Dynamic expectation strength and precision shape human pain perception through shared and dissociable α-oscillatory mechanisms.

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Paper

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The authors' code

MATLAB · 82 lines · 3.1 KB · no license

%% Mediation analysis basic walkthrough
% This script is explained in more detail in the powerpoint titled
% Mediation_sample_data_walkthrough
% mediation_example_script1 and 2 do the same analysis.
% ...script1 is very terse, and includes the essential commands only.
% ...script2 is longer and includes more checking that files are available, etc.
%% Step 1: Make a new analysis directory to save results, and go there
% Make a new analysis directory to save results, and go there
andir = 'Test_mediation';
mkdir(andir)
cd(andir)
%% Step 2: Load image data and behavioral variables
dinf = what('Wager_et_al_2008_Neuron_EmotionReg');
%imgs = filenames(fullfile(dinf.path,'con_*img'), 'char', 'absolute');
imgs = fullfile(dinf.path, 'Wager_2008_emo_reg_vs_look_neg_contrast_images.nii.gz');
behav_dat = importdata(fullfile(dinf.path,'Wager_2008_emotionreg_behavioral_data.txt'))
%% Step 3: Load and display mask
% The mask determines which voxels are analyzed.
% The standard mask is in the CanlabCore Tools repository, so you need the
% folder containing it (and other CanlabCore folders) on your path.
mask = which('gray_matter_mask.img')
canlab_results_fmridisplay(mask, 'compact2');
%% Step 4 : Run mediation
% Run mediation without bootstrapping (fast)
% Test that things are working
x=behav_dat.data(:,1);
y=behav_dat.data(:,2);
names = {'X:RVLPFC' 'Y:Reappraisal_Success' 'M:BrainMediator'};
% This is what you would run:
% results = mediation_brain(x,y,imgs,'names',names,'mask', mask);
% We run this instead to suppress output for report publishing
str = 'results = mediation_brain(x,y,imgs,''names'',names,''mask'', mask);';
disp(['Running with output suppressed (for report-generation): ' str]);
evalc(str);
% "Legacy" version: reslice mask to same space first:
% scn_map_image(mask,deblank(imgs(1,:)), 'write', 'resliced_mask.img');
% results = mediation_brain(x,y,imgs,'names',names,'mask', 'resliced_mask.img');
% OR
% ---------------------------------
% Uncomment the lines below to run with bootstrapping
% Make yourself a cup of tea while the results are compiled as this is going to take a while
% pre-compiled results are also available in
% 'mediation_Example_Data_Wager2008_Msearch_R_XisRIFGstim_norobust'
% results = mediation_brain(x,y,imgs,'names',names,'mask', mask,'boot','pvals',5, 'bootsamples', 10000);
%% Step 5: Get and save results figures, tables, and report
% First, change to the mediation analysis directory, if you're not there already.
% Then, run one of several batch results functions.
%
% The most complete way to create and publish a time- and date-stamped HTML
% report with figures and tables is to run "publish_mediation_report.m"
% (This is commented out because this script is published, and we can't
% have nested publish commands)
%
% publish_mediation_report;
%
% ---------------------------------------------------------------------
% Another option is to use this batch script to create figures and tables,
% and save .mat files with results:
mediation_brain_results_all_script;
% This function runs a series of calls to mediation_brain_results.m, which
% is also a stand-alone function. Type "help mediation_brain_results" for more options.

mediation_brain_single_level_walkthrough1.m at commit 46bdc01, no license · at the source

Overview

Authors: Jia Li¹, Shihao Chen¹, Libo Zhang², Lingling Weng¹, Xinxin Lin¹, Yiheng Tu^3,4, Weiwei Peng¹

ORCID iDs: Jia Li, Weiwei Peng

Institutions: Shenzhen University (China); Dartmouth College (United States); Chinese Academy of Sciences (China); Institute of Psychology, Chinese Academy of Sciences (China); University of Chinese Academy of Sciences (China)

Journal: PLoS biology, volume 24, issue 3, article e3003675

Dates: received 15 December 2025; accepted 13 February 2026; published online 2 March 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pbio.3003675 · PMID 41770797 · PMCID PMC12965688 · OpenAlex W7133199735

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: EEG (modality), human (organism), pain (population)

Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Physiology & signal measures

