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Visual field inhomogeneities and the architectonics of early visual cortex shape visual working memory.

Code ↔ Paper

3 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 3 matches
  1. [1] § Methods › Behavioral analysis methods ↔ behavioral analysis scripts/02_make_measures_17.R, lines 463–501 · score 0.67 · Holm Bonferroni correction, confidence intervals, Pearson, bootstrap, height, correlation
  2. [2] § Results › Behavioral results ↔ behavioral analysis scripts/02_make_measures_17.R, lines 367–453 · score 0.59 · bootstrap CI, visual field asymmetries, polar angle, Pearson, variance, HVA
  3. [3] § Methods › Behavioral task ↔ behavioral analysis scripts/02_make_measures_17.R, lines 367–453 · score 0.58 · confidence interval, visual field asymmetry, polar angle, variance, bootstrapped, axis

Paper

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The authors' code

R · 502 lines · 19 KB · no license · 3 matches

  1. library(tidyr)
  2. library(ez)
  3. library(tidyverse)
  4. library(reshape)
  5. library(ggthemes)
  6. library(dplyr)
  7. library(effsize)
  8. library(boot)
  9. #library(ggpubfigs)
  11. rm(list=ls()) # Clear workspace
  13. # -----------------------------------------------------------------------------------------------------
  14. # Load data
  16. script_folder = "/Users/juliapapiernik-klodzinska/Desktop/PhD/_17_Simon_WM/analysis/"
  17. setwd(script_folder)
  19. DAT = read.csv(paste(script_folder,'all_clean_17_shuffled.csv', sep = ""), header = TRUE, sep = ",") #length(unique(dat$ID))
  21. # Dataframe for distances
  22. datDIST = DAT[,c(1:10)]
  23. datDIST = gather(datDIST, loc, dist, distance0:distance7, factor_key=TRUE)
  24. datDIST = datDIST[order(datDIST$participant,datDIST$trial),]
  25. levels(datDIST$loc) = c(0:7)
  26. datDIST$dist = datDIST$dist / 6.8 # Normalize
  28. mdat = melt(datDIST, id=c("loc","trial"), measure="dist", var = "THS")
  29. cdat = data.frame(cast(mdat, loc+trial ~ ..., mean))
  31. cdat$dist <- cdat$dist*6.8 #Use only for plotting!
  34. # ggplot(data=cdat, aes(x=trial, y=dist, group=loc)) + geom_line(aes(color=loc,linewidth=1)) +
  35. # guides(color = guide_legend(override.aes = list(linewidth = 1)))
  37. # plt_1 <- ggplot(data=cdat, aes(x=trial, y=dist, color=loc)) +
  38. # geom_line(aes(group=loc),linewidth=1) +
  39. # labs(title = "Grand Average of Distance Changes Across Trials", y = "Distance", x = "Trial", color = "Location") +
  40. # scale_color_manual(values = friendly_pal("tol_eight"), labels = c("Right", "Upper Right", "Up", "Upper Left", "Left", "Lower Left", "Down", "Lower Right")) +
  41. # theme_minimal() +
  42. # theme(plot.title = element_text(size = 12), # Title text size
  43. # axis.title.x = element_text(size = 11), # X-axis title size
  44. # axis.title.y = element_text(size = 11), # Y-axis title size
  45. # axis.text.y = element_text(size = 10), # Y-axis tick labels size
  46. # axis.text.x = element_text(size = 10), # X-axis tick labels size
  47. # legend.title = element_text(size = 9), # Legend title size
  48. # legend.text = element_text(size = 8) )
  49. # plt_1
  51. # ggsave("plot_grand_avg_stair.png", plot = last_plot(), width = 15, height = 9, units = "cm")
  53. # Dataframe for reaction times
  54. mdat = melt(DAT, id=c("participant","targetpos"), measure="rt", var = "THS")
  55. datRT = data.frame(cast(mdat, participant+targetpos ~ ..., c(mean,sd) ))
  56. datRT$participant = factor(datRT$participant)
  57. datRT$targetpos = factor(datRT$targetpos)
  59. mdat = melt(datRT, id=c("targetpos"), measure="rt_mean", var = "THS")
