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Working memory demands modulate memory brain state engagement.

Code ↔ Paper

11 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 11 matches
  1. [1] § STAR★Methods › Quantification and statistical analysis › EEG data preprocessing ↔ codes/anaCodes/eegPrepro.py, lines 59–103 · score 0.91 · notch filter, Hurst exponent, high pass filter, Bad electrodes, harmonics, noise
  2. [2] § Results › Memory brain states are modulated by working memory demands ↔ codes/anaCodes/permStats.Rmd, lines 273–325 · score 0.81 · gradual increase, rmANOVA, response selectively, Response locked memory, indicating greater retrieval, assessed response locked
  3. [3] § STAR★Methods › Quantification and statistical analysis › EEG data analysis ↔ codes/anaCodes/config.py, lines 271–314 · score 0.79 · Morlet wavelet transform, logarithmically spaced frequencies, 100 Hz
  4. [4] § STAR★Methods › Method details › Procedure and design › General overview ↔ codes/expCodes/perm.py, lines 169–246 · score 0.75 · response mappings, perception blocks, memory blocks, response phase, jitter, locations
  5. [5] § STAR★Methods › Quantification and statistical analysis › Statistical analyses ↔ codes/anaCodes/perm_group_memState.py, lines 307–357 · score 0.59 · logistic regression, linear regression, predicts, memory state evidence, delay, perception
  6. [6] § Results › Memory brain states are modulated by working memory demands ↔ codes/anaCodes/permStats.Rmd, lines 327–370 · score 0.59 · pre response dissociation, post hoc, perception responses, anticipated, SD, interval
  7. [7] § STAR★Methods › Method details › Procedure and design › Memory task ↔ codes/expCodes/perm.py, lines 169–246 · score 0.58 · memory blocks, response phase, change detection, locations, squares
  8. [8] § Results › Set size modulates target and change detection behavior ↔ codes/anaCodes/permStats.Rmd, lines 372–455 · score 0.55 · logistic regression, memory accuracy, linear regression, RT, SD, perception
  9. [9] § Results › Memory brain states are modulated by working memory demands ↔ codes/anaCodes/perm_stats.py, lines 357–402 · score 0.54 · 500–2500 ms, delay, retrieval, memory
  10. [10] § Results › Memory brain states are modulated by working memory demands ↔ codes/anaCodes/permStats.Rmd, lines 457–497 · score 0.51 · memory response, pre response, BF, hit, CRs, SD
  11. [11] § STAR★Methods › Quantification and statistical analysis › EEG data analysis ↔ codes/anaCodes/zpowerAna.py, lines 15–98 · score 0.50 · log transforming, onset, power, electrodes, EEG

