OSCR

Positive mood enhances accessibility of unrelated concepts in the first language but not in the foreign language.

Code ↔ Paper

7 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 7 matches
  1. [1] § 2. Materials and methods › 2.6. Data analysis › 2.6.2. Electrophysiological measures. ↔ Matlab scripts/C_MoBSeR_ERPs.m, lines 70–82 · score 0.75 · Artifact rejection, pop_jointprob, SDs, EEGLAB, Matlab, rejected
  2. [2] § 2. Materials and methods › 2.6. Data analysis › 2.6.1. Self-report measures: Mood ratings. ↔ R scripts/MoBSeR_word_pairs.R, lines 52–122 · score 0.69 · linear mixed, principal component, lme4, justified, PCA, fit
  3. [3] § 3. Results › 3.1. Self-report data: Mood ratings ↔ R scripts/MoBSeR_mood.R, lines 292–350 · score 0.64 · physiological arousal, Mood induction phase, arousal ratings, valence
  4. [4] § 3. Results › 3.2. Electrophysiological data: N400 (300–500 ms) › 3.2.1. Polish (L1) prime–target pairs. ↔ R scripts/MoBSeR_ERP_R1.R, lines 574–634 · score 0.60 · Mood induction progression, positive mood induction, negative mood induction, Word pair, slopes, N400 amplitudes
  5. [5] § 2. Materials and methods › 2.6. Data analysis › 2.6.2. Electrophysiological measures. ↔ R scripts/MoBSeR_ERP_R1.R, lines 378–425 · score 0.59 · films watched, Mood induction progression, Weakly related, block, predictor, Negative mood
  6. [6] § 2. Materials and methods › 2.6. Data analysis › 2.6.1. Self-report measures: Mood ratings. ↔ R scripts/MoBSeR_ERP.R, lines 198–251 · score 0.57 · principal component, lme4, variance, PCA, fit, intercepts
  7. [7] § 2. Materials and methods › 2.6. Data analysis › 2.6.2. Electrophysiological measures. ↔ R scripts/MoBSeR_ERP.R, lines 198–251 · score 0.51 · smpsize_lmm, medium, ICC, power, correlation, model