  60. cdat = data.frame(cast(mdat, targetpos ~ ..., mean))
  61. ggplot(data=cdat, aes(x=targetpos, y=rt_mean)) +
  62. geom_bar(stat="identity",aes(fill=targetpos)) +
  63. scale_y_continuous(limits = c(0, 1.25), breaks = seq(0, 1.25, by = 0.2))
  65. #------------------------------------------------------------------------------------------------------
  66. #RENAME THE LOCATIONS AND CHANGE THE TITLE BEFORE DOING FINAL PLOTS
  68. mdat <- melt(DAT, id=c('participant'),
  69. measure=c('distance0','distance1','distance2','distance3','distance4','distance5','distance6','distance7'), var = "DISTANCE")
  70. cdat <- cast(mdat, participant ~ ..., mean)
  72. library(tidyr)
  73. data_long <- gather(cdat, distance, mean, distance0:distance7, factor_key=TRUE)
  74. data_long <- data_long[order(data_long$participant),]
  76. cdat <- cast(mdat, participant ~ ..., sd)
  77. fuu <- gather(cdat, distance, sd, distance0:distance7, factor_key=TRUE)
  78. fuu <- fuu[order(fuu$participant),]
  79. data_long$sd = fuu$sd
  81. # for (s in c(1:nrow(cdat))){
  82. # fuu = DAT[DAT$participant == cdat$participant[s],]
  83. # fuu_long <- gather(fuu, distance, val, distance0:distance7, factor_key=TRUE)
  84. # fuu_long <- data.frame(fuu_long[order(fuu_long$trial),])
  85. # str(fuu_long)
  86. #
  87. # participant_number <- fuu_long$participant[1]
  88. #
  89. # custom_labels <- c("distance0" = "Right",
  90. # "distance1" = "Upper right",
  91. # "distance2" = "Up",
  92. # "distance3" = "Upper left",
  93. # "distance4" = "Left",
  94. # "distance5" = "Lower left",
  95. # "distance6" = "Down",
  96. # "distance7" = "Lower right")
  97. #
  98. #
  99. # # Map the levels of the 'distance' variable to the custom labels
  100. # levels(fuu_long$distance) <- custom_labels
  101. #
  102. # # Plots of individual PPs
  103. #
  104. # plt_1 <- ggplot(data=fuu_long, aes(x=trial, y=val, color=distance)) +
  105. # geom_step(aes(group=distance),linewidth=0.7) + ylim(1,12) +
  106. # labs(title = paste("Participant", participant_number, " - Performance Across Locations"),
  107. # y = tools::toTitleCase("distance from the center"),
  108. # x = tools::toTitleCase("trial"),
  109. # color = "Location") +
  110. # theme_light() +
  111. # theme(legend.key.size = unit(0.5, 'cm'),
  112. # legend.title = element_text(size = 8),
  113. # plot.title = element_text(hjust = 0.3, margin = margin(b = 20), size = 12),
  114. # axis.title.x = element_text(size = 8),
  115. # axis.title.y = element_text(size = 8),
  116. # axis.text.x = element_text(size = 10),
  117. # axis.text.y = element_text(size = 10),
  118. # axis.ticks = element_line(size = 0.5))
  119. #
  120. # print(plt_1)
  121. #
  122. # #ggsave(plot = last_plot(), path = "C:/Users/julia/OneDrive/Pulpit/PhD/_17_Simon_WM/analysis/presentation_plots", device = "png", filename = paste("Rplot participant", participant_number, ".png"), units = "cm", width = 10, height = 6)
  123. # }
  126. # -----------------------------------------------------------------------------------------------------
  127. # Group the distance data and calculate necessary statistics
  129. dat <- datDIST %>%
  130. group_by(participant, loc) %>%
  131. summarize(
  132. dist_M = mean(dist, na.rm = TRUE),
  133. dist_SD = sd(dist, na.rm = TRUE),
  134. dist_M_5 = mean(tail(dist, 5), na.rm = TRUE), # Last 5 trials or fewer
  135. dist_SD_5 = sd(tail(dist,5), na.rm = TRUE), # SD of last 5 trials
  136. dist_FIN = tail(dist, 1), # Last trial
  137. dist_M_5_true = mean(tail(unique(dist), 5), na.rm = TRUE),
  138. .groups = 'drop'
  139. )
  142. # Ensure the factors are aligned in datRT
