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The Role of the Anterior Temporal Lobe in Reading: An HD-tDCS Study.

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  1. [1] § METHODS › Analysis ↔ PicturePlausibility_code.qmd, lines 260–337 · score 0.85 · log transformed, residual SD, brms, Picture Plausibility, Student, chains

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The authors' code

Quarto · 403 lines · 10 KB · no license · 1 match

  1. ---
  2. title: "PP tDCS 1 & 2"
  3. format: html
  4. editor: visual
  5. ---
  7. # Combining data
  9. ```{r}
  10. library(patchwork)
  11. library(readxl)
  12. library(ggplot2)
  13. library(gridExtra)
  14. library(openxlsx)
  15. library(dplyr)
  16. library(corrplot)
  17. library(prettyR)
  18. library(hrbrthemes)
  19. library(lme4)
  20. library(lmerTest)
  21. library(tidyverse)
  22. library(rstan)
  23. library(brms)
  24. library(bridgesampling)
  25. library(bayestestR)
  26. library(tidyverse)
  29. data <- read_excel("/Users/sophie.arheix/Desktop/Studies/2.tDCS1&2_WN/DATA/PP_repository.xlsx")
  31. ```
  33. Cleaning data (subjects, outliers + wrong answer
  35. ```{r}
  38. data_clean <- subset(data, RT != 0,)
  40. #Excluding upper limits
  41. data_median <- median(data_clean$RT)
  42. data_clean <- subset(data_clean, RT < 2000)
  43. data_clean <- subset(data_clean, RT > 400)
  46. #outliers
  47. remove_outliers <- function(data_clean, sd_threshold = 3) {
  48. result <- data.frame()
  49. subjects <- unique(data_clean$Subject)
  50. for (subject in subjects) {
  51. subset_df <- data_clean[data_clean$Subject == subject, ]
  52. mean_rt <- mean(subset_df$RT)
  53. sd_rt <- sd(subset_df$RT)
  54. subset_df <- subset_df[abs(subset_df$RT - mean_rt) <= sd_threshold * sd_rt, ]
  55. result <- rbind(result, subset_df)
  56. }
  57. return(result)
  58. }
  59. data_clean <- remove_outliers(data_clean)
  62. #Accuracy
  63. accuracy<-data_clean
  64. data_clean <- filter (data_clean, (Accuracy !=0 ))
  65. ```
  67. ### Summarize function
  69. ```{r}
  71. ## Summarizes data.
  72. ## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
  73. ## data: a data frame.
  74. ## measurevar: the name of a column that contains the variable to be summariezed
  75. ## groupvars: a vector containing names of columns that contain grouping variables
  76. ## na.rm: a boolean that indicates whether to ignore NA's
  77. ## conf.interval: the percent range of the confidence interval (default is 95%)
  78. summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE, conf.interval=.95, .drop=TRUE) {
  79. require(plyr)
  81. # New version of length which can handle NA's: if na.rm==T, don't count them
  82. length2 <- function (x, na.rm=FALSE) {
  83. if (na.rm) sum(!is.na(x))
  84. else length(x)
  85. }
  87. # This is does the summary; it's not easy to understand...
  88. datac <- ddply(data, groupvars, .drop=.drop,
  89. .fun= function(xx, col, na.rm) {
  90. c( N = length2(xx[,col], na.rm=na.rm),
  91. mean = mean (xx[,col], na.rm=na.rm),
  92. sd = sd (xx[,col], na.rm=na.rm)
  93. )
  94. },
  95. measurevar,
  96. na.rm
  97. )
  99. # Rename the "mean" column
  100. datac <- plyr::rename(datac, c("mean"=measurevar))
  102. datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
  104. # Confidence interval multiplier for standard error
  105. # Calculate t-statistic for confidence interval:
  106. # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
  107. ciMult <- qt(conf.interval/2 + .5, datac$N-1)
  108. datac$ci <- datac$se * ciMult
  110. return(datac)
  111. }
  115. ```
  117. ### Subsets
  119. ```{r}
  121. #subsets
  122. ATL_2 <- subset(data_clean, Target=="ATL")
  123. TOC_2 <- subset(data_clean, Target=="TOC")
  125. ATL_accuracy <- subset(accuracy, Target=="ATL")
  126. TOC_accuracy <- subset(accuracy, Target=="TOC")
  128. ```
  130. # Visualization
  132. ## Main effect
  134. ```{r}
  137. #TOC
  138. RT_4 <- summarySE(data=TOC_2, measurevar="RT", groupvars=c("Stimulation", "cond"))
