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Pupillary responses to invisible brightness escape from attentional modulation.

Code ↔ Paper

5 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 5 matches
  1. [1] § STAR★Methods › Quantification and statistical analysis › Statistical analysis › Bayes Factor analysis ↔ Analysis_LME/analysis_CFS_pupil_LME_Exp1_Final.r, lines 94–165 · score 0.72 · Bayes Factors, alternative model, additive, BF01, H0, H1
  2. [2] § Results › Experiment 1: Feature-based attention › Pupil responses to luminance, visibility, and feature-based attention ↔ Analysis_LME/analysis_CFS_pupil_LME_Exp1_Final.r, lines 94–165 · score 0.70 · Likelihood ratio, gaze position, reduced model, full model, LRT, LME
  3. [3] § Results › Experiment 1: Feature-based attention › Pupil responses to luminance, visibility, and feature-based attention ↔ Analysis_LME/analysis_CFS_pupil_LME_Exp2_Final.r, lines 50–115 · score 0.69 · Likelihood ratio, gaze position, reduced model, full model, LRT, suppression
  4. [4] § STAR★Methods › Method details › Procedure ↔ Data_csv/analysis_CFS_pupil_Final.m, lines 2–49 · score 0.56 · attend orientation, subjective visibility, attend luminance, eye, objective, Gabor
  5. [5] § STAR★Methods › Quantification and statistical analysis › Statistical analysis › Linear Mixed-Effects (LME) analysis ↔ Analysis_LME/analysis_CFS_pupil_LME_Exp1_Final.r, lines 1–47 · score 0.53 · Linear Mixed, lme4, models, Visible, CFS

