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Prefrontal and Cerebellar Contributions to Semantic Memory Retrieval.

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  1. [1] § MATERIALS AND METHODS › Statistical Analysis ↔ R_syntax/R3_Bayesian_mediation.R, lines 41 to 129 · score 0.91 · mediation parameters, Bayesian mediation, mediator model, brms, outcome model, Bernoulli · Report a mismatch
  2. [2] § RESULTS › Bayesian Mediation Analysis ↔ R_syntax/R3_Bayesian_mediation.R, lines 41 to 129 · score 0.61 · mediator model, outcome model, mediation, indirect, Bayesian, mediated · Report a mismatch

Paper

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The authors' code

R · 129 lines · 4.7 KB · no license · 2 matches

  1. ################################################################################
  2. # Project: ADT tDCS Study — Mediation analysis
  3. # Author: M. Marko (contact: [email hidden])
  4. ################################################################################
  6. #This study employed a Bayesian mediation analysis to investigate whether the
  7. #effect of prefrontal transcranial direct current stimulation (tDCS) on
  8. #response time (RT) is mediated by intrusions (an intermediate variable).
  11. ## 1) Packages -----------------------------------------------------------------
  13. require(readr)
  14. library(mediation)
  15. library(broom.mixed)
  16. library(lme4)
  17. library(boot)
  18. library(brms)
  19. library(bayestestR)
  20. library(brms)
  21. library(tidybayes)
  23. ## 2) Data import --------------------------------------------------------------
  24. DATA_ADT <- read_delim("C:/WORKSTATION/DATA_ADT.txt",
  25. delim = "\t",
  26. escape_double = FALSE,
  27. trim_ws = TRUE,
  28. locale = locale(encoding = "Windows-1250"))
  30. # Select correct dissociative trials from sham and PFC groups
  31. DATA_ADT_DA = subset(DATA_ADT, Exclude == 0)
  32. DATA_ADT_DA = subset(DATA_ADT_DA, Retrieval == "Unrelated")
  33. DATA_ADT_DA = subset(DATA_ADT_DA, tDCS != "CER")
  34. DATA_ADT_DA = na.omit(DATA_ADT_DA)
  36. ## 3) Bayesian Mediation -------------------------------------------------------
  38. # Rename SHAM -> CONTROL
  39. DATA_ADT_DA$tDCS = ifelse(DATA_ADT_DA$tDCS == "PFC", "PFC", "CONTROL")
  41. # Conversion to factors
  42. DATA_ADT_DA$tDCS <- factor(DATA_ADT_DA$tDCS, levels = c("CONTROL", "PFC"))
  43. DATA_ADT_DA$Block <- factor(DATA_ADT_DA$Block, levels = c("B1_PRE", "B2_POST"))
  45. # Mediator model
  46. brm_med <- brm(INT ~ Block * tDCS + (1+Block|ID) + (1|Stimulus),
  47. data = DATA_ADT_DA, family = bernoulli(),
  48. iter = 4000, warmup = 1000, chains = 4, cores = 4, seed = 4321,
  49. control = list(adapt_delta = 0.95))
  51. # Outcome model
  52. brm_out <- brm(wRT ~ Block * tDCS + INT + (1+Block|ID) + (1|Stimulus),
  53. data = DATA_ADT_DA, family = Gamma("log"),
  54. iter = 4000, warmup = 1000, chains = 4, cores = 4, seed = 4321,
  55. control = list(adapt_delta = 0.95))
  57. summary(brm_med)
  58. summary(brm_out)
  60. capture.output(summary(brm_med), file = "modmed_brm_med.txt")
  61. capture.output(summary(brm_out), file = "modmed_brm_out.txt")
  62. sink()
  64. ## Posterior draws and mediation parameters ------------------------------------
  66. # Extract posterior draws for brm_med (INT ~ Block * tDCS)
  67. post_med <- as_draws_df(brm_med)
  68. colnames(post_med)
  70. # Extract posterior draws for brm_out (wRT ~ Block * tDCS + INT)
  71. post_out <- as_draws_df(brm_out)
  72. colnames(post_out)
  74. # Extract coefficients
  75. a <- post_med$`b_BlockB2_POST:tDCSPFC` # Effect of Block*tDCS on INT
  76. b <- post_out$`b_INT` # Effect of INT on wRT
  78. # Compute indirect effect
  79. indirect_effect <- a * b
  81. # Compute direct and total effects (+proportion mediated)
  82. direct_effect <- post_out$`b_BlockB2_POST:tDCSPFC`
  83. total_effect <- indirect_effect + direct_effect
  84. prop_mediated <- indirect_effect / total_effect
  86. # Analysis summary
  87. posterior_summary <- describe_posterior(data.frame(
  88. Indirect_Effect = indirect_effect,
  89. Direct_Effect = direct_effect,
  90. Total_Effect = total_effect,
  91. Prop_Mediated = prop_mediated
  92. ))
  94. posterior_summary
  96. # Transform to interpretable response format -----------------------------------
  98. baseline_RT_pretest <- mean(DATA_ADT_DA$wRT[DATA_ADT_DA$Block == "B1_PRE"])
  100. # Function to transform effects to RT and percentage change
  101. transform_effect <- function(effect) {
  102. RT_change <- baseline_RT_pretest * (exp(effect) - 1)
  103. perc_change <- (exp(effect) - 1) * 100
  104. return(c(RT_change, perc_change))
  105. }
  107. {
  108. # Fetch effect values dynamically from the summary table
  109. effects <- posterior_summary$Median[posterior_summary$Parameter %in% c("Indirect_Effect", "Direct_Effect", "Total_Effect")]
  111. # Apply transformation
  112. transformed_values <- t(sapply(effects, transform_effect))
  114. # Add transformed values to the table
  115. posterior_summary$RT_Change <- NA
  116. posterior_summary$Percent_Change <- NA
  118. # Assign transformed values only to effect rows
  119. posterior_summary$RT_Change[posterior_summary$Parameter %in% c("Indirect_Effect", "Direct_Effect", "Total_Effect")] <- transformed_values[,1]
  120. posterior_summary$Percent_Change[posterior_summary$Parameter %in% c("Indirect_Effect", "Direct_Effect", "Total_Effect")] <- transformed_values[,2]
  122. # Print the updated posterior summary table
  123. print(posterior_summary)
  124. }
  126. write.table(posterior_summary,"modmed_posterior_summary.txt",sep="\t",row.names=FALSE)
  127. sink()
  129. #-------------------------------------------------------------------------------

