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Faster but less precise: expectation enhances response speed while reducing sensory fidelity.

Code ↔ Paper

9 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 9 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and Methods › EEG acquisition and preprocessing ↔ code.zip/code/subfunctions/preprocess_eeg_data.m, the whole file · a weak match · score 0.83 · 200–500 ms, EEGLAB, SASICA, artifacts, component, drifts
  2. [2] § Materials and Methods › Pupillometry ↔ code.zip/code/subfunctions/preprocess_eye_data.m, the whole file · a weak match · score 0.77 · removing blinks, removing outliers, preprocessed, bandpass, buffer, scored
  3. [3] § Materials and Methods › Neural decoding of stimulus location ↔ code.zip/code/subfunctions/misc/shuffle_labels.m, lines 38–168 · score 0.73 · training trials, cross validation, channel responses, fold, sensor, filter
  4. [4] § Materials and Methods › Stimuli, task, and procedure ↔ code.zip/code/analyze_eeg_data.m, lines 81–136 · score 0.71 · vertical bias, horizontal bias, task irrelevant, blocks, match, position
  5. [5] § Materials and Methods › Neural decoding of stimulus location ↔ code.zip/code/subfunctions/misc/binary_classification.m, lines 37–170 · score 0.71 · training trials, cross validation, channel responses, fold, filter, electrodes
  6. [6] § Materials and Methods › Stimuli, task, and procedure ↔ code.zip/code/subfunctions/preprocess_behavioural_data.m, the whole file · a weak match · score 0.70 · vertical bias, horizontal bias, task irrelevant, blocks, reproduction, position
  7. [7] § Materials and Methods › Neural decoding of stimulus location ↔ code.zip/code/analyze_eeg_data.m, lines 1–39 · score 0.61 · forward model, cross validation, fold, EEG, 360 deg, channels
  8. [8] § Materials and Methods › Neural decoding of stimulus location ↔ code.zip/code/subfunctions/misc/shuffle_labels.m, lines 38–168 · score 0.61 · cross validation, channel responses, fold, sensors, training, matrix
  9. [9] § Materials and Methods › Pupillometry ↔ code.zip/code/subfunctions/misc/circ_detect_outliers.m, lines 1–52 · score 0.55 · median absolute deviation, outliers

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 169 lines · 6.2 KB · no license · 2 matches