MeSH: Alpha Rhythm*, Pain Perception*, Adult, Anticipation, Psychological, Bayes Theorem, Cues, Electroencephalography, Female, Humans, Male, Outcome Expectations, Prefrontal Cortex, Sensorimotor Cortex, Young Adult (* major topic)

Journal subjects: Medicine and Health Sciences, Clinical Medicine, Signs and Symptoms, Pain, Biology and Life Sciences, Neuroscience, Cognitive Science, Cognitive Psychology, Perception, Sensory Perception, Psychology, Social Sciences, Physiology, Sensory Physiology, Somatosensory System, Pain Sensation, Sensory Systems, Sensory Cues, Research and Analysis Methods, Bioassays and Physiological Analysis, Electrophysiological Techniques, Brain Electrophysiology, Electroencephalography, Electrophysiology, Neurophysiology, Brain Mapping, Clinical Neurophysiology, Imaging Techniques, Neuroimaging, Anatomy, Brain, Prefrontal Cortex, Engineering and Technology, Signal Processing, Signal Amplification, Physical Sciences, Chemistry, Chemical Elements, Nickel

Topic: Pain Mechanisms and Treatments (Physiology, Medicine), according to OpenAlex

Funding: STI2030-Major Projects by the Ministry of Science and Technology of China (2022ZD0206400); National Natural Science Foundation of China (National Science Foundation of China) (32271105); Shenzhen Basic Research Project (JCYJ20230808105805012); Shenzhen University 2035 Program for Excellent Research under Grant (2024C003); Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions (2021SHIBS0003)

Citations: cited by 3 papers (Europe PMC); 102 references in the paper

Abstract

Human pain perception is not solely driven by sensory input but is dynamically modulated by what we expect to feel and how confident we are in those expectations. Yet, the temporal mechanisms through which evolving expectations shape pain remain poorly understood. Here, we combined a probabilistic cueing paradigm with computational modeling and EEG to dissociate two core components of expectation: strength (a recency-weighted estimate of predicted pain) and precision (the inverse variability of recent predictions). Trial-wise strength estimates closely tracked subjective expectations and outperformed static cue labels, validating the model’s psychological relevance. Expectation strength and precision exerted dissociable effects on pain processing: strength enhanced, whereas precision suppressed, pain-evoked responses. Critically, anticipatory α-band activity mediated these effects via distinct topographical patterns—expectation strength reduced fronto-central α power (reflecting heightened vigilance), while precision increased contralateral sensorimotor α-synchronization (supporting sensory gating). Source-level mediation analyses identified a right-lateralized dorsolateral prefrontal–sensorimotor cortices (DLPFC-SM1) integrating both components, with strength-specific engagement of the medial prefrontal cortex (mPFC). These effects were supported by Bayesian inference and pooled mega-analyses, underscoring their robustness. Together, these findings highlight cortical α-oscillations as dual-control mechanisms for predictive integration, with DLPFC–SM1 as a shared expectation hub and mPFC as a strength-specific node. By moving beyond static cue-based models, this framework captures the adaptive dynamics of expectation and provides a neurocomputational foundation for targeted interventions in chronic pain.

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

Repository

Its files are read in the Code ↔ Paper reader above.

canlab/MediationToolbox

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 46bdc01df8c3b22900a8cf89090fe8eea27ecc2d, 11 September 2026

Languages: MATLAB (201)

Size: 297 files, 201 scripts

Software Heritage: not archived

Found in: the text, “Multilevel mediation analysis”

Holds: README, 4 notebooks

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

Tools: Statistics and Machine Learning Toolbox (35 files), SPM (14 files), Optimization Toolbox (3 files)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