Paper

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The authors' code

R · 1,719 lines · 85 KB · no license · 2 matches

  1. #################################
  2. ########## MoBSeR: ERPs ########
  3. #################################
  5. # positive = "#EB0030",negative = "#404A80"
  6. # Polish = "#353269",English = "#120BDE"
  7. # "UN" = "#ffc000", "WR" = "#ff2f92", "CR" = "#404A80"
  9. #### libraries ####
  10. library(tidyverse)
  11. library(patchwork)
  12. library(ggplot2)
  13. library(lmerTest)
  14. library(ggpubr)
  15. library(lme4)
  16. library(emmeans)
  17. library(survival)
  18. library(car)
  19. library(stats)
  20. library(lattice)
  21. library(dplyr)
  22. library(reshape2)
  23. library(ez)
  24. library(multcomp)
  25. library(lsmeans)
  26. library(tidyr)
  27. library(apaTables)
  28. library(RColorBrewer)
  29. library(afex)
  30. library(purrr)
  31. library(ggridges)
  32. library(MuMIn)
  33. library(devtools)
  34. library(Hmisc)
  35. library(corrplot)
  36. library(PerformanceAnalytics)
  37. library(vcd)
  38. library(psych)
  39. library(irr)
  40. library(datarium)
  41. library(tables)
  42. library(ggrepel)
  43. library(sjPlot)
  44. library(sjmisc)
  45. library(sjlabelled)
  46. library(ggprism)
  47. library(sjstats)
  49. #### data preparation ####
  50. # import data
  51. dir()
  52. setwd(dirname(rstudioapi::getSourceEditorContext()$path))
  53. dir()
  54. df <- read.csv(file = "MoBSeR_ERP_R1.csv", fileEncoding = "UTF-8-BOM", sep = ',')
  55. summary(df)
  56. # str(df)
  57. # View(df)
  59. # set factors
  60. df$mood <- as.factor(df$mood)
  61. df$language <- as.factor(df$language)
  62. df$type <- as.factor(df$type)
  63. df$type <- factor(df$type, levels=c("UN", "WR", "CR"))
  64. df$mood <- factor(df$mood, levels=c("positive", "negative"))
  65. df$language <- factor(df$language, levels=c("Polish", "English"))
  66. df$chan <- factor(df$chan, levels=c("F1", "Fz", "F2", "FC1", "FCz", "FC2", "C1", "Cz", "C2", "CP1", "CPz", "CP2", "P1", "Pz", "P2"))
  67. df$chan1 <- with(df, ifelse(chan %in% c("FC1", "FCz", "FC2"), "FC",ifelse(chan %in% c("C1", "Cz", "C2"), "C",ifelse(chan %in% c("CP1", "CPz", "CP2"), "CP",ifelse(chan %in% c("P1", "Pz", "P2"), "P",NA)))))
  68. df$chan1 <- factor(df$chan1, levels=c("FC", "C", "CP","P"))
  69. df$amp <- as.numeric(df$amp)
  70. names(df)[names(df) == "trgtrig"] <- "word"
  71. names(df)[names(df) == "cndtrig"] <- "cnd"
  72. df <- na.omit(df)
  74. FC_C_CP_3 <- c("FC1","FCz","FC2","C1","Cz","C2","CP1","CPz","CP2")
  75. FC_C_CP_P_3 <- c("FC1","FCz","FC2","C1","Cz","C2","CP1","CPz","CP2","P1","Pz","P2")
  76. FCs <- c("FC1","FCz","FC2")
  77. Cs <- c("C1","Cz","C2")
  78. CPs <- c("CP1","CPz","CP2")
  79. Ps <- c("P1", "Pz", "P2")
  81. #### _________________ N400 mood x type x mood induction progression (film1) _________________ ####
  83. #### LMMs: N400 Polish [L1] ####
  84. # df4 --- N400 Polish
  85. df4 <- df %>% subset(chan %in% FC_C_CP_P_3 & time >= 300 & time <=500 & language %in% "Polish") %>% droplevels() %>%
  86. dplyr::group_by(mood, type, ptp, word, chan, film1) %>%
  87. dplyr::summarise(mean_amp = mean(amp))
  89. # contrasts
  90. df4$mood <- factor(df4$mood, levels=c("positive", "negative"))
  91. df4$type <- factor(df4$type, levels=c("UN", "WR", "CR"))
  92. contrasts(df4$mood) <- contr.sum(levels(df4$mood))
  93. df4$type <- relevel(df4$type, ref = "UN")
  95. # lmer
  96. N4_L1_film_model0 <- lmer(mean_amp ~ mood*type*film1 + (1|ptp) + (1|word), data=df4)
  97. N4_L1_film_model1 <- lmer(mean_amp ~ mood*type*film1 + (1+type+mood|ptp) + (1+type+mood|word), data=df4)
  98. N4_L1_film_model2 <- lmer(mean_amp ~ mood*type*film1 + (1+type|ptp) + (1+type|word), data=df4)
  100. summary(N4_L1_film_model2)
  101. anova(N4_L1_film_model2)
  102. anova(m0, N4_L1_film_model2)
  104. ' Estimate Std. Error df t value Pr(>|t|)
  105. (Intercept) -1.509e+00 2.898e-01 4.327e+01 -5.207 5.04e-06 ***
  106. mood1 -2.505e-01 9.690e-02 5.910e+03 -2.585 0.009763 **
  107. typeWR 2.884e-01 1.814e-01 2.801e+02 1.590 0.113052
  108. typeCR 4.259e-01 2.266e-01 1.353e+02 1.879 0.062370 .
  109. mood1:typeWR 3.212e-01 1.368e-01 1.055e+04 2.348 0.018886 *
  110. mood1:typeCR 3.758e-01 1.297e-01 1.411e+04 2.898 0.003760 **
  111. mood1:typeWR:film1 -7.683e-02 2.734e-02 8.222e+03 -2.810 0.004970 **
  112. mood1:typeCR:film1 -8.381e-02 2.668e-02 1.170e+04 -3.141 0.001688 ** '
  114. summary(rePCA(N4_L1_film_model2)) # singular fit? ==> relative Principal Component Analysis
  116. relgrad <- with(N4_L1_film_model1@optinfo$derivs,solve(Hessian,gradient)) # convergence problem?
  117. max(abs(relgrad)) # <.001? ==> the model is close to convergence
  119. plot_model(N4_L1_chan1_model2) # plot the model
  120. plot(N4_L1_chan1_model2) # reasonably equal amount of spread?
  121. hist(residuals(N4_L1_chan1_model2)) # more or less normally distributed?
  122. qqnorm(residuals(N4_L1_chan1_model2)) # reasonably aligned? '/'
  123. #tab_model(N4_L1_chan1_model2,transform = NULL, show.est=TRUE, show.ci=TRUE, show.se=TRUE, show.df=TRUE, show.stat=TRUE, wrap.labels=2, df.method = "satterthwaite", title = "N400 Polish (L1)", file = "N400_L1_model_specs.html")
  125. # POST-HOCS
  126. # mood
  127. Polish_film_fixed_mood <- emmeans(N4_L1_film_model2, pairwise ~ mood,adjust = "bonferroni")
  128. print(Polish_film_fixed_mood)
  129. plot(Polish_film_fixed_mood)
  131. ' mood emmean SE df asymp.LCL asymp.UCL
  132. positive -1.05 0.27 Inf -1.58 -0.524
  133. negative -1.20 0.27 Inf -1.73 -0.675'
  135. ' contrast estimate SE df z.ratio p.value
  136. positive - negative 0.15 0.0407 Inf 3.693 0.0002'
  138. # type
  139. Polish_film_fixed_type <- emmeans(N4_L1_film_model2, pairwise ~ type,adjust = "bonferroni")
  140. print(Polish_film_fixed_type)
  141. plot(Polish_film_fixed_type)
  143. ' type emmean SE df asymp.LCL asymp.UCL
  144. UN -1.679 0.274 Inf -2.22 -1.143
  145. WR -1.235 0.282 Inf -1.79 -0.683
  146. CR -0.469 0.300 Inf -1.06 0.118'
  148. 'contrast estimate SE df z.ratio p.value
  149. UN - WR -0.444 0.128 Inf -3.459 0.0016
  150. UN - CR -1.210 0.189 Inf -6.412 <.0001
  151. WR - CR -0.766 0.173 Inf -4.420 <.0001'
  153. # mood x type
  154. Polish_film_int_mood_type1 <- emmeans(N4_L1_film_model2, pairwise ~ mood|type, adjust = "bonferroni")
  155. print(Polish_film_int_mood_type1)
  156. plot(Polish_film_int_mood_type1)
  158. 'type = UN:
  159. mood emmean SE df asymp.LCL asymp.UCL
  160. positive -1.595 0.276 Inf -2.136 -1.0541
  161. negative -1.764 0.276 Inf -2.305 -1.2226
  163. type = WR:
  164. mood emmean SE df asymp.LCL asymp.UCL
  165. positive -1.176 0.284 Inf -1.733 -0.6198
  166. negative -1.295 0.284 Inf -1.851 -0.7379
  168. type = CR:
  169. mood emmean SE df asymp.LCL asymp.UCL
  170. positive -0.387 0.302 Inf -0.979 0.2045
  171. negative -0.551 0.302 Inf -1.143 0.0404'
  174. 'type = UN:
  175. contrast estimate SE df z.ratio p.value
  176. positive - negative 0.169 0.0704 Inf 2.395 0.0166
  178. type = WR:
  179. contrast estimate SE df z.ratio p.value
  180. positive - negative 0.118 0.0701 Inf 1.686 0.0918
  182. type = CR:
  183. contrast estimate SE df z.ratio p.value
  184. positive - negative 0.164 0.0711 Inf 2.313 0.0207'
  187. Polish_film_int_mood_type2 <- emmeans(N4_L1_film_model2, pairwise ~ type|mood, adjust = "bonferroni")
  188. print(Polish_film_int_mood_type2)
  189. plot(Polish_film_int_mood_type2)
  191. 'mood = positive:
  192. type emmean SE df asymp.LCL asymp.UCL
  193. UN -1.595 0.276 Inf -2.136 -1.0541
  194. WR -1.176 0.284 Inf -1.733 -0.6198
  195. CR -0.387 0.302 Inf -0.979 0.2045
  197. mood = negative:
  198. type emmean SE df asymp.LCL asymp.UCLx
  199. UN -1.764 0.276 Inf -2.305 -1.2226
  200. WR -1.295 0.284 Inf -1.851 -0.7379
  201. CR -0.551 0.302 Inf -1.143 0.0404'
  203. 'mood = positive:
  204. contrast estimate SE df z.ratio p.value
  205. UN - WR -0.419 0.138 Inf -3.044 0.0070
  206. UN - CR -1.208 0.195 Inf -6.191 <.0001
  207. WR - CR -0.789 0.180 Inf -4.378 <.0001
  209. mood = negative:
  210. contrast estimate SE df z.ratio p.value
  211. UN - WR -0.469 0.138 Inf -3.407 0.0020
  212. UN - CR -1.212 0.195 Inf -6.205 <.0001
  213. WR - CR -0.743 0.181 Inf -4.117 0.0001'
  215. # mood x type x film1
  216. emm_trends <- emtrends(N4_L1_film_model2,specs = ~ mood * type,var = "film1")
  217. summary(emm_trends)
  219. 'mood type film1.trend SE df asymp.LCL asymp.UCL
  220. positive UN 0.036478 0.0279 Inf -0.0182 0.0912
  221. negative UN -0.112016 0.0276 Inf -0.1661 -0.0579
  222. positive WR -0.005858 0.0281 Inf -0.0610 0.0492
  223. negative WR -0.000689 0.0263 Inf -0.0522 0.0508
  224. positive CR 0.126571 0.0265 Inf 0.0746 0.1785
  225. negative CR 0.145692 0.0270 Inf 0.0928 0.1986'
  227. contrast(emm_trends,method = "pairwise",adjust = "holm", by ="mood")
  228. 'mood = positive:
  229. contrast estimate SE df z.ratio p.value
  230. UN - WR 0.0423 0.0396 Inf 1.070 0.2847
  231. UN - CR -0.0901 0.0385 Inf -2.339 0.0387
  232. WR - CR -0.1324 0.0387 Inf -3.426 0.0018
  234. mood = negative:
  235. contrast estimate SE df z.ratio p.value
  236. UN - WR -0.1113 0.0381 Inf -2.924 0.0035
  237. UN - CR -0.2577 0.0387 Inf -6.654 <.0001
  238. WR - CR -0.1464 0.0376 Inf -3.888 0.0002'
  240. contrast(emm_trends,method = "pairwise",adjust = "holm", by ="type")
  241. 'type = UN:
  242. contrast estimate SE df z.ratio p.value
  243. positive - negative 0.14849 0.0382 Inf 3.884 0.0001
  245. type = WR:
  246. contrast estimate SE df z.ratio p.value
  247. positive - negative -0.00517 0.0393 Inf -0.132 0.8953
  249. type = CR:
  250. contrast estimate SE df z.ratio p.value
  251. positive - negative -0.01912 0.0371 Inf -0.516 0.6060'
  254. # observed power
  255. vc <- as.data.frame(VarCorr(N4_L1_film_model2))
  256. sigma_ptp <- vc$vcov[vc$grp == "ptp" & vc$var1 == "(Intercept)"][1] # Participant intercept variance (scalar)
  257. sigma_resid <- vc$vcov[vc$grp == "Residual"][1] # Residual variance (scalar)
  258. print(icc_ptp <- sigma_ptp / (sigma_ptp + sigma_resid))
  260. smpsize_lmm(
  261. eff.size = 0.25, # medium effect size
  262. df.n = 2, # (3-1)*(2-1) -- 3x2 design [for a two-way interaction]
  263. power = .96, # observed power for 40 items per condition
  264. sig.level = 0.05,
  265. k = 32, # participants
  266. n = 240, # trials per participant (40*6)
  267. icc = 0.05837853)
  270. #### Polish [L1] - PLOTS ####
  271. #### Polish -- N400: type x mood [GAV] ####
  272. plotstats2c <- df %>% subset(chan %in% FC_C_CP_P_3 & time >= -200 & time <= 800 & language %in% "Polish") %>% droplevels() %>%
  273. dplyr::group_by(time, type, mood) %>%
  274. dplyr::summarise(mean_amp = mean(amp),CIlower = Rmisc::CI(amp, ci = 0.95)["lower"],CIupper = Rmisc::CI(amp, ci = 0.95)["upper"])
  275. plotstats2c <- plotstats2c %>% group_by(type, mood) %>% mutate(baseline = mean_amp[time == 0],mean_amp = mean_amp - baseline, CIlower = CIlower - baseline,CIupper = CIupper - baseline) %>% dplyr::select(-baseline)
  276. plotstats2c <- plotstats2c %>% arrange(time) %>% group_by(type, mood) %>% mutate(mean_amp = zoo::rollmean(mean_amp, k = 10, fill = NA, align = "center"), CIlower = zoo::rollmean(CIlower, k = 10, fill = NA, align = "center"), CIupper = zoo::rollmean(CIupper, k = 17, fill = NA, align = "center"))
  278. # by type
  279. ggplot(plotstats2c, aes(time, mean_amp, color = type, group = type)) +
  280. geom_ribbon(aes(ymin = CIlower, ymax = CIupper, fill = type),
  281. alpha = 0.2, linetype = 0) +
  282. facet_wrap(.~mood, ncol =2, labeller = as_labeller(c("positive" = "Positive mood", "negative" = "Negative mood"))) +
  283. geom_line(size = 0.75) +
  284. guides(fill = "none") +
  285. labs(x = "Time (ms)", y = expression(paste("Amplitude (", mu, "V)")), colour = "") +
  286. scale_shape_prism() +
  287. #theme_prism() +
  288. scale_y_reverse(breaks = c(1,0, -1, -2, -3), limits = c(1,-3), guide = "prism_offset") +
  289. scale_x_continuous(breaks = c(-100, 0, 100, 300, 500, 700), guide = "prism_offset") +
  290. theme_classic2()+
  291. geom_vline(xintercept = 0, linetype = "dotted", alpha = 0.5) +
  292. geom_hline(yintercept = 0, linetype = "dotted", alpha = 0.5) +
  293. geom_vline(xintercept = 300, linetype = "solid", alpha = 1, color = "black") +
  294. geom_vline(xintercept = 500, linetype = "solid", alpha = 1, color = "black") +
  295. theme(text = element_text(size = 20, color = "black", family = "Helvetica"),
  296. axis.line.x = element_line(colour = "black", size = .5),
  297. axis.line.y = element_line(colour = "black", size = .5),
  298. axis.text = element_text(size = 18, color = "black", family = "Helvetica"),
  299. strip.text.x.top = element_text(size = 20, color = "black", family = "Helvetica"),
  300. plot.title = element_text(hjust = 0, family = "Helvetica"),
  301. legend.position = "bottom",
  302. legend.title = element_text(size = 20),
  303. legend.text = element_text(size = 20)) +
  304. scale_fill_manual(values = c("#ffc000","#ff2f92","#404A80")) +
  305. scale_color_manual(name = "", values = c("#ffc000","#ff2f92","#404A80"), labels = c("Unrelated (UR) pairs", "Weakly related (WR) pairs", "Closely related (CR) pairs"))+
  306. guides(colour = guide_legend(override.aes = list(linetype = 1, shape = 16, size = 5)))
  308. ggsave(filename ="L1_mood_type_R1.png", plot = last_plot(), width = 30,height = 12, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  311. # by mood
  312. ggplot(plotstats2c, aes(time, mean_amp, color = mood, group = mood)) +
  313. geom_ribbon(aes(ymin = CIlower, ymax = CIupper, fill = mood),
  314. alpha = 0.2, linetype = 0) +
  315. facet_wrap(.~type, ncol =3, labeller = as_labeller(c("UN" = "Unrelated pairs", "WR" = "Weakly related pairs", "CR" = "Closely related pairs"))) +
  316. geom_line(size = 0.75) +
  317. guides(fill = "none") +
  318. labs(x = "Time (ms)", y = expression(paste("Amplitude (", mu, "V)")), colour = "") +
  319. scale_shape_prism() +
  320. #theme_prism() +
  321. scale_y_reverse(breaks = c(0, -1, -2, -3), limits = c(0.75,-2.75), guide = "prism_offset") +
  322. scale_x_continuous(breaks = c(-100, 0, 100, 300, 500, 700), guide = "prism_offset") +
  323. theme_classic2()+
  324. geom_vline(xintercept = 0, linetype = "dotted", alpha = 0.5) +
  325. geom_hline(yintercept = 0, linetype = "dotted", alpha = 0.5) +
  326. geom_vline(xintercept = 300, linetype = "solid", alpha = 1, color = "black") +
  327. geom_vline(xintercept = 500, linetype = "solid", alpha = 1, color = "black") +
  328. theme(text = element_text(size = 20, color = "black", family = "Helvetica"),
  329. axis.line.x = element_line(colour = "black", size = .5),
  330. axis.line.y = element_line(colour = "black", size = .5),
  331. axis.text = element_text(size = 18, color = "black", family = "Helvetica"),
  332. strip.text.x.top = element_text(size = 20, color = "black", family = "Helvetica"),
  333. plot.title = element_text(hjust = 0, family = "Helvetica"),
  334. legend.position = "bottom",
  335. legend.title = element_text(size = 20),
  336. legend.text = element_text(size = 20)) +