  1. clear all
  2. close all
  4. try
  5. parpool('local',8)
  6. catch
  7. end
  9. addpath(genpath('/Users/uqrridea/OneDrive - The University of Queensland/MATLAB/toolboxes/decoding'))
  10. addpath(genpath('/Users/uqrridea/Documents/inverted_adaptation/eeg/functions/misc'))
  11. addpath(genpath('/Users/uqrridea/Documents/inverted_adaptation/eeg/functions/analysis'))
  12. addpath('/Users/uqrridea/OneDrive - The University of Queensland/MATLAB/toolboxes/eeglab2021.1')
  13. addpath(genpath('/Users/uqrridea/OneDrive - The University of Queensland/MATLAB/toolboxes/eeglab2021.1/functions'))
  15. eeg_data_folder = '/Users/uqrridea/Documents/inverted_adaptation/eeg/data/processed_data';
  16. mat_data_folder = '/Users/uqrridea/Documents/inverted_adaptation/eeg/data/raw_data';
  18. output = 'decoded_shuffle-labels_5chan';
  20. trials = 1:3600;
  21. nt = numel(trials);
  22. temporal_window = [-50,500];
  23. temporal_smoothing = 0;
  24. rear_electrodes = [20:31,57:64];
  25. n_ori_chans = 5;
  26. use_rear_sensors = true;
  28. eeg_file = wildcardsearch(eeg_data_folder,'*-cleaned.set');
  29. stim_pre = wildcardsearch(mat_data_folder,'*pre.mat');
  30. stim_post = wildcardsearch(mat_data_folder,'*post.mat');
  31. % eeg_file = eeg_file([1:34,36,35,37]);
  33. processed = wildcardsearch(eeg_data_folder,['*',output,'.mat']);
  34. for i = 1:numel(processed)
  35. processed{i} = processed{i}(numel(eeg_data_folder)+2:end-5-numel(output));
  36. end
  38. %%
  39. for participant = 1:numel(eeg_file)
  40. [~,sID,~] = fileparts(eeg_file{participant});
  41. % if isempty(strmatch(sID(1:end-8),processed,'exact'))
  42. disp(num2str(participant))
  43. tic
  45. meta_pre = load(stim_pre{participant});
  46. meta_post = load(stim_post{participant});
  47. [folder,fname,~] = fileparts(eeg_file{participant});
  48. EEG = pop_loadset('filename', [fname,'.set'], 'filepath',folder);
  49. EEG = eeg_checkset( EEG );
  51. t_idx = EEG.times>temporal_window(1) & EEG.times<temporal_window(2);
  52. times = EEG.times(t_idx);
  53. if temporal_smoothing>0
  54. EEG.data = filtfast(EEG.data,2,[],'gaussian',temporal_smoothing); % temporally filters the data
  55. end
  57. if use_rear_sensors
  58. Y = reshape(EEG.data(rear_electrodes,t_idx,:),numel(rear_electrodes),numel(times),nt,2);
  59. else
  60. Y = reshape(EEG.data(:,t_idx,:),EEG.nbchan,numel(times),nt,2);
  61. end
  63. % enable to sensors, treating time as features
  64. % Y = permute(Y, [2 1 3 4]);
  66. orientations_pre = cell2mat(meta_pre.sparam.np.ori(:));
  67. orientations_pre = reshape(orientations_pre',[prod(size(orientations_pre)),1]);
  68. orientations_post = cell2mat(meta_post.sparam.np.ori(:));
  69. orientations_post = reshape(orientations_post',[prod(size(orientations_post)),1]);
  70. orientations = [orientations_pre;orientations_post];
  71. adaptor_ori = meta_post.dparam.orientation;
  74. % bin orientations
  75. delta_ori = wrapToPi(circ_dist(orientations*2,adaptor_ori*2)/2);
  76. delta_ori = delta_ori + pi/2;
  77. edges = linspace(0,pi,n_ori_chans+1);
  78. labels = delta_ori*nan;
  79. for b = 1:n_ori_chans
  80. labels(delta_ori>edges(b)&delta_ori<edges(b+1)) = round(mean(edges(b:b+1))*180/pi);
  81. end
  82. chans = unique(labels);
  83. labels = reshape(labels,[],2);
  84. labels = Shuffle(labels);
  87. % create the design matrix
  88. funType = @(xx,mu) (cosd(xx-mu)).^(n_ori_chans-mod(n_ori_chans,2));
  89. xx = linspace(1,180,180);
  90. basis_set = nan(numel(xx),n_ori_chans);
  92. for cc = 1:n_ori_chans
  93. basis_set(:,cc) = funType(xx,chans(cc));
  94. end
  96. % 1.2. Cross-validation
  97. n_folds = 10;
  98. folds = cell(n_folds,2);
  99. for i = 1:2
  100. for iCond = 1:n_ori_chans
  101. % Find indices
  102. index = find(labels(trials,i) == chans(iCond));
  103. nIndex = length(index);
  105. % Shuffle
  106. index = index(randperm(nIndex));
  108. % Distribute across folds
  109. groupNumber = floor((0:(nIndex-1))*(n_folds/nIndex))+1;
  111. for iFold = 1:n_folds
  112. folds{iFold,i} = [folds{iFold,i}, index(groupNumber==iFold)'];
  113. end
  114. end
  115. [~,order(:,i)] = sort([folds{:,i}]);
  116. stim_mask(:,:,i) = zeros(length(labels(:,i)),length(xx));
  118. for tt = 1:size(stim_mask,1) % loop over trials
  119. stim_mask(tt,labels(tt,i),i) = 1;
  120. end
  121. end
  123. % Generate design matrix
  124. for i = 1:2
  125. design(:,:,i) = (stim_mask(:,:,i)*basis_set)';
  126. end
  128. numT = size(Y,2);
  129. estimatedChannelResponse = nan(n_ori_chans,numT,nt,3,'single');
  131. parfor time = 1:numT
  132. for i = 1:2
  133. tempResponses = [];
  134. tempAccuracy = [];
  135. DataTime = squeeze(Y(:,time,:,i));
  136. for fold = 1:n_folds
  137. TestTrials = folds{fold,i};
  138. TrainTrials = find(~ismember(trials,TestTrials));
  139. DesignTrain = design(:,TrainTrials,i);
  141. Y_train = DataTime(:,TrainTrials);
  142. Y_test = DataTime(:,TestTrials);
  145. cfg = [];
  146. decoder = train_beamformerMT(cfg,DesignTrain,Y_train);
  147. tempResponses = cat(2,tempResponses,decode_beamformer(cfg, decoder,Y_test));
  148. end
  149. estimatedChannelResponse(:,time,:,i) = tempResponses(:,order(:,i));
  150. end
  151. end
  154. parfor time = 1:numT
  155. % post-adaptation
  156. Y_train = squeeze(Y(:,time,:,1));
  157. Y_test = squeeze(Y(:,time,:,2));
  158. DesignTrain = design(:,:,1);
  160. cfg = [];
  161. decoder = train_beamformerMT(cfg,DesignTrain,Y_train);
  162. estimatedChannelResponse(:,time,:,3) = decode_beamformer(cfg, decoder,Y_test);
  163. end
  165. save([folder,filesep,fname(1:end-8),'-',output,'.mat'],'estimatedChannelResponse','basis_set','labels','trials','chans','temporal_smoothing')
  166. toc
  167. % end
  168. end