202 files

Mediation_walkthrough/bmrk3_download_and_prep_data.mlx, MATLAB, not shown here
Mediation_walkthrough/mediation_1_basics.mlx, MATLAB, not shown here
Mediation_walkthrough/mediation_brain_multilevel_walkthrough1.mlx, MATLAB, not shown here
Mediation_walkthrough/mediation_brain_single_level_walkthrough1.mlx, MATLAB, not shown here
Mediation_walkthrough/older/mediation_brain_single_level_walkthrough1.m, MATLAB, 82 lines
Mediation_walkthrough/older/mediation_brain_single_level_walkthrough2.m, MATLAB, 114 lines
PDM_toolbox/BootPDM.m, MATLAB, 150 lines
PDM_toolbox/BootPDMJoint.m, MATLAB, 145 lines
PDM_toolbox/Multivariate_Mediation_ExampleScript.m, MATLAB, 111 lines
PDM_toolbox/PDMN.m, MATLAB, 193 lines
PDM_toolbox/PVD.m, MATLAB, 264 lines
PDM_toolbox/multivariateMediation.m, MATLAB, 494 lines
PDM_toolbox/plotPDM.m, MATLAB, 41 lines
PDM_toolbox/runBootstrapPDM.m, MATLAB, 96 lines
PDM_toolbox/runPDM.m, MATLAB, 96 lines
PDM_toolbox/thresholdPDM.m, MATLAB, 112 lines
geom2d/Contents.m, MATLAB, 158 lines
geom2d/angle3Points.m, MATLAB, 34 lines
geom2d/angleSort.m, MATLAB, 62 lines
geom2d/bisector.m, MATLAB, 59 lines
geom2d/cart2geod.m, MATLAB, 43 lines
geom2d/cartesianLine.m, MATLAB, 33 lines
geom2d/centroid.m, MATLAB, 63 lines
geom2d/circleArcAsCurve.m, MATLAB, 40 lines
geom2d/circleAsPolygon.m, MATLAB, 33 lines
geom2d/clipEdge.m, MATLAB, 109 lines
geom2d/clipLineRect.m, MATLAB, 47 lines
geom2d/clipPolygon.m, MATLAB, 63 lines
geom2d/clipPolygonHP.m, MATLAB, 86 lines
geom2d/convexification.m, MATLAB, 74 lines
geom2d/crackPattern.m, MATLAB, 158 lines
geom2d/crackPattern2.m, MATLAB, 119 lines
geom2d/createCircle.m, MATLAB, 44 lines
geom2d/createDirectedCircle.m, MATLAB, 49 lines
geom2d/createEdge.m, MATLAB, 92 lines
geom2d/createLine.m, MATLAB, 126 lines
geom2d/createMedian.m, MATLAB, 50 lines
geom2d/curvature.m, MATLAB, 152 lines
geom2d/curveCentroid.m, MATLAB, 58 lines
geom2d/curveLength.m, MATLAB, 56 lines
geom2d/distancePointEdge.m, MATLAB, 64 lines
geom2d/distancePointLine.m, MATLAB, 53 lines
geom2d/distancePoints.m, MATLAB, 126 lines
geom2d/drawArrow.m, MATLAB, 118 lines
geom2d/drawCenteredEdge.m, MATLAB, 75 lines
geom2d/drawCircle.m, MATLAB, 48 lines
geom2d/drawCircleArc.m, MATLAB, 53 lines
geom2d/drawCurve.m, MATLAB, 104 lines
geom2d/drawEdge.m, MATLAB, 93 lines
geom2d/drawEllipse.m, MATLAB, 91 lines
geom2d/drawEllipseArc.m, MATLAB, 77 lines
geom2d/drawLabels.m, MATLAB, 35 lines
geom2d/drawLine.m, MATLAB, 54 lines
geom2d/drawParabola.m, MATLAB, 57 lines
geom2d/drawPoint.m, MATLAB, 74 lines
geom2d/drawPolygon.m, MATLAB, 86 lines
geom2d/drawRay.m, MATLAB, 74 lines
geom2d/drawRect.m, MATLAB, 73 lines
geom2d/drawRect2.m, MATLAB, 80 lines
geom2d/drawShape.m, MATLAB, 78 lines
geom2d/edgeAngle.m, MATLAB, 22 lines
geom2d/edgeLength.m, MATLAB, 31 lines
geom2d/ellipseAsPolygon.m, MATLAB, 49 lines
geom2d/enclosingCircle.m, MATLAB, 66 lines
geom2d/fillPolygon.m, MATLAB, 81 lines
geom2d/geod2cart.m, MATLAB, 34 lines
geom2d/hexagonalGrid.m, MATLAB, 88 lines
geom2d/homothecy.m, MATLAB, 28 lines
geom2d/inCircle.m, MATLAB, 23 lines
geom2d/intersectEdges.m, MATLAB, 95 lines
geom2d/intersectLineEdge.m, MATLAB, 78 lines
geom2d/intersectLinePolygon.m, MATLAB, 43 lines
geom2d/intersectLines.m, MATLAB, 80 lines
geom2d/invertLine.m, MATLAB, 28 lines
geom2d/isLeftOriented.m, MATLAB, 26 lines
geom2d/lineAngle.m, MATLAB, 33 lines
geom2d/lineFit.m, MATLAB, 96 lines
geom2d/linePosition.m, MATLAB, 55 lines
geom2d/lineSymmetry.m, MATLAB, 34 lines