Paper

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The authors' code

R Markdown · 497 lines · 17 KB · no license · 4 matches

  1. ---
  2. title: "perm stats"
  3. author: "Nicole Long & DT Nguyen"
  4. date: "1/31/2025"
  5. output:
  6. pdf_document:
  7. toc: yes
  8. latex_engine: xelatex
  9. mainfont: Helvetica
  10. cache: yes
  11. ---
  13. # Set desired parameters for the markdown output and load libraries
  14. ```{r setup, include=FALSE}
  15. knitr::opts_chunk$set(echo = FALSE)
  16. knitr::opts_chunk$set(cache = TRUE)
  17. knitr::opts_chunk$set(warning = FALSE)
  18. knitr::opts_chunk$set(message = FALSE)
  20. library(tidyverse)
  21. library(dplyr)
  22. library(glue) # prints results
  23. library(sjstats) # calculates eta squared
  24. library(emmeans)
  25. library(BayesFactor)
  26. ```
  28. # Specify subjects and directories
  29. ```{r}
  30. expr = "dtn4gn"
  31. N = 39
  32. dataDir = paste("/Users/",expr,"/eeg/perm/data/group/",sep="")
  33. #dataDir = paste("/Users/",expr,"/Library/CloudStorage/Box-Box/DT/perm/analysis/data/group/",sep="")
  34. demoDir = paste("/Users/",expr,"/eeg/perm/data/demographics/",sep="")
  35. ```
  37. # Demographics
  38. ```{r}
  39. demoData = read.csv(paste(demoDir, "eeg_demoPerm.csv", sep="")) %>%
  40. dplyr::select(-Study, -X, -X.1) # remove unnecessary columns
  42. ageM = round(mean(demoData$Age), 4)
  43. print(glue("Mean age is {ageM}"))
  45. ageSD = round(sd(demoData$Age), 4)
  46. print(glue("Standard deviation of age is {ageSD}"))
  48. ```
  50. # Load data
  51. ```{r}
  52. # behavioral data
  53. data = read.csv(paste(paste(dataDir,"N", N,"_perm_performance_Acc_RT.csv", sep=""))) %>%
  54. dplyr::select(-X) # remove this X column
  56. ## separate by task
  57. percDF = data[data$task=="perception",]
  58. memoDF = data[data$task=="memory",]
  60. # stimulus-locked data
  61. dataStimANOVA = read.csv(paste(paste0(dataDir,"N39_perm_trained_N57_repo_0to2000_to_stimLocked_0to3500in100ms_whole_brain_allFreqs_retEvi_Task.csv", sep=""))) %>%
  62. select(-X) %>% # remove this X column
  63. rename(
  64. retEvi = retrieval.evidence,
  65. time = time..ms.)
  67. dataRetEviDelay = read_csv(paste(paste0(dataDir, "N39_perm_trained_N57_repo_0to2000_to_stimLocked_500to2500in2000ms_whole_brain_allFreqs_retEvi_TaskXSetSize.csv"))) %>%
  68. select(-1) %>% # remove this X column
  69. rename(
  70. retEvi = `retrieval evidence`,
  71. time = `time (ms)`,
  72. setSize = `set size`)
  74. ## memory task only
  75. memoDelayDF = dataRetEviDelay[dataRetEviDelay$task=="memory",] %>%
  76. select(-3,-4)
  78. dataCapLimit = read.csv(paste(paste0(dataDir,"N39_perm_trained_N57_repo_0to2000_to_stimLocked_500to2500in2000ms_whole_brain_allFreqs_accuracy_SetSize.csv", sep=""))) %>%
  79. select(-X) %>% # remove this X column
  80. rename(
  81. setSize = set.size,
  82. capGroup = capacity.group)
  84. # response-locked data
  85. dataRespANOVA = read.csv(paste(paste0(dataDir,"N39_perm_trained_N57_repo_0to2000_to_respLocked_-500to500in100ms_whole_brain_allFreqs_retEvi_Task.csv", sep=""))) %>%
  86. select(-X) %>% # remove this X column
  87. rename(
  88. retEvi = retrieval.evidence,
  89. time = time..ms.)
  91. ```
  93. # ANOVAs
  95. ## 1x4 rmANOVA
  96. To examine the effect of set size on reaction time on perceptual trials, we conducted a one-way repeated-measures ANOVA with factors of set size (1, 2, 4, 6) as the IV and reaction time as the DV.
  98. To examine the effect of set size on response accuracy on memory trials, we conducted a one-way repeated-measures ANOVA with factors of set size (1, 2, 4, 6) as the IV and response accuracy as the DV.
  100. ### Analysis
  101. ```{r}
  102. # 1 x 4 ANOVA (perception)
  103. model = aov(rt ~ setSize + Error(subject/(setSize)), data=percDF)
  104. es_res = effectsize::eta_squared(model)
  105. summary(model)
  107. FACTOR = 2
  108. pval = summary(model)[[FACTOR]][[1]]['Pr(>F)'][[1]][[1]]
