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Meditation-Specific Neural Predictors of State Mindfulness During Eyes Open Meditation.

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The authors' code

R Markdown · 212 lines · 5 KB · no license

  1. ---
  2. title: "SMS_Flanker_Accuracy"
  3. output: pdf_document
  4. date: "2023-07-28"
  5. ---
  7. ```{r setup, include=FALSE}
  8. knitr::opts_chunk$set(echo = FALSE)
  9. ```
  11. ```{r include=FALSE}
  12. library(tidyverse)
  13. library(dplyr)
  14. library(rstatix)
  15. library(brms)
  16. library(bayestestR)
  17. library(ggmcmc)
  18. library(mcmcplots)
  19. library(interactions)
  20. ```
  22. ```{r}
  23. flank_behavioral_input <- read.csv("FlankBehavioral_Output.csv", header = TRUE, sep = ",")
  24. ```
  26. ```{r include= FALSE}
  27. acc_data <- data.frame(flank_behavioral_input) %>%
  28. filter(Target.RT != 0) %>% #remove missed trials
  29. mutate(Target.ACC = factor(Target.ACC))%>%
  30. mutate(TrialType = factor(TrialType, levels = c( "Incon", "Con")))%>%
  31. mutate(Induction = factor(Induction, levels = c( "FA", "OM", "C"))) %>%
  32. dplyr::rename(Session_Number = Session)%>%
  33. dplyr::rename(Task_Number = Task_Order)%>%
  34. mutate(Session_Number = as.numeric(Session_Number))%>%
  35. mutate(Task_Number = as.numeric(Task_Number))%>%
  36. mutate(Subject = factor(Subject))
  38. acc_data_R <- acc_data %>%
  39. mutate(Induction = factor(Induction, levels = c( "C", "OM", "FA")))
  41. acc_data_dummy_c<- acc_data %>%
  42. mutate(Induction = factor(Induction, levels = c( "C", "OM", "FA")))
  44. acc_data_dummy_fa<- acc_data %>%
  45. mutate(Induction = factor(Induction, levels = c( "FA", "OM", "C")))
  48. contrasts(acc_data$Induction) = contr.sum(3)
  49. contrasts(acc_data$TrialType) = contr.sum(2)
  50. contrasts(acc_data$Target.ACC) = contr.sum(2)
  52. contrasts(acc_data_R$Induction) = contr.sum(3)
  53. contrasts(acc_data_R$TrialType) = contr.sum(2)
  54. contrasts(acc_data_R$Target.ACC) = contr.sum(2)
  56. contrasts(acc_data_dummy_c$TrialType) = contr.sum(2)
  57. contrasts(acc_data_dummy_c$Target.ACC) = contr.sum(2)
  59. contrasts(acc_data_dummy_fa$TrialType) = contr.sum(2)
  60. contrasts(acc_data_dummy_fa$Target.ACC) = contr.sum(2)
  61. ```
  64. # Preregistered Analysis Contrast: Target.ACC ~ 1 + TrialType * Induction + Mindfulness_Comp_Z + Session_Number + Task_Number + (1 | Subject)
  66. ```{r}
  67. #build model
  68. acc_m1 <- Target.ACC ~ 1 + TrialType * Induction + Mindfulness_Comp_Z + Task_Number + Session_Number + (1 | Subject)
  69. ```
  71. ```{r include = FALSE}
  72. #run model
  73. bayes_acc_m1 <- brm(acc_m1,
  74. data = acc_data,
  75. family= bernoulli(link = "logit"),
  76. sample_prior = TRUE,
  77. seed = 3)
  78. ```
  80. ```{r}