Paper

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The authors' code

R · 166 lines · 6.5 KB · no license · 3 matches

  1. # ==============================================================================
  2. # 1. SETUP & DATA CLEANING
  3. # ==============================================================================
  4. library(lme4) # For the Linear Mixed Effects model
  5. library(lmerTest) # To get p-values for the LME
  6. library(dplyr) # For easy data manipulation
  7. library(bayestestR) # For Bayes Factors
  8. library(emmeans)
  11. setwd("~/Google 雲端硬碟/CFS_Pupil/CFS_Pupil_Luminance/Data/data/LME")
  12. # Load your CSV (Exported from MATLAB)
  13. # Replace '1' with '2' for Experiment 2
  14. data <- read.csv("LME_Data_Exp1.csv")
  16. # --- FILTERING & PREPROCESSING ---
  17. data_clean <- data %>%
  18. # 1. Remove rows with missing data (NaNs)
  19. filter(!is.na(PupilSize) & !is.na(GazeX) & !is.na(GazeY)) %>%
  21. # 2. Center Gaze Coordinates (Critical for correcting Foreshortening)
  22. # Screen Center: (960, 540)
  23. mutate(
  24. GazeX_Cent = GazeX - 960,
  25. GazeY_Cent = GazeY - 540
  26. ) %>%
  28. # 3. Convert Variables to Factors (Categorical)
  29. # IMPORTANT: Check your MATLAB export codes.
  30. # Example: 1=CFS, 2=Visible; 1=Dark, 2=Bright...
  31. mutate(
  32. Visibility = factor(Visibility, levels = c(1, 2), labels = c("CFS", "Visible")),
  33. Luminance = factor(Luminance, levels = c(1, 2), labels = c("Dark", "Bright")),
  34. Attention = factor(Attention, levels = c(1, 2), labels = c("Att_Lum", "Att_Ori")), # Or Spatial Attention labels for Exp2
  35. SubjectID = factor(SubjectID)
  36. )
  38. # ==============================================================================
  39. # 2. RUNNING THE LINEAR MIXED EFFECTS MODEL (LME)
  40. # ==============================================================================
  41. # Formula:
  42. # Pupil ~ (Conditions Interaction) + (Gaze Control) + (Random Effect)
  44. full_model <- lmer(PupilSize ~ Visibility * Luminance * Attention +
  45. GazeX_Cent + GazeY_Cent +
  46. (1 | SubjectID),
  47. data = data_clean)
  49. # Print the ANOVA table (Type III is standard for interactions)
  50. print("--- LME ANOVA Results ---")
  51. print(anova(full_model, type = 3))
  52. summary(full_model)
  57. # ==============================================================================
  58. # 3. CALCULATE ESTIMATED MARGINAL MEANS
  59. # ==============================================================================
  60. # We want to see the mean pupil size for each Task, split by Visibility.
  61. VisAtten <- emmeans(full_model, ~ Attention | Visibility)
  62. print(VisAtten)
  63. pairs(VisAtten, adjust = "bonferroni")
  66. AttenVis <- emmeans(full_model, ~ Visibility | Attention)
  67. print(AttenVis)
  68. pairs(AttenVis, adjust = "bonferroni")
  70. VisLum <- emmeans(full_model, ~ Luminance | Visibility)
  71. print(VisLum)
  72. pairs(VisLum, adjust = "bonferroni")
  75. # ==============================================================================
  76. # 3. (Optional) PLOT THE INTERACTION
  77. # ==============================================================================
  78. # This helps you visually confirm the "crossing" or divergence.
  79. emmip(full_model, Visibility ~ Attention, CIs = TRUE) +
  80. labs(title = "Interaction Plot: Visibility × Attention Task",
  81. y = "Predicted Pupil Size (z-score)",
  82. x = "Attention Task") +
  83. theme_minimal()
  86. emmip(full_model, Visibility ~ Luminance, CIs = TRUE) +
  87. labs(title = "Interaction Plot: Visibility × Luminance",
  88. y = "Predicted Pupil Size (z-score)",
  89. x = "Luminance") +
  90. theme_minimal()
  94. # ==============================================================================
  95. # 4. BAYES FACTOR (Testing the Null Hypothesis)
  96. # ==============================================================================
  97. # The editor wants to know: Does Attention exist under Suppression?
  98. # Standard p-values can't prove the null (no effect). Bayes Factors can.
  99. # We will check if the Null Model is better than the Alternative for the CFS condition.
  101. # A. Subset Data: Look ONLY at CFS trials
  102. data_cfs <- data_clean %>% filter(Visibility == "CFS")
  104. # B. Build Two Models for CFS
  105. # H1 (Alternative): Attention has an effect on Pupil
  106. # H1 (Alternative): Full Model (Includes Interaction: Luminance * Attention)
  107. # This assumes Attention CHANGES how the pupil responds to Luminance.
  108. model_H1 <- lmer(PupilSize ~ Luminance * Attention + GazeX_Cent + GazeY_Cent + (1|SubjectID),
  109. data = data_cfs, REML = FALSE)
  111. # H0 (Null): Additive Model (Luminance + Attention)
  112. # This assumes Luminance works, and Attention might change baseline,
  113. # BUT Attention does NOT change the response to Luminance (No Interaction).
  114. model_H0 <- lmer(PupilSize ~ Luminance + Attention + GazeX_Cent + GazeY_Cent + (1|SubjectID),
  115. data = data_cfs, REML = FALSE)
  117. # C. Calculate Bayes Factor using BIC Approximation
  118. # This method is robust and commonly accepted for LME comparisons
  119. bic_H1 <- BIC(model_H1)
  120. bic_H0 <- BIC(model_H0)
  122. # Formula: BF01 = exp((BIC_H1 - BIC_H0) / 2)
  123. # BF > 1 favors Null (No Attention effect)
  124. # BF < 1 favors Alternative (Attention effect exists)
  125. BF_01 <- exp((bic_H1 - bic_H0) / 2)
  127. print("--- Bayes Factor Analysis (CFS Condition Only) ---")
  128. print(paste("BIC for Null Model (No Attention):", bic_H0))
  129. print(paste("BIC for Alternative Model (With Attention):", bic_H1))
  130. print(paste("Bayes Factor (BF01) favoring Null:", round(BF_01, 2)))
  132. if(BF_01 > 3){
  133. print("Result: Moderate/Strong evidence that Attention does NOT modulate pupil under suppression.")
  134. } else if(BF_01 < 0.33){
  135. print("Result: Evidence supports that Attention DOES modulate pupil.")
  136. } else {
  137. print("Result: Evidence is inconclusive.")
  138. }
  141. # ==============================================================================
  142. # 5. LIKELIHOOD RATIO TEST (LRT) FOR GAZE ARTIFACTS
  143. # ==============================================================================
  144. # NOTE: When comparing models with different Fixed Effects, you MUST use REML=FALSE (Maximum Likelihood).
  145. # If you use REML=TRUE (default), the likelihoods are not comparable.
  147. # 1. The Full Model (Includes Gaze Position)
  148. # This is what you believe is true: Gaze affects Pupil.
  149. m_full <- lmer(PupilSize ~ Visibility * Luminance * Attention +
  150. GazeX_Cent + GazeY_Cent +
  151. (1 | SubjectID),
  152. data = data_clean, REML = FALSE)
  154. # 2. The Reduced Model (Removes Gaze Position)
  155. # This is the Null Hypothesis: Gaze does NOT affect Pupil.
  156. m_reduced <- lmer(PupilSize ~ Visibility * Luminance * Attention +
  157. (1 | SubjectID),
  158. data = data_clean, REML = FALSE)
  160. # 3. Compare them
  161. # This runs the Chi-Square test on the difference in Likelihoods.
  162. lrt_result <- anova(m_reduced, m_full)
  164. print("--- Likelihood Ratio Test Results ---")
  165. print(lrt_result)