  140. ggplot(RT_4, aes(x = cond, y =RT, fill = Stimulation)) +
  141. geom_bar(stat = "identity", position = "dodge") +
  142. geom_errorbar(width = 0.15, position = position_dodge(1), aes(ymin = RT - se, ymax = RT + se)) +
  143. scale_y_continuous("Mean RT") +
  144. xlab("Stimulation type") +
  145. scale_fill_manual(values = c("Real" = "darkgoldenrod1", "Sham" = "cyan4")) +
  146. coord_cartesian(ylim = c(600, 1200)) + labs(title="TOC Stimulation")
  148. #ATL
  150. RT_5 <- summarySE(data=ATL_2, measurevar="RT", groupvars=c("Stimulation", "cond"))
  152. ggplot(RT_5, aes(x = cond, y =RT, fill = Stimulation)) +
  153. geom_bar(stat = "identity", position = "dodge") +
  154. geom_errorbar(width = 0.15, position = position_dodge(1), aes(ymin = RT - se, ymax = RT + se)) +
  155. scale_y_continuous("Mean RT") +
  156. xlab("Stimulation type") +
  157. scale_fill_manual(values = c("Real" = "darkgoldenrod1", "Sham" = "cyan4")) +
  158. coord_cartesian(ylim = c(600, 1200)) + labs(title="ATL Stimulation")
  160. #####Accuracy
  163. RT_6 <- summarySE(data=TOC_accuracy, measurevar="Accuracy", groupvars=c("Stimulation", "cond"))
  165. ggplot(RT_6, aes(x = cond, y =Accuracy, fill = Stimulation)) +
  166. geom_bar(stat = "identity", position = "dodge") +
  167. geom_errorbar(width = 0.15, position = position_dodge(1), aes(ymin = Accuracy - se, ymax = Accuracy + se)) +
  168. scale_y_continuous("Mean Accuracy") +
  169. xlab("Stimulation type") +
  170. scale_fill_manual(values = c("Real" = "darkgoldenrod1", "Sham" = "cyan4")) +
  171. coord_cartesian(ylim = c(0.6, 1)) + labs(title ="TOC Stimulation")
  173. RT_7 <- summarySE(data=ATL_accuracy, measurevar="Accuracy", groupvars=c("Stimulation", "cond"))
  175. ggplot(RT_7, aes(x = cond, y =Accuracy, fill = Stimulation)) +
  176. geom_bar(stat = "identity", position = "dodge") +
  177. geom_errorbar(width = 0.15, position = position_dodge(1), aes(ymin = Accuracy - se, ymax = Accuracy + se)) +
  178. scale_y_continuous("Mean Accuracy") +
  179. xlab("Stimulation type") +
  180. scale_fill_manual(values = c("Real" = "darkgoldenrod1", "Sham" = "cyan4")) +
  181. coord_cartesian(ylim = c(0.6, 1)) + labs(title="ATL Stimulation")
  182. ```
  184. # Omnibus model
  186. ## RT
  188. ```{r}
  191. print("#################### ATL ###########################")
  192. ATL_2 <- subset(data_clean, Target=="ATL")
  193. ATL_plaus <- subset(ATL_2, cond == "plausible")
  194. ATL_implaus <- subset(ATL_2, cond == "implaus")
  197. omnibus_model <- lmer(RT ~ cond + Stimulation + (1|Subject), data=ATL_2)
  198. summary(omnibus_model)
  200. condition <- lmer(RT ~ Stimulation + (1|Subject), data=ATL_plaus)
  201. summary(condition)
  203. condition <- lmer(RT ~ Stimulation + (1|Subject), data=ATL_implaus)
  204. summary(condition)
  206. print("#################### TOC ###########################")
  207. TOC_2 <- subset(data_clean, Target=="TOC")
  208. TOC_plaus <- subset(TOC_2, cond == "plausible")
  209. TOC_implaus <- subset(TOC_2, cond == "implaus")
  212. omnibus_model <- lmer(RT ~ cond + Stimulation + (1|Subject), data=TOC_2)
  213. summary(omnibus_model)
  215. condition <- lmer(RT ~ Stimulation + (1|Subject), data=TOC_plaus)
  216. summary(condition)
  218. condition <- lmer(RT ~ Stimulation + (1|Subject), data=TOC_implaus)
  219. summary(condition)
  224. ```
  226. ## Accuracy
  228. ```{r}
  230. #################### ATL ###########################
  231. ATL_2 <- subset(accuracy, Target=="ATL")
  232. ATL_plaus <- subset(ATL_2, cond == "plausible")
  233. ATL_implaus <- subset(ATL_2, cond == "implaus")
  236. omnibus_model <- glmer(Accuracy ~ cond + Stimulation + (1|Subject), data=ATL_2, family=binomial())
  237. summary(omnibus_model)
  239. condition <- glmer(Accuracy ~ Stimulation + (1|Subject), data=ATL_plaus, family=binomial())
  240. summary(condition)
  242. condition <- glmer(Accuracy ~ Stimulation + (1|Subject), data=ATL_implaus, family=binomial())
  243. summary(condition)
  245. #################### TOC ###########################