  81. print(summary(bayes_acc_m1), digits = 4)
  82. ```
  84. \newpage
  86. # Accuracy contrast, reverse coded to get C estimates
  88. ```{r include = FALSE}
  89. #run model
  90. bayes_acc_m1_R <- brm(acc_m1,
  91. data = acc_data_R,
  92. family= bernoulli(link = "logit"),
  93. sample_prior = TRUE,
  94. seed = 3)
  95. ```
  97. ```{r}
  98. summary(bayes_acc_m1_R)
  99. ```
  101. \newpage
  102. # ERs for Accuracy Contrast Coded Models
  104. ## ER for TrialType:FA > 0
  105. ```{r}
  106. h1_acc <- hypothesis(bayes_acc_m1, "TrialType1:Induction1 > 0")
  108. print(h1_acc, digits = 2)
  109. ```
  111. ## ER for OM > 0
  112. ```{r}
  113. h2_acc <- hypothesis(bayes_acc_m1, "Induction2 > 0")
  115. print(h2_acc, digits = 2)
  116. ```
  120. \newpage
  121. # Preregistered Analysis Dummy C as Baseline:
  122. # Target.ACC ~ 1 + TrialType * Induction + Mindfulness_Comp_Z + Session_Number + Task_Number + (1 | Subject)
  124. ```{r include = FALSE}
  125. #run model
  126. bayes_acc_m1_dummy_c <- brm(acc_m1,
  127. data = acc_data_dummy_c,
  128. family= bernoulli(link = "logit"),
  129. sample_prior = TRUE,
  130. seed = 3)
  131. ```
  133. ```{r}
  134. print(summary(bayes_acc_m1_dummy_c), digits = 4)
  135. ```
  138. ## ER for TrialType:FA > 0
  139. ```{r}
  140. h3_acc <- hypothesis(bayes_acc_m1_dummy_c, "TrialType1:InductionFA > 0")
  142. print(h3_acc, digits = 2)
  143. ```
  145. ## ER for OM > 0
  146. ```{r}
  147. h4_acc <- hypothesis(bayes_acc_m1_dummy_c, "InductionOM > 0")
  149. print(h4_acc, digits = 2)
  150. ```
  153. \newpage
  154. # Preregistered Analysis Dummy FA as Baseline:
  155. # Target.ACC ~ 1 + TrialType * Induction + Mindfulness_Comp_Z + Session_Number + Task_Number + (1 | Subject)
  157. ```{r include = FALSE}
  158. #run model
  159. bayes_acc_m1_dummy_fa <- brm(acc_m1,
  160. data = acc_data_dummy_fa,
  161. family= bernoulli(link = "logit"),
  162. sample_prior = TRUE,
  163. seed = 3)
  164. ```
  166. ```{r}
  167. print(summary(bayes_acc_m1_dummy_fa), digits = 4)
  168. ```
  170. ## ER for OM aka InductionOM > 0
  171. ```{r}
  172. h5_acc <- hypothesis(bayes_acc_m1_dummy_fa, "InductionOM > 0")
  174. print(h5_acc, digits = 2)
  175. ```
  179. \newpage
  180. # Visualizations for original contrast coded model
  182. ```{r include = FLASE}
  183. #run model
  184. acc_linear_regression <- glmer(acc_m1,
  185. data = acc_data, family = binomial)
  187. print(summary(acc_linear_regression), digits = 3)
  188. ```
  195. ```{r}
  196. acc_fig <- cat_plot(model = bayes_acc_m1,
  197. pred = TrialType,
  198. modx = Induction,
  199. interval = TRUE,
  200. int.type = "confidence",
  201. int.width = 0.95,
  202. data = acc_data,
  204. line.thickness = 1.2,
  205. vary.lty = FALSE)
  206. acc_fig
  208. ```