  109. factor_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[1]]
  110. resid_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[2]]
  111. f_stat = round(summary(model)[[FACTOR]][[1]]['F value'][[1]][[1]],4)
  112. pval_round = round(pval,4)
  113. # partial eta sq is one "off" because there's no eta for the subjects factor
  114. part_eta_sq = round(es_res$Eta2_partial,4)[FACTOR-1]
  116. print('Main effect of set size (perception trials)')
  117. print(glue("F({factor_df},{resid_df}) = {f_stat}; p = {pval_round}; pEta2 = {part_eta_sq}"))
  119. # 1 x 4 ANOVA (memory)
  120. model = aov(accuracy ~ setSize + Error(subject/(setSize)), data=memoDF)
  121. es_res = effectsize::eta_squared(model)
  122. summary(model)
  124. FACTOR = 2
  125. pval = summary(model)[[FACTOR]][[1]]['Pr(>F)'][[1]][[1]]
  126. factor_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[1]]
  127. resid_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[2]]
  128. f_stat = round(summary(model)[[FACTOR]][[1]]['F value'][[1]][[1]],4)
  129. pval_round = round(pval,4)
  130. # partial eta sq is one "off" because there's no eta for the subjects factor
  131. part_eta_sq = round(es_res$Eta2_partial,4)[FACTOR-1]
  133. print('Main effect of set size (memory trials)')
  134. print(glue("F({factor_df},{resid_df}) = {f_stat}; p = {pval_round}; pEta2 = {part_eta_sq}"))
  136. ```
  138. ### M & SD
  139. (if the interaction is significant, report the means and SDs of each of the four conditions)
  140. ```{r}
  141. perc_ss1 = percDF %>%
  142. filter(setSize==1)
  144. perc_ss2 = percDF %>% # perc_ss2 and perc_ss4 NS for post-hoc
  145. filter(setSize==2)
  147. perc_ss4 = percDF %>%
  148. filter(setSize==4)
  150. perc_ss6 = percDF %>%
  151. filter(setSize==6)
  153. # ----- perception: set size 1 ----- #
  154. muF1L1_F2L1 = round(mean(perc_ss1$rt),4)
  155. sdF1L1_F2L1 = round(sd(perc_ss1$rt),4)
  157. print(paste('perception: set size 1 mu = ',muF1L1_F2L1))
  158. print(paste('perception: set size 1 sd = ',sdF1L1_F2L1))
  160. # ----- perception: set size 2 ----- #
  161. muF1L1_F2L2 = round(mean(perc_ss2$rt),4)
  162. sdF1L1_F2L2 = round(sd(perc_ss2$rt),4)
  164. print(paste('perception: set size 2 mu = ',muF1L1_F2L2))
  165. print(paste('perception: set size 2 sd = ',sdF1L1_F2L2))
  167. # ----- perception: set size 4 ----- #
  168. muF1L2_F2L1 = round(mean(perc_ss4$rt),4)
  169. sdF1L2_F2L1 = round(sd(perc_ss4$rt),4)
  171. print(paste('perception: set size 4 mu = ',muF1L2_F2L1))
  172. print(paste('perception: set size 4 sd = ',sdF1L2_F2L1))
  174. # ----- perception: set size 6 ----- #
  175. muF1L2_F2L2 = round(mean(perc_ss6$rt),4)
  176. sdF1L2_F2L2 = round(sd(perc_ss6$rt),4)
  178. print(paste('perception: set size 6 mu = ',muF1L2_F2L2))
  179. print(paste('perception: set size 6 sd = ',sdF1L2_F2L2))
  181. # memory
  182. memo_ss1 = memoDF %>% # memo_ss1 and memo_ss2 NS for post-hoc
  183. filter(setSize==1)
  185. memo_ss2 = memoDF %>%
  186. filter(setSize==2)
  188. memo_ss4 = memoDF %>%
  189. filter(setSize==4)
  191. memo_ss6 = memoDF %>%
  192. filter(setSize==6)
  194. # ----- memory: set size 1 ----- #
  195. muF1L1_F2L1 = round(mean(memo_ss1$accuracy),4)
  196. sdF1L1_F2L1 = round(sd(memo_ss1$accuracy),4)
  198. print(paste('memory: set size 1 mu = ',muF1L1_F2L1))
  199. print(paste('memory: set size 1 sd = ',sdF1L1_F2L1))
  201. # ----- memory: set size 2 ----- #
  202. muF1L1_F2L2 = round(mean(memo_ss2$accuracy),4)
  203. sdF1L1_F2L2 = round(sd(memo_ss2$accuracy),4)
  205. print(paste('memory: set size 2 mu = ',muF1L1_F2L2))
  206. print(paste('memory: set size 2 sd = ',sdF1L1_F2L2))
  208. # ----- memory: set size 4 ----- #
  209. muF1L2_F2L1 = round(mean(memo_ss4$accuracy),4)
  210. sdF1L2_F2L1 = round(sd(memo_ss4$accuracy),4)
  212. print(paste('memory: set size 4 mu = ',muF1L2_F2L1))
  213. print(paste('memory: set size 4 sd = ',sdF1L2_F2L1))
  215. # ----- memory: set size 6 ----- #