  246. TOC_2 <- subset(accuracy, Target=="TOC")
  247. TOC_plaus <- subset(TOC_2, cond == "plausible")
  248. TOC_implaus <- subset(TOC_2, cond == "implaus")
  250. omnibus_model <- glmer(Accuracy ~ cond + Stimulation + (1|Subject), data=TOC_2, family=binomial())
  251. summary(omnibus_model)
  253. condition <- glmer(Accuracy ~ Stimulation + (1|Subject), data=TOC_plaus, family=binomial())
  254. summary(condition)
  256. condition <- glmer(Accuracy ~ Stimulation + (1|Subject), data=TOC_implaus, family=binomial())
  257. summary(condition)
  258. ```
  260. # Bayesian
  262. ### ATL
  264. ```{r}
  267. #log-transform
  268. data_clean <- data_clean %>%
  269. mutate(
  270. RT_ms = RT,
  271. logRT = log(RT_ms),
  272. Stimulation = factor(Stimulation),
  273. cond = factor(cond),
  274. Target = factor(Target),
  275. Subject = factor(Subject)
  276. )
  280. ATL_2 <- filter(data_clean, Target == "ATL")
  282. mean(ATL_2$logRT)
  284. # Priors log(RT)
  285. # b: fixed effect centered on 0, sd 0.04
  286. # intercept: centered on mean(logRT)
  288. priors <- c(
  289. set_prior("normal(0, 0.04)", class = "b"), # fixed effects
  290. set_prior("student_t(3, 6.87, 0.5)", class = "Intercept"),
  291. set_prior("exponential(1/0.5)", class = "sd"), # random-subject SD
  292. set_prior("exponential(1/0.5)", class = "sigma") # residual SD
  293. )
  296. ATL_full <- brm(
  297. formula = logRT ~ cond + Stimulation + (1 | Subject),
  298. data = ATL_2,
  299. family = gaussian(),
  300. prior = priors,
  301. iter = 2000, warmup = 1000, chains = 4,
  302. control = list(adapt_delta = 0.95)
  303. )
  305. summary(ATL_full)
  308. #### SUBSET FOR PLAUSIBILITY
  311. ATL_2_plausible <- filter(ATL_2 , cond == "plausible")
  312. ATL_2_implausible <- filter(ATL_2 , cond == "implaus")
  315. ATL_plausible <- brm(
  316. formula = logRT ~ Stimulation + (1 | Subject),
  317. data = ATL_2_plausible,
  318. family = gaussian(),
  319. prior = priors,
  320. iter = 2000, warmup = 1000, chains = 4,
  321. control = list(adapt_delta = 0.95)
  322. )
  324. summary(ATL_plausible)
  326. ATL_implausible <- brm(
  327. formula = logRT ~ Stimulation + (1 | Subject),
  328. data = ATL_2_implausible,
  329. family = gaussian(),
  330. prior = priors,
  331. iter = 2000, warmup = 1000, chains = 4,
  332. control = list(adapt_delta = 0.95)
  333. )
  335. summary(ATL_implausible)
  337. ```
  339. ### TOC
  341. ```{r}
  342. TOC_2 <- filter(data_clean, Target == "TOC")
  344. mean(TOC_2$logRT)
  347. # Priors on log(RT)
  348. # b: fixed effects centered on 0, sd 0.04 ( ±8% ±77 ms at 964ms)
  349. # intercept: centered on mean(logRT)
  350. # sd / sigma: weak regularization
  352. priors <- c(
  353. set_prior("normal(0, 0.04)", class = "b"), # fixed effects
  354. set_prior("student_t(3, 6.87, 0.5)", class = "Intercept"),
  355. set_prior("exponential(1/0.5)", class = "sd"), # random-subject SD
  356. set_prior("exponential(1/0.5)", class = "sigma") # residual SD
  357. )
  360. TOC_full <- brm(
  361. formula = logRT ~ cond + Stimulation + (1 | Subject),
  362. data = TOC_2,
  363. family = gaussian(),
  364. prior = priors,
  365. iter = 2000, warmup = 1000, chains = 4,
  366. control = list(adapt_delta = 0.95)
  367. )
  369. summary(TOC_full)
  372. #### SUBSET FOR PLAUSIBILITY
  375. TOC_2_plausible <- filter(TOC_2, cond == "plausible")
  376. TOC_2_implausible <- filter(TOC_2, cond == "implaus")
  379. TOC_plausible <- brm(
  380. formula = logRT ~ Stimulation + (1 | Subject),
  381. data = TOC_2_plausible,
  382. family = gaussian(),
  383. prior = priors,
  384. iter = 2000, warmup = 1000, chains = 4,
  385. control = list(adapt_delta = 0.95)
  386. )
  388. summary(TOC_plausible)
  391. TOC_implausible <- brm(
  392. formula = logRT ~ Stimulation + (1 | Subject),
  393. data = TOC_2_implausible,
  394. family = gaussian(),
  395. prior = priors,
  396. iter = 2000, warmup = 1000, chains = 4,
  397. control = list(adapt_delta = 0.95)
  398. )
  400. summary(TOC_implausible)
  403. ```