  143. datRT$participant <- factor(datRT$participant)
  144. datRT$targetpos <- factor(datRT$targetpos)
  146. # Join the summarized distance data with reaction time data
  147. dat$rt_mean <- datRT$rt_mean
  148. dat$rt_SD <- datRT$rt_sd
  151. # -----------------------------------------------------------------------------------------------------
  152. # Plot distributions of the distances and SD
  154. # Final distance
  155. stat <- ezANOVA(data=dat, dv=.(dist_FIN), wid=.(participant), within=.(loc), type=3)
  156. print(stat)
  157. mdat <- melt(dat, id=c("loc"), measure="dist_FIN", var = "THS")
  158. cdat <- cast(mdat, loc ~ ..., function(x) c( M=mean(x), SE=sd(x)/sqrt(length(x))))
  159. limits <- aes(ymax = cdat$dist_FIN_M + cdat$dist_FIN_SE, ymin= cdat$dist_FIN_M - cdat$dist_FIN_SE, width=0.2)
  160. p <- ggplot(data=cdat, aes(x=loc, y=dist_FIN_M)) +
  161. geom_bar(stat="identity",aes(fill=loc)) +
  162. geom_errorbar(limits)
  163. p
  165. # Average distance over final 5 trials
  166. stat <- ezANOVA(data=dat, dv=.(dist_M_5), wid=.(participant), within=.(loc), type=3)
  167. print(stat)
  168. mdat <- melt(dat, id=c("loc"), measure="dist_M_5", var = "THS")
  169. cdat <- cast(mdat, loc ~ ..., function(x) c( M=mean(x), SE=sd(x)/sqrt(length(x))))
  170. limits <- aes(ymax = cdat$dist_M_5_M + cdat$dist_M_5_SE, ymin= cdat$dist_M_5_M - cdat$dist_M_5_SE, width=0.2)
  171. p <- ggplot(data=cdat, aes(x=loc, y=dist_M_5_M)) +
  172. geom_bar(stat="identity",aes(fill=loc)) +
  173. geom_errorbar(limits)
  174. p
  176. # Average distance over final 5 trials
  177. stat <- ezANOVA(data=dat, dv=.(dist_M_5_true), wid=.(participant), within=.(loc), type=3)
  178. print(stat)
  179. mdat <- melt(dat, id=c("loc"), measure="dist_M_5_true", var = "THS")
  180. cdat <- cast(mdat, loc ~ ..., function(x) c( M=mean(x), SE=sd(x)/sqrt(length(x))))
  181. limits <- aes(ymax = cdat$dist_M_5_true_M + cdat$dist_M_5_true_SE, ymin= cdat$dist_M_5_true_M - cdat$dist_M_5_true_SE, width=0.2)
  182. p <- ggplot(data=cdat, aes(x=loc, y=dist_M_5_true_M)) +
  183. geom_bar(stat="identity",aes(fill=loc)) +
  184. geom_errorbar(limits)
  185. p
  188. # SD of distance over final 5 trials
  189. stat <- ezANOVA(data=dat, dv=.(dist_SD_5), wid=.(participant), within=.(loc), type=3)
  190. print(stat)
  191. mdat <- melt(dat, id=c("loc"), measure="dist_SD_5", var = "THS")
  192. cdat <- cast(mdat, loc ~ ..., function(x) c( M=mean(x), SE=sd(x)/sqrt(length(x))))
  193. limits <- aes(ymax = cdat$dist_SD_5_M + cdat$dist_SD_5_SE, ymin= cdat$dist_SD_5_M - cdat$dist_SD_5_SE, width=0.2)
  194. p <- ggplot(data=cdat, aes(x=loc, y=dist_SD_5_M)) +
  195. geom_bar(stat="identity",aes(fill=loc)) +
  196. geom_errorbar(limits)
  197. p
  200. # SD over all trials
  201. stat <- ezANOVA(data=dat, dv=.(dist_SD), wid=.(participant), within=.(loc), type=3)
  202. print(stat)
  203. mdat <- melt(dat, id=c("loc"), measure="dist_SD", var = "THS")
  204. cdat <- cast(mdat, loc ~ ..., function(x) c( M=mean(x), SE=sd(x)/sqrt(length(x))))
  205. limits <- aes(ymax = cdat$dist_SD_M + cdat$dist_SD_SE, ymin= cdat$dist_SD_M - cdat$dist_SD_SE, width=0.2)
  206. p <- ggplot(data=cdat, aes(x=loc, y=dist_SD_M)) +
  207. geom_bar(stat="identity",aes(fill=loc)) +
  208. geom_errorbar(limits)
  209. p
  212. # Average over all trials
  213. stat <- ezANOVA(data=dat, dv=.(dist_M), wid=.(participant), within=.(loc), type=3)
  214. print(stat)
  215. mdat <- melt(dat, id=c("loc"), measure="dist_M", var = "THS")
  216. cdat <- cast(mdat, loc ~ ..., function(x) c( M=mean(x), SE=sd(x)/sqrt(length(x))))
  217. limits <- aes(ymax = cdat$dist_M_M + cdat$dist_M_SE, ymin= cdat$dist_M_M - cdat$dist_M_SE, width=0.2)