geom2d/medialAxisConvex.m, MATLAB, 126 lines
geom2d/medianLine.m, MATLAB, 49 lines
geom2d/minDistance.m, MATLAB, 38 lines
geom2d/minDistancePoints.m, MATLAB, 167 lines
geom2d/normalize.m, MATLAB, 28 lines
geom2d/onCircle.m, MATLAB, 22 lines
geom2d/onEdge.m, MATLAB, 52 lines
geom2d/onLine.m, MATLAB, 56 lines
geom2d/onRay.m, MATLAB, 42 lines
geom2d/orthogonalLine.m, MATLAB, 31 lines
geom2d/parallelLine.m, MATLAB, 34 lines
geom2d/parametrize.m, MATLAB, 40 lines
geom2d/pointOnLine.m, MATLAB, 21 lines
geom2d/polarPoint.m, MATLAB, 52 lines
geom2d/polyfit2.m, MATLAB, 183 lines
geom2d/polygonArea.m, MATLAB, 53 lines
geom2d/polygonCentroid.m, MATLAB, 41 lines
geom2d/polygonClipHP.m, MATLAB, 18 lines
geom2d/polygonExpand.m, MATLAB, 64 lines
geom2d/polygonLength.m, MATLAB, 37 lines
geom2d/polygonNormalAngle.m, MATLAB, 51 lines
geom2d/projPointOnLine.m, MATLAB, 41 lines
geom2d/readPolygon.m, MATLAB, 30 lines
geom2d/rectAsPolygon.m, MATLAB, 48 lines
geom2d/rotation.m, MATLAB, 57 lines
geom2d/scaling.m, MATLAB, 45 lines
geom2d/squareGrid.m, MATLAB, 50 lines
geom2d/steinerPoint.m, MATLAB, 43 lines
geom2d/steinerPolygon.m, MATLAB, 26 lines
geom2d/supportFunction.m, MATLAB, 48 lines
geom2d/surfaceCurvature.m, MATLAB, 28 lines
geom2d/transformEdge.m, MATLAB, 41 lines
geom2d/transformLine.m, MATLAB, 39 lines
geom2d/transformPoint.m, MATLAB, 66 lines
geom2d/translation.m, MATLAB, 38 lines
geom2d/triangleGrid.m, MATLAB, 35 lines
geom2d/vecnorm_geom2d_renamed.m, MATLAB, 57 lines
mediation_toolbox/Boot_samples_needed_fcns/Bneeded.m, MATLAB, 79 lines
mediation_toolbox/Boot_samples_needed_fcns/GLScalc_for_boot_L2M.m, MATLAB, 221 lines
mediation_toolbox/Boot_samples_needed_fcns/boot_rel_contrib_B.m, MATLAB, 32 lines
mediation_toolbox/Boot_samples_needed_fcns/v_from_alphaaccept.m, MATLAB, 13 lines
mediation_toolbox/Boot_samples_needed_fcns/v_from_targetu.m, MATLAB, 18 lines
mediation_toolbox/Boot_samples_needed_fcns/var2b.m, MATLAB, 32 lines
mediation_toolbox/Boot_samples_needed_fcns/var_prctile_script3.m, MATLAB, 111 lines
mediation_toolbox/EM_step.m, MATLAB, 116 lines
mediation_toolbox/HLM_EM_working.m, MATLAB, 138 lines
mediation_toolbox/M3.m, MATLAB, 8 lines
mediation_toolbox/RB_empirical_bayes_params_old.m, MATLAB, 87 lines
mediation_toolbox/RB_hlm_working.m, MATLAB, 85 lines
mediation_toolbox/ar_iterate_core.m, MATLAB, 92 lines
mediation_toolbox/bootbca_ci.m, MATLAB, 97 lines
mediation_toolbox/bootbca_pval.m, MATLAB, 136 lines
mediation_toolbox/bootbca_pval_onetail.m, MATLAB, 177 lines
mediation_toolbox/bootstrp_havesamples.m, MATLAB, 78 lines
mediation_toolbox/dcm_sim.m, MATLAB, 106 lines
mediation_toolbox/glmfit_multilevel_brain_wrapper.m, MATLAB, 79 lines
mediation_toolbox/mediation.m, MATLAB, 2,126 lines
mediation_toolbox/mediationIVObserver.m, MATLAB, 78 lines
mediation_toolbox/mediation_M_search.m, MATLAB, 21 lines
mediation_toolbox/mediation_X_search.m, MATLAB, 15 lines
mediation_toolbox/mediation_X_search_mask.m, MATLAB, 8 lines
mediation_toolbox/mediation_Y_search.m, MATLAB, 12 lines
mediation_toolbox/mediation_brain.m, MATLAB, 466 lines
mediation_toolbox/mediation_brain_corrected_threshold.m, MATLAB, 164 lines
mediation_toolbox/mediation_brain_multilev_wrapper.m, MATLAB, 121 lines
mediation_toolbox/mediation_brain_multilevel.m, MATLAB, 293 lines
mediation_toolbox/mediation_brain_print_tables.m, MATLAB, 366 lines
mediation_toolbox/mediation_brain_results.m, MATLAB, 1,678 lines
mediation_toolbox/mediation_brain_results_a_b_overlap.m, MATLAB, 309 lines