  216. muF1L2_F2L2 = round(mean(memo_ss6$accuracy),4)
  217. sdF1L2_F2L2 = round(sd(memo_ss6$accuracy),4)
  219. print(paste('memory: set size 6 mu = ',muF1L2_F2L2))
  220. print(paste('memory: set size 6 sd = ',sdF1L2_F2L2))
  221. ```
  223. ## 2x35 rmANOVA
  224. We expect to find a significant main effect of time and a significant interaction between task and time. We expect a decrease in memory state evidence (reflecting encoding state engagement) during the display phase and an increase in memory state evidence (reflecting retrieval state engagement) during the delay phase, the latter of which will be larger for memory compared to perception trials.
  226. ### Analysis (task)
  227. ```{r}
  228. model = aov(retEvi ~ task*time + Error(subject/(task*time)), data=dataStimANOVA)
  229. es_res = effectsize::eta_squared(model)
  230. summary(model)
  232. # task
  233. FACTOR = 2
  234. pval = summary(model)[[FACTOR]][[1]]['Pr(>F)'][[1]][[1]]
  235. factor_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[1]]
  236. resid_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[2]]
  237. f_stat = round(summary(model)[[FACTOR]][[1]]['F value'][[1]][[1]],4)
  238. pval_round = round(pval,4)
  239. # partial eta sq is one "off" because there's no eta for the subjects factor
  240. part_eta_sq = round(es_res$Eta2_partial,4)[FACTOR-1]
  242. print('Main effect of task:')
  243. print(glue("F({factor_df},{resid_df}) = {f_stat}; p = {pval_round}; pEta2 = {part_eta_sq}"))
  245. # time
  246. FACTOR = 3
  247. pval = summary(model)[[FACTOR]][[1]]['Pr(>F)'][[1]][[1]]
  248. factor_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[1]]
  249. resid_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[2]]
  250. f_stat = round(summary(model)[[FACTOR]][[1]]['F value'][[1]][[1]],4)
  251. pval_round = round(pval,4)
  252. # partial eta sq is one "off" because there's no eta for the subjects factor
  253. part_eta_sq = round(es_res$Eta2_partial,4)[FACTOR-1]
  255. print('Main effect of time:')
  256. print(glue("F({factor_df},{resid_df}) = {f_stat}; p = {pval_round}; pEta2 = {part_eta_sq}"))
  258. # task:time
  259. FACTOR = 4
  260. pval = summary(model)[[FACTOR]][[1]]['Pr(>F)'][[1]][[1]]
  261. factor_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[1]]
  262. resid_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[2]]
  263. f_stat = round(summary(model)[[FACTOR]][[1]]['F value'][[1]][[1]],4)
  264. pval_round = round(pval,4)
  265. # partial eta sq is one "off" because there's no eta for the subjects factor
  266. part_eta_sq = round(es_res$Eta2_partial,4)[FACTOR-1]
  268. print('Interaction between task and time:')
  269. print(glue("F({factor_df},{resid_df}) = {f_stat}; p = {pval_round}; pEta2 = {part_eta_sq}"))
  271. ```
  273. ## 2x10 rmANOVA
  274. We will assess response-locked memory state evidence during the response interval of each task which will provide insights into the extent to which the retrieval state is modulated by decision making. There are two alternative outcomes:
  275. - First, to the extent that the retrieval state is recruited for any decision, we should find a gradual increase in memory state evidence (indicating greater retrieval state engagement) leading up to the response on both memory and perception trials.
  276. - Alternatively, to the extent that the retrieval state is specifically recruited for a decision based on internal, rather than external, information, we should find a gradual increase in memory state evidence leading up to the response selectively for memory trials.
  278. We expect to find a significant main effect of time and a significant interaction between task and time.
  280. ### Analysis
  281. ```{r}
  282. model = aov(retEvi ~ task*time + Error(subject/(task*time)), data=dataRespANOVA)
  283. es_res = effectsize::eta_squared(model)
  284. summary(model)
  286. # task
  287. FACTOR = 2
  288. pval = summary(model)[[FACTOR]][[1]]['Pr(>F)'][[1]][[1]]