  218. p <- ggplot(data=cdat, aes(x=loc, y=dist_M_M)) +
  219. geom_bar(stat="identity",aes(fill=loc)) +
  220. geom_errorbar(limits)
  221. p
  225. # SD of reaction times
  226. stat <- ezANOVA(data=dat, dv=.(rt_SD), wid=.(participant), within=.(loc), type=3)
  227. print(stat)
  228. mdat <- melt(dat, id=c("loc"), measure="rt_SD", var = "THS")
  229. cdat <- cast(mdat, loc ~ ..., function(x) c( M=mean(x), SE=sd(x)/sqrt(length(x))))
  230. limits <- aes(ymax = cdat$rt_SD_M + cdat$rt_SD_SE, ymin= cdat$rt_SD_M - cdat$rt_SD_SE, width=0.2)
  231. p <- ggplot(data=cdat, aes(x=loc, y=rt_SD_M)) +
  232. geom_bar(stat="identity",aes(fill=loc)) +
  233. geom_errorbar(limits)
  234. p
  236. write.csv(dat, paste(script_folder,'long_data_final_shuffled.csv',sep=""), row.names = FALSE)
  237. # -----------------------------------------------------------------------------------------------------
  238. # Make indices
  239. inds = data.frame(IDs = unique(dat$participant))
  241. inds$MD = NA # mean distance over all 8 locations
  242. inds$HVA = NA # horizontal-vertical asymmetry
  243. inds$VMA = NA # vertical meridian asymmetry
  244. inds$LRA = NA # left-right asymmetry
  245. inds$LR_VFA = NA #left-right visual field asymmetry
  246. inds$UD_VFA = NA #up-down visual field asymmetry
  247. inds$TA = NA # total asymmetry
  248. inds$RDA = NA # right-down asymmetry
  250. for (r in c(1:nrow(inds))) {
  252. fuu = dat$dist_M_5[which(dat$participant == inds$IDs[r])]
  254. inds$MD[r] = mean(fuu)
  255. inds$HVA[r] = (( mean(fuu[c(1,5)]) - mean(fuu[c(3,7)]) ) / mean(fuu[c(1,3,5,7)]))*100
  256. inds$VMA[r] = ( fuu[7] - fuu[3] ) / mean(fuu[c(3,7)]) * 100
  257. inds$LRA[r] = ( fuu[1] - fuu[5] ) / mean(fuu[c(1,5)])
  258. inds$LR_VFA[r] = ((mean (fuu[c(4,5,6)] - mean(fuu[c(1,2,8)]) / mean(fuu[c(1,2,4,5,6,8)])))) * 100
  259. inds$UD_VFA[r] = (( mean(fuu[c(6,7,8)] - mean (fuu[c(2,3,4)]) / mean(fuu[c(2,3,4,6,7,8)])))) * 100
  260. inds$TA[r] = sd(fuu)
  261. inds$RDA[r] = ( fuu[1] ) / (fuu[7])
  263. }
  265. hist(inds$MD)
  266. hist(inds$HVA)
  267. hist(inds$VMA)
  268. hist(inds$LRA)
  269. hist(inds$LR_VFA)
  270. hist(inds$UD_VFA)
  271. hist(inds$TA)
  272. hist(inds$RDA)
  274. cor.test(inds$HVA,inds$MD)
  275. ggplot(inds, aes(x=HVA, y=MD)) + geom_point(size=2) + geom_smooth(method=lm,col="red")
  278. # -----------------------------------------------------------------------------------------------------
  280. # Add sex and age from DAT
  281. for (i in 1:nrow(inds)) {
  282. participant_id <- inds$IDs[i]
  283. participant_data <- DAT[DAT$participant == participant_id, ]
  285. inds$sex[i] <- participant_data$sex[1]
  286. inds$age[i] <- participant_data$age[1]
  287. inds$eye.dominance[i] <- participant_data$eye.dominance[1]
  288. inds$height[i] <- participant_data$height[1]
  289. inds$weight[i] <- participant_data$weight[1]
  290. }
  292. #Correcting one weird datapoint, as I suppose it was a typo
  293. inds$height[inds$height == 0.57] <- 1.57
  294. # descriptives of HVA and VMA
  296. mean(inds$HVA)
  297. min(inds$HVA)
  298. max(inds$HVA)
  299. sd(inds$HVA)
  301. mean(inds$VMA)
  302. min(inds$VMA)
  303. max(inds$VMA)
  304. sd(inds$VMA)
  306. # descriptives of VFIs
  307. mean(inds$LR_VFA)
  308. min(inds$LR_VFA)
  309. max(inds$LR_VFA)
  310. sd(inds$LR_VFA)
  312. mean(inds$UD_VFA)
  313. min(inds$UD_VFA)
  314. max(inds$UD_VFA)
  315. sd(inds$UD_VFA)
  317. # bootstrapped confidence intervals for indices
  319. # Define a function to calculate variance for bootstrapping
  320. variance_function <- function(data, indices) {
  321. sample_data <- data[indices] # Select the bootstrap sample