mediation_toolbox/mediation_brain_results_all_script.m, MATLAB, 351 lines
mediation_toolbox/mediation_brain_results_detail.m, MATLAB, 276 lines
mediation_toolbox/mediation_brain_results_report.m, MATLAB, 399 lines
mediation_toolbox/mediation_brain_results_surface_movie.m, MATLAB, 69 lines
mediation_toolbox/mediation_brain_surface_figs.m, MATLAB, 125 lines
mediation_toolbox/mediation_dcm_sim1.m, MATLAB, 488 lines
mediation_toolbox/mediation_extract_data.m, MATLAB, 178 lines
mediation_toolbox/mediation_latent.m, MATLAB, 218 lines
mediation_toolbox/mediation_latent_sse.m, MATLAB, 129 lines
mediation_toolbox/mediation_multilev_reformat_cl.m, MATLAB, 56 lines
mediation_toolbox/mediation_multilevel_loess_plots.m, MATLAB, 49 lines
mediation_toolbox/mediation_nmds_results.m, MATLAB, 304 lines
mediation_toolbox/mediation_path_coefficients.m, MATLAB, 487 lines
mediation_toolbox/mediation_path_coefficients_threepaths.m, MATLAB, 222 lines
mediation_toolbox/mediation_path_coefficients_threepaths_singlelevel.m, MATLAB, 330 lines
mediation_toolbox/mediation_path_diagram.m, MATLAB, 167 lines
mediation_toolbox/mediation_permutation_svc_fwe.m, MATLAB, 193 lines
mediation_toolbox/mediation_plots.m, MATLAB, 375 lines
mediation_toolbox/mediation_power.m, MATLAB, 307 lines
mediation_toolbox/mediation_problem1.m, MATLAB, 58 lines
mediation_toolbox/mediation_region_stepwise.m, MATLAB, 63 lines
mediation_toolbox/mediation_results.m, MATLAB, 31 lines
mediation_toolbox/mediation_results_XY_exclusive_mask.m, MATLAB, 117 lines
mediation_toolbox/mediation_results_interactive_view_init.m, MATLAB, 175 lines
mediation_toolbox/mediation_results_p2z.m, MATLAB, 31 lines
mediation_toolbox/mediation_scatterplots.m, MATLAB, 222 lines
mediation_toolbox/mediation_search.m, MATLAB, 176 lines
mediation_toolbox/mediation_shift.m, MATLAB, 198 lines
mediation_toolbox/mediation_shift_sse.m, MATLAB, 93 lines
mediation_toolbox/mediation_sim1.m, MATLAB, 701 lines
mediation_toolbox/mediation_sim2_igls.m, MATLAB, 471 lines
mediation_toolbox/mediation_sim3.m, MATLAB, 246 lines
mediation_toolbox/mediation_sim_output_figs.m, MATLAB, 143 lines
mediation_toolbox/mediation_sim_single_level1.m, MATLAB, 402 lines
mediation_toolbox/mediation_sim_single_level2.m, MATLAB, 205 lines
mediation_toolbox/mediation_sort_xy_proximity_plot.m, MATLAB, 743 lines
mediation_toolbox/mediation_threepaths.m, MATLAB, 1,437 lines
mediation_toolbox/mediation_threepaths_sim1.m, MATLAB, 590 lines
mediation_toolbox/mediation_threepaths_sim_output_figs.m, MATLAB, 130 lines
mediation_toolbox/mediation_threepaths_singlelevel.m, MATLAB, 1,579 lines
mediation_toolbox/moderation.m, MATLAB, 64 lines
mediation_toolbox/multivariate_mediation_brain_results_report.m, MATLAB, 375 lines
mediation_toolbox/onsets2singletrial.m, MATLAB, 265 lines
mediation_toolbox/onsets2singletrial_tor.m, MATLAB, 265 lines
mediation_toolbox/optimal_delay_mediation_search.m, MATLAB, 26 lines
mediation_toolbox/publish_mediation_report.m, MATLAB, 61 lines
mediation_toolbox/publish_multivariate_mediation_report.m, MATLAB, 67 lines
mediation_toolbox/setup_boot_samples.m, MATLAB, 60 lines
mediation_toolbox/spm_config_mediation.m, MATLAB, 297 lines
mediation_toolbox/timeseries_interactive_plot.m, MATLAB, 109 lines
mediation_toolbox/verification/mediation_unit_test1.m, MATLAB, 172 lines
mediation_toolbox/verification/verify_bootstrap.m, MATLAB, 24 lines
mediation_toolbox/xcorr_multisubject_old.m, MATLAB, 254 lines
README.md, Text, 147 lines