PicturePlausibility_code.qmd, no license · at the source

Overview

Authors: Sophie Arheix-Parras1, Cheng Xiao1,2, Sidney Crouse1, Nicholas Riccardi1, Karim Johari3, Rutvik H. Desai1,4
ORCID iDs: Sophie Arheix-Parras, Cheng Xiao, Nicholas Riccardi, Karim Johari, Rutvik H. Desai
  1. Department of Psychology, University of South Carolina, Columbia, SC, USA
  2. Linguistics Program, University of South Carolina, Columbia, SC, USA
  3. Human Neurophysiology and Neuromodulation Lab, Communication Sciences and Disorders, Louisiana State University, Baton Rouge, LA, USA
  4. Institute for Mind and Brain, University of South Carolina, Columbia, SC, USA
Institutions: University of South Carolina (United States); Louisiana State University (United States)
Journal: Neurobiology of language (Cambridge, Mass.), volume 7, article NOL.a.266
Dates: received 21 April 2025; accepted 16 April 2026; published online 1 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/nol.a.266 · PMID 42491894 · PMCID PMC13379303 · OpenAlex W7155515413
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), cognitive (subfield)
Methods: Statistics
Keywords: ATL, lexical, reading, semantic, tDCS
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIH/NIDCD (R01DC017162)
Citations: not cited yet (Europe PMC); 101 references in the paper