  322. return(var(sample_data)) # Calculate and return variance
  323. }
  325. # Perform bootstrapping
  326. set.seed(123) # For reproducibility
  327. bootstrap_results <- boot(data = inds$HVA, statistic = variance_function, R = 1000) # for HVA
  329. # Calculate and display the 95% confidence interval for the variance
  330. boot_ci <- boot.ci(bootstrap_results, type = "perc") # Use percentile method for CI
  331. print(boot_ci)
  333. sample_variance <- var(inds$HVA)
  334. # Calculate the standard error of variance (normal approximation)
  335. n <- length(inds)
  336. se_variance <- sqrt(2 * sample_variance^2 / (n - 1))
  338. # 95% CI using normal approximation
  339. ci_lower <- sample_variance - 1.96 * se_variance
  340. ci_upper <- sample_variance + 1.96 * se_variance
  342. c(ci_lower, ci_upper)
  344. sample_variance <- var(inds$VMA)
  345. # Calculate the standard error of variance (normal approximation)
  346. n <- length(inds)
  347. se_variance <- sqrt(2 * sample_variance^2 / (n - 1))
  349. # 95% CI using normal approximation
  350. ci_lower <- sample_variance - 1.96 * se_variance
  351. ci_upper <- sample_variance + 1.96 * se_variance
  353. c(ci_lower, ci_upper)
  355. # Perform bootstrapping
  356. set.seed(123) # For reproducibility
  357. bootstrap_results <- boot(data = inds$VMA, statistic = variance_function, R = 1000)
  359. # Calculate and display the 95% confidence interval for the variance
  360. boot_ci <- boot.ci(bootstrap_results, type = "perc") # Use percentile method for CI
  361. print(boot_ci)
  363. # Perform bootstrapping
  364. set.seed(123) # For reproducibility
  365. bootstrap_results <- boot(data = inds$UD_VFA, statistic = variance_function, R = 1000) # for UD_VFA
  367. # Calculate and display the 95% confidence interval for the variance
  368. boot_ci <- boot.ci(bootstrap_results, type = "perc") # Use percentile method for CI
  369. print(boot_ci)
  371. sample_variance <- var(inds$UD_VFA)
  372. # Calculate the standard error of variance (normal approximation)
  373. n <- length(inds)
  374. se_variance <- sqrt(2 * sample_variance^2 / (n - 1))
  376. # 95% CI using normal approximation
  377. ci_lower <- sample_variance - 1.96 * se_variance
  378. ci_upper <- sample_variance + 1.96 * se_variance
  380. c(ci_lower, ci_upper)
  382. bootstrap_results <- boot(data = inds$LR_VFA, statistic = variance_function, R = 1000) # for LR_VFA
  384. # Calculate and display the 95% confidence interval for the variance
  385. boot_ci <- boot.ci(bootstrap_results, type = "perc") # Use percentile method for CI
  386. print(boot_ci)
  388. sample_variance <- var(inds$LR_VFA)
  389. # Calculate the standard error of variance (normal approximation)
  390. n <- length(inds)
  391. se_variance <- sqrt(2 * sample_variance^2 / (n - 1))
  393. # 95% CI using normal approximation
  394. ci_lower <- sample_variance - 1.96 * se_variance
  395. ci_upper <- sample_variance + 1.96 * se_variance
  397. c(ci_lower, ci_upper)
  399. write.csv(inds, paste(script_folder,'VFA_17_field_final_shuffled.csv',sep=""), row.names = FALSE)
  401. correlation <-cor.test(inds$VMA, inds$HVA, method = 'pearson')
  402. correlation <- cor(inds$VMA, inds$HVA, method = 'pearson')
  404. ggplot(inds, aes(x = VMA, y = HVA)) +
  405. geom_point(color = "#4477AA",alpha = 0.6, shape = 16, size = 1) +
  406. labs(x = "Vertical Meridian Anisotropy", y = "Horizontal-Vertical Asymmetry", title = "Scatter Plot of Polar Angle\n Asymmetry Indices") +
  407. geom_smooth(method = "lm", se = FALSE, color = "grey40", linetype = "solid", linewidth = 0.75) +
  408. annotate("text", x = max(inds$VMA) * 0.9, y = max(inds$HVA) * 0.4,