shuffle_labels.m, no license · at the source

Overview

Authors: Ziyue Hu1, Dominic M. D. Tran1, Reuben Rideaux1
ORCID iDs: Ziyue Hu, Dominic M. D. Tran, Reuben Rideaux
  1. School of Psychology, The University of Sydney, Camperdown 2050, Australia
Institutions: The University of Sydney (Australia)
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience, volume 46, issue 28, article e0154262026
Dates: received 26 January 2026; accepted 11 May 2026; published online 22 June 2026; in print 15 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/jneurosci.0154-26.2026 · PMID 42331630 · PMCID PMC13375892 · OpenAlex W7165519231
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Physiology & signal measures
Keywords: EEG, prediction, selective attention, vision
Journal subjects: Behavioral/Cognitive
Topic: Motor Control and Adaptation (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: DHAC | National Health and Medical Research Council (NHMRC) (2026318)
Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

The brain's remarkable ability to process continuous sensory inputs with adaptive efficiency—balancing flexibility while minimizing metabolic cost—is thought to rely on predictive mechanisms that generate and update internal models that leverage statistical regularities in the environment. However, it remains unclear whether this efficiency arises from prioritizing reliable, expected events or informative, unexpected ones, as they offer complementary adaptive advantages. To isolate genuine expectation effects, we combined electroencephalography (EEG), pupillometry, and behavioral measures in a paradigm that independently manipulated task relevance (selective attention) and stimulus predictability, while minimizing stimulus repetition at identical spatial locations to control for low-level adaptation. Human participants (both sexes) responded faster and more accurately to expected events, which was enhanced when attention was engaged; however, these events were reproduced with lower precision, independent of attention. Feature-specific neural decoding revealed prestimulus effects of attention and poststimulus effects of expectation, with no interaction between the two. Attention increased decoding accuracy, while expectation reduced accuracy. The reduced representational fidelity for expected events appeared rapidly (∼100–200 ms after stimulus onset) and correlated with individual differences in perceptual precision. Collectively, our findings indicate two complementary processes that define how the brain leverages redundancy in the environment: an early (prestimulus) mechanism, which supports rapid motor responses to expected events and is mediated by attention, and a later (poststimulus) process, which dampens sensory responses to expected events and is unaffected by 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 9 matches between paragraphs and lines of code.

OSF nekp7

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 40 files
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (11 files), CircStat (10 files), Image Processing Toolbox (9 files), EEGLAB (4 files), Parallel Computing Toolbox (4 files), Signal Processing Toolbox (3 files), shadedErrorBar (2 files), Optimization Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
50 files
At the source: doi.org/10.17605/OSF.IO/NEKP7

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 50 scripts, each with its path and the digest of its content;
  • 9 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

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

Data and code availability

The raw data and analysis code are publicly available at the following repository: https://doi.org/10.17605/OSF.IO/NEKP7

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 4 keywords, 1 funder, 53 references.

Cite

This paper

Hu, Z., Tran, D. M. D., & Rideaux, R. (2026). Faster but less precise: expectation enhances response speed while reducing sensory fidelity. The Journal of neuroscience : the official journal of the Society for Neuroscience, 46(28), e0154262026. https://doi.org/10.1523/jneurosci.0154-26.2026

BibTeX

@article{hu2026faster,
author = {Hu, Ziyue and Tran, Dominic M. D. and Rideaux, Reuben},
title = {{Faster but less precise: expectation enhances response speed while reducing sensory fidelity}},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
year = {2026},
month = jun,
volume = {46},
number = {28},
pages = {e0154262026},
publisher = {Society for Neuroscience},
issn = {0270-6474},
doi = {10.1523/jneurosci.0154-26.2026},
url = {https://doi.org/10.1523/jneurosci.0154-26.2026},
pmid = {42331630},
pmcid = {PMC13375892}
}

RIS

TY - JOUR
AU - Hu, Ziyue
AU - Tran, Dominic M. D.
AU - Rideaux, Reuben
TI - Faster but less precise: expectation enhances response speed while reducing sensory fidelity
T2 - The Journal of neuroscience : the official journal of the Society for Neuroscience
J2 - J Neurosci
PY - 2026
DA - 2026/06/22
VL - 46
IS - 28
SP - e0154262026
SN - 0270-6474
PB - Society for Neuroscience
DO - 10.1523/jneurosci.0154-26.2026
UR - https://doi.org/10.1523/jneurosci.0154-26.2026
LA - en
ER -

CSL-JSON

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