  337. scale_fill_manual(values = c("red","blue")) +
  338. scale_color_manual(name = "", values = c("red","blue"), labels = c("Positive mood", "Negative mood"))+
  339. guides(colour = guide_legend(override.aes = list(linetype = 1, shape = 16, size = 5)))
  342. #### Polish -- N400: type x mood [distribution] ####
  343. plotstats2c <- df %>% subset(chan %in% FC_C_CP_P_3 & time >= 300 & time <= 500 & language %in% "Polish") %>% droplevels() %>%
  344. dplyr::group_by(type, mood, word) %>%
  345. dplyr::summarise(mean_amp = mean(amp),CIlower = Rmisc::CI(amp, ci = 0.95)["lower"],CIupper = Rmisc::CI(amp, ci = 0.95)["upper"])
  347. ggplot(plotstats2c, aes(x = type, y = mean_amp, colour = type, fill = type)) +
  348. facet_wrap(.~mood, labeller = as_labeller(c("positive" = "Positive mood", "negative" = "Negative mood", "Polish" = "Polish (L1)", "English" = "English (L2)"))) +
  349. # ggdist::stat_halfeye(aes(colour = type), width = 0.8, .width = 0, alpha = 0.75, justification = -0.3, point_colour = NA) +
  350. geom_point(aes(colour = type), size = 1.0, alpha = 0.2, position = position_jitter(seed = 1, width = 0.1)) +
  351. geom_boxplot(aes(colour = type), width = .25, alpha = 0.25, outlier.shape = NA) +
  352. scale_shape_prism() +
  353. #theme_prism() +
  354. scale_y_reverse(limits = c(3,-6), breaks = c(-6,-3, 0,3), guide = "prism_offset") +
  355. #coord_cartesian(ylim = c(-3, 0.5), clip = "off") +
  356. scale_x_discrete(labels = c("UN" = "Unrelated", "WR" = "Weakly related", "CR" = "Closely related"), guide = "prism_bracket") +
  357. labs(y = expression(paste("Amplitude (", mu, "V)")), x = "Word pair") +
  358. scale_fill_manual(values = c("#ffc000","#ff2f92","#404A80")) +
  359. scale_colour_manual(values = c("#ffc000","#ff2f92","#404A80")) +
  360. theme_classic2() +
  361. theme(legend.position="none") +
  362. theme(panel.background = element_rect(fill = "white"),
  363. strip.background = element_rect(fill = "white",color="black",size=1),
  364. legend.key = element_rect(fill = "white"),
  365. axis.line.x = element_line(colour = "black", size = .5),
  366. axis.line.y = element_line(colour = "black", size = .5),
  367. axis.title = element_text(size = 15, color= "black"),
  368. strip.text = element_text(size = 15, color= "black"),
  369. legend.text = element_text(size = 15, color= "black"),
  370. legend.title = element_text(size = 15, color= "black"),
  371. strip.text.x.top = element_text(size = 15, color= "black"),
  372. axis.text = element_text(size = 15, color= "black"))
  375. ggsave(filename ="L1_distribution.png", plot = last_plot(), width = 25,height = 8, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  378. #### Polish -- N400: type x mood x mood induction progression [slopes] ####
  379. block_points1 <- quantile(df4$film1, probs = c(.05, .5, .95))
  381. emm_plot1 <- emmeans(N4_L1_film_model2,~ mood * type | film1,at = list(film1 = block_points1))
  382. plot_df1 <- as.data.frame(emm_plot1)
  384. plot_df1$type <- factor(plot_df1$type, levels=c("CR", "WR", "UN"))
  386. # by mood
  387. ggplot(plot_df1,aes(x = film1, y = emmean, color = type)) +
  388. geom_line(aes(group = type), linewidth = 1) +
  389. geom_point(size = 3) +
  390. geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL),width = 0.1) +
  391. facet_wrap(~ mood,ncol = 2,labeller = as_labeller(c("positive" = "Positive mood","negative" = "Negative mood"))) +
  392. scale_color_manual(name = "",values = c("#ffc000", "#ff2f92", "#404A80"),labels = c("Unrelated (UR) pairs","Weakly related (WR) pairs","Closely related (CR) pairs")) +
  393. scale_x_continuous(name = "Block progression",breaks = c(1,2,3,4,5,6,7, 8),guide = "prism_offset") +
  394. scale_y_reverse(name = "Predicted mean N400 amplitude (µV)",breaks = c(1,0, -1, -2, -3),limits = c(1, -3),guide = "prism_offset") +
  395. theme_classic2() +
  396. scale_shape_prism() +
  397. theme(text = element_text(size = 20, family = "Helvetica", color = "black"),
  398. axis.text = element_text(size = 18),
  399. axis.line = element_line(size = 0.5),
  400. strip.text = element_text(size = 20),
  401. strip.background = element_blank(),
  402. legend.position = "bottom",
  403. legend.text = element_text(size = 20))
  405. # by type
  406. ggplot(plot_df1,aes(x = film1, y = emmean, color = mood)) +
  407. geom_line(aes(group = mood), linewidth = 1) +
  408. geom_point(size = 2) +
  409. geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL),width = 0.1) +
  410. facet_wrap(.~type, ncol =3, labeller = as_labeller(c("UN" = "Unrelated \npairs", "WR" = "Weakly \nrelated pairs", "CR" = "Closely \nrelated pairs"))) +
  411. scale_color_manual(name = "",values = c("firebrick2","darkblue"),labels = c("Positive mood","Negative mood")) +
  412. scale_x_continuous(name = "Number of films watched",breaks = c(1,2,3,4,5,6,7, 8),guide = "prism_offset") +
  413. scale_y_reverse(name = "Predicted mean N400 amplitude (µV)",breaks = c(1,0, -1, -2, -3),limits = c(1, -3),guide = "prism_offset") +
  414. scale_shape_prism() +
  415. theme_classic2() +
  416. theme(text = element_text(size = 20, family = "Helvetica", color = "black"),
  417. axis.text = element_text(size = 18),
  418. axis.line = element_line(size = 0.5),
  419. strip.text = element_text(size = 20),
  420. strip.background = element_blank(),
  421. legend.position = "bottom",
  422. legend.text = element_text(size = 20))
  425. ggsave(filename ="L1_predicted_films.png", plot = last_plot(), width = 15,height = 14, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  428. #### LMMs: N400 English [L2] ####
  429. # df5 --- N400 English
  430. df5 <- df %>% subset(chan %in% FC_C_CP_P_3 & time >= 300 & time <=500 & language %in% "English") %>% droplevels() %>%
  431. dplyr::group_by(mood, type, ptp, word, chan, film1) %>%
  432. dplyr::summarise(mean_amp = mean(amp))
  434. # contrasts
  435. df5$mood <- factor(df5$mood, levels=c("positive", "negative"))
  436. df5$type <- factor(df5$type, levels=c("UN", "WR", "CR"))
  437. contrasts(df5$mood) <- contr.sum(levels(df5$mood))
  438. df5$type <- relevel(df5$type, ref = "UN")
  440. # lmer
  441. N4_L2_film_model0 <- lmer(mean_amp ~ mood*type*film1 + (1|ptp) + (1|word), data=df5)
  442. N4_L2_film_model1 <- lmer(mean_amp ~ mood*type*film1 + (1+type+mood|ptp) + (1+type+mood|word), data=df5)
  443. N4_L2_film_model2 <- lmer(mean_amp ~ mood*type*film1 + (1+mood|ptp) + (1|word), data=df5)
  445. summary(N4_L2_film_model2)
  446. anova(N4_L2_film_model2)
  447. anova(m0, N4_L2_film_model2)
  449. ' Estimate Std. Error df t value Pr(>|t|)
  450. (Intercept) -1.372e+00 2.717e-01 4.011e+01 -5.050 1.00e-05 ***
  451. mood1 9.008e-02 9.191e-02 5.040e+02 0.980 0.327
  452. typeWR -5.580e-03 1.140e-01 6.683e+04 -0.049 0.961
  453. typeCR 7.777e-01 1.164e-01 6.483e+04 6.679 2.42e-11 ***
  454. mood1:typeWR -4.807e-02 1.135e-01 6.941e+04 -0.424 0.672
  455. mood1:typeCR 1.852e-01 1.150e-01 7.218e+04 1.611 0.107
  456. mood1:typeWR:film1 -4.171e-03 2.298e-02 6.224e+04 -0.181 0.856
  457. mood1:typeCR:film1 -1.565e-02 2.278e-02 6.709e+04 -0.687 0.492 '
  459. summary(rePCA(N4_L2_film_model2)) # singular fit? ==> relative Principal Component Analysis
  461. relgrad <- with(N4_L2_film_model2@optinfo$derivs,solve(Hessian,gradient)) # convergence problem?
  462. max(abs(relgrad)) # <.001? ==> the model is close to convergence
  464. plot_model(N4_L2_film_model2) # plot the model
  465. plot(N4_L2_film_model2) # reasonably equal amount of spread?
  466. hist(residuals(N4_L2_film_model2)) # more or less normally distributed?
  467. qqnorm(residuals(N4_L2_film_model2)) # reasonably aligned? '/'
  468. #tab_model(N4_L2_film_model2,transform = NULL, show.est=TRUE, show.ci=TRUE, show.se=TRUE, show.df=TRUE, show.stat=TRUE, wrap.labels=2, df.method = "satterthwaite", title = "N400 Polish (L1)", file = "N400_L1_model_specs.html")
  470. # POST-HOCS
  471. # type
  472. English_film_fixed_type <- emmeans(N4_L2_film_model2, pairwise ~ type,adjust = "bonferroni")
  473. print(English_film_fixed_type)
  474. plot(English_film_fixed_type)
  476. ' type emmean SE df asymp.LCL asymp.UCL
  477. UN -1.72 0.261 Inf -2.234 -1.211
  478. WR -1.13 0.261 Inf -1.639 -0.616
  479. CR -0.43 0.261 Inf -0.941 0.082'
  481. ' contrast estimate SE df z.ratio p.value
  482. UN - WR -0.595 0.0494 Inf -12.038 <.0001
  483. UN - CR -1.293 0.0493 Inf -26.222 <.0001
  484. WR - CR -0.698 0.0493 Inf -14.158 <.0001'
  487. # observed power
  488. vc <- as.data.frame(VarCorr(N4_L2_film_model2))
  489. sigma_ptp <- vc$vcov[vc$grp == "ptp" & vc$var1 == "(Intercept)"][1] # Participant intercept variance (scalar)
  490. sigma_resid <- vc$vcov[vc$grp == "Residual"][1] # Residual variance (scalar)
  491. print(icc_ptp <- sigma_ptp / (sigma_ptp + sigma_resid))
  493. smpsize_lmm(
  494. eff.size = 0.25, # medium effect size
  495. df.n = 2, # (3-1)*(2-1) -- 3x2 design [for a two-way interaction]
  496. power = .97, # observed power for 40 items per condition
  497. sig.level = 0.05,
  498. k = 32, # participants
  499. n = 240, # trials per participant (40*6)
  500. icc = 0.05552603)
  503. #### English [L2] - PLOTS ####
  504. #### English -- N400: type x mood [GAV] ####
  505. plotstats2f <- df %>% subset(chan %in% FC_C_CP_P_3 & time >= -200 & time <= 800 & language %in% "English") %>% droplevels() %>%
  506. dplyr::group_by(time, type, mood) %>%
  507. dplyr::summarise(mean_amp = mean(amp),CIlower = Rmisc::CI(amp, ci = 0.95)["lower"],CIupper = Rmisc::CI(amp, ci = 0.95)["upper"])
  508. plotstats2f <- plotstats2f %>% group_by(type, mood) %>% mutate(baseline = mean_amp[time == 0],mean_amp = mean_amp - baseline, CIlower = CIlower - baseline,CIupper = CIupper - baseline) %>% dplyr::select(-baseline)
  509. plotstats2f <- plotstats2f %>% arrange(time) %>% group_by(type, mood) %>% mutate(mean_amp = zoo::rollmean(mean_amp, k = 10, fill = NA, align = "center"), CIlower = zoo::rollmean(CIlower, k = 10, fill = NA, align = "center"), CIupper = zoo::rollmean(CIupper, k = 17, fill = NA, align = "center"))
  511. # by type
  512. ggplot(plotstats2f, aes(time, mean_amp, color = type, group = type)) +
  513. geom_ribbon(aes(ymin = CIlower, ymax = CIupper, fill = type),
  514. alpha = 0.2, linetype = 0) +
  515. facet_wrap(.~mood, ncol =2, labeller = as_labeller(c("positive" = "Positive mood", "negative" = "Negative mood"))) +
  516. geom_line(size = 0.75) +
  517. guides(fill = "none") +
  518. labs(x = "Time (ms)", y = expression(paste("Amplitude (", mu, "V)")), colour = "") +
  519. scale_shape_prism() +
  520. #theme_prism() +
  521. scale_y_reverse(breaks = c(1,0, -1, -2, -3), limits = c(1,-3), guide = "prism_offset") +
  522. scale_x_continuous(breaks = c(-100, 0, 100, 300, 500, 700), guide = "prism_offset") +
  523. theme_classic2()+
  524. geom_vline(xintercept = 0, linetype = "dotted", alpha = 0.5) +
  525. geom_hline(yintercept = 0, linetype = "dotted", alpha = 0.5) +
  526. geom_vline(xintercept = 300, linetype = "solid", alpha = 1, color = "black") +
  527. geom_vline(xintercept = 500, linetype = "solid", alpha = 1, color = "black") +
  528. theme(text = element_text(size = 20, color = "black", family = "Helvetica"),
  529. axis.line.x = element_line(colour = "black", size = .5),
  530. axis.line.y = element_line(colour = "black", size = .5),
  531. axis.text = element_text(size = 18, color = "black", family = "Helvetica"),
  532. strip.text.x.top = element_text(size = 20, color = "black", family = "Helvetica"),
  533. plot.title = element_text(hjust = 0, family = "Helvetica"),
  534. legend.position = "bottom",
  535. legend.title = element_text(size = 20),
  536. legend.text = element_text(size = 20)) +
  537. scale_fill_manual(values = c("#ffc000","#ff2f92","#404A80")) +
  538. scale_color_manual(name = "", values = c("#ffc000","#ff2f92","#404A80"), labels = c("Unrelated (UR) pairs", "Weakly related (WR) pairs", "Closely related (CR) pairs"))+
  539. guides(colour = guide_legend(override.aes = list(linetype = 1, shape = 16, size = 5)))
  541. ggsave(filename ="L2_mood_type_R1.png", plot = last_plot(), width = 30,height = 12, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  544. # by mood
  545. ggplot(plotstats2f, aes(time, mean_amp, color = mood, group = mood)) +
  546. geom_ribbon(aes(ymin = CIlower, ymax = CIupper, fill = mood),
  547. alpha = 0.2, linetype = 0) +
  548. facet_wrap(.~type, ncol =3, labeller = as_labeller(c("UN" = "Unrelated pairs", "WR" = "Weakly related pairs", "CR" = "Closely related pairs"))) +
  549. geom_line(size = 0.75) +
  550. guides(fill = "none") +
  551. labs(x = "Time (ms)", y = expression(paste("Amplitude (", mu, "V)")), colour = "") +
  552. scale_shape_prism() +
  553. #theme_prism() +
  554. scale_y_reverse(breaks = c(0, -1, -2, -3), limits = c(0.75,-2.75), guide = "prism_offset") +
  555. scale_x_continuous(breaks = c(-100, 0, 100, 300, 500, 700), guide = "prism_offset") +
  556. theme_classic2()+
  557. geom_vline(xintercept = 0, linetype = "dotted", alpha = 0.5) +
  558. geom_hline(yintercept = 0, linetype = "dotted", alpha = 0.5) +
  559. geom_vline(xintercept = 300, linetype = "solid", alpha = 1, color = "black") +
  560. geom_vline(xintercept = 500, linetype = "solid", alpha = 1, color = "black") +
  561. theme(text = element_text(size = 20, color = "black", family = "Helvetica"),
  562. axis.line.x = element_line(colour = "black", size = .5),
  563. axis.line.y = element_line(colour = "black", size = .5),
  564. axis.text = element_text(size = 18, color = "black", family = "Helvetica"),
  565. strip.text.x.top = element_text(size = 20, color = "black", family = "Helvetica"),
  566. plot.title = element_text(hjust = 0, family = "Helvetica"),
  567. legend.position = "bottom",
  568. legend.title = element_text(size = 20),
  569. legend.text = element_text(size = 20)) +
  570. scale_fill_manual(values = c("red","blue")) +
  571. scale_color_manual(name = "", values = c("red","blue"), labels = c("Positive mood", "Negative mood"))+
  572. guides(colour = guide_legend(override.aes = list(linetype = 1, shape = 16, size = 5)))
  574. #### English -- N400: type x mood [distribution] ####
  575. plotstats2f <- df %>% subset(chan %in% FC_C_CP_3 & time >= 300 & time <= 500 & language %in% "English") %>% droplevels() %>%
  576. dplyr::group_by(type, mood, word) %>%
  577. dplyr::summarise(mean_amp = mean(amp),CIlower = Rmisc::CI(amp, ci = 0.95)["lower"],CIupper = Rmisc::CI(amp, ci = 0.95)["upper"])
  579. ggplot(plotstats2f, aes(x = type, y = mean_amp, colour = type, fill = type)) +
  580. facet_wrap(.~mood, labeller = as_labeller(c("positive" = "Positive mood", "negative" = "Negative mood", "Polish" = "Polish (L1)", "English" = "English (L2)"))) +
  581. # ggdist::stat_halfeye(aes(colour = type), width = 0.8, .width = 0, alpha = 0.75, justification = -0.3, point_colour = NA) +
  582. geom_point(aes(colour = type), size = 1.0, alpha = 0.2, position = position_jitter(seed = 1, width = 0.1)) +
  583. geom_boxplot(aes(colour = type), width = .25, alpha = 0.25, outlier.shape = NA) +
  584. scale_shape_prism() +