  409. label = paste("r =", round(correlation, 2)),
  410. size = 2, color = "grey40") +
  411. theme_minimal() +
  412. theme(plot.title = element_text(size = 9), # Title text size
  413. axis.title.x = element_text(size = 6.5), # X-axis title size
  414. axis.title.y = element_text(size = 6.5), # Y-axis title size
  415. axis.text.y = element_text(size = 7), # Y-axis tick labels size
  416. axis.text.x = element_text(size = 7), # X-axis tick labels size
  417. legend.title = element_text(size = 8), # Legend title size
  418. legend.text = element_text(size = 8) )
  420. #ggsave("plot_cor_VMA_HVA.png", plot = last_plot(), width = 5.5, height = 5, units = "cm")
  423. correlation <-cor.test(inds$LR_VFA, inds$UD_VFA, method = 'pearson')
  424. correlation <- cor(inds$VMA, inds$HVA, method = 'pearson')
  428. ggplot(inds, aes(x = LR_VFA, y = UD_VFA)) +
  429. geom_point(color = "#4477AA",alpha = 0.6, shape = 16, size = 1) +
  430. labs(x = "Left-Right Visual Field Asymmetry", y = "Up-Down Visual Field Asymmetry", title = "Scatter Plot of Visual Field\n Asymmetry Indices") +
  431. geom_smooth(method = "lm", se = FALSE, color = "grey40", linetype = "solid", linewidth = 0.75) +
  432. annotate("text", x = max(inds$LR_VFA) * 0.9, y = max(inds$UD_VFA) * 0.4,
  433. label = paste("r =", round(correlation, 2)),
  434. size = 2, color = "grey40") +
  435. theme_minimal() +
  436. theme(plot.title = element_text(size = 9), # Title text size
  437. axis.title.x = element_text(size = 6.5), # X-axis title size
  438. axis.title.y = element_text(size = 6.5), # Y-axis title size
  439. axis.text.y = element_text(size = 7), # Y-axis tick labels size
  440. axis.text.x = element_text(size = 7), # X-axis tick labels size
  441. legend.title = element_text(size = 8), # Legend title size
  442. legend.text = element_text(size = 8) )
  444. #ggsave("plot_cor_VFI.png", plot = last_plot(), width = 5.5, height = 5, units = "cm")
  447. cor.test(inds$LR_VFA, inds$UD_VFA, method = 'pearson')
  448. cor.test(inds$VMA, inds$HVA, method = 'pearson')
  449. cor.test(inds$VMA, inds$UD_VFA, method = 'pearson')
  453. set.seed(2020)
  455. ## Define a function to calculate the difference of correlations
  456. diff_corr <- function(data, indices) {
  457. data <- data[indices, ]
  458. cor1 <- cor(data$VMA, data$HVA)
  459. cor2 <- cor(data$LR_VFA, data$UD_VFA)
  460. return(cor1 - cor2)
  461. }
  463. ## Apply the bootstrap procedure with 999 draws
  464. res_boot <- boot(data = inds,
  465. R = 999,
  466. statistic = diff_corr,
  467. stype = "i")
  469. ## Retrieve the empirical 95% confidence interval
  470. boot.ci(res_boot, type = "perc", conf = 0.95)
  472. t.test(inds$HVA)
  473. t.test(inds$VMA)
  474. t.test(inds$LR_VFA)
  475. t.test(inds$UD_VFA)
  477. cohen.d(inds$HVA, f = NA)
  478. cohen.d(inds$VMA, f = NA)
  479. cohen.d(inds$LR_VFA, f = NA)
  480. cohen.d(inds$UD_VFA, f = NA)
  482. p <- c(2.2e-16, 7.486e-07, 5.799e-08, 2.2e-16)
  484. # Holm-Bonferroni Correction for the t-tests
  485. p.adjust(p, method = "bonferroni")
  488. hist(inds$HVA)
  489. hist(inds$VMA)
  490. hist(inds$LR_VFA)
  491. hist(inds$UD_VFA)
  493. # Correlations with height
  494. cor.test(inds$UD_VFA, inds$height, method = "pearson", use = "complete.obs")
  495. cor.test(inds$LR_VFA, inds$height, method = "pearson", use = "complete.obs")
  496. cor.test(inds$VMA, inds$height, method = "pearson", use = "complete.obs")
  497. cor.test(inds$HVA, inds$height, method = "pearson", use = "complete.obs")
  499. p <- c(0.5327,0.4609, 0.8535, 0.03606) # 1) UD 2) LR 3) VMA 4) HVA
  501. p.adjust(p, "fdr", n = length(p))