R3_Bayesian_mediation.R, no license · at the source

Overview

Authors: Adam Kubinec1, Rastislav Rovný1, Igor Riečanský1,2, Martin Marko1,3
ORCID iDs: Adam Kubinec, Rastislav Rovný, Igor Riečanský, Martin Marko
  1. Department of Behavioural Neuroscience, Institute of Normal and Pathological Physiology, Centre of Experimental Medicine, Slovak Academy of Sciences, Bratislava, Slovakia
  2. Department of Psychiatry, Faculty of Medicine, Slovak Medical University in Bratislava, Bratislava, Slovakia
  3. Department of Applied Informatics, Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Bratislava, Slovakia
Institutions: Slovak Academy of Sciences (Slovakia); Centre of Experimental Medicine of the Slovak Academy of Sciences (Slovakia); Institute of Normal and Pathological Physiology of the Slovak Academy of Sciences (Slovakia); Slovak Medical University (Slovakia); Comenius University Bratislava (Slovakia)
Journal: Neurobiology of language (Cambridge, Mass.), volume 7, article NOL.a.235
Dates: received 2 October 2025; accepted 6 January 2026; published online 26 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/nol.a.235 · PMID 41971750 · PMCID PMC13065103 · OpenAlex W7119598968
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: human (organism), cognitive (subfield)
Methods: Statistics, Physiology & signal measures
Keywords: cerebellum, executive functions, inhibition, language, noninvasive neuromodulation, prefrontal cortex, semantic memory
Topic: Memory Processes and Influences (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Vedecká Grantová Agentúra MŠVVaŠ SR a SAV (2/0052/23, 2/0067/25); Agentúra na Podporu Výskumu a Vývoja (APVV-23-0145, APVV-23)
Citations: not cited yet (Europe PMC); 112 references in the paper

