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Environment geometry alters sequential route learning and its integration into cognitive maps.

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  1. ---
  2. title: "CURCI Figures"
  3. output: html_document
  4. date: "2024-06-08"
  5. ---
  7. ```{r setup, include=FALSE #Libraries and fonts}
  8. knitr::opts_chunk$set(echo = FALSE)
  9. library(readr)
  10. options(readr.show_col_types = FALSE)
  11. library(tidyr)
  12. library(influence.ME)
  13. library(MuMIn)
  14. library(car)
  15. library(reshape2)
  16. library(gdata)
  17. library(dplyr)
  18. library(afex)
  19. library(ggplot2)
  20. install.packages("debug")
  21. library(plotrix)
  22. library(showtext)
  23. font_add_google("Quicksand", family = "Quicksand")
  24. showtext_auto()
  25. ```
  27. ```{r #Retrace SME by Shape}
  29. CURCI <- read_csv("C:/Users/clm/Documents/CURCI-G Procrustes/Output - with scaling/Final_CURCIG_FP_procrustes.csv")
  30. CURCI$Shape = factor(CURCI$Shape)
  32. CURCI_red <- (CURCI %>%
  33. group_by(ID,Shape) %>%
  34. #group_by(Shape) %>%
  35. summarise(Mean_SME = mean(Mean_SME, na.rm = TRUE)))
  37. gd <- (CURCI %>%
  38. group_by(Shape) %>%
  39. summarise(Mean_SME = mean(Mean_SME, na.rm = TRUE)))
  41. gdstde <- (CURCI %>%
  42. group_by(Shape) %>%
  43. summarise(stde = std.error(Mean_SME, na.rm = TRUE)))
  45. windows.options(width = 1, height = 1, reset = FALSE)
  46. SMEbyShape <- ggplot(CURCI_red, aes(x = Shape, y = Mean_SME,fill=Shape,color=Shape)) +
  47. geom_point() +
  48. #geom_errorbar(data = gd, aes(x = Shape, ymin=Mean_SME-unlist(gdstde[,2]),ymax=Mean_SME+unlist(gdstde[,2])), width=.5, color = "black") +
  49. geom_line(aes(group = CURCI_red$ID), color = "black",alpha = .2 ) +
