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The behavioral evidence of processing congruences and incongruences between Self- and Other-perspective-related representations during both perspective judgments.

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  1. [1] § Methods › Statistical analysis ↔ yk2d9/R code for Congruency Beh analysis to share.R, lines 24–64 · score 0.55 · emmeans, lmerTest, lme4, marginal, model, Satterthwaite

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The authors' code

R · 892 lines · 25 KB · no license · 1 match

  1. ## Cong/Incong Beh analysis from (iEEG VPT exp)
  3. library(lme4) # usd in Multilevel MOdeling Center (Bristol) tutorial. gives the same results as nlme but this is more versatile for complicated nestings
  4. library(readxl)
  5. library(nlme) # used in Field's book
  6. library(car)
  7. library(tidyverse)
  8. library(rstatix)
  9. library(emmeans)
  10. library(ggpubr)
  11. library(ggplot2)
  12. library(lmerTest)
  13. library(dplyr)
  14. library(scales)
  15. library(simr)
  17. # for copying results
  18. write.excel <- function(res.aov,row.names=FALSE,col.names=TRUE,...) {
  19. write.table(res.aov,"clipboard",sep="\t",row.names=row.names,col.names=col.names,...)
  20. }
  24. # 19.08.25
  27. ## LMM for left/right Congruence effect on Accuracy
  29. dat <- read_excel("datapath")
  31. rintercept <- lmer(Accuracy ~ Congruence*Perspective + (1|ParticipantID), data = dat, REML = FALSE) # this by default gives treatment contrast with a reference level with alphabetical oder, i.e., SPJ in Perspective and Cong in Congruence
  32. summary(rintercept) # therefore the estimates are Perspective effect in Cong and Congruence effect in SPJ
  33. # When overal effect over all levels of another factor is needed marginal means averaged over all lvels of another factor is obtained by lsmeans function from emmeans
  34. # or default treatment coding can be manually adjusted to sum to zero coding before running the lmer
  35. # using this > dat$Congruence <- factor(dat$Congruence, levels = c("Cong", "Incong")); dat$Perspective <- factor(dat$Perspective, levels = c("SPJ", "VPT")); contrasts(dat$Congruence) <- cbind(Congruence = c(-0.5, 0.5)) in this paper about contrasts in detail Schad et al., 2020 but this sum to zero coding may not be optimal for ublanaced data (e.g. if some levels of a factor has more datapoints)
  36. #contrasts(dat$Perspective) <- cbind(Perspective = c(-0.5, 0.5))
  37. # contrasts(dat$Congruence)
  38. #contrasts(dat$Perspective)
  43. rslope <- lmer(Accuracy ~ Congruence*Perspective + (1+Congruence|ParticipantID), data = dat, REML = FALSE)
  44. summary(rslope)
  46. # comparing the models
  47. anova (rintercept,rslope)
  51. # Comparing marginal means of Congruence averaged over both levels of Perspective
  52. lsmeans(rslope, pairwise~Congruence, lmer.df = "satterthwaite", lmerTest.limit = 5777)
  54. # Comparing marginal means of Perspective averaged over both levels of Congruence
  55. lsmeans(rslope, pairwise~Perspective, lmer.df = "satterthwaite", lmerTest.limit = 5777)
  59. ## Plot by default plots with SEM- standard error of the mean
  60. bxp <- ggerrorplot(
  61. dat, x = "Perspective", y = "Accuracy", size = 1.3,
  62. color = "Congruence", palette = c("#0072B2", "#E69FBB")
  63. )
  64. bxp
  67. #
  68. bxp <- ggerrorplot(
  69. dat, x = "Perspective", y = "Accuracy", size = 1.3,
  70. color = "Congruence", palette = c("#0072B2", "#E69FBB"),
  71. facet.by = "Angle"
  72. )
  73. bxp
  80. # plot with VPT and SPJ with different shapes, Angle x axis, Congruence different colors
  82. library(ggplot2)
  83. library(dplyr)
  85. # Ensure your data is clean
  86. dat <- dat %>%
  87. filter(!is.na(Accuracy)) %>%
  88. mutate(
  89. Perspective = factor(Perspective, levels = c("SPJ", "VPT")),
  90. Congruence = factor(Congruence, levels = c("Cong", "Incong")),
