','ThetaDir','clockwise','ThetaTick',...
  164. [0,120,240],'ThetaTickLabel',{'Correct','+1 Error','-1 Error'},...
  165. 'TickDir','out','RLim',[10, 65],'RGrid','off', 'GridAlpha',.5,...
  166. 'RTick',gridlines,'RTickLabel',sprintfc('%i',gridlines), ...
  167. 'RColor', [0 0 0], 'Linewidth', 2, 'Fontsize', 12)
  168. % legend properties
  169. legend(plt_h,{'Correct','+1 Error','-1 Error','Shuffle'}, 'Units','centimeters', 'Position',...
  170. [8.5 9 3 2])

visualization_ErrorTrials.m, under CC-BY-4.0 · at the source

Overview

Authors: Laura E Seidler1, Stephanie Westendorff1, Andreas Nieder1
ORCID iDs: Laura E Seidler, Andreas Nieder
  1. Animal Physiology Unit, Institute of Neurobiology, University of Tübingen, Tübingen, Germany
Institutions: University of Tübingen (Germany)
Journal: Nature communications, volume 17, issue 1, article 4227
Dates: received 21 July 2025; accepted 28 April 2026; published online 11 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-73037-9 · PMID 42115156 · PMCID PMC13161402 · OpenAlex W7160831768
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: non-human primate (organism)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing, Single-unit activity, calcium imaging
Keywords: Cognitive control, Intelligence
MeSH: Parietal Lobe*, Animals, Cognition, Macaca mulatta, Male, Neurons, Photic Stimulation, Psychomotor Performance, Visual Perception (* major topic)
Topic: Cognitive and developmental aspects of mathematical skills (Statistics and Probability, Mathematics), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (German Research Foundation) (NI 618/13-1 (FOR 5159))
Citations: not cited yet (Europe PMC); 52 references in the paper

Abstract

The neuronal mechanisms by which the brain flexibly transforms perceived numerical values into corresponding numbers of self-generated actions remain poorly understood. Here, we investigated this sensorimotor transformation process in the parietal cortex of two male rhesus macaques performing a manual counting task. Monkeys viewed visual numerical cues and produced a corresponding number of hand movements. Single-neuron recordings from the ventral intraparietal area (VIP)—a region known to represent perceived numerosity—revealed tuning to the number of intended actions during motor planning. These neurons showed both sustained and transient activity patterns, reflecting static and dynamic codes that support numerical sensorimotor transformation. Population decoding confirmed that VIP encoded intended action number and reflected systematic over- and underestimation errors. Our findings reveal a neural mechanism by which the primate brain converts abstract numerical input into goal-directed motor output, providing insight into the sensorimotor foundations of numerical cognition.

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 3 matches between paragraphs and lines of code.

figshare 31096300

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (6)
Size: 14 files, 6 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
7 files
At the source:

Code availability

The data and code that support the findings of this study are available from Figshare (10.6084/m9.figshare.31096300).

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 6 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);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Data availability

Source data are provided with this paper Source data are provided with this paper.

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

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 2 keywords, 9 MeSH terms, 1 funder, 49 references.

Cite

This paper

Seidler, L. E., Westendorff, S., & Nieder, A. (2026). Sensorimotor transformation of number in the primate parietal cortex. Nature communications, 17(1), 4227. https://doi.org/10.1038/s41467-026-73037-9

BibTeX

@article{seidler2026sensorimotor,
author = {Seidler, Laura E and Westendorff, Stephanie and Nieder, Andreas},
title = {{Sensorimotor transformation of number in the primate parietal cortex}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {4227},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73037-9},
url = {https://doi.org/10.1038/s41467-026-73037-9},
pmid = {42115156},
pmcid = {PMC13161402}
}

RIS

TY - JOUR
AU - Seidler, Laura E
AU - Westendorff, Stephanie
AU - Nieder, Andreas
TI - Sensorimotor transformation of number in the primate parietal cortex
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/11
VL - 17
IS - 1
SP - 4227
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73037-9
UR - https://doi.org/10.1038/s41467-026-73037-9
LA - en
ER -

CSL-JSON

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"container-title": "Nature communications",
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"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "4227",
"DOI": "10.1038/s41467-026-73037-9",
"PMID": "42115156",
"PMCID": "PMC13161402",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
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