  585. #theme_prism() +
  586. scale_y_reverse(limits = c(3,-6), breaks = c(-6,-3, 0,3), guide = "prism_offset") +
  587. #coord_cartesian(ylim = c(-3, 0.5), clip = "off") +
  588. scale_x_discrete(labels = c("UN" = "Unrelated", "WR" = "Weakly related", "CR" = "Closely related"), guide = "prism_bracket") +
  589. labs(y = expression(paste("Amplitude (", mu, "V)")), x = "Word pair") +
  590. scale_fill_manual(values = c("#ffc000","#ff2f92","#404A80")) +
  591. scale_colour_manual(values = c("#ffc000","#ff2f92","#404A80")) +
  592. theme_classic2() +
  593. theme(legend.position="none") +
  594. theme(panel.background = element_rect(fill = "white"),
  595. strip.background = element_rect(fill = "white",color="black",size=1),
  596. legend.key = element_rect(fill = "white"),
  597. axis.line.x = element_line(colour = "black", size = .5),
  598. axis.line.y = element_line(colour = "black", size = .5),
  599. axis.title = element_text(size = 15, color= "black"),
  600. strip.text = element_text(size = 15, color= "black"),
  601. legend.text = element_text(size = 15, color= "black"),
  602. legend.title = element_text(size = 15, color= "black"),
  603. strip.text.x.top = element_text(size = 15, color= "black"),
  604. axis.text = element_text(size = 15, color= "black"))
  607. ggsave(filename ="L2_distribution.png", plot = last_plot(), width = 25,height = 8, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  610. #### English -- N400: type x mood x mood induction progression [slopes] ####
  611. block_points2 <- quantile(df4$film1, probs = c(.05, .5, .95))
  613. emm_plot2 <- emmeans(N4_L2_film_model2,~ mood * type | film1,at = list(film1 = block_points2))
  614. emm_plot2 <- as.data.frame(emm_plot2)
  616. emm_plot2$type <- factor(emm_plot2$type, levels=c("CR", "WR", "UN"))
  618. ggplot(emm_plot2,aes(x = film1, y = emmean, color = type)) +
  619. geom_line(aes(group = type), linewidth = 1) +
  620. geom_point(size = 2) +
  621. geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL),width = 0.1) +
  622. facet_wrap(~ mood,ncol = 2,labeller = as_labeller(c("positive" = "Positive mood","negative" = "Negative mood"))) +
  623. scale_color_manual(name = "",values = c("#ffc000", "#ff2f92", "#404A80"),labels = c("Unrelated (UR) pairs","Weakly related (WR) pairs","Closely related (CR) pairs")) +
  624. scale_x_continuous(name = "Block progression",breaks = c(1,2,3,4,5,6,7,8),guide = "prism_offset") +
  625. scale_y_reverse(name = "Predicted mean N400 amplitude (µV)",breaks = c(1,0, -1, -2, -3),limits = c(1, -3),guide = "prism_offset") +
  626. theme_classic2() +
  627. scale_shape_prism() +
  628. theme(text = element_text(size = 20, family = "Helvetica", color = "black"),
  629. axis.text = element_text(size = 18),
  630. axis.line = element_line(size = 0.5),
  631. strip.text = element_text(size = 20),
  632. strip.background = element_blank(),
  633. legend.position = "bottom",
  634. legend.text = element_text(size = 20))
  636. # by type
  637. ggplot(emm_plot2,aes(x = film1, y = emmean, color = mood)) +
  638. geom_line(aes(group = mood), linewidth = 1) +
  639. geom_point(size = 2) +
  640. geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL),width = 0.1) +
  641. facet_wrap(.~type, ncol =3, labeller = as_labeller(c("UN" = "Unrelated \npairs", "WR" = "Weakly \nrelated pairs", "CR" = "Closely \nrelated pairs"))) +
  642. scale_color_manual(name = "",values = c("firebrick2","darkblue"),labels = c("Positive mood","Negative mood")) +
  643. scale_x_continuous(name = "Number of films watched",breaks = c(1,2,3,4,5,6,7, 8),guide = "prism_offset") +
  644. scale_y_reverse(name = "Predicted mean N400 amplitude (µV)",breaks = c(1,0, -1, -2, -3),limits = c(1, -3),guide = "prism_offset") +
  645. theme_classic2() +
  646. scale_shape_prism() +
  647. theme(text = element_text(size = 20, family = "Helvetica", color = "black"),
  648. axis.text = element_text(size = 18),
  649. axis.line = element_line(size = 0.5),
  650. strip.text = element_text(size = 20),
  651. strip.background = element_blank(),
  652. legend.position = "bottom",
  653. legend.text = element_text(size = 20))
  655. ggsave(filename ="L2_predicted_films.png", plot = last_plot(), width = 15,height = 14, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  659. #### _________________ FCs+Cs+CPs+Ps _________________ ####
  660. #### LMMs: N400 Polish [L1] ####
  661. # df2 --- N400 Polish
  662. df2 <- df %>% subset(chan %in% FC_C_CP_P_3 & time >= 300 & time <=500 & language %in% "Polish") %>% droplevels() %>%
  663. dplyr::group_by(mood, type, ptp, word,chan1) %>%
  664. dplyr::summarise(mean_amp = mean(amp))
  666. # contrasts
  667. df2$mood <- factor(df2$mood, levels=c("positive", "negative"))
  668. df2$type <- factor(df2$type, levels=c("UN", "WR", "CR"))
  669. contrasts(df2$mood) <- contr.sum(levels(df2$mood))
  670. df2$type <- relevel(df2$type, ref = "UN")
  671. contrasts(df2$chan1) <- contr.sum(levels(df2$chan1))
  672. df2$chan1 <- relevel(df2$chan1, ref = "FC")
  674. summary(df2)
  675. # lmer
  676. N4_L1_chan1_model0 <- lmer(mean_amp ~ mood*type*chan1 + (1|ptp) + (1|word), data=df2)
  677. N4_L1_chan1_model1 <- lmer(mean_amp ~ mood*type*chan1 + (1+type+mood|ptp) + (1+type+mood|word), data=df2)
  678. N4_L1_chan1_model2 <- lmer(mean_amp ~ mood*type*chan1 + (1+type|ptp) + (1|word), data=df2)
  680. summary(N4_L1_chan1_model2)
  681. anova(N4_L1_chan1_model2)
  682. anova(N4_L1_chan1_model0, N4_L1_chan1_model2)
  684. ' Estimate Std. Error df t value Pr(>|t|)
  685. (Intercept) -2.496e+00 3.490e-01 7.934e+01 -7.151 3.77e-10 ***
  686. mood1 2.019e-01 9.365e-02 2.932e+04 2.156 0.03107 *
  687. typeWR 4.015e-01 1.490e-01 1.834e+02 2.695 0.00769 **
  688. typeCR 1.118e+00 1.888e-01 7.819e+01 5.920 8.12e-08 ***
  689. chan1C -2.048e-01 3.587e-01 1.126e+02 -0.571 0.56916
  690. chan1CP 8.099e-01 3.587e-01 1.126e+02 2.258 0.02587 *
  691. chan1P 2.582e+00 3.587e-01 1.126e+02 7.200 7.22e-11 ***
  692. mood1:typeWR -2.484e-01 1.322e-01 2.932e+04 -1.879 0.06027 .
  693. mood1:typeCR -1.087e-01 1.326e-01 2.933e+04 -0.819 0.41267
  694. mood1:chan1C -6.886e-02 1.324e-01 2.930e+04 -0.520 0.60303
  695. mood1:chan1CP -1.492e-01 1.324e-01 2.930e+04 -1.127 0.25968
  696. mood1:chan1P -1.581e-01 1.324e-01 2.930e+04 -1.194 0.23236
  697. typeWR:chan1C 8.896e-02 1.869e-01 2.930e+04 0.476 0.63414
  698. typeCR:chan1C 4.866e-02 1.875e-01 2.930e+04 0.259 0.79527
  699. typeWR:chan1CP -7.093e-02 1.869e-01 2.930e+04 -0.379 0.70436
  700. typeCR:chan1CP 1.340e-01 1.875e-01 2.930e+04 0.715 0.47486
  701. typeWR:chan1P 1.534e-01 1.869e-01 2.930e+04 0.821 0.41176
  702. typeCR:chan1P 9.579e-02 1.875e-01 2.930e+04 0.511 0.60950
  703. mood1:typeWR:chan1C 1.309e-01 1.869e-01 2.930e+04 0.700 0.48377
  704. mood1:typeCR:chan1C 1.319e-01 1.875e-01 2.930e+04 0.703 0.48180
  705. mood1:typeWR:chan1CP 3.135e-01 1.869e-01 2.930e+04 1.677 0.09351 .
  706. mood1:typeCR:chan1CP 1.788e-01 1.875e-01 2.930e+04 0.953 0.34045
  707. mood1:typeWR:chan1P 3.742e-01 1.869e-01 2.930e+04 2.002 0.04529 *
  708. mood1:typeCR:chan1P 1.226e-01 1.875e-01 2.930e+04 0.654 0.51322 '
  710. summary(rePCA(N4_L1_chan1_model2)) # singular fit? ==> relative Principal Component Analysis
  712. relgrad <- with(N4_L1_chan1_model2@optinfo$derivs,solve(Hessian,gradient)) # convergence problem?
  713. max(abs(relgrad)) # <.001? ==> the model is close to convergence
  715. plot_model(N4_L1_chan1_model2) # plot the model
  716. plot(N4_L1_chan1_model2) # reasonably equal amount of spread?
  717. hist(residuals(N4_L1_chan1_model2)) # more or less normally distributed?
  718. qqnorm(residuals(N4_L1_chan1_model2)) # reasonably aligned? '/'
  719. #tab_model(N4_L1_chan1_model2,transform = NULL, show.est=TRUE, show.ci=TRUE, show.se=TRUE, show.df=TRUE, show.stat=TRUE, wrap.labels=2, df.method = "satterthwaite", title = "N400 Polish (L1)", file = "N400_L1_model_specs.html")
  721. # POST-HOCS
  722. # mood
  723. Polish_chan1_fixed_mood <- emmeans(N4_L1_chan1_model2, pairwise ~ mood,adjust = "bonferroni")
  724. print(Polish_chan1_fixed_mood)
  725. plot(Polish_chan1_fixed_mood)
  727. ' mood emmean SE df asymp.LCL asymp.UCL
  728. positive -1.06 0.271 Inf -1.59 -0.531
  729. negative -1.25 0.271 Inf -1.78 -0.717'
  731. ' contrast estimate SE df z.ratio p.value
  732. positive - negative 0.186 0.0541 Inf 3.446 0.0006'
  734. # type
  735. Polish_chan1_fixed_type <- emmeans(N4_L1_chan1_model2, pairwise ~ type,adjust = "bonferroni")
  736. print(Polish_chan1_fixed_type)
  737. plot(Polish_chan1_fixed_type)
  739. ' type emmean SE df asymp.LCL asymp.UCL
  740. UN -1.699 0.271 Inf -2.23 -1.1671
  741. WR -1.254 0.276 Inf -1.79 -0.7142
  742. CR -0.511 0.291 Inf -1.08 0.0586'
  744. ' contrast estimate SE df z.ratio p.value
  745. UN - WR -0.444 0.0953 Inf -4.662 <.0001
  746. UN - CR -1.187 0.1500 Inf -7.923 <.0001
  747. WR - CR -0.743 0.1290 Inf -5.747 <.0001'
  749. # mood x type x chan1 [1]
  750. Polish_int_mood_chan1_type1 <- emmeans(N4_L1_chan1_model2, pairwise ~ mood|type,by="chan1",adjust = "bonferroni")
  751. print(Polish_int_mood_chan1_type1)
  752. plot(Polish_int_mood_chan1_type1)
  754. '
  755. # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> FC
  756. type = UN, chan1 = FC:
  757. contrast estimate SE df z.ratio p.value
  758. positive - negative 0.4039 0.187 Inf 2.156 0.0311
  760. type = WR, chan1 = FC:
  761. contrast estimate SE df z.ratio p.value
  762. positive - negative -0.0930 0.187 Inf -0.498 0.6184
  764. type = CR, chan1 = FC:
  765. contrast estimate SE df z.ratio p.value
  766. positive - negative 0.1865 0.188 Inf 0.993 0.3208
  768. # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> C
  770. type = UN, chan1 = C:
  771. contrast estimate SE df z.ratio p.value
  772. positive - negative 0.2662 0.187 Inf 1.421 0.1553
  774. type = WR, chan1 = C:
  775. contrast estimate SE df z.ratio p.value
  776. positive - negative 0.0311 0.187 Inf 0.167 0.8677
  778. type = CR, chan1 = C:
  779. contrast estimate SE df z.ratio p.value
  780. positive - negative 0.3126 0.188 Inf 1.664 0.0961
  782. # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> CP
  783. type = UN, chan1 = CP:
  784. contrast estimate SE df z.ratio p.value
  785. positive - negative 0.1054 0.187 Inf 0.563 0.5737
  787. type = WR, chan1 = CP:
  788. contrast estimate SE df z.ratio p.value
  789. positive - negative 0.2356 0.187 Inf 1.262 0.2069
  791. type = CR, chan1 = CP:
  792. contrast estimate SE df z.ratio p.value
  793. positive - negative 0.2456 0.188 Inf 1.307 0.1912
  795. # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> P
  797. type = UN, chan1 = P:
  798. contrast estimate SE df z.ratio p.value
  799. positive - negative 0.0876 0.187 Inf 0.468 0.6400
  801. type = WR, chan1 = P:
  802. contrast estimate SE df z.ratio p.value
  803. positive - negative 0.3392 0.187 Inf 1.817 0.0692
  805. type = CR, chan1 = P:
  806. contrast estimate SE df z.ratio p.value
  807. positive - negative 0.1155 0.188 Inf 0.615 0.5388'
  809. # mood x type x chan1 [2]
  810. Polish_int_mood_chan1_type2 <- emmeans(N4_L1_chan1_model2, pairwise ~ type|mood,by="chan1",adjust = "bonferroni")
  811. print(Polish_int_mood_chan1_type2)
  812. plot(Polish_int_mood_chan1_type2)
  815. '# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> FC
  816. mood = positive, chan1 = FC:
  817. contrast estimate SE df z.ratio p.value
  818. UN - WR -0.153 0.199 Inf -0.770 1.0000
  819. UN - CR -1.009 0.231 Inf -4.373 <.0001
  820. WR - CR -0.856 0.218 Inf -3.930 0.0003
  822. mood = negative, chan1 = FC:
  823. contrast estimate SE df z.ratio p.value
  824. UN - WR -0.650 0.200 Inf -3.256 0.0034
  825. UN - CR -1.226 0.231 Inf -5.315 <.0001
  826. WR - CR -0.577 0.218 Inf -2.650 0.0241'
  828. '# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> C
  829. mood = positive, chan1 = C:
  830. contrast estimate SE df z.ratio p.value
  831. UN - WR -0.373 0.199 Inf -1.876 0.1819
  832. UN - CR -1.190 0.231 Inf -5.155 <.0001
  833. WR - CR -0.817 0.218 Inf -3.750 0.0005
  835. mood = negative, chan1 = C:
  836. contrast estimate SE df z.ratio p.value
  837. UN - WR -0.608 0.200 Inf -3.046 0.0070
  838. UN - CR -1.143 0.231 Inf -4.955 <.0001
  839. WR - CR -0.535 0.218 Inf -2.460 0.0417'
  842. '# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> CP
  843. mood = positive, chan1 = CP:
  844. contrast estimate SE df z.ratio p.value
  845. UN - WR -0.396 0.199 Inf -1.991 0.1396
  846. UN - CR -1.322 0.231 Inf -5.728 <.0001
  847. WR - CR -0.926 0.218 Inf -4.252 0.0001
  849. mood = negative, chan1 = CP:
  850. contrast estimate SE df z.ratio p.value
  851. UN - WR -0.265 0.200 Inf -1.330 0.5507
  852. UN - CR -1.182 0.231 Inf -5.121 <.0001
  853. WR - CR -0.916 0.218 Inf -4.211 0.0001'
  855. '# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> P
  856. mood = positive, chan1 = P:
  857. contrast estimate SE df z.ratio p.value
  858. UN - WR -0.681 0.199 Inf -3.425 0.0018
  859. UN - CR -1.228 0.231 Inf -5.319 <.0001
  860. WR - CR -0.547 0.218 Inf -2.510 0.0362
  862. mood = negative, chan1 = P:
  863. contrast estimate SE df z.ratio p.value
  864. UN - WR -0.429 0.200 Inf -2.150 0.0947
  865. UN - CR -1.200 0.231 Inf -5.199 <.0001
  866. WR - CR -0.771 0.218 Inf -3.542 0.0012'
  869. # observed power
  870. vc <- as.data.frame(VarCorr(N4_L1_chan1_model2))
  871. sigma_ptp <- vc$vcov[vc$grp == "ptp" & vc$var1 == "(Intercept)"][1] # Participant intercept variance (scalar)
  872. sigma_resid <- vc$vcov[vc$grp == "Residual"][1] # Residual variance (scalar)
  873. print(icc_ptp <- sigma_ptp / (sigma_ptp + sigma_resid))
  875. smpsize_lmm(
  876. eff.size = 0.25, # medium effect size
  877. df.n = 6, # (3-1)*(2-1)*(4-1) -- 3x2x4 design
  878. power = .19, # observed power for 40 items per condition
  879. sig.level = 0.05,
  880. k = 32, # participants
  881. n = 960, # trials per participant (40*6*4)
  882. icc = 0.05919195)
  885. #### LMMs: N400 English [L2] ####
  886. # df3 --- N400 English
  887. df3 <- df %>% subset(chan %in% FC_C_CP_P_3 & time >= 300 & time <=500 & language %in% "English") %>% droplevels() %>%
  888. dplyr::group_by(mood, type, ptp, word,chan1) %>%
  889. dplyr::summarise(mean_amp = mean(amp))
  891. # contrasts
  892. df3$mood <- factor(df3$mood, levels=c("positive", "negative"))
  893. df3$type <- factor(df3$type, levels=c("UN", "WR", "CR"))
  894. contrasts(df3$mood) <- contr.sum(levels(df3$mood))
  895. df3$type <- relevel(df3$type, ref = "UN")
  896. contrasts(df3$chan1) <- contr.sum(levels(df3$chan1))
  897. df3$chan1 <- relevel(df3$chan1, ref = "FC")
  900. # lmer
  901. N4_L2_chan1_model0 <- lmer(mean_amp ~ mood*type*chan1 + (1|ptp) + (1|word) + (1|ptp:chan1), data=df3)
  902. N4_L2_chan1_model1 <- lmer(mean_amp ~ mood*type*chan1 + (1+type+mood|ptp) + (1+type+mood|word) + (1|ptp:chan1), data=df3)
  903. N4_L2_chan1_model2 <- lmer(mean_amp ~ mood*type*chan1 + (1+mood|ptp) + (1|word) + (1|ptp:chan1), data=df3)
  905. summary(N4_L2_chan1_model2)
  906. anova(N4_L1_chan1_model2)
  907. anova(N4_L1_chan1_model0, N4_L1_chan1_model2)