  289. factor_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[1]]
  290. resid_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[2]]
  291. f_stat = round(summary(model)[[FACTOR]][[1]]['F value'][[1]][[1]],4)
  292. pval_round = round(pval,4)
  293. # partial eta sq is one "off" because there's no eta for the subjects factor
  294. part_eta_sq = round(es_res$Eta2_partial,4)[FACTOR-1]
  296. print('Main effect of tas:')
  297. print(glue("F({factor_df},{resid_df}) = {f_stat}; p = {pval_round}; pEta2 = {part_eta_sq}"))
  299. # time
  300. FACTOR = 3
  301. pval = summary(model)[[FACTOR]][[1]]['Pr(>F)'][[1]][[1]]
  302. factor_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[1]]
  303. resid_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[2]]
  304. f_stat = round(summary(model)[[FACTOR]][[1]]['F value'][[1]][[1]],4)
  305. pval_round = round(pval,4)
  306. # partial eta sq is one "off" because there's no eta for the subjects factor
  307. part_eta_sq = round(es_res$Eta2_partial,4)[FACTOR-1]
  309. print('Main effect of time:')
  310. print(glue("F({factor_df},{resid_df}) = {f_stat}; p = {pval_round}; pEta2 = {part_eta_sq}"))
  312. # task:time
  313. FACTOR = 4
  314. pval = summary(model)[[FACTOR]][[1]]['Pr(>F)'][[1]][[1]]
  315. factor_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[1]]
  316. resid_df = summary(model)[[FACTOR]][[1]]['Df'][[1]][[2]]
  317. f_stat = round(summary(model)[[FACTOR]][[1]]['F value'][[1]][[1]],4)
  318. pval_round = round(pval,4)
  319. # partial eta sq is one "off" because there's no eta for the subjects factor
  320. part_eta_sq = round(es_res$Eta2_partial,4)[FACTOR-1]
  322. print('Interaction between task and time:')
  323. print(glue("F({factor_df},{resid_df}) = {f_stat}; p = {pval_round}; pEta2 = {part_eta_sq}"))
  325. ```
  327. ### Post-hoc t-tests
  328. (if interaction is significant, perform post-hoc tests on specific conditions)
  329. Because we anticipate a pre-response dissociation between memory and perception responses, we will conduct post-hoc analyses on the pre-response (-500 to 0 ms) time interval.
  330. ```{r}
  332. # define pre-response time interval bins
  333. bins = seq(-500, -100, by = 100) # 100 ms intervals from 500 ms pre-response to 0 ms response
  334. time_order = paste0(bins, "to", bins + 100)
  336. # average across pre-response bins
  337. preResp = dataRespANOVA %>%
  338. filter(time %in% time_order) %>%
  339. group_by(subject, task) %>%
  340. summarise(muRetEvi = mean(retEvi))
  342. # separate by task
  343. percPreResp = preResp[preResp$task == "perception",]
  344. memoPreResp = preResp[preResp$task == "memory",]
  346. # compare F1L1_F2L1 to F1L1_F2L2
  347. res = t.test(memoPreResp$muRetEvi,percPreResp$muRetEvi,paired=TRUE)
  348. t_val = round(res$statistic[[1]],4)
  349. p_val = round(res$p.value[[1]],4)
  350. CI1 = round(res$conf.int[[1]],4)
  351. CI2 = round(res$conf.int[[2]],4)
  353. # cohen's d
  354. M1 = mean(memoPreResp$muRetEvi)
  355. S1 = sd(memoPreResp$muRetEvi)
  356. M2 = mean(percPreResp$muRetEvi)
  357. S2 = sd(percPreResp$muRetEvi)
  359. Spooled = sqrt(((S1^2)+(S2^2))/2)
  360. cohD = round((M1-M2)/Spooled,4)
  362. print(glue('Memory vs. Perception: t = {t_val}; p = {p_val}; d = {cohD}, CI = [{CI1},{CI2}]'))
  364. print(paste('memory: mu = ',M1))
  365. print(paste('memory: sd = ',S1))
  367. print(paste('perception: mu = ',M2))
  368. print(paste('perception: sd = ',S2))
  370. ```
  372. # Resub
  374. ## Bayes factors
  375. ### Regressions
  376. ```{r}
  378. # DISPLAY: MEMORY STATE EVIDENCE & MEMORY ACCURACY (LOGISTIC REGRESSION)
  379. retEviReg_display = read.csv(paste(paste(dataDir,"N", N,"_perm_trained_N57_repo_0to2000_to_stimLocked_0to500in500ms_whole_brain_allFreqs_retEvi_Display_Reg.csv", sep="")))
  381. statData = retEviReg_display %>%
  382. filter(task == 'memory')
  384. # Means and SDs
  385. M = round(mean(statData$beta),4)
  386. S = round(sd(statData$beta),4)
  387. print(glue('display (memory): M = {M}, SD = {S}'))