02_make_measures_17.R, no license · at the source

Overview

Authors: Julia Papiernik-Kłodzińska1,2,3, Simon Hviid Del Pin4, Kristian Sandberg5,6, Michał Wierzchoń1,3, Marisa Carrasco7, Renate Rutiku1,3
ORCID iDs: Julia Papiernik-Kłodzińska, Simon Hviid Del Pin, Kristian Sandberg, Michał Wierzchoń, Marisa Carrasco, Renate Rutiku
  1. Consciousness lab, Institute of Psychology, Jagiellonian University, 6 Ingardena Street, 30-060, Kraków, Poland
  2. Doctoral School in the Social Sciences, Jagiellonian University, Main Square 34, 31-010, Kraków, Poland
  3. Centre for Brain Research, Jagiellonian University, 50 Kopernika Street, 31-501, Kraków, Poland
  4. Department of Computer Science, Norwegian University of Science and Technology, Teknologiveien 22NO-2815, Gjøvik, Norway
  5. Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University and Aarhus University Hospital Universitetsbyen, Building 1710, Universitetsbyen 38000, Aarhus, Denmark
  6. Neurobiology Research Unit, Department of Neurology, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen, Denmark
  7. Department of Psychology and Center for Neural Science, New York University, 4 Washington Place, New York, NY 10003, United States
Institutions: Jagiellonian University (Poland); Norwegian University of Science and Technology (Norway); Aarhus University (Denmark); Aarhus University Hospital (Denmark); Copenhagen University Hospital (Denmark); Rigshospitalet (Denmark); New York University (United States)
Journal: Cerebral cortex (New York, N.Y. : 1991), volume 36, issue 5, article bhag058
Dates: received 9 November 2025; accepted 1 April 2026; published online 22 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/cercor/bhag058 · PMID 42172102 · PMCID PMC13196602 · OpenAlex W7162081658
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, fMRI & imaging, Preprocessing
Keywords: multiparametric mapping, polar angle asymmetries, quantitative MRI, V1, visual working memory
MeSH: Memory, Short-Term*, Primary Visual Cortex*, Visual Cortex*, Visual Fields*, Visual Perception*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Cooperation in Science and Technology (COST) (CA18106); Future Society Priority Research Area and the Quality of Life Priority Research Area; Strategic Programme of Excellence Initiative at the Jagiellonian University
Citations: not cited yet (Europe PMC); 123 references in the paper