  910. ' Estimate Std. Error df t value Pr(>|t|)
  911. (Intercept) -2.281e+00 3.554e-01 9.589e+01 -6.418 5.25e-09 ***
  912. mood1 9.539e-02 9.961e-02 7.059e+02 0.958 0.338613
  913. typeWR 4.646e-01 1.308e-01 2.900e+04 3.552 0.000383 ***
  914. typeCR 1.250e+00 1.309e-01 2.900e+04 9.549 < 2e-16 ***
  915. chan1C -4.528e-01 3.932e-01 1.084e+02 -1.152 0.252023
  916. chan1CP 3.809e-01 3.932e-01 1.084e+02 0.969 0.334859
  917. chan1P 2.264e+00 3.932e-01 1.084e+02 5.757 8.08e-08 ***
  918. mood1:typeWR -1.318e-01 1.308e-01 2.899e+04 -1.008 0.313399
  919. mood1:typeCR 6.028e-02 1.309e-01 2.900e+04 0.461 0.645063
  920. mood1:chan1C -2.149e-02 1.309e-01 2.898e+04 -0.164 0.869553
  921. mood1:chan1CP 4.181e-02 1.309e-01 2.898e+04 0.319 0.749373
  922. mood1:chan1P 1.071e-01 1.309e-01 2.898e+04 0.818 0.413154
  923. typeWR:chan1C 1.100e-01 1.849e-01 2.898e+04 0.595 0.551902
  924. typeCR:chan1C 1.751e-01 1.850e-01 2.898e+04 0.946 0.344058
  925. typeWR:chan1CP 2.923e-01 1.849e-01 2.898e+04 1.581 0.113952
  926. typeCR:chan1CP 2.085e-01 1.850e-01 2.898e+04 1.127 0.259682
  927. typeWR:chan1P 1.327e-01 1.849e-01 2.898e+04 0.718 0.473061
  928. typeCR:chan1P -1.564e-01 1.850e-01 2.898e+04 -0.845 0.397996
  929. mood1:typeWR:chan1C 1.162e-01 1.849e-01 2.898e+04 0.629 0.529545
  930. mood1:typeCR:chan1C 8.101e-02 1.850e-01 2.898e+04 0.438 0.661499
  931. mood1:typeWR:chan1CP 1.231e-01 1.849e-01 2.898e+04 0.666 0.505468
  932. mood1:typeCR:chan1CP 7.412e-02 1.850e-01 2.898e+04 0.401 0.688675
  933. mood1:typeWR:chan1P 1.108e-03 1.849e-01 2.898e+04 0.006 0.995220
  934. mood1:typeCR:chan1P -2.562e-02 1.850e-01 2.898e+04 -0.138 0.889862 '
  936. #POST-HOCS
  938. # mood
  939. English_chan1_fixed_mood <- emmeans(N4_L2_chan1_model2, pairwise ~ mood,adjust = "bonferroni")
  940. print(English_chan1_fixed_mood)
  941. plot(English_chan1_fixed_mood)
  943. ' mood emmean SE df asymp.LCL asymp.UCL
  944. positive -0.964 0.250 Inf -1.45 -0.473
  945. negative -1.232 0.274 Inf -1.77 -0.694'
  947. ' contrast estimate SE df z.ratio p.value
  948. positive - negative 0.268 0.0909 Inf 2.952 0.0032'
  950. # type
  951. Polish_chan1_fixed_type <- emmeans(N4_L2_chan1_model2, pairwise ~ type,adjust = "bonferroni")
  952. print(Polish_chan1_fixed_type)
  953. plot(Polish_chan1_fixed_type)
  955. ' type emmean SE df asymp.LCL asymp.UCL
  956. UN -1.733 0.261 Inf -2.245 -1.221
  957. WR -1.135 0.261 Inf -1.647 -0.622
  958. CR -0.426 0.261 Inf -0.939 0.086'
  960. ' contrast estimate SE df z.ratio p.value
  961. UN - WR -0.598 0.0655 Inf -9.135 <.0001
  962. UN - CR -1.307 0.0655 Inf -19.937 <.0001
  963. WR - CR -0.708 0.0655 Inf -10.817 <.0001'
  965. #### PLOTS: N400 ####
  967. #### Polish -- N400: type x mood by chan1 [POSITIVE MOOD] ####
  968. plotstats2a <- df %>% subset(chan %in% FC_C_CP_P_3 & time >= -200 & time <= 800 & language %in% "Polish" & mood %in% "positive") %>% droplevels() %>%
  969. dplyr::group_by(time, type, chan1) %>%
  970. dplyr::summarise(mean_amp = mean(amp),CIlower = Rmisc::CI(amp, ci = 0.95)["lower"],CIupper = Rmisc::CI(amp, ci = 0.95)["upper"])
  971. plotstats2a <- plotstats2a %>% group_by(type, chan1) %>% mutate(baseline = mean_amp[time == 0],mean_amp = mean_amp - baseline, CIlower = CIlower - baseline,CIupper = CIupper - baseline) %>% dplyr::select(-baseline)
  972. plotstats2a <- plotstats2a %>% arrange(time) %>% group_by(type, chan1) %>% mutate(mean_amp = zoo::rollmean(mean_amp, k = 10, fill = NA, align = "center"), CIlower = zoo::rollmean(CIlower, k = 10, fill = NA, align = "center"), CIupper = zoo::rollmean(CIupper, k = 17, fill = NA, align = "center"))
  975. ggplot(plotstats2a, aes(time, mean_amp, color = type, group = type)) +
  976. geom_ribbon(aes(ymin = CIlower, ymax = CIupper, fill = type),
  977. alpha = 0.2, linetype = 0) +
  978. #facet_wrap(.~mood, ncol =2, labeller = as_labeller(c("positive" = "Positive mood", "negative" = "Negative mood"))) +
  979. facet_wrap(.~chan1, ncol=4)+
  980. geom_line(size = 0.75) +
  981. guides(fill = "none") +
  982. labs(x = "Time (ms)", y = expression(paste("Amplitude (", mu, "V)")), colour = "") +
  983. scale_shape_prism() +
  984. #theme_prism() +
  985. scale_y_reverse(breaks = c( 3,2,1,0, -1, -2, -3), limits = c(3,-3.7), guide = "prism_offset") +
  986. scale_x_continuous(breaks = c(-100, 0, 100, 300, 500, 700), guide = "prism_offset") +
  987. theme_classic2()+
  988. geom_vline(xintercept = 0, linetype = "dotted", alpha = 0.5) +
  989. geom_hline(yintercept = 0, linetype = "dotted", alpha = 0.5) +
  990. geom_vline(xintercept = 300, linetype = "solid", alpha = 1, color = "black") +
  991. geom_vline(xintercept = 500, linetype = "solid", alpha = 1, color = "black") +
  992. theme(text = element_text(size = 20, color = "black", family = "Helvetica"),
  993. axis.line.x = element_line(colour = "black", size = .5),
  994. axis.line.y = element_line(colour = "black", size = .5),
  995. axis.text = element_text(size = 18, color = "black", family = "Helvetica"),
  996. strip.text.x.top = element_text(size = 20, color = "black", family = "Helvetica"),
  997. plot.title = element_text(hjust = 0, family = "Helvetica"),
  998. legend.position = "bottom",
  999. legend.title = element_text(size = 20),
  1000. legend.text = element_text(size = 20)) +
  1001. scale_fill_manual(values = c("#ffc000","#ff2f92","#404A80")) +
  1002. scale_color_manual(name = "", values = c("#ffc000","#ff2f92","#404A80"), labels = c("Unrelated (UR) pairs", "Weakly related (WR) pairs", "Closely related (CR) pairs"))+
  1003. guides(colour = guide_legend(override.aes = list(linetype = 1, shape = 16, size = 5)))
  1005. ggsave(filename ="L1_negative_chan1.png", plot = last_plot(), width = 30,height = 12, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  1008. #### Polish -- N400: type x mood by chan1 [NEGATIVE MOOD] ####
  1009. plotstats2b <- df %>% subset(chan %in% FC_C_CP_P_3 & time >= -200 & time <= 800 & language %in% "Polish" & mood %in% "negative") %>% droplevels() %>%
  1010. dplyr::group_by(time, type, chan1) %>%
  1011. dplyr::summarise(mean_amp = mean(amp),CIlower = Rmisc::CI(amp, ci = 0.95)["lower"],CIupper = Rmisc::CI(amp, ci = 0.95)["upper"])
  1012. plotstats2b <- plotstats2b %>% group_by(type, chan1) %>% mutate(baseline = mean_amp[time == 0],mean_amp = mean_amp - baseline, CIlower = CIlower - baseline,CIupper = CIupper - baseline) %>% dplyr::select(-baseline)
  1013. plotstats2b <- plotstats2b %>% arrange(time) %>% group_by(type, chan1) %>% mutate(mean_amp = zoo::rollmean(mean_amp, k = 10, fill = NA, align = "center"), CIlower = zoo::rollmean(CIlower, k = 10, fill = NA, align = "center"), CIupper = zoo::rollmean(CIupper, k = 17, fill = NA, align = "center"))
  1015. ggplot(plotstats2b, aes(time, mean_amp, color = type, group = type)) +
  1016. geom_ribbon(aes(ymin = CIlower, ymax = CIupper, fill = type),
  1017. alpha = 0.2, linetype = 0) +
  1018. #facet_wrap(.~mood, ncol =2, labeller = as_labeller(c("positive" = "Positive mood", "negative" = "Negative mood"))) +
  1019. facet_wrap(.~chan1, ncol=4)+
  1020. geom_line(size = 0.75) +
  1021. guides(fill = "none") +
  1022. labs(x = "Time (ms)", y = expression(paste("Amplitude (", mu, "V)")), colour = "") +
  1023. scale_shape_prism() +
  1024. #theme_prism() +
  1025. scale_y_reverse(breaks = c( 3,2,1,0, -1, -2, -3), limits = c(3,-3.7), guide = "prism_offset") +
  1026. scale_x_continuous(breaks = c(-100, 0, 100, 300, 500, 700), guide = "prism_offset") +
  1027. theme_classic2()+
  1028. geom_vline(xintercept = 0, linetype = "dotted", alpha = 0.5) +
  1029. geom_hline(yintercept = 0, linetype = "dotted", alpha = 0.5) +
  1030. geom_vline(xintercept = 300, linetype = "solid", alpha = 1, color = "black") +
  1031. geom_vline(xintercept = 500, linetype = "solid", alpha = 1, color = "black") +
  1032. theme(text = element_text(size = 20, color = "black", family = "Helvetica"),
  1033. axis.line.x = element_line(colour = "black", size = .5),
  1034. axis.line.y = element_line(colour = "black", size = .5),
  1035. axis.text = element_text(size = 18, color = "black", family = "Helvetica"),
  1036. strip.text.x.top = element_text(size = 20, color = "black", family = "Helvetica"),
  1037. plot.title = element_text(hjust = 0, family = "Helvetica"),
  1038. legend.position = "bottom",
  1039. legend.title = element_text(size = 20),
  1040. legend.text = element_text(size = 20)) +
  1041. scale_fill_manual(values = c("#ffc000","#ff2f92","#404A80")) +
  1042. scale_color_manual(name = "", values = c("#ffc000","#ff2f92","#404A80"), labels = c("Unrelated (UR) pairs", "Weakly related (WR) pairs", "Closely related (CR) pairs"))+
  1043. guides(colour = guide_legend(override.aes = list(linetype = 1, shape = 16, size = 5)))
  1045. ggsave(filename ="L1_negative_chan1.png", plot = last_plot(), width = 30,height = 12, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  1047. #### English -- N400: type x mood by chan1 [POSITIVE MOOD] ####
  1048. plotstats2d <- df %>% subset(chan %in% FC_C_CP_P_3 & time >= -200 & time <= 800 & language %in% "English" & mood %in% "positive") %>% droplevels() %>%
  1049. dplyr::group_by(time, type, chan1) %>%
  1050. dplyr::summarise(mean_amp = mean(amp),CIlower = Rmisc::CI(amp, ci = 0.95)["lower"],CIupper = Rmisc::CI(amp, ci = 0.95)["upper"])
  1051. plotstats2d <- plotstats2d %>% group_by(type, chan1) %>% mutate(baseline = mean_amp[time == 0],mean_amp = mean_amp - baseline, CIlower = CIlower - baseline,CIupper = CIupper - baseline) %>% dplyr::select(-baseline)
  1052. plotstats2d <- plotstats2d %>% arrange(time) %>% group_by(type, chan1) %>% mutate(mean_amp = zoo::rollmean(mean_amp, k = 10, fill = NA, align = "center"), CIlower = zoo::rollmean(CIlower, k = 10, fill = NA, align = "center"), CIupper = zoo::rollmean(CIupper, k = 17, fill = NA, align = "center"))
  1055. ggplot(plotstats2d, aes(time, mean_amp, color = type, group = type)) +
  1056. geom_ribbon(aes(ymin = CIlower, ymax = CIupper, fill = type),
  1057. alpha = 0.2, linetype = 0) +
  1058. #facet_wrap(.~mood, ncol =2, labeller = as_labeller(c("positive" = "Positive mood", "negative" = "Negative mood"))) +
  1059. facet_wrap(.~chan1, ncol=4)+
  1060. geom_line(size = 0.75) +
  1061. guides(fill = "none") +
  1062. labs(x = "Time (ms)", y = expression(paste("Amplitude (", mu, "V)")), colour = "") +
  1063. scale_shape_prism() +
  1064. #theme_prism() +
  1065. scale_y_reverse(breaks = c( 3,2,1,0, -1, -2, -3), limits = c(3,-3.7), guide = "prism_offset") +
  1066. scale_x_continuous(breaks = c(-100, 0, 100, 300, 500, 700), guide = "prism_offset") +
  1067. theme_classic2()+
  1068. geom_vline(xintercept = 0, linetype = "dotted", alpha = 0.5) +
  1069. geom_hline(yintercept = 0, linetype = "dotted", alpha = 0.5) +
  1070. geom_vline(xintercept = 300, linetype = "solid", alpha = 1, color = "black") +
  1071. geom_vline(xintercept = 500, linetype = "solid", alpha = 1, color = "black") +
  1072. theme(text = element_text(size = 20, color = "black", family = "Helvetica"),
  1073. axis.line.x = element_line(colour = "black", size = .5),
  1074. axis.line.y = element_line(colour = "black", size = .5),
  1075. axis.text = element_text(size = 18, color = "black", family = "Helvetica"),
  1076. strip.text.x.top = element_text(size = 20, color = "black", family = "Helvetica"),
  1077. plot.title = element_text(hjust = 0, family = "Helvetica"),
  1078. legend.position = "bottom",
  1079. legend.title = element_text(size = 20),
  1080. legend.text = element_text(size = 20)) +
  1081. scale_fill_manual(values = c("#ffc000","#ff2f92","#404A80")) +
  1082. scale_color_manual(name = "", values = c("#ffc000","#ff2f92","#404A80"), labels = c("Unrelated (UR) pairs", "Weakly related (WR) pairs", "Closely related (CR) pairs"))+
  1083. guides(colour = guide_legend(override.aes = list(linetype = 1, shape = 16, size = 5)))
  1085. ggsave(filename ="L2_positive_chan1.png", plot = last_plot(), width = 30,height = 12, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  1089. #### _________________ semantic vectors x language _________________ ####
  1091. df8 <- df %>%
  1092. dplyr::group_by(language, type, word) %>%
  1093. dplyr::summarise(vector = mean(vector))
  1096. # contrasts
  1097. df8$type <- factor(df8$type, levels=c("UN", "WR", "CR"))
  1098. df8$type <- relevel(df8$type, ref = "UN")
  1100. # lmer
  1101. vct_model0 <- lmer(vector ~ language*type + (1|word), data=df8)
  1102. vct_model1 <- lmer(vector ~ language*type + (1|word), data=df8)
  1104. ' Estimate Std. Error df t value Pr(>|t|)
  1105. (Intercept) 0.143665 0.009863 702.880235 14.566 < 2e-16 ***
  1106. languageEnglish -0.047342 0.013948 702.880235 -3.394 0.000727 ***
  1107. typeCR 0.331080 0.013520 471.999996 24.487 < 2e-16 ***
  1108. typeWR 0.215297 0.013520 471.999995 15.924 < 2e-16 ***
  1109. languageEnglish:typeCR 0.086150 0.019121 471.999995 4.506 8.36e-06 ***
  1110. languageEnglish:typeWR 0.019210 0.019121 471.999995 1.005 0.315573 '
  1112. summary(vct_model1)
  1113. anova(vct_model1)
  1114. anova(vct_model0, vct_model1)
  1116. summary(rePCA(vct_model1)) # singular fit? ==> relative Principal Component Analysis
  1118. relgrad <- with(vct_model1@optinfo$derivs,solve(Hessian,gradient)) # convergence problem?
  1119. max(abs(relgrad)) # <.001? ==> the model is close to convergence
  1121. plot_model(vct_model1) # plot the model
  1122. plot(vct_model1) # reasonably equal amount of spread?
  1123. hist(residuals(vct_model1)) # more or less normally distributed?
  1124. qqnorm(residuals(vct_model1)) # reasonably aligned? '/'
  1126. #POST-HOCS
  1127. # means
  1128. summary_table <- df8 %>%
  1129. dplyr::group_by(language, type) %>%
  1130. dplyr::summarize(
  1131. mean_vector = mean(vector, na.rm = TRUE),
  1132. n = sum(!is.na(vector)),
  1133. sd_vector = sd(vector, na.rm = TRUE),
  1134. se_vector = sd_vector / sqrt(n),
  1135. ci_lower = mean_vector - qt(0.975, df = n - 1) * se_vector,
  1136. ci_upper = mean_vector + qt(0.975, df = n - 1) * se_vector,
  1137. .groups = "drop"
  1138. )
  1140. print(summary_table)
  1142. ' language type mean_vector n sd_vector se_vector ci_lower ci_upper
  1143. <fct> <fct> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
  1144. 1 Polish UN 0.144 119 0.0832 0.00762 0.129 0.159
  1145. 2 Polish WR 0.359 119 0.114 0.0104 0.338 0.380
  1146. 3 Polish CR 0.475 119 0.115 0.0106 0.454 0.496
  1147. 4 English UN 0.0963 119 0.0713 0.00654 0.0834 0.109
  1148. 5 English WR 0.331 119 0.107 0.00980 0.311 0.350
  1149. 6 English CR 0.514 119 0.141 0.0129 0.488 0.539'
  1152. # language
  1153. fixed_lng <- emmeans(vct_model1, pairwise ~ language, adjust = "bonferroni")
  1154. print(fixed_lng)
  1155. plot(fixed_lng)
  1157. ' language emmean SE df lower.CL upper.CL
  1158. Polish 0.326 0.00603 236 0.314 0.338
  1159. English 0.314 0.00603 236 0.302 0.325'
  1161. ' contrast estimate SE df t.ratio p.value
  1162. Polish - English 0.0122 0.00852 236 1.434 0.1530'
  1164. # type
  1165. fixed_type <- emmeans(vct_model1, pairwise ~ type, adjust = "bonferroni")
  1166. print(fixed_type)