  91. Angle = factor(Angle, levels = sort(unique(Angle)))
  92. )
  94. pd <- position_dodge(width = 0.6)
  96. ggplot(dat, aes(x = Angle, y = Accuracy*100, color = Congruence, shape = Perspective)) +
  97. stat_summary(
  98. fun = mean,
  99. geom = "point",
  100. size = 4,
  101. position = pd,
  102. na.rm = TRUE
  103. ) +
  104. stat_summary(
  105. fun.data = mean_se, # SEM
  106. geom = "errorbar",
  107. width = 0, # straight lines
  108. size = 1.2,
  109. position = pd,
  110. na.rm = TRUE
  111. ) +
  112. scale_color_manual(values = c("Cong" = "#0072B2", "Incong" = "#E69FBB")) +
  113. scale_shape_manual(values = c("SPJ" = 16, "VPT" = 17)) +
  114. labs(x = "Angle (°)", y = "Accuracy (%)", shape = "Perspective") +
  115. theme_classic(base_size = 14) +
  116. theme(
  117. axis.text.x = element_text(size = 14, angle = 0, hjust = 0.5, color = "black"),
  118. axis.text.y = element_text(size = 14, color = "black"),
  119. axis.title.x = element_text(size = 16, face = "bold", color = "black"),
  120. axis.title.y = element_text(size = 16, face = "bold", color = "black"),
  121. axis.line = element_line(size = 1.2, color = "black"), # thicker axes
  122. legend.title = element_text(size = 14),
  123. legend.text = element_text(size = 13),
  124. legend.position = "right"
  125. )
  128. ## the same as above only connecting lines between datpoints are added
  131. library(ggplot2)
  132. library(dplyr)
  134. # Ensure your data is clean
  135. dat <- dat %>%
  136. filter(!is.na(Accuracy)) %>%
  137. mutate(
  138. Perspective = factor(Perspective, levels = c("SPJ", "VPT")),
  139. Congruence = factor(Congruence, levels = c("Cong", "Incong")),
  140. Angle = factor(Angle, levels = sort(unique(Angle)))
  141. )
  143. pd <- position_dodge(width = 0.6)
  145. ggplot(dat, aes(x = Angle,
  146. y = Accuracy*100,
  147. color = Congruence,
  148. shape = Perspective,
  149. group = interaction(Congruence, Perspective))) +
  151. stat_summary(
  152. fun = mean,
  153. geom = "line",
  154. size = 1.2, # same thickness as axis lines
  155. position = pd,
  156. na.rm = TRUE
  157. ) +
  159. stat_summary(
  160. fun = mean,
  161. geom = "point",
  162. size = 4,
  163. position = pd,
  164. na.rm = TRUE
  165. ) +
  167. stat_summary(
  168. fun.data = mean_se,
  169. geom = "errorbar",
  170. width = 0,
  171. size = 1.2,
  172. position = pd,
  173. na.rm = TRUE
  174. ) +
  176. scale_color_manual(values = c("Cong" = "#0072B2", "Incong" = "#E69FBB")) +
  177. scale_shape_manual(values = c("SPJ" = 16, "VPT" = 17)) +
  178. labs(x = "Angle (°)", y = "Accuracy (%)", shape = "Perspective") +
  180. theme_classic(base_size = 14) +
  181. theme(
  182. axis.text.x = element_text(size = 14, angle = 0, hjust = 0.5, color = "black"),
  183. axis.text.y = element_text(size = 14, color = "black"),
  184. axis.title.x = element_text(size = 16, face = "bold", color = "black"),
  185. axis.title.y = element_text(size = 16, face = "bold", color = "black"),
  186. axis.line = element_line(size = 1.2, color = "black"),
  187. legend.title = element_text(size = 14),
  188. legend.text = element_text(size = 13),
  189. legend.position = "right"
  190. )
  194. ## compute means and SE
  195. means <- dat %>%
  196. group_by(Perspective, Congruence) %>%
  197. get_summary_stats(Accuracy, type = "mean_se")
  199. print(means)
  200. write.excel(means)
  204. ## compute means and SE
  205. means <- dat %>%
  206. group_by(Perspective, Angle, Congruence) %>%
  207. get_summary_stats(Accuracy, type = "mean_se")
  209. print(means)
  210. write.excel(means)
  216. ## LMM for left/right Congruence effect on RT
  218. dat <- read_excel("datapath")
  220. rintercept <- lmer(RT ~ Congruence*Perspective + (1|ParticipantID), data = dat, REML = FALSE)
  221. summary(rintercept)
  223. rslope <- lmer(RT ~ Congruence*Perspective + (1+Congruence|ParticipantID), data = dat, REML = FALSE)