Abstract

The anterior temporal lobe (ATL) is suggested as a semantic hub that may support reading inconsistent words via semantic access. Surface alexia is characterized by difficulty in reading these inconsistent words, and often co-occurs with ATL atrophy and semantic impairments. However, the role of ATL in word reading is unclear, as evidenced by cases of alexia without semantic impairments and semantic impairments without surface alexia. To test its role in reading, we stimulated the ATL using transcranial Direct Current Stimulation in neurotypical participants performing a Word Naming task including consistent and inconsistent words, and a Picture Plausibility (PP) task involving nonverbal semantic judgments. We also stimulated the left tempo-occipital cortex (TOC) during the PP task. Low-frequency inconsistent words were read more slowly than high-frequency inconsistent words in the sham condition. This interaction disappeared with ATL stimulation. We found an interaction between consistency and stimulation in the low-frequency subset, but not in the high-frequency subset. In the PP task, ATL stimulation had no effect, whereas TOC stimulation significantly influenced reaction time. These findings support the left ATL’s role in reading inconsistent words, aligning with surface alexia with ATL atrophy. The results also suggest that the left lateral ATL may be involved in lexical rather than purely semantic processes, consistent with lesion studies demonstrating a dissociation between semantic impairments and surface alexia, and possibly also with graded modality-specific specialization of bilateral ATLs. These findings reconcile conflicting findings and elucidate the role of left lateral ATL in reading.

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 1 match between paragraphs and lines of code.

OSF 2nmcr

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Quarto (2)
Size: 4 files, 2 scripts
Software Heritage: not checked
Found in: “DATA AND CODE AVAILABILITY STATEMENT”
Holds: 2 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (2 files), lme4 (2 files), lmerTest (2 files), patchwork (2 files), tidyverse (2 files), brms (1 file), easystats (1 file), Stan (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
2 files
At the source: osf.io/2nmcr/overview?view_only=cb893f6a012e410a8eae83b714001267

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;
  • 2 scripts, each with its path and the digest of its content;
  • 1 match 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 statement

Data and code for this study are available on OSF: https://osf.io/2nmcr/overview?view_only=cb893f6a012e410a8eae83b714001267.

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, pages, dates, 6 authors, 5 keywords, 1 funder, 100 references.

Cite

This paper

Arheix-Parras, S., Xiao, C., Crouse, S., Riccardi, N., Johari, K., & Desai, R. H. (2026). The Role of the Anterior Temporal Lobe in Reading: An HD-tDCS Study. Neurobiology of language (Cambridge, Mass.), 7, NOL.a.266. https://doi.org/10.1162/nol.a.266

BibTeX

@article{arheixparras2026role,
author = {Arheix-Parras, Sophie and Xiao, Cheng and Crouse, Sidney and Riccardi, Nicholas and Johari, Karim and Desai, Rutvik H.},
title = {{The Role of the Anterior Temporal Lobe in Reading: An HD-tDCS Study}},
journal = {Neurobiology of language (Cambridge, Mass.)},
year = {2026},
month = jul,
volume = {7},
pages = {NOL.a.266},
publisher = {MIT Press},
issn = {2641-4368},
doi = {10.1162/nol.a.266},
url = {https://doi.org/10.1162/nol.a.266},
pmid = {42491894},
pmcid = {PMC13379303}
}

RIS

TY - JOUR
AU - Arheix-Parras, Sophie
AU - Xiao, Cheng
AU - Crouse, Sidney
AU - Riccardi, Nicholas
AU - Johari, Karim
AU - Desai, Rutvik H.
TI - The Role of the Anterior Temporal Lobe in Reading: An HD-tDCS Study
T2 - Neurobiology of language (Cambridge, Mass.)
J2 - Neurobiol Lang (Camb)
PY - 2026
DA - 2026/07/01
VL - 7
SP - NOL.a.266
SN - 2641-4368
PB - MIT Press
DO - 10.1162/nol.a.266
UR - https://doi.org/10.1162/nol.a.266
LA - en
ER -

CSL-JSON

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"id": "10.1162/nol.a.266",
"type": "article-journal",
"title": "The Role of the Anterior Temporal Lobe in Reading: An HD-tDCS Study",
"container-title": "Neurobiology of language (Cambridge, Mass.)",
"author": [
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"family": "Arheix-Parras",
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{
"family": "Xiao",
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"given": "Sidney"
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{
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{
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"given": "Rutvik H."
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],
"container-title-short": "Neurobiol Lang (Camb)",
"volume": "7",
"page": "NOL.a.266",
"DOI": "10.1162/nol.a.266",
"PMID": "42491894",
"PMCID": "PMC13379303",
"ISSN": "2641-4368",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/nol.a.266",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
1
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]
}
}

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In common: Stan, brms, easystats, 2 other tools, cognitive, 1 reference

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