  1167. plot(fixed_type)
  1169. ' contrast estimate SE df t.ratio p.value
  1170. UN - WR -0.225 0.00956 472 -23.524 <.0001
  1171. UN - CR -0.374 0.00956 472 -39.136 <.0001
  1172. WR - CR -0.149 0.00956 472 -15.611 <.0001'
  1174. ' type emmean SE df lower.CL upper.CL
  1175. UN 0.120 0.00697 703 0.106 0.134
  1176. WR 0.345 0.00697 703 0.331 0.359
  1177. CR 0.494 0.00697 703 0.480 0.508'
  1179. # # language x type
  1180. # vct_lng_type1 <- emmeans(vct_model1, pairwise ~ language|type, adjust = "bonferroni")
  1181. # print(vct_lng_type1)
  1182. # plot(vct_lng_type1)
  1183. #
  1184. # 'type = UN:
  1185. # contrast estimate SE df t.ratio p.value
  1186. # Polish - English 0.0473 0.0139 703 3.394 0.0007
  1187. #
  1188. # type = WR:
  1189. # contrast estimate SE df t.ratio p.value
  1190. # Polish - English 0.0281 0.0139 703 2.017 0.0441
  1191. #
  1192. # type = CR:
  1193. # contrast estimate SE df t.ratio p.value
  1194. # Polish - English -0.0388 0.0139 703 -2.782 0.0055'
  1195. #
  1196. # 'type = UN:
  1197. # language emmean SE df lower.CL upper.CL
  1198. # Polish 0.1437 0.00986 703 0.124 0.163
  1199. # English 0.0963 0.00986 703 0.077 0.116
  1200. #
  1201. # type = WR:
  1202. # language emmean SE df lower.CL upper.CL
  1203. # Polish 0.3590 0.00986 703 0.340 0.378
  1204. # English 0.3308 0.00986 703 0.311 0.350
  1205. #
  1206. # type = CR:
  1207. # language emmean SE df lower.CL upper.CL
  1208. # Polish 0.4747 0.00986 703 0.455 0.494
  1209. # English 0.5136 0.00986 703 0.494 0.533'
  1210. #
  1211. # vct_lng_type2 <- emmeans(vct_model1, pairwise ~ type|language, adjust = "bonferroni")
  1212. # print(vct_lng_type2)
  1213. # plot(vct_lng_type2)
  1214. #
  1215. # 'language = Polish:
  1216. # contrast estimate SE df t.ratio p.value
  1217. # UN - WR -0.215 0.0135 472 -15.924 <.0001
  1218. # UN - CR -0.331 0.0135 472 -24.487 <.0001
  1219. # WR - CR -0.116 0.0135 472 -8.563 <.0001
  1220. #
  1221. # language = English:
  1222. # contrast estimate SE df t.ratio p.value
  1223. # UN - WR -0.235 0.0135 472 -17.345 <.0001
  1224. # UN - CR -0.417 0.0135 472 -30.859 <.0001
  1225. # WR - CR -0.183 0.0135 472 -13.514 <.0001'
  1226. #
  1227. # 'language = Polish:
  1228. # type emmean SE df lower.CL upper.CL
  1229. # UN 0.1437 0.00986 703 0.124 0.163
  1230. # WR 0.3590 0.00986 703 0.340 0.378
  1231. # CR 0.4747 0.00986 703 0.455 0.494
  1232. #
  1233. # language = English:
  1234. # type emmean SE df lower.CL upper.CL
  1235. # UN 0.0963 0.00986 703 0.077 0.116
  1236. # WR 0.3308 0.00986 703 0.311 0.350
  1237. # CR 0.5136 0.00986 703 0.494 0.533'
  1239. #### _________________ N400 x semantic vectors _________________ ####
  1240. #### N400 Polish [L1] ####
  1241. df6 <- df %>% subset(chan %in% FC_C_CP_P_3 & time >= 300 & time <=500 & language %in% "Polish") %>% droplevels() %>%
  1242. dplyr::group_by(mood, type, ptp, word,vector) %>%
  1243. dplyr::summarise(mean_amp = mean(amp))
  1245. # contrasts
  1246. df6$mood <- factor(df6$mood, levels=c("positive", "negative"))
  1247. df6$type <- factor(df6$type, levels=c("UN", "WR", "CR"))
  1248. contrasts(df6$mood) <- contr.sum(levels(df6$mood))
  1249. df6$type <- relevel(df6$type, ref = "UN")
  1251. # lmer
  1252. N4_L1_vct_model0 <- lmer(mean_amp ~ mood*type*vector + (1|ptp) + (1|word), data=df6)
  1253. N4_L1_vct_model1 <- lmer(mean_amp ~ mood*type*vector + (1+type+mood|ptp) + (1+type+mood|word), data=df6)
  1254. N4_L1_vct_model2 <- lmer(mean_amp ~ mood*type*vector + (1+type|ptp) + (1+mood|word), data=df6)
  1256. '(Intercept) -1.71230 0.29660 45.98929 -5.773 6.33e-07 ***
  1257. mood1 0.23508 0.13275 1101.16563 1.771 0.0769 .
  1258. typeWR 0.34981 0.26492 1153.40508 1.320 0.1869
  1259. typeCR 0.53566 0.34596 693.27999 1.548 0.1220
  1260. vector 0.09186 0.84098 2190.51093 0.109 0.9130
  1261. mood1:typeWR -0.17480 0.25189 1688.42684 -0.694 0.4878
  1262. mood1:typeCR -0.72402 0.30938 1450.31427 -2.340 0.0194 *
  1263. mood1:vector -0.86860 0.80161 1264.54526 -1.084 0.2788
  1264. typeWR:vector 0.20325 1.03078 2393.92563 0.197 0.8437
  1265. typeCR:vector 1.31299 1.03798 2120.10267 1.265 0.2060
  1266. mood1:typeWR:vector 0.88584 0.98317 1380.95736 0.901 0.3677
  1267. mood1:typeCR:vector 2.12366 0.98697 1222.05738 2.152 0.0316 * '
  1269. summary(N4_L1_vct_model2)
  1270. anova(N4_L1_vct_model2)
  1271. anova(N4_L1_vct_model0, N4_L1_vct_model2)
  1273. summary(rePCA(N4_L1_vct_model2)) # singular fit? ==> relative Principal Component Analysis
  1275. relgrad <- with(N4_L1_vct_model2@optinfo$derivs,solve(Hessian,gradient)) # convergence problem?
  1276. max(abs(relgrad)) # <.001? ==> the model is close to convergence
  1278. plot_model(N4_L1_vct_model2) # plot the model
  1279. plot(N4_L1_vct_model2) # reasonably equal amount of spread?
  1280. hist(residuals(N4_L1_vct_model2)) # more or less normally distributed?
  1281. qqnorm(residuals(N4_L1_vct_model2)) # reasonably aligned? '/'
  1283. #POST-HOCS
  1284. # mood x type
  1285. Polish_vct_int_mood_type1 <- emmeans(N4_L1_vct_model2, pairwise ~ mood|type, adjust = "bonferroni")
  1286. print(Polish_vct_int_mood_type1)
  1287. plot(Polish_vct_int_mood_type1)
  1289. 'type = UN:
  1290. contrast estimate SE df z.ratio p.value
  1291. positive - negative -0.0952 0.320 Inf -0.298 0.7659
  1293. type = WR:
  1294. contrast estimate SE df z.ratio p.value
  1295. positive - negative 0.1318 0.137 Inf 0.963 0.3356
  1297. type = CR:
  1298. contrast estimate SE df z.ratio p.value
  1299. positive - negative -0.1610 0.216 Inf -0.744 0.4567'
  1301. Polish_vct_int_mood_type2 <- emmeans(N4_L1_vct_model2, pairwise ~ type|mood, adjust = "bonferroni")
  1302. print(Polish_vct_int_mood_type2)
  1303. plot(Polish_vct_int_mood_type2)
  1305. 'mood = positive:
  1306. contrast estimate SE df z.ratio p.value
  1307. UN - WR -0.529 0.250 Inf -2.118 0.1024
  1308. UN - CR -0.930 0.302 Inf -3.081 0.0062
  1309. WR - CR -0.401 0.204 Inf -1.964 0.1485
  1311. mood = negative:
  1312. contrast estimate SE df z.ratio p.value
  1313. UN - WR -0.302 0.249 Inf -1.214 0.6740
  1314. UN - CR -0.996 0.300 Inf -3.321 0.0027
  1315. WR - CR -0.693 0.202 Inf -3.431 0.0018'
  1317. # mood x type x vector
  1318. emm_trends <- emtrends(N4_L1_vct_model2,specs = ~ mood * type,var = "vector")
  1319. summary(emm_trends)
  1321. ' mood type vector.trend SE df asymp.LCL asymp.UCL
  1322. positive UN -0.777 1.160 Inf -3.06 1.50
  1323. negative UN 0.960 1.160 Inf -1.31 3.23
  1324. positive WR 0.312 0.848 Inf -1.35 1.97
  1325. negative WR 0.278 0.824 Inf -1.34 1.89
  1326. positive CR 2.660 0.841 Inf 1.01 4.31 <- significant (does not include 0)
  1327. negative CR 0.150 0.826 Inf -1.47 1.77'
  1329. contrast(emm_trends,method = "pairwise",adjust = "holm", by ="mood")
  1331. 'mood = positive:
  1332. contrast estimate SE df z.ratio p.value
  1333. UN - WR -1.089 1.43 Inf -0.760 0.4471
  1334. UN - CR -3.437 1.44 Inf -2.388 0.0508
  1335. WR - CR -2.348 1.19 Inf -1.969 0.0980
  1337. mood = negative:
  1338. contrast estimate SE df z.ratio p.value
  1339. UN - WR 0.683 1.42 Inf 0.482 1.0000
  1340. UN - CR 0.811 1.43 Inf 0.569 1.0000
  1341. WR - CR 0.128 1.17 Inf 0.110 1.0000'
  1344. block_points2 <- quantile(df6$vector, probs = c(.05, .5, .95))
  1346. emm_plot2 <- emmeans(N4_L1_vct_model2,~ mood * type | vector,at = list(vector = block_points2))
  1347. emm_plot2 <- as.data.frame(emm_plot2)
  1349. emm_plot2$type <- factor(emm_plot2$type, levels=c("CR", "WR", "UN"))
  1351. ggplot(emm_plot2,aes(x = vector, y = emmean, color = type)) +
  1352. geom_line(aes(group = type), linewidth = 1) +
  1353. geom_point(size = 2) +
  1354. geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL),width = 0.1) +
  1355. facet_wrap(~ mood,ncol = 2,labeller = as_labeller(c("positive" = "Positive mood","negative" = "Negative mood"))) +
  1356. scale_color_manual(name = "",values = c("#ffc000", "#ff2f92", "#404A80"),labels = c("Unrelated (UR) pairs","Weakly related (WR) pairs","Closely related (CR) pairs")) +
  1357. #scale_x_continuous(name = "Block progression",breaks = c(1,2,3,4,5,6,7,8),guide = "prism_offset") +
  1358. scale_y_reverse(name = "Predicted mean N400 amplitude (µV)",breaks = c(1,0, -1, -2, -3, -4),limits = c(1, -4),guide = "prism_offset") +
  1359. theme_classic2() +
  1360. scale_shape_prism() +
  1361. theme(text = element_text(size = 20, family = "Helvetica", color = "black"),
  1362. axis.text = element_text(size = 18),
  1363. axis.line = element_line(size = 0.5),
  1364. strip.text = element_text(size = 20),
  1365. strip.background = element_blank(),
  1366. legend.position = "bottom",
  1367. legend.text = element_text(size = 20))
  1369. # by type
  1370. ggplot(emm_plot2,aes(x = vector, y = emmean, color = mood)) +
  1371. geom_line(aes(group = mood), linewidth = 1) +
  1372. geom_point(size = 2) +
  1373. geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL),width = 0.1) +
  1374. facet_wrap(.~type, ncol =3, labeller = as_labeller(c("UN" = "Unrelated \npairs", "WR" = "Weakly \nrelated pairs", "CR" = "Closely \nrelated pairs"))) +
  1375. scale_color_manual(name = "",values = c("firebrick2","darkblue"),labels = c("Positive mood","Negative mood")) +
  1376. scale_x_continuous(name = "Semantic similarity",breaks = c(0.0, 0.2, 0.4,0.6),guide = "prism_offset") +
  1377. scale_y_reverse(name = "Predicted mean N400 amplitude (µV)",breaks = c(1, 0, -1, -2, -3),limits = c(1, -3),guide = "prism_offset") +
  1378. theme_classic2() +
  1379. scale_shape_prism() +
  1380. theme(text = element_text(size = 20, family = "Helvetica", color = "black"),
  1381. axis.text = element_text(size = 18),
  1382. axis.line = element_line(size = 0.5),
  1383. strip.text = element_text(size = 20),
  1384. strip.background = element_blank(),
  1385. legend.position = "bottom",
  1386. legend.text = element_text(size = 20))
  1388. ggsave(filename ="L1_predicted_similarity.png", plot = last_plot(), width = 20,height = 14, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  1391. #### N400 English [L2] ####
  1392. df7 <- df %>% subset(chan %in% FC_C_CP_P_3 & time >= 300 & time <=500 & language %in% "English") %>% droplevels() %>%
  1393. dplyr::group_by(mood, type, ptp, word,vector) %>%
  1394. dplyr::summarise(mean_amp = mean(amp))
  1396. # contrasts
  1397. df7$mood <- factor(df7$mood, levels=c("positive", "negative"))
  1398. df7$type <- factor(df7$type, levels=c("UN", "WR", "CR"))
  1399. contrasts(df7$mood) <- contr.sum(levels(df7$mood))
  1400. df7$type <- relevel(df7$type, ref = "UN")
  1402. # lmer
  1403. N4_L2_vct_model0 <- lmer(mean_amp ~ mood*type*vector + (1|ptp) + (1|word), data=df7)
  1404. N4_L2_vct_model1 <- lmer(mean_amp ~ mood*type*vector + (1+type+mood|ptp) + (1+type+mood|word), data=df7)
  1405. N4_L2_vct_model2 <- lmer(mean_amp ~ mood*type*vector + (1+type|ptp) + (1+mood|word), data=df7)
  1407. summary(N4_L1_vct_model2)
  1408. anova(N4_L1_vct_model2)
  1409. anova(N4_L1_vct_model0, N4_L1_vct_model2)
  1411. ' Estimate Std. Error df t value Pr(>|t|)
  1412. (Intercept) -1.8010 0.2970 39.4702 -6.063 4.05e-07 ***
  1413. mood1 0.1056 0.1118 1053.5220 0.945 0.3449
  1414. typeWR 0.1565 0.2552 869.4058 0.613 0.5401
  1415. typeCR 0.7242 0.3014 609.7491 2.402 0.0166 *
  1416. vector 0.6812 0.9718 2277.0085 0.701 0.4834
  1417. mood1:typeWR -0.3553 0.2382 1891.8090 -1.491 0.1361
  1418. mood1:typeCR 0.3406 0.2729 1702.8740 1.248 0.2122
  1419. mood1:vector 0.2537 0.9304 1396.8918 0.273 0.7852
  1420. typeWR:vector 0.8672 1.1558 2590.3667 0.750 0.4531
  1421. typeCR:vector 0.5884 1.0876 2330.1993 0.541 0.5885
  1422. mood1:typeWR:vector 0.6686 1.1107 1597.5895 0.602 0.5473
  1423. mood1:typeCR:vector -0.6980 1.0416 1451.2130 -0.670 0.5029 '
  1425. summary(rePCA(N4_L1_vct_model2)) # singular fit? ==> relative Principal Component Analysis
  1427. relgrad <- with(N4_L1_vct_model2@optinfo$derivs,solve(Hessian,gradient)) # convergence problem?
  1428. max(abs(relgrad)) # <.001? ==> the model is close to convergence
  1430. plot_model(N4_L1_vct_model2) # plot the model
  1431. plot(N4_L1_vct_model2) # reasonably equal amount of spread?
  1432. hist(residuals(N4_L1_vct_model2)) # more or less normally distributed?
  1433. qqnorm(residuals(N4_L1_vct_model2)) # reasonably aligned? '/'
  1435. #POST-HOCS
  1437. # type
  1438. English_vct_fixed_type <- emmeans(N4_L1_vct_model2, pairwise ~ type,adjust = "bonferroni")
  1439. print(English_vct_fixed_type)
  1440. plot(English_vct_fixed_type)
  1442. 'contrast estimate SE df z.ratio p.value
  1443. UN - WR -0.416 0.181 Inf -2.301 0.0642
  1444. UN - CR -0.963 0.232 Inf -4.145 0.0001
  1445. WR - CR -0.547 0.160 Inf -3.423 0.0019'
  1447. ' type emmean SE df asymp.LCL asymp.UCL
  1448. UN -1.682 0.311 Inf -2.29 -1.073
  1449. WR -1.266 0.276 Inf -1.81 -0.726
  1450. CR -0.719 0.303 Inf -1.31 -0.127'
  1452. block_points2 <- quantile(df7$vector, probs = c(.05, .5, .95))
  1454. emm_plot2 <- emmeans(N4_L2_vct_model2,~ mood * type | vector,at = list(vector = block_points2))
  1455. emm_plot2 <- as.data.frame(emm_plot2)
  1457. emm_plot2$type <- factor(emm_plot2$type, levels=c("CR", "WR", "UN"))
  1459. ggplot(emm_plot2,aes(x = vector, y = emmean, color = type)) +
  1460. geom_line(aes(group = type), linewidth = 1) +
  1461. geom_point(size = 2) +
  1462. geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL),width = 0.1) +
  1463. facet_wrap(~ mood,ncol = 2,labeller = as_labeller(c("positive" = "Positive mood","negative" = "Negative mood"))) +
  1464. scale_color_manual(name = "",values = c("#ffc000", "#ff2f92", "#404A80"),labels = c("Unrelated (UR) pairs","Weakly related (WR) pairs","Closely related (CR) pairs")) +
  1465. #scale_x_continuous(name = "Block progression",breaks = c(1,2,3,4,5,6,7,8),guide = "prism_offset") +
  1466. scale_y_reverse(name = "Predicted mean N400 amplitude (µV)",breaks = c(1,0, -1, -2, -3, -4),limits = c(1, -4),guide = "prism_offset") +
  1467. theme_classic2() +
  1468. scale_shape_prism() +
  1469. theme(text = element_text(size = 20, family = "Helvetica", color = "black"),
  1470. axis.text = element_text(size = 18),
  1471. axis.line = element_line(size = 0.5),
  1472. strip.text = element_text(size = 20),
  1473. strip.background = element_blank(),
  1474. legend.position = "bottom",
  1475. legend.text = element_text(size = 20))
  1477. # by type
  1478. ggplot(emm_plot2,aes(x = vector, y = emmean, color = mood)) +
  1479. geom_line(aes(group = mood), linewidth = 1) +
  1480. geom_point(size = 2) +
  1481. geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL),width = 0.1) +
  1482. facet_wrap(.~type, ncol =3, labeller = as_labeller(c("UN" = "Unrelated \npairs", "WR" = "Weakly \nrelated pairs", "CR" = "Closely \nrelated pairs"))) +
  1483. scale_color_manual(name = "",values = c("firebrick2","darkblue"),labels = c("Positive mood","Negative mood")) +
  1484. scale_x_continuous(name = "Semantic similarity",breaks = c(0.0, 0.2, 0.4,0.6),guide = "prism_offset") +
  1485. scale_y_reverse(name = "Predicted mean N400 amplitude (µV)",breaks = c(1, 0, -1, -2, -3),limits = c(1, -3),guide = "prism_offset") +