  224. summary(rslope)
  226. # comparing the models
  227. anova (rintercept,rslope)
  230. # comparing estimated marginal means of Congruence averaged over both levels of Perspective
  231. lsmeans(rintercept, pairwise~Congruence, lmer.df = "satterthwaite", lmerTest.limit = 5685) # for RT on all responses incl 180 and 0 deg
  233. # comparing estimated marginal means of Congruence averaged over both levels of Perspective
  234. lsmeans(rintercept, pairwise~Perspective, lmer.df = "satterthwaite", lmerTest.limit = 5685)
  239. # posthoc
  240. lsmeans(rintercept, pairwise~Congruence|Perspective, adjust="tukey", lmer.df = "satterthwaite", lmerTest.limit = 5685) # for RT on all responses incl 180 and 0 deg
  244. ## Plot by default plots with SEM- standard error of the mean
  245. bxp <- ggerrorplot(
  246. dat, x = "Perspective", y = "RT", size = 1.3,
  247. color = "Congruence", palette = c("#0072B2", "#E69FBB")
  248. )
  249. bxp
  252. #
  253. bxp <- ggerrorplot(
  254. dat, x = "Perspective", y = "RT", size = 1.3,
  255. color = "Congruence", palette = c("#0072B2", "#E69FBB"),
  256. facet.by = "Angle"
  257. )
  258. bxp
  263. # plot with VPT and SPJ with different shapes, Angle x axis, Congruence different colors
  265. library(ggplot2)
  266. library(dplyr)
  268. # Ensure your data is clean
  269. dat <- dat %>%
  270. filter(!is.na(RT)) %>%
  271. mutate(
  272. Perspective = factor(Perspective, levels = c("SPJ", "VPT")),
  273. Congruence = factor(Congruence, levels = c("Cong", "Incong")),
  274. Angle = factor(Angle, levels = sort(unique(Angle)))
  275. )
  277. pd <- position_dodge(width = 0.6)
  279. ggplot(dat, aes(x = Angle, y = RT, color = Congruence, shape = Perspective)) +
  280. stat_summary(
  281. fun = mean,
  282. geom = "point",
  283. size = 4,
  284. position = pd,
  285. na.rm = TRUE
  286. ) +
  287. stat_summary(
  288. fun.data = mean_se, # SEM
  289. geom = "errorbar",
  290. width = 0, # straight lines
  291. size = 1.2,
  292. position = pd,
  293. na.rm = TRUE
  294. ) +
  295. scale_color_manual(values = c("Cong" = "#0072B2", "Incong" = "#E69FBB")) +
  296. scale_shape_manual(values = c("SPJ" = 16, "VPT" = 17)) +
  297. labs(x = "Angle (°)", y = "RT (s)", shape = "Perspective") +
  298. theme_classic(base_size = 14) +
  299. theme(
  300. axis.text.x = element_text(size = 14, angle = 0, hjust = 0.5, color = "black"),
  301. axis.text.y = element_text(size = 14, color = "black"),
  302. axis.title.x = element_text(size = 16, face = "bold", color = "black"),
  303. axis.title.y = element_text(size = 16, face = "bold", color = "black"),
  304. axis.line = element_line(size = 1.2, color = "black"), # thicker axes
  305. legend.title = element_text(size = 14),
  306. legend.text = element_text(size = 13),
  307. legend.position = "right"
  308. )
  312. ## same as above just with added lines to connedct thed datapoints
  315. library(ggplot2)
  316. library(dplyr)
  318. # Ensure your data is clean
  319. dat <- dat %>%
  320. filter(!is.na(RT)) %>%
  321. mutate(
  322. Perspective = factor(Perspective, levels = c("SPJ", "VPT")),
  323. Congruence = factor(Congruence, levels = c("Cong", "Incong")),
  324. Angle = factor(Angle, levels = sort(unique(Angle)))
  325. )
  327. pd <- position_dodge(width = 0.6)
  330. ggplot(dat, aes(x = Angle,
  331. y = RT,
  332. color = Congruence,
  333. shape = Perspective,
  334. group = interaction(Congruence, Perspective))) +
  336. stat_summary(
  337. fun = mean,
  338. geom = "line",
  339. size = 1.2, # same thickness as axis lines
  340. position = pd,
  341. na.rm = TRUE
  342. ) +
  344. stat_summary(
  345. fun = mean,
  346. geom = "point",
  347. size = 4,
  348. position = pd,
  349. na.rm = TRUE
  350. ) +
  352. stat_summary(
  353. fun.data = mean_se,
  354. geom = "errorbar",
  355. width = 0,
  356. size = 1.2,
  357. position = pd,
  358. na.rm = TRUE
  359. ) +