Abstract

Whether the primary visual cortex (V1) is essential for visual working memory (vWM) remains a topic of scientific debate. The current study expanded upon previous findings by examining whether idiosyncratic architectural properties of V1, particularly those underlying visual field inhomogeneities such as polar angle asymmetries, predict interindividual differences in vWM performance. A total of 292 participants underwent quantitative MRI (qMRI) using a multiparametric mapping sequence to generate four microstructural maps per participant: magnetization transfer, proton density, longitudinal relaxation rate (R1), and transverse relaxation rate (R2*). In a separate session, the participants completed a vWM task designed to probe visual field inhomogeneities. Behavioral results are consistent with previously reported asymmetries in vWM, particularly the inverted polarity of the vertical meridian asymmetry (VMA). Quantitative MRI analysis revealed significant associations between VMA and multiple qMRI parameters within V1, indicating that V1 microarchitecture contributes to variability in vWM performance. Additionally, cortical thickness measures linked V3 to left–right asymmetry, suggesting that structural variability in the early visual cortex beyond V1 also shapes vWM performance. These findings are consistent with the sensory recruitment hypothesis and demonstrate that fine-grained architectural characteristics of early visual areas constrain vWM performance.

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

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OSF pjmgb

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (6), Python (1)
Size: 221 files, 7 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), reshape2 (5 files), lme4 (2 files), cowplot (1 file), ggplot2 (1 file), NumPy (1 file), PsychoPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
7 files
At the source: osf.io/pjmgb

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

Tracing map

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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;
  • 7 scripts, each with its path and the digest of its content;
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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 12 MeSH terms, 3 funders, 117 references.

Cite

This paper

Papiernik-Kłodzińska, J., Del Pin, S. H., Sandberg, K., Wierzchoń, M., Carrasco, M., & Rutiku, R. (2026). Visual field inhomogeneities and the architectonics of early visual cortex shape visual working memory. Cerebral cortex (New York, N.Y. : 1991), 36(5), bhag058. https://doi.org/10.1093/cercor/bhag058

BibTeX

@article{papiernikkodzinska2026visual,
author = {Papiernik-Kłodzińska, Julia and Del Pin, Simon Hviid and Sandberg, Kristian and Wierzchoń, Michał and Carrasco, Marisa and Rutiku, Renate},
title = {{Visual field inhomogeneities and the architectonics of early visual cortex shape visual working memory}},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
year = {2026},
month = may,
volume = {36},
number = {5},
pages = {bhag058},
publisher = {Oxford University Press},
issn = {1047-3211},
doi = {10.1093/cercor/bhag058},
url = {https://doi.org/10.1093/cercor/bhag058},
pmid = {42172102},
pmcid = {PMC13196602}
}

RIS

TY - JOUR
AU - Papiernik-Kłodzińska, Julia
AU - Del Pin, Simon Hviid
AU - Sandberg, Kristian
AU - Wierzchoń, Michał
AU - Carrasco, Marisa
AU - Rutiku, Renate
TI - Visual field inhomogeneities and the architectonics of early visual cortex shape visual working memory
T2 - Cerebral cortex (New York, N.Y. : 1991)
J2 - Cereb Cortex
PY - 2026
DA - 2026/05/01
VL - 36
IS - 5
SP - bhag058
SN - 1047-3211
PB - Oxford University Press
DO - 10.1093/cercor/bhag058
UR - https://doi.org/10.1093/cercor/bhag058
LA - en
ER -

CSL-JSON

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