  1486. theme_classic2() +
  1487. scale_shape_prism() +
  1488. theme(text = element_text(size = 20, family = "Helvetica", color = "black"),
  1489. axis.text = element_text(size = 18),
  1490. axis.line = element_line(size = 0.5),
  1491. strip.text = element_text(size = 20),
  1492. strip.background = element_blank(),
  1493. legend.position = "bottom",
  1494. legend.text = element_text(size = 20))
  1496. ggsave(filename ="L2_predicted_similarity.png", plot = last_plot(), width = 20,height = 14, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  1501. #### _________________ correlations _________________ ####
  1503. df7 <- df %>% subset(chan %in% FC_C_CP_P_3 & time >= 300 & time <=500) %>% droplevels() %>%
  1504. dplyr::group_by(mood, type, language, word, relatedness, vector) %>%
  1505. dplyr::summarise(mean_amp = mean(amp))
  1507. df7$type <- factor(df7$type, levels=c("UN", "WR", "CR"))
  1508. df7$mood <- factor(df7$mood, levels=c("positive", "negative"))
  1509. df7$language <- factor(df7$language, levels=c("Polish", "English"))
  1511. df7Pl <- df7[df7$language=="Polish",]
  1512. df7En <- df7[df7$language=="English",]
  1514. df7PlPos <- df7Pl[df7Pl$mood=="positive",]
  1515. df7PlNeg <- df7Pl[df7Pl$mood=="negative",]
  1517. df7EnPos <- df7En[df7En$mood=="positive",]
  1518. df7EnNeg <- df7En[df7En$mood=="negative",]
  1520. cor.test(df7$mean_amp, df7$relatedness,method = "pearson")
  1521. cor.test(df7PlPos$mean_amp, df7PlPos$relatedness,method = "pearson")
  1522. cor.test(df7PlNeg$mean_amp, df7PlNeg$relatedness,method = "pearson")
  1523. cor.test(df7EnPos$mean_amp, df7EnPos$relatedness,method = "pearson")
  1524. cor.test(df7EnNeg$mean_amp, df7EnNeg$relatedness,method = "pearson")
  1526. cor.test(df7$mean_amp, df7$vector,method = "pearson")
  1527. cor.test(df7PlPos$mean_amp, df7PlPos$vector,method = "pearson")
  1528. cor.test(df7PlNeg$mean_amp, df7PlNeg$vector,method = "pearson")
  1529. cor.test(df7EnPos$mean_amp, df7EnPos$vector,method = "pearson")
  1530. cor.test(df7EnNeg$mean_amp, df7EnNeg$vector,method = "pearson")
  1532. cor.test(df7$vector, df7$relatedness,method = "pearson")
  1535. # N400 x semantic relatedness ratings
  1536. ggplot(df7, aes(x = mean_amp, y = relatedness)) +
  1537. geom_point(aes(color = type)) + # color by type for points only
  1538. facet_grid(language~mood, labeller = as_labeller(c("positive" = "Positive mood", "negative" = "Negative mood", "Polish" = "Polish (L1)", "English" = "English (L2)"))) +
  1539. geom_smooth(method = "lm", se = TRUE, color = "black") + # overall regression line
  1540. scale_color_manual(values = c("#ffc000","#ff2f92","#404A80"), labels = c("UN" = "Unrelated pairs", "WR" = "Weakly related pairs", "CR" = "Closely related pairs"), name = "") +
  1541. geom_rug(aes(color = type)) + # keep rug lines color-coded too
  1542. scale_shape_prism() +
  1543. #theme_prism() +
  1544. scale_y_continuous(limits = c(1,7), breaks = c(1, 2, 3, 4, 5, 6, 7), guide = "prism_offset") +
  1545. scale_x_continuous(limits = c(-5,4), breaks = c(-7,-6,-5, -4, -3, -2, -1, 0, 1, 2,3), guide = "prism_offset") +
  1546. #scale_y_reverse(limits = c(7,0), breaks = c(1, 2,3,4,5,6,7), guide = "prism_offset") +
  1547. xlab(expression(paste("Mean N400 amplitude (", mu, "V)"))) +
  1548. ylab("Mean semantic relatedness rating") +
  1549. theme_classic2() +
  1550. theme(legend.position="bottom") +
  1551. theme(plot.title = element_text(hjust = 0.5))+
  1552. theme(panel.background = element_rect(fill = "white"),
  1553. strip.background = element_rect(fill = "white",color="black",size=1),
  1554. legend.key = element_rect(fill = "white"),
  1555. axis.line.x = element_line(colour = "black", size = .5),
  1556. axis.line.y = element_line(colour = "black", size = .5),
  1557. axis.title = element_text(size = 15, color= "black"),
  1558. strip.text = element_text(size = 15, color= "black"),
  1559. legend.text = element_text(size = 15, color= "black"),
  1560. legend.title = element_text(size = 15, color= "black"),
  1561. strip.text.x.top = element_text(size = 15, color= "black"),
  1562. axis.text = element_text(size = 15, color= "black"))
  1564. ggsave(filename ="Correlation_plot_relatedness.png", plot = last_plot(), width = 20,height = 15, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  1566. # ggplot(df7, aes(x = mean_amp, y = relatedness)) +
  1567. # geom_point(aes(color = type)) + # color by type for points only
  1568. # #facet_grid(language~mood, labeller = as_labeller(c("positive" = "Positive mood", "negative" = "Negative mood", "Polish" = "Polish (L1)", "English" = "English (L2)"))) +
  1569. # geom_smooth(method = "lm", se = TRUE, color = "black") + # overall regression line
  1570. # scale_color_manual(values = c("#ffc000","#ff2f92","#404A80"), labels = c("UN" = "Unrelated pairs", "WR" = "Weakly related pairs", "CR" = "Closely related pairs"), name = "") +
  1571. # geom_rug(aes(color = type)) + # keep rug lines color-coded too
  1572. # scale_shape_prism() +
  1573. # #theme_prism() +
  1574. # #scale_y_continuous(limits = c(1,7), breaks = c(1, 2, 3, 4, 5, 6, 7), guide = "prism_offset") +
  1575. # #scale_x_continuous(limits = c(-5,2), breaks = c(-5, -4, -3, -2, -1, 0, 1, 2), guide = "prism_offset") +
  1576. # #scale_y_reverse(limits = c(2,-5), breaks = c(-5, -4, -3, -2, -1, 0, 1, 2), guide = "prism_offset") +
  1577. # xlab(expression(paste("Mean N400 amplitudes (", mu, "V)"))) +
  1578. # ylab("Mean semantic relatedness relatednesss") +
  1579. # theme_classic2() +
  1580. # theme(legend.position="bottom") +
  1581. # theme(plot.title = element_text(hjust = 0.5))+
  1582. # theme(panel.background = element_rect(fill = "white"),
  1583. # strip.background = element_rect(fill = "white",color="black",size=1),
  1584. # legend.key = element_rect(fill = "white"),
  1585. # axis.line.x = element_line(colour = "black", size = .5),
  1586. # axis.line.y = element_line(colour = "black", size = .5),
  1587. # axis.title = element_text(size = 15, color= "black"),
  1588. # strip.text = element_text(size = 15, color= "black"),
  1589. # legend.text = element_text(size = 15, color= "black"),
  1590. # legend.title = element_text(size = 15, color= "black"),
  1591. # strip.text.x.top = element_text(size = 15, color= "black"),
  1592. # axis.text = element_text(size = 15, color= "black"))
  1593. #
  1594. # ggsave(filename ="Correlation_plot_relatedness_all.png", plot = last_plot(), width = 20,height = 15, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  1597. # N400 x vectors
  1598. ggplot(df7, aes(x = mean_amp, y = vector)) +
  1599. geom_point(aes(color = type)) + # color by type for points only
  1600. facet_grid(language~mood, labeller = as_labeller(c("positive" = "Positive mood", "negative" = "Negative mood", "Polish" = "Polish (L1)", "English" = "English (L2)"))) +
  1601. geom_smooth(method = "lm", se = TRUE, color = "black") + # overall regression line
  1602. scale_color_manual(values = c("#ffc000","#ff2f92","#404A80"), labels = c("UN" = "Unrelated pairs", "WR" = "Weakly related pairs", "CR" = "Closely related pairs"), name = "") +
  1603. geom_rug(aes(color = type)) + # keep rug lines color-coded too
  1604. scale_shape_prism() +
  1605. scale_y_continuous(limits = c(-0.1,0.8), breaks = c(0, 0.2, 0.4, 0.6, 0.8), guide = "prism_offset") +
  1606. scale_x_continuous(limits = c(-5,4), breaks = c(-7,-6,-5, -4, -3, -2, -1, 0, 1, 2,3), guide = "prism_offset") +
  1607. xlab(expression(paste("Mean N400 amplitude (", mu, "V)"))) +
  1608. ylab("Mean semantic similarity") +
  1609. theme_classic2() +
  1610. theme(legend.position="bottom") +
  1611. theme(plot.title = element_text(hjust = 0.5))+
  1612. theme(panel.background = element_rect(fill = "white"),
  1613. strip.background = element_rect(fill = "white",color="black",size=1),
  1614. legend.key = element_rect(fill = "white"),
  1615. axis.line.x = element_line(colour = "black", size = .5),
  1616. axis.line.y = element_line(colour = "black", size = .5),
  1617. axis.title = element_text(size = 15, color= "black"),
  1618. strip.text = element_text(size = 15, color= "black"),
  1619. legend.text = element_text(size = 15, color= "black"),
  1620. legend.title = element_text(size = 15, color= "black"),
  1621. strip.text.x.top = element_text(size = 15, color= "black"),
  1622. axis.text = element_text(size = 15, color= "black"))
  1624. ggsave(filename ="Correlation_plot_vector.png", plot = last_plot(), width = 20,height = 15, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  1626. #
  1627. # ggplot(df7, aes(x = mean_amp, y = vector)) +
  1628. # geom_point(aes(color = type)) + # color by type for points only
  1629. # #facet_grid(language~mood, labeller = as_labeller(c("positive" = "Positive mood", "negative" = "Negative mood", "Polish" = "Polish (L1)", "English" = "English (L2)"))) +
  1630. # geom_smooth(method = "lm", se = TRUE, color = "black") + # overall regression line
  1631. # scale_color_manual(values = c("#ffc000","#ff2f92","#404A80"), labels = c("UN" = "Unrelated pairs", "WR" = "Weakly related pairs", "CR" = "Closely related pairs"), name = "") +
  1632. # geom_rug(aes(color = type)) + # keep rug lines color-coded too
  1633. # scale_shape_prism() +
  1634. # #theme_prism() +
  1635. # #scale_y_continuous(limits = c(1,7), breaks = c(1, 2, 3, 4, 5, 6, 7), guide = "prism_offset") +
  1636. # #scale_x_continuous(limits = c(-5,2), breaks = c(-5, -4, -3, -2, -1, 0, 1, 2), guide = "prism_offset") +
  1637. # #scale_y_reverse(limits = c(2,-5), breaks = c(-5, -4, -3, -2, -1, 0, 1, 2), guide = "prism_offset") +
  1638. # xlab(expression(paste("Mean ERP amplitudes (", mu, "V)"))) +
  1639. # ylab("Mean semantic vector") +
  1640. # theme_classic2() +
  1641. # theme(legend.position="bottom") +
  1642. # theme(plot.title = element_text(hjust = 0.5))+
  1643. # theme(panel.background = element_rect(fill = "white"),
  1644. # strip.background = element_rect(fill = "white",color="black",size=1),
  1645. # legend.key = element_rect(fill = "white"),
  1646. # axis.line.x = element_line(colour = "black", size = .5),
  1647. # axis.line.y = element_line(colour = "black", size = .5),
  1648. # axis.title = element_text(size = 15, color= "black"),
  1649. # strip.text = element_text(size = 15, color= "black"),
  1650. # legend.text = element_text(size = 15, color= "black"),
  1651. # legend.title = element_text(size = 15, color= "black"),
  1652. # strip.text.x.top = element_text(size = 15, color= "black"),
  1653. # axis.text = element_text(size = 15, color= "black"))
  1654. #
  1655. # ggsave(filename ="Correlation_plot_vector_all.png", plot = last_plot(), width = 20,height = 15, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  1658. # semantic relatedness ratings - distribution
  1659. ggplot(df7, aes(x = language, y = relatedness, colour = type, fill = type)) +
  1660. facet_wrap(~type, labeller = as_labeller(c("UN" = "Unrelated pairs", "WR" = "Weakly related pairs", "CR" = "Closely related pairs"))) +
  1661. # ggdist::stat_halfeye(aes(colour = type), width = 0.8, .width = 0, alpha = 0.75, justification = -0.3, point_colour = NA) +
  1662. geom_point(aes(colour = type), size = 1.0, alpha = 0.2, position = position_jitter(seed = 1, width = 0.1)) +
  1663. geom_boxplot(aes(colour = type), width = .25, alpha = 0.25, outlier.shape = NA) +
  1664. scale_shape_prism() +
  1665. #theme_prism() +
  1666. scale_y_continuous(limits = c(1,7), breaks = c(1, 2, 3, 4, 5, 6, 7), guide = "prism_offset") +
  1667. #coord_cartesian(ylim = c(1, 7), clip = "off") +
  1668. scale_x_discrete(labels = c("Polish" = "Polish (L1)", "English" = "English (L2)"), guide = "prism_bracket") +
  1669. labs(y = "Mean semantic relatedness rating", x = "Language of operation") +
  1670. scale_fill_manual(values = c("#ffc000","#ff2f92","#404A80")) +
  1671. scale_colour_manual(values = c("#ffc000","#ff2f92","#404A80")) +
  1672. #scale_y_continuous(breaks = c(1, 2, 3, 4, 5, 6, 7))+
  1673. theme_classic() +
  1674. theme(legend.position="none") +
  1675. theme(panel.background = element_rect(fill = "white"),
  1676. strip.background = element_rect(fill = "white",color="black",size=1),
  1677. legend.key = element_rect(fill = "white"),
  1678. axis.line.x = element_line(colour = "black", size = .5),
  1679. axis.line.y = element_line(colour = "black", size = .5),
  1680. axis.title = element_text(size = 15, color= "black"),
  1681. strip.text = element_text(size = 15, color= "black"),
  1682. legend.text = element_text(size = 15, color= "black"),
  1683. legend.title = element_text(size = 15, color= "black"),
  1684. strip.text.x.top = element_text(size = 15, color= "black"),
  1685. axis.text = element_text(size = 15, color= "black"))
  1687. ggsave(filename ="Mean semantic relatedness rating.png", plot = last_plot(), width = 21,height = 9, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")
  1690. # vectors - distribution
  1691. ggplot(df7, aes(x = language, y = vector, colour = type, fill = type)) +
  1692. facet_wrap(~type, labeller = as_labeller(c("UN" = "Unrelated pairs", "WR" = "Weakly related pairs", "CR" = "Closely related pairs"))) +
  1693. # ggdist::stat_halfeye(aes(colour = type), width = 0.8, .width = 0, alpha = 0.75, justification = -0.3, point_colour = NA) +
  1694. geom_point(aes(colour = type), size = 1.0, alpha = 0.2, position = position_jitter(seed = 1, width = 0.1)) +
  1695. geom_boxplot(aes(colour = type), width = .25, alpha = 0.25, outlier.shape = NA) +
  1696. scale_shape_prism() +
  1697. #theme_prism() +
  1698. scale_y_continuous(limits = c(-0.1,0.8), breaks = c(0, 0.2, 0.4, 0.6, 0.8), guide = "prism_offset") +
  1699. #coord_cartesian(ylim = c(1, 7), clip = "off") +
  1700. scale_x_discrete(labels = c("Polish" = "Polish (L1)", "English" = "English (L2)"), guide = "prism_bracket") +
  1701. labs(y = "Mean semantic similarity", x = "Language of operation") +
  1702. scale_fill_manual(values = c("#ffc000","#ff2f92","#404A80")) +
  1703. scale_colour_manual(values = c("#ffc000","#ff2f92","#404A80")) +
  1704. #scale_y_continuous(breaks = c(1, 2, 3, 4, 5, 6, 7))+
  1705. theme_classic() +
  1706. theme(legend.position="none") +
  1707. theme(panel.background = element_rect(fill = "white"),
  1708. strip.background = element_rect(fill = "white",color="black",size=1),
  1709. legend.key = element_rect(fill = "white"),
  1710. axis.line.x = element_line(colour = "black", size = .5),
  1711. axis.line.y = element_line(colour = "black", size = .5),
  1712. axis.title = element_text(size = 15, color= "black"),
  1713. strip.text = element_text(size = 15, color= "black"),
  1714. legend.text = element_text(size = 15, color= "black"),
  1715. legend.title = element_text(size = 15, color= "black"),
  1716. strip.text.x.top = element_text(size = 15, color= "black"),
  1717. axis.text = element_text(size = 15, color= "black"))
  1719. ggsave(filename ="Mean semantic vector.png", plot = last_plot(), width = 21,height = 9, units = "cm", bg = "white", path="/Users/m/Desktop/MoBSeR/R /plots")