  361. scale_color_manual(values = c("Cong" = "#0072B2", "Incong" = "#E69FBB")) +
  362. scale_shape_manual(values = c("SPJ" = 16, "VPT" = 17)) +
  363. labs(x = "Angle (°)", y = "RT(S)", shape = "Perspective") +
  365. theme_classic(base_size = 14) +
  366. theme(
  367. axis.text.x = element_text(size = 14, angle = 0, hjust = 0.5, color = "black"),
  368. axis.text.y = element_text(size = 14, color = "black"),
  369. axis.title.x = element_text(size = 16, face = "bold", color = "black"),
  370. axis.title.y = element_text(size = 16, face = "bold", color = "black"),
  371. axis.line = element_line(size = 1.2, color = "black"),
  372. legend.title = element_text(size = 14),
  373. legend.text = element_text(size = 13),
  374. legend.position = "right"
  375. )
  379. ## same as above the RT plot with lines but the line is cut for Cong at 180 deg
  381. library(ggplot2)
  382. library(dplyr)
  384. # Ensure your data is clean
  385. dat <- dat %>%
  386. filter(!is.na(RT)) %>%
  387. mutate(
  388. Perspective = factor(Perspective, levels = c("SPJ", "VPT")),
  389. Congruence = factor(Congruence, levels = c("Cong", "Incong")),
  390. Angle = factor(Angle, levels = sort(unique(Angle)))
  391. )
  394. pd <- position_dodge(width = 0.6)
  396. ggplot(dat, aes(x = Angle,
  397. y = RT,
  398. color = Congruence,
  399. shape = Perspective,
  400. group = interaction(Congruence, Perspective))) +
  402. stat_summary(
  403. aes(y = RT), # ??? breaks Cong line at 180
  404. fun = mean,
  405. geom = "line",
  406. size = 1.2,
  407. position = pd,
  408. na.rm = TRUE
  409. ) +
  411. stat_summary(
  412. fun = mean,
  413. geom = "point",
  414. size = 4,
  415. position = pd,
  416. na.rm = TRUE
  417. ) +
  419. stat_summary(
  420. fun.data = mean_se,
  421. geom = "errorbar",
  422. width = 0,
  423. size = 1.2,
  424. position = pd,
  425. na.rm = TRUE
  426. ) +
  428. scale_color_manual(values = c("Cong" = "#0072B2", "Incong" = "#E69FBB")) +
  429. scale_shape_manual(values = c("SPJ" = 16, "VPT" = 17)) +
  430. labs(x = "Angle (°)", y = "RT (S)", shape = "Perspective") +
  432. theme_classic(base_size = 14) +
  433. theme(
  434. axis.text.x = element_text(size = 14, angle = 0, hjust = 0.5, color = "black"),
  435. axis.text.y = element_text(size = 14, color = "black"),
  436. axis.title.x = element_text(size = 16, face = "bold", color = "black"),
  437. axis.title.y = element_text(size = 16, face = "bold", color = "black"),
  438. axis.line = element_line(size = 1.2, color = "black"),
  439. legend.title = element_text(size = 14),
  440. legend.text = element_text(size = 13),
  441. legend.position = "right"
  442. )
  452. ## LMM for Horizontal/Overhead View Plane effect on Accuracy
  455. dat <- read_excel("datapath")
  458. rintercept <- lmer(Accuracy ~ Perspective*View + (1|ParticipantID), data = dat, REML = FALSE)
  459. summary(rintercept)
  462. rslope <- lmer(Accuracy ~ Perspective*View + (1+View|ParticipantID), data = dat, REML = FALSE)
  463. summary(rslope)
  465. anova (rintercept, rslope)
  468. # comparing marginal means for the effect of Perspective averaged over both levels of View plane
  470. lsmeans(rslope, pairwise~Perspective, lmer.df = "satterthwaite", lmerTest.limit = 5777)
  473. # comparing marginal means for the effect of Perspective averaged over both levels of Perspective
  474. lsmeans(rslope, pairwise~View, lmer.df = "satterthwaite", lmerTest.limit = 5777)
  477. #Posthoc on View Plane
  479. lsmeans(rslope, pairwise~View|Perspective, adjust="tukey", lmer.df = "satterthwaite", lmerTest.limit = 5685) # lmerTest.limit - number may need adjustment- if R recommnds
  481. #Posthoc on Perspective
  483. lsmeans(rslope, pairwise~Perspective|View, adjust="tukey", lmer.df = "satterthwaite", lmerTest.limit = 5685) # lmerTest.limit - number may need adjustment- if R recommnds
  486. # Plot
  487. bxp <- ggerrorplot(