SMPFlankerAcc_PreReg.Rmd, no license · at the source

Overview

Authors: Marne L White1, Yanli Lin2, Todd S Braver3
  1. University of Maryland, College Park, College Park, United States
  2. University of Arkansas at Fayetteville, Fayetteville, United States
  3. Washington University in St. Louis, St. Louis, United States
Institutions: University of Maryland, College Park (United States); University of Arkansas at Fayetteville (United States); Washington University in St. Louis (United States)
Journal: Mindfulness, volume 17, issue 7, pages 1969-1981
Dates: received 29 August 2025; accepted 19 April 2026; published online 11 May 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s12671-026-02850-6 · PMID 42483313 · PMCID PMC13385244 · OpenAlex W7160822238
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Physiology & signal measures
Keywords: EEG, Mindfulness, Meditation, Alpha, Theta
Topic: Mindfulness and Compassion Interventions (Clinical Psychology, Psychology), according to OpenAlex
Funding: NIA NIH HHS (F32 AG069499)
Citations: cited by 1 paper (Europe PMC); 49 references in the paper

Abstract

Objectives: Focused attention (FA) and open monitoring (OM) are distinct mindfulness practices that produce unique psychological effects. Prior research has frequently investigated the neural correlates of FA and OM states utilizing electroencephalographic (EEG) methods, focusing on changes in spectral power within theta and alpha bands. Yet the functional significance of these neural changes has remained unclear. Here we utilized a fully within-subject state induction protocol to more directly test whether EEG spectral power during FA and OM is differentially associated with subjective ratings of state mindfulness.

Method: While continuous EEG was recorded, participants engaged in eyes-open audio-guided FA and OM practices, as well as an active control condition (C), and then self-reported their state mindfulness afterwards. Linear mixed-effects models were used to rigorously assess how condition-level variation in spectral power predicted state mindfulness scores across the three inductions.

Results: Validating the approach, participants reported higher state mindfulness and decreased theta power during both FA and OM relative to C; additionally, reduced alpha power was found in OM relative to FA. Most importantly, increased theta power was associated with higher state mindfulness in FA and even more strongly in OM, whereas reduced alpha power was linked to higher state mindfulness selectively in OM.

Conclusions: These findings add to the growing literature suggesting that FA and OM represent distinct mindfulness states. We further establish that the functional significance of theta and alpha power is context-dependent, clearly linking these neural measures to the subjective quality of specific meditation states and practices.

Supplementary Information: The online version contains supplementary material available at 10.1007/s12671-026-02850-6.

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

Repository

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

OSF uv9yn

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (10)
Size: 22 files, 10 scripts
Software Heritage: not checked
Found in: “Data Availability”
Holds: 10 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (10 files), rstatix (8 files), brms (6 files), easystats (6 files), lme4 (4 files), lmerTest (4 files), lavaan (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
10 files
At the source: osf.io/uv9yn/

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.

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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;
  • 10 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 Availability

All data, materials, protocols, and analysis code, including full model specifications and output, are available in the OSF repository (https://osf.io/uv9yn/).

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

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 1 funder, 45 references.

Cite

This paper

White, M. L., Lin, Y., & Braver, T. S. (2026). Meditation-Specific Neural Predictors of State Mindfulness During Eyes Open Meditation. Mindfulness, 17(7), 1969-1981. https://doi.org/10.1007/s12671-026-02850-6

BibTeX

@article{white2026meditation,
author = {White, Marne L and Lin, Yanli and Braver, Todd S},
title = {{Meditation-Specific Neural Predictors of State Mindfulness During Eyes Open Meditation}},
journal = {Mindfulness},
year = {2026},
month = may,
volume = {17},
number = {7},
pages = {1969--1981},
publisher = {Springer Science+Business Media},
issn = {1868-8527},
doi = {10.1007/s12671-026-02850-6},
url = {https://doi.org/10.1007/s12671-026-02850-6},
pmid = {42483313},
pmcid = {PMC13385244}
}

RIS

TY - JOUR
AU - White, Marne L
AU - Lin, Yanli
AU - Braver, Todd S
TI - Meditation-Specific Neural Predictors of State Mindfulness During Eyes Open Meditation
T2 - Mindfulness
J2 - Mindfulness (N Y)
PY - 2026
DA - 2026/05/11
VL - 17
IS - 7
SP - 1969
EP - 1981
SN - 1868-8527
PB - Springer Science+Business Media
DO - 10.1007/s12671-026-02850-6
UR - https://doi.org/10.1007/s12671-026-02850-6
LA - en
ER -

CSL-JSON

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