MoBSeR_ERP_R1.R, no license · at the source

Overview

Authors: Piotr Żukowski1, Marcin Naranowicz1,2
ORCID iDs: Piotr Żukowski, Marcin Naranowicz
  1. Laboratory for Social Neuroscience, Faculty of English, Adam Mickiewicz University, Poznań, Poland
  2. Cognitive Neuroscience Center, Adam Mickiewicz University, Poznań, Poland
Institutions: Adam Mickiewicz University in Poznań (Poland)
Journal: PloS one, volume 21, issue 8, article e0353990
Dates: received 21 February 2026; accepted 1 July 2026; published online 12 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0353990 · PMID 42585154 · PMCID PMC13465833 · OpenAlex W7106226611
Open access: gold, a free copy (OpenAlex)
Preprint: osf.io/nq4vz
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Connectivity, Physiology & signal measures
MeSH: Affect*, Language*, Multilingualism*, Adult, Electroencephalography, Evoked Potentials, Female, Humans, Memory, Semantics, Young Adult (* major topic)
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Uniwersytet im. Adama Mickiewicza w Poznaniu (140/04/POB5/00, 140/04/POB5/0010)
Citations: not cited yet (Europe PMC); 83 references in the paper

Abstract

Prior research has shown that, compared to a negative mood, a positive mood facilitates spreading activation within semantic memory in the first language. Yet, little is still known about neurocognitive mechanisms underlying mood effects on semantic processes in the foreign language. Here, we show that a positive mood enhances accessibility of unrelated concepts within semantic memory in the first but not the foreign language. Highly proficient Polish–English bilingual women were induced into positive and negative mood states with animated films and made semantic relatedness judgements about closely related, weakly related, and unrelated prime–target pairs in their first and foreign languages. Mean N400 responses were analysed as neural markers of spreading activation within semantic memory. Our results revealed reduced N400 responses to unrelated word pairs under a positive compared to a negative mood only in the first language. Critically, no mood-driven effects emerged for the foreign language. These findings provide novel evidence that a positive mood broadens spreading activation within semantic memory only in the first language context, making distant concepts more accessible. Critically, they also highlight mood-independent responding when bilinguals operate in their foreign language.

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

Repository

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OSF nq4vz

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State: the link answers, verified on 27 September 2026
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Languages: R (6), MATLAB (3)
Size: 25 files, 9 scripts
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Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: afex (6 files), emmeans (6 files), ggplot2 (6 files), lme4 (6 files), lmerTest (6 files), psych (6 files), tidyverse (6 files), car (5 files), ggpubr (5 files), multcomp (5 files), patchwork (5 files), reshape2 (5 files), survival (5 files), EEGLAB (2 files), ERPLAB (1 file), ICLabel (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
9 files
At the source: doi.org/10.17605/OSF.IO/NQ4VZ

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

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Data

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Data Availability

Pre-processed data, experimental stimuli, model specifications, as well as R and Matlab scripts are publicly available on the Open Science Framework at https://doi.org/10.17605/OSF.IO/NQ4VZ.

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 3, 28 September 2026

  • Funding: added Uniwersytet im. Adama Mickiewicza w Poznaniu: 140/04/POB5/00, 140/04/POB5/0010

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 11 MeSH terms, 72 references.

Cite

This paper

Żukowski, P., & Naranowicz, M. (2026). Positive mood enhances accessibility of unrelated concepts in the first language but not in the foreign language. PloS one, 21(8), e0353990. https://doi.org/10.1371/journal.pone.0353990

BibTeX

@article{zukowski2026positive,
author = {Żukowski, Piotr and Naranowicz, Marcin},
title = {{Positive mood enhances accessibility of unrelated concepts in the first language but not in the foreign language}},
journal = {PloS one},
year = {2026},
month = aug,
volume = {21},
number = {8},
pages = {e0353990},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0353990},
url = {https://doi.org/10.1371/journal.pone.0353990},
pmid = {42585154},
pmcid = {PMC13465833}
}

RIS

TY - JOUR
AU - Żukowski, Piotr
AU - Naranowicz, Marcin
TI - Positive mood enhances accessibility of unrelated concepts in the first language but not in the foreign language
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/08/12
VL - 21
IS - 8
SP - e0353990
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0353990
UR - https://doi.org/10.1371/journal.pone.0353990
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pone.0353990",
"type": "article-journal",
"title": "Positive mood enhances accessibility of unrelated concepts in the first language but not in the foreign language",
"container-title": "PloS one",
"author": [
{
"family": "Żukowski",
"given": "Piotr"
},
{
"family": "Naranowicz",
"given": "Marcin"
}
],
"container-title-short": "PLoS One",
"volume": "21",
"issue": "8",
"page": "e0353990",
"DOI": "10.1371/journal.pone.0353990",
"PMID": "42585154",
"PMCID": "PMC13465833",
"ISSN": "1932-6203",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pone.0353990",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
12
]
]
}
}

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