  488. dat, x = "Perspective", y = "Accuracy", size = 1.3,
  489. color = "View", palette = c("#E69F00", "#56B4E9")
  490. )
  491. bxp
  494. # Plot for each VPT target angle (is different from Degree because all 8 anglees are separatelly without clockwise- counterclockwise grouping) separatelly
  495. bxp <- ggerrorplot(
  496. dat, x = "Perspective", y = "Accuracy", size = 1.3,
  497. color = "View", palette = c("#E69F00", "#56B4E9"),
  498. facet.by = "Angle"
  499. )
  500. bxp
  504. ## LMM for View Plane effect on RT
  506. dat <- read_excel("datapath")
  507. rintercept <- lmer(RT ~ Perspective*View + (1|ParticipantID), data = dat, REML = FALSE)
  508. summary(rintercept)
  511. rslope <- lmer(RT ~ Perspective*View + (1+View|ParticipantID), data = dat, REML = FALSE)
  512. summary(rslope)
  515. anova (rintercept, rslope)
  518. # comparing marginal means for the effect of Perspective averaged over both levels of View plane
  520. lsmeans(rslope, pairwise~Perspective, lmer.df = "satterthwaite", lmerTest.limit = 5777)
  523. # comparing marginal means for the effect of Perspective averaged over both levels of Perspective
  524. lsmeans(rslope, pairwise~View, lmer.df = "satterthwaite", lmerTest.limit = 5777)
  530. # posthoc on the effect of View
  531. lsmeans(rslope, pairwise~View|Perspective, adjust="tukey", lmer.df = "satterthwaite", lmerTest.limit = 5685) # lmerTest.limit - number may need adjustment- if R recommnds
  533. # posthoc on the effect of Perspective
  534. lsmeans(rslope, pairwise~Perspective|View, adjust="tukey", lmer.df = "satterthwaite", lmerTest.limit = 5685) # lmerTest.limit - number may need adjustment- if R recommnds
  537. # Plot
  538. bxp <- ggerrorplot(
  539. dat, x = "Perspective", y = "RT", size = 1.3,
  540. color = "View", palette = c("#E69F00", "#56B4E9")
  541. )
  542. bxp
  545. # Plot for each VPT target angle (is different from Degree because all 8 anglees are separatelly without clockwise- counterclockwise grouping) separatelly
  546. bxp <- ggerrorplot(
  547. dat, x = "Perspective", y = "RT", size = 1.3,
  548. color = "View", palette = c("#E69F00", "#56B4E9"),
  549. facet.by = "Angle"
  550. )
  551. bxp
  558. ## LMM for Degree effect on Accuracy
  561. dat <- read_excel("datapath")
  564. rintercept <- lmer(Accuracy ~ Perspective*Degree+ (1|ParticipantID), data = dat, REML = FALSE)
  565. summary(rintercept)
  568. rslope <- lmer(Accuracy ~ Perspective*Degree + (1+View|ParticipantID), data = dat, REML = FALSE)
  569. summary(rslope)
  571. anova (rintercept, rslope)
  573. #Posthoc on Degree
  575. lsmeans(rintercept, pairwise~Degree|Perspective, adjust="tukey", lmer.df = "satterthwaite", lmerTest.limit = 5685) # lmerTest.limit - number may need adjustment- if R recommnds
  577. #Posthoc on Perspective
  579. lsmeans(rintercept, pairwise~Perspective|Degree, adjust="tukey", lmer.df = "satterthwaite", lmerTest.limit = 5685) # lmerTest.limit - number may need adjustment- if R recommnds
  582. # Plot
  583. bxp <- ggerrorplot(
  584. dat, x = "Perspective", y = "Accuracy", size = 1.3,
  585. color = "Degree", palette = c("#7A7E8A", "#009E73", "#CC79A7")
  586. )
  587. bxp
  590. # Plot for each VPT target angle (is different from Degree because all 8 anglees are separatelly without clockwise- counterclockwise grouping) separatelly
  591. bxp <- ggerrorplot(
  592. dat, x = "Perspective", y = "Accuracy", size = 1.3,
  593. color = "Degree", palette = c("#7A7E8A", "#009E73", "#CC79A7"),
  594. facet.by = "Congruence"
  595. )
  596. bxp
  600. ## LMM for Degree effect on RT
  602. dat <- read_excel("datapath")
  603. rintercept <- lmer(RT ~ Perspective*Degree + (1|ParticipantID), data = dat, REML = FALSE)
  604. summary(rintercept)
  607. rslope <- lmer(RT ~ Perspective*Degree + (1+View|ParticipantID), data = dat, REML = FALSE)
  608. summary(rslope)
  611. anova (rintercept, rslope)
  613. # posthoc on the effect of View