  389. res = t.test(statData$beta,mu=0)
  390. df = nrow(statData) - 1
  391. t_val = round(res$statistic[[1]],4)
  392. p_val = round(res$p.value[[1]],4)
  394. bf = ttestBF(x = statData$beta,mu=0)
  395. bf = extractBF(bf)
  396. # You know that people don't always want to print things directly to the console. Right? Right?!?!?!
  397. bf = round(bf$bf,4)
  399. # Cohen's d
  400. cohD = round(M/S,4)
  402. print(glue('vs 0: t({df}) = {t_val}; p = {p_val}; d = {cohD}, BF = {bf}'))
  404. # DELAY: MEMORY STATE EVIDENCE & ACCURACY PER SET SIZE (LOGISTIC REGRESSION)
  405. retEviReg_delay = read.csv(paste(paste(dataDir,"N", N,"_perm_trained_N57_repo_0to2000_to_stimLocked_500to2500in2000ms_whole_brain_allFreqs_retEvi_Delay_Reg_perSetSize.csv", sep="")))
  407. statData = retEviReg_delay %>%
  408. group_by(subject) %>%
  409. summarise(beta = mean(beta), .groups = "drop")
  411. # Means and SDs
  412. M = round(mean(statData$beta),4)
  413. S = round(sd(statData$beta),4)
  414. print(glue('delay (memory): M = {M}, SD = {S}'))
  416. res = t.test(statData$beta,mu=0)
  417. df = nrow(statData) - 1
  418. t_val = round(res$statistic[[1]],4)
  419. p_val = round(res$p.value[[1]],4)
  421. bf = ttestBF(x = statData$beta,mu=0)
  422. bf = extractBF(bf)
  423. bf = round(bf$bf,4)
  425. # Cohen's d
  426. cohD = round(M/S,4)
  428. print(glue('vs 0: t({df}) = {t_val}; p = {p_val}; d = {cohD}, BF = {bf}'))
  430. # DELAY: MEMORY STATE EVIDENCE & PERCEPTION RT (LINEAR REGRESSION)
  431. retEviReg_delay_perc = read.csv(paste(paste(dataDir,"N", N,"_perm_trained_N57_repo_0to2000_to_stimLocked_500to2500in2000ms_whole_brain_allFreqs_retEvi_Delay_Reg_SetSize.csv", sep="")))
  433. statData = retEviReg_delay_perc %>%
  434. filter(task == 'perception')
  436. # Means and SDs
  437. M = round(mean(statData$beta),4)
  438. S = round(sd(statData$beta),4)
  439. print(glue('delay (perception): M = {M}, SD = {S}'))
  441. res = t.test(statData$beta,mu=0)
  442. df = nrow(statData) - 1
  443. t_val = round(res$statistic[[1]],4)
  444. p_val = round(res$p.value[[1]],4)
  446. bf = ttestBF(x = statData$beta,mu=0)
  447. bf = extractBF(bf)
  448. bf = round(bf$bf,4)
  450. # Cohen's d
  451. cohD = round(M/S,4)
  453. print(glue('vs 0: t({df}) = {t_val}; p = {p_val}; d = {cohD}, BF = {bf}'))
  455. ```
  457. ### Hits vs CRs
  458. ```{r}
  459. # basically turning the python code in perm_stats.py to R here, so going a little against the template. sorry, nicole!
  461. # read in data
  462. retEviResponse = read.csv(paste(paste(dataDir,"N", N,"_perm_trained_N57_repo_0to2000_to_respLocked_-500to500in100ms_whole_brain_allFreqs_retEvi_Memory_Response.csv", sep="")))
  464. bins = seq(-500, -100, by = 100)
  465. preResponse = paste0(bins, "to", bins + 100)
  467. responseDF <- retEviResponse %>%
  468. filter(task == "memory",
  469. response %in% c("hit", "cr"),
  470. time..ms. %in% preResponse) %>% # this is ugly but keeping it like this since we only need the BF anyway
  471. group_by(subject, response) %>%
  472. summarise(retrieval.evidence = mean(retrieval.evidence, na.rm = TRUE), .groups = "drop")
  474. responseDF <- responseDF %>%
  475. pivot_wider(names_from = response, values_from = retrieval.evidence)
  477. hitRetEvi = responseDF$hit
  478. crRetEvi = responseDF$cr
  480. res = t.test(hitRetEvi, crRetEvi, paired = TRUE)
  481. df = res$parameter
  482. t_val = round(res$statistic, 4)
  483. p_val = round(res$p.value, 4)
  485. # cohen's d (for paired: mean of difference scores / sd of difference scores)
  486. diff = hitRetEvi - crRetEvi
  487. cohD = round(mean(diff) / sd(diff), 4)
  489. print(paste0("Hit mean: ", round(mean(hitRetEvi), 4)))
  490. print(paste0("Hit sd: ", round(sd(hitRetEvi), 4)))
  492. print(paste0("CR mean: ", round(mean(crRetEvi), 4)))
  493. print(paste0("CR sd: ", round(sd(crRetEvi), 4)))
  495. print(glue('Hit vs CR: t({df}) = {t_val}; p = {p_val}; BF = {bf}'))
  497. ```