Abstract

Semantic memory retrieval is essential for language, thought, and adaptive behavior. Although both the prefrontal cortex (PFC) and the cerebellum have been implicated in this function, the role of the PFC remains poorly understood and the contribution of the cerebellum largely overlooked in current neurocognitive models. To address these gaps, we conducted a double-blind, randomized, placebo-controlled experiment in which healthy adults received anodal transcranial direct current stimulation (tDCS) targeting the left lateral PFC, the right posterior cerebellum, or sham stimulation. Participants completed a novel process-sensitive paradigm comprising lexical decision, free-associative (FA; automatic) retrieval, dissociative (DA; controlled) retrieval, and intrusion monitoring, while manipulating response predictability and rule switching. Cerebellar tDCS selectively impaired FA performance, particularly for cues evoking predictable responses, supporting its role in automatic access to overlearned associations. In contrast, prefrontal tDCS disrupted DA performance and increased associative intrusions, implicating the PFC in retrieval inhibition. Importantly, mediation analysis showed that the reduction in DA fluency was largely explained by higher probability of intrusions, indicating a perturbation of proactive inhibitory control that normally prevents irrelevant memory activations from entering working memory. Further exploratory analyses ruled out several alternative accounts of these stimulation effects, underscoring their process specificity. Together, these findings advance models of semantic cognition by demonstrating complementary contributions of the cerebellum to automatic retrieval and of the PFC to inhibitory control over intrusions.

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

Repository

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OSF y7edf

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: R (4)
Size: 116 files, 4 scripts
Software Heritage: not checked
Found in: “Code and Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: brms (4 files), easystats (4 files), lme4 (4 files), tidyverse (4 files), car (3 files), glmmTMB (3 files), lmerTest (3 files), multcomp (3 files), psych (3 files), reshape2 (3 files), broom (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
4 files
At the source: osf.io/y7edf/

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

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Code and Data Availability Statement

The data, materials, and codes are available on the Open Science Framework website (https://osf.io/y7edf/).

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 7 keywords, 2 funders, 112 references.

Cite

This paper

Kubinec, A., Rovný, R., Riečanský, I., & Marko, M. (2026). Prefrontal and Cerebellar Contributions to Semantic Memory Retrieval. Neurobiology of language (Cambridge, Mass.), 7, NOL.a.235. https://doi.org/10.1162/nol.a.235

BibTeX

@article{kubinec2026prefrontal,
author = {Kubinec, Adam and Rovný, Rastislav and Riečanský, Igor and Marko, Martin},
title = {{Prefrontal and Cerebellar Contributions to Semantic Memory Retrieval}},
journal = {Neurobiology of language (Cambridge, Mass.)},
year = {2026},
month = mar,
volume = {7},
pages = {NOL.a.235},
publisher = {MIT Press},
issn = {2641-4368},
doi = {10.1162/nol.a.235},
url = {https://doi.org/10.1162/nol.a.235},
pmid = {41971750},
pmcid = {PMC13065103}
}

RIS

TY - JOUR
AU - Kubinec, Adam
AU - Rovný, Rastislav
AU - Riečanský, Igor
AU - Marko, Martin
TI - Prefrontal and Cerebellar Contributions to Semantic Memory Retrieval
T2 - Neurobiology of language (Cambridge, Mass.)
J2 - Neurobiol Lang (Camb)
PY - 2026
DA - 2026/03/26
VL - 7
SP - NOL.a.235
SN - 2641-4368
PB - MIT Press
DO - 10.1162/nol.a.235
UR - https://doi.org/10.1162/nol.a.235
LA - en
ER -

CSL-JSON

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