  614. lsmeans(rslope, pairwise~Degree|Perspective, adjust="tukey", lmer.df = "satterthwaite", lmerTest.limit = 5685) # lmerTest.limit - number may need adjustment- if R recommnds
  616. # posthoc on the effect of Perspective
  617. lsmeans(rslope, pairwise~Perspective|Degree, adjust="tukey", lmer.df = "satterthwaite", lmerTest.limit = 5685) # lmerTest.limit - number may need adjustment- if R recommnds
  620. # Plot
  621. bxp <- ggerrorplot(
  622. dat, x = "Perspective", y = "RT", size = 1.3,
  623. color = "Degree", palette = c("#7A7E8A", "#009E73", "#CC79A7")
  624. )
  625. bxp
  628. # Plot for each level of left/right Congruence
  629. bxp <- ggerrorplot(
  630. dat, x = "Perspective", y = "RT", size = 1.3,
  631. color = "Degree", palette = c("#7A7E8A", "#009E73", "#CC79A7"),
  632. facet.by = "Congruence"
  633. )
  634. bxp
  638. ###### simulation based sensitivity power analysis for lmer to justify the sample size
  640. library(simr)
  642. #### for accuracy
  644. ## the effect of Congruence in SPJ, Effect of Perspective in Congruent trials
  645. ## and Congruence Perspective interaction
  647. m <- lmer(
  648. Accuracy ~ Congruence * Perspective +
  649. (1 + Congruence | ParticipantID),
  650. data = dat,
  651. REML = FALSE
  652. )
  655. fixef(m)
  657. ### to determine with what power the analysis can detect 5% change
  659. fixef(m)["CongruenceIncong:PerspectiveVPT"] <- 0.05 # 0.05 depends on the unit of data, for accuracy it's 5% change
  662. ### test power for the interaction
  663. powerSim(m, test = fixed("CongruenceIncong:PerspectiveVPT", "sa"), nsim = 1000) # "sa" - satherwhite, nsim - number of simulations
  665. # as the results of the above code showed that
  666. # the 5% change in Accuracy can be detected with 92% power
  667. # now we test what difference can be detected with 80%, thus we decrease the effect size
  668. # https://thechangelab.stanford.edu/tutorials/power-analysis/post-hoc-power-sensitivity-analysis-using-the-sesoi/
  670. fixef(m)["CongruenceIncong:PerspectiveVPT"] <- 0.042
  671. powerSim(m, test = fixed("CongruenceIncong:PerspectiveVPT", "sa"), nsim = 1000)
  674. ### Congruence effect in SPJ
  675. m_acc<-fixef(m)["CongruenceIncong"] <- 0.05 # as this gives less than 80% power, below we increase the effect size
  676. powerSim(m, test = fixed("CongruenceIncong", "sa"), nsim = 1000)
  678. m_acc<-fixef(m)["CongruenceIncong"] <- 0.0551
  679. powerSim(m, test = fixed("CongruenceIncong", "sa"), nsim = 1000)
  683. ### Perspective effect in Congruent trials
  684. m_persp<-fixef(m)["PerspectiveVPT"] <- 0.05 # as this gives 100% power, below we decrease the effect size
  685. powerSim(m, test = fixed("PerspectiveVPT", "sa"), nsim = 1000)
  687. m_persp<-fixef(m)["PerspectiveVPT"] <- 0.03 # as this gives 100% power, below we decrease the effect size
  688. powerSim(m, test = fixed("PerspectiveVPT", "sa"), nsim = 1000)
  693. ### Congruence effect in VPT
  695. dat$Perspective <- factor(dat$Perspective,
  696. levels = c("SPJ", "VPT"))
  698. levels(dat$Perspective)
  700. dat$Perspective <- relevel(dat$Perspective, ref = "VPT") # so that reference level is VPT in the data to receive the identical results as
  701. # lsmeans(rslope, pairwise~Congruence, lmer.df = "satterthwaite", lmerTest.limit = 5777)
  702. # for VPT does and because simr can not woork with lsmeans
  703. # model for the reference level perspective
  704. m_VPTref <- lmer(
  705. Accuracy ~ Congruence * Perspective +
  706. (1 + Congruence | ParticipantID),
  707. data = dat,
  708. REML = FALSE
  709. )
  711. # with what power can the analysis detect the 4.2% change between Congruent and Incongruent conditions during VPT
  712. fixef(m_VPTref)["CongruenceIncong"] <- 0.055
  713. powerSim(m_VPTref, test = fixed("CongruenceIncong", "sa"), nsim = 1000)
  717. ### Perspective effect in Incongruent
  719. dat$Perspective <- factor(dat$Perspective,