analysis_CFS_pupil_LME_Exp1_Final.r, no license · at the source

Overview

Authors: Yung-Hao Yang1,2, Hsin-I Liao1
ORCID iDs: Yung-Hao Yang, Hsin-I Liao
  1. Human Information Science Laboratory, Communication Science Laboratories, NTT, Inc., Kanagawa, Japan
  2. Cognitive Informatics Lab, Graduate School of Informatics, Kyoto University, Kyoto, Japan
Institutions: Kyoto University (Japan); NTT (Japan) (Japan)
Journal: iScience, volume 29, issue 6, article 115919
Dates: received 25 November 2025; accepted 24 April 2026; published online 28 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.115919 · PMID 42205707 · PMCID PMC13207345 · OpenAlex W4417167668
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.2139/ssrn.5887871
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Statistics, Preprocessing, Physiology & signal measures
Keywords: biological process, biological sciences, biology of human development, systems biology
Topic: Visual perception and processing mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 37 references in the paper
Research resources: MATLAB 2018b RRID:SCR_001622, Psychophysics Toolbox Version 3 RRID:SCR_002881, RRID:SCR_009602, G∗Power RRID:SCR_013726, R package: lme4 RRID:SCR_015654, R package: lmerTest RRID:SCR_015656, RRID:SCR_015823

Abstract

The pupillary light response (PLR) is traditionally regarded as a reflex to retinal illumination; however, recent studies have shown that it also reflects perceived or attended brightness. We examined whether pupils respond to perceptually invisible luminance under interocular suppression and most importantly, whether attention modulates such response. In experiment 1, a single bright or dark visual Gabor pattern evoked attenuated but reliable PLRs under interocular suppression, which was independent of feature-based attention manipulation. In experiment 2, spatial attention was directed to one of two Gabors (black and white) using a sustained rapid serial visual presentation task. Spatial attention modulated PLRs when the stimuli were visible but not when they were invisible under interocular suppression. These findings demonstrate that while unconscious brightness signals reach subcortical pathways, spatial attentional modulation of the PLR depends on visual awareness, revealing a cortical-subcortical gating mechanism for pupil control.

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 5 matches between paragraphs and lines of code.

OSF cgvw8

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: R (2), MATLAB (1)
Size: 11 files, 3 scripts
Software Heritage: not checked
Found in: “Data and code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: emmeans (2 files), lme4 (2 files), lmerTest (2 files), tidyverse (2 files), easystats (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
3 files
At the source: doi.org/10.17605/OSF.IO/CGVW8

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;
  • 3 scripts, each with its path and the digest of its content;
  • 5 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

• Raw data have been deposited on the Open Science Framework and are publicly available (DOI: https://doi.org/10.17605/OSF.IO/CGVW8). • The analysis codes have been deposited on the Open Science Framework and are publicly available (DOI: https://doi.org/10.17605/OSF.IO/CGVW8). • Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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, 2 authors, 4 keywords, 36 references, 7 RRIDs.

Cite

This paper

Yang, Y.-H., & Liao, H.-I. (2026). Pupillary responses to invisible brightness escape from attentional modulation. iScience, 29(6), 115919. https://doi.org/10.1016/j.isci.2026.115919

BibTeX

@article{yang2026pupillary,
author = {Yang, Yung-Hao and Liao, Hsin-I},
title = {{Pupillary responses to invisible brightness escape from attentional modulation}},
journal = {iScience},
year = {2026},
month = apr,
volume = {29},
number = {6},
pages = {115919},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.115919},
url = {https://doi.org/10.1016/j.isci.2026.115919},
pmid = {42205707},
pmcid = {PMC13207345}
}

RIS

TY - JOUR
AU - Yang, Yung-Hao
AU - Liao, Hsin-I
TI - Pupillary responses to invisible brightness escape from attentional modulation
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/04/28
VL - 29
IS - 6
SP - 115919
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.115919
UR - https://doi.org/10.1016/j.isci.2026.115919
LA - en
ER -

CSL-JSON

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