permStats.Rmd, no license · at the source

Overview

Authors: DT Nguyen1, Nicole M Long1
ORCID iDs: DT Nguyen, Nicole M Long
  1. Department of Psychology, University of Virginia, Charlottesville, VA, USA
Institutions: University of Virginia (United States)
Journal: iScience, volume 29, issue 9, article 117285
Dates: received 24 April 2026; accepted 5 August 2026; published online 25 August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.isci.2026.117285 · PMID 42699593 · PMCID PMC13543892 · OpenAlex W7154531691
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.64898/2026.04.14.718495
Status: code verified
Categories: EEG (modality), cognitive (subfield)
Methods: Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Spectral & time-frequency, Complexity, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: working memory, encoding, retrieval, external attention, internal attention, brain states, scalp EEG
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institutes of Health; National Institute of Neurological Disorders and Stroke (R01 NS132872)
Citations: not cited yet (Europe PMC); 99 references in the paper
Research resources: R studio RRID:SCR_000432, Psychopy RRID:SCR_006571

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

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OSF m6knv

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (25), R (1)
Size: 53 files, 26 scripts
Software Heritage: not checked
Found in: the text, “EEG data preprocessing”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (19 files), pandas (18 files), Matplotlib (13 files), seaborn (8 files), SciPy (7 files), statsmodels (2 files), BayesFactor (1 file), easystats (1 file), emmeans (1 file), MNE-Python (1 file), PsychoPy (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
26 files
At the source: osf.io/m6knv

longtermmemorylab.com/papers

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Evidence: the link answers
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Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: longtermmemorylab.com/papers/

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Tracing map

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What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 26 scripts, each with its path and the digest of its content;
  • 11 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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Data

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Read it in the paper: doi.org/10.1016/j.isci.2026.117285.

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Version 2, 28 September 2026

  • Authors: added DT Nguyen (0009-0007-8321-2277); Nicole M Long (0000-0003-3766-2764); removed DT Nguyen; Nicole M Long

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 7 keywords, 2 funders, 98 references, 2 RRIDs.

Cite

This paper

Nguyen, D., & Long, N. M. (2026). Working memory demands modulate memory brain state engagement. iScience, 29(9), 117285. https://doi.org/10.1016/j.isci.2026.117285

BibTeX

@article{nguyen2026working,
author = {Nguyen, DT and Long, Nicole M},
title = {{Working memory demands modulate memory brain state engagement}},
journal = {iScience},
year = {2026},
month = aug,
volume = {29},
number = {9},
pages = {117285},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.117285},
url = {https://doi.org/10.1016/j.isci.2026.117285},
pmid = {42699593},
pmcid = {PMC13543892}
}

RIS

TY - JOUR
AU - Nguyen, DT
AU - Long, Nicole M
TI - Working memory demands modulate memory brain state engagement
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/08/25
VL - 29
IS - 9
SP - 117285
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.117285
UR - https://doi.org/10.1016/j.isci.2026.117285
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13543892",
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