  720. levels = c("SPJ", "VPT"))
  721. dat$Congruence <- factor(dat$Congruence,
  722. levels = c("Cong", "Incong"))
  724. levels(dat$Congruence)
  725. levels(dat$Perspective)
  728. dat$Congruence <- relevel(dat$Congruence, ref = "Incong") # changing the reference level
  730. m_IncongRef <- lmer(
  731. Accuracy ~ Congruence * Perspective +
  732. (1 + Congruence | ParticipantID),
  733. data = dat,
  734. REML = FALSE
  735. )
  738. fixef(m_IncongRef)["PerspectiveVPT"] <- 0.03
  739. powerSim(m_IncongRef, test = fixed("PerspectiveVPT", "sa"), nsim = 1000)
  746. ##### sensitivity power analysis for RT
  747. m_rt <- lmer(
  748. RT ~ Congruence * Perspective +
  749. (1 | ParticipantID),
  750. data = dat,
  751. REML = FALSE
  752. )
  754. # to determine with what power can the analysis detect 0.05 second change
  756. fixef(m_rt)["CongruenceIncong:PerspectiveVPT"] <- 0.05 # 0.05 depends on the unit of data, for accuracy it's 5% change
  759. ### test power for the interaction
  760. powerSim(m_rt, test = fixed("CongruenceIncong:PerspectiveVPT", "sa"), nsim = 1000) # "sa" - satherwhite, nsim - number of simulations
  763. # as the results of the above code showed that
  764. # the 0.05 second change in RTs can be detected with below 80% power
  765. # now we test what difference can be detected with 80%, thus we increase the effect size
  767. fixef(m_rt)["CongruenceIncong:PerspectiveVPT"] <- 0.0524
  768. powerSim(m_rt, test = fixed("CongruenceIncong:PerspectiveVPT", "sa"), nsim = 1000)
  772. ### Congruence effect in SPJ
  773. fixef(m_rt)["CongruenceIncong"] <- 0.05
  775. powerSim(
  776. m_rt,
  777. test = fixed("CongruenceIncong", "sa"),
  778. nsim = 1000
  779. )
  783. fixef(m_rt)["CongruenceIncong"] <- 0.037 # decreasing the effect size as 0.05 showed 97% power
  785. powerSim(
  786. m_rt,
  787. test = fixed("CongruenceIncong", "sa"),
  788. nsim = 1000
  789. )
  793. ### Perspective effect in Congruent trials
  795. fixef(m_rt)["PerspectiveVPT"] <- 0.037
  797. powerSim(
  798. m_rt,
  799. test = fixed("PerspectiveVPT", "sa"),
  800. nsim = 1000
  801. )
  805. ### Congruent in VPT
  806. ## relevel the model to VPT reference
  808. dat$Perspective <- factor(dat$Perspective,
  809. levels = c("SPJ", "VPT"))
  811. levels(dat$Perspective)
  812. dat_VPTref <- dat
  813. dat_VPTref$Perspective <- relevel(dat_VPTref$Perspective, ref = "VPT")
  815. m_rt_VPTref <- lmer(
  816. RT ~ Congruence * Perspective +
  817. (1 | ParticipantID),
  818. data = dat_VPTref,
  819. REML = FALSE
  820. )
  823. fixef(m_rt_VPTref)["CongruenceIncong"] <- 0.038
  825. powerSim(
  826. m_rt_VPTref,
  827. test = fixed("CongruenceIncong", "sa"),
  828. nsim = 1000
  829. )
  833. ### Perspective in Incongruent
  835. ## relevel the model to Incongruent reference
  838. dat$Perspective <- factor(dat$Perspective,
  839. levels = c("SPJ", "VPT"))
  840. dat$Congruence <- factor(dat$Congruence,
  841. levels = c("Cong", "Incong"))
  843. levels(dat$Congruence)
  844. levels(dat$Perspective)
  846. dat_IncongRef <- dat
  847. dat_IncongRef$Congruence <- relevel(dat_IncongRef$Congruence, ref = "Incong")
  849. m_rt_IncongRef <- lmer(
  850. RT ~ Congruence * Perspective +
  851. (1 | ParticipantID),
  852. data = dat_IncongRef,
  853. REML = FALSE
  854. )
  858. fixef(m_rt_IncongRef)["PerspectiveVPT"] <- 0.037
  860. powerSim(
  861. m_rt_IncongRef,
  862. test = fixed("PerspectiveVPT", "sa"),
  863. nsim = 1000
  864. )
  872. ## Sensitivity analysis to justify the sample size using t test and anova model
  874. # using t test
  875. library(pwr)
  876. pwr.t.test(
  877. n = 26, # number of participants
  878. sig.level = 0.05,
  879. power = 0.80,
  880. type = "paired"
  881. )
  884. ## using anova to test interaction terms
  885. pwr.anova.test(
  886. k = 4, # 2 (Congruence) × 2 (Perspective)
  887. n = 26,
  888. sig.level = 0.05,
  889. power = 0.80
  890. )