At the source: github.com/canlab/MediationToolbox

The paper's code and data availability statement is in the Data section.

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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;
201 scripts, each with its path and the digest of its content;
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neither the text of the paper nor the code itself.

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Data

Datasets cited

zenodo:18503056, at Zenodo; found in the text, “Validation of expectation manipulation”

Data Availability

All data needed to reproduce the conclusions and figures in the paper are present in the public repository of Zenodo (https://doi.org/10.5281/zenodo.18503056). The toolbox and codes of main data analysis and visualization are available on Zenodo (https://doi.org/10.5281/zenodo.18503056).

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 14 MeSH terms, 5 funders, 102 references.

Cite

This paper

Li, J., Chen, S., Zhang, L., Weng, L., Lin, X., Tu, Y., & Peng, W. (2026). Dynamic expectation strength and precision shape human pain perception through shared and dissociable α-oscillatory mechanisms. PLoS biology, 24(3), e3003675. https://doi.org/10.1371/journal.pbio.3003675

BibTeX

@article{li2026dynamic, author = {Li, Jia and Chen, Shihao and Zhang, Libo and Weng, Lingling and Lin, Xinxin and Tu, Yiheng and Peng, Weiwei}, title = {{Dynamic expectation strength and precision shape human pain perception through shared and dissociable α-oscillatory mechanisms}}, journal = {PLoS biology}, year = {2026}, month = mar, volume = {24}, number = {3}, pages = {e3003675}, publisher = {PLOS}, issn = {1544-9173}, doi = {10.1371/journal.pbio.3003675}, url = {https://doi.org/10.1371/journal.pbio.3003675}, pmid = {41770797}, pmcid = {PMC12965688} }

RIS

TY - JOUR AU - Li, Jia AU - Chen, Shihao AU - Zhang, Libo AU - Weng, Lingling AU - Lin, Xinxin AU - Tu, Yiheng AU - Peng, Weiwei TI - Dynamic expectation strength and precision shape human pain perception through shared and dissociable α-oscillatory mechanisms T2 - PLoS biology J2 - PLoS Biol PY - 2026 DA - 2026/03/02 VL - 24 IS - 3 SP - e3003675 SN - 1544-9173 PB - PLOS DO - 10.1371/journal.pbio.3003675 UR - https://doi.org/10.1371/journal.pbio.3003675 LA - en ER -

CSL-JSON

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