R code for Congruency Beh analysis to share.R, no license · at the source

Overview

Authors: Anna Gunia1,2,3, Adam Kalina4, Alena Javůrková4, Kamil Vlček1,2,4
  1. Institute of Physiology, The Czech Academy of Sciences,Prague, Czech Republic
  2. Institute of Psychology, The Czech Academy of Sciences,Prague, Czech Republic
  3. Third Faculty of Medicine, Charles University,Prague, Czech Republic
  4. Department of Neurology, Second Faculty of Medicine, Charles University, Motol University Hospital,Prague, Czech Republic
Institutions: Czech Academy of Sciences (Czechia); Charles University (Czechia)
Journal: Psychological research, volume 90, issue 4, article 134
Dates: received 25 September 2025; accepted 5 May 2026; published online 13 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s00426-026-02311-8 · PMID 42439946 · PMCID PMC13364830 · OpenAlex W7168139286
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Statistics, Physiology & signal measures
MeSH: Judgment*, Self Concept*, Social Perception*, Space Perception*, Temporal Lobe*, Visual Perception*, Adult, Female, Humans, Male, Parietal Lobe, Young Adult (* major topic)
Topic: Spatial Cognition and Navigation (Automotive Engineering, Engineering), according to OpenAlex
Funding: Grant Agency of Charles University (GAUK 254422); ERDF-Project Brain dynamics (CZ.02.01.01/00/22_008/0004643); Johannes Amos Comenius Programme (OP JAC) provided by MSMT (CZ.02.01.01/00/23_025/0008715); Czech Science Foundation (22-16874S)
Citations: not cited yet (Europe PMC); 70 references in the paper

Abstract

Daily, we make visuospatial self-perspective judgments from our actual perspective (SPJ). However, social contexts often require imagining another viewpoint—visuospatial perspective-taking (VPT). VPT is costlier than SPJ, and gets even costlier in autism, schizophrenia, and aging. One explanation is interference from self-related representations. This interference may reflect a mechanism that processes various self- and other-related representations, supported by the temporoparietal junction (TPJ). Moreover, reportedly other-related representations also influence SPJ, though this remains debated. An additional explanation for VPT difficulty in literature is the mental body-schema transformation needed to adopt another perspective at greater angular disparities between perspectives. However, studies often confounded angle with left/right representational incongruence between perspectives. In our previous iEEG study, we found robust TPJ activity during both SPJ and VPT, possibly reflecting the processing of self-other representations. To test whether self-other representations are processed during both perspective judgments, we analyzed behavioral data from the same participants. We categorized trials by an object’s left/right congruence between the perspectives. We hypothesized that if self-other representations are processed during both judgments, participants will show greater difficulty in incongruent vs. congruent scenarios during SPJ and VPT. Linear mixed model analysis confirmed this: for both SPJ and VPT, errors were higher on incongruent than congruent trials. These findings show that self-other representations are processed in both SPJ and VPT, and incongruent ones cause interference, highlight left/right congruence as a key factor in VPT studies, and motivate further research with a similar approach on TPJ’s role in processing multimodal self-other representations.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s00426-026-02311-8.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

OSF wuapg

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

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

The data and code that support the findings of this study are available in the Open Science Framework at https://osf.io/wuapg/.

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

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 12 MeSH terms, 4 funders, 62 references.

Cite

This paper

Gunia, A., Kalina, A., Javůrková, A., & Vlček, K. (2026). The behavioral evidence of processing congruences and incongruences between Self- and Other-perspective-related representations during both perspective judgments. Psychological research, 90(4), 134. https://doi.org/10.1007/s00426-026-02311-8

BibTeX

@article{gunia2026behavioral,
author = {Gunia, Anna and Kalina, Adam and Javůrková, Alena and Vlček, Kamil},
title = {{The behavioral evidence of processing congruences and incongruences between Self- and Other-perspective-related representations during both perspective judgments}},
journal = {Psychological research},
year = {2026},
month = jul,
volume = {90},
number = {4},
pages = {134},
publisher = {Springer Science+Business Media},
issn = {0340-0727},
doi = {10.1007/s00426-026-02311-8},
url = {https://doi.org/10.1007/s00426-026-02311-8},
pmid = {42439946},
pmcid = {PMC13364830}
}

RIS

TY - JOUR
AU - Gunia, Anna
AU - Kalina, Adam
AU - Javůrková, Alena
AU - Vlček, Kamil
TI - The behavioral evidence of processing congruences and incongruences between Self- and Other-perspective-related representations during both perspective judgments
T2 - Psychological research
J2 - Psychol Res
PY - 2026
DA - 2026/07/13
VL - 90
IS - 4
SP - 134
SN - 0340-0727
PB - Springer Science+Business Media
DO - 10.1007/s00426-026-02311-8
UR - https://doi.org/10.1007/s00426-026-02311-8
LA - en
ER -

CSL-JSON

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"id": "10.1007/s00426-026-02311-8",
"type": "article-journal",
"title": "The behavioral evidence of processing congruences and incongruences between Self- and Other-perspective-related representations during both perspective judgments",
"container-title": "Psychological research",
"author": [
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"family": "Gunia",
"given": "Anna"
},
{
"family": "Kalina",
"given": "Adam"
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"family": "Javůrková",
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{
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"container-title-short": "Psychol Res",
"volume": "90",
"issue": "4",
"page": "134",
"DOI": "10.1007/s00426-026-02311-8",
"PMID": "42439946",
"PMCID": "PMC13364830",
"ISSN": "0340-0727",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s00426-026-02311-8",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
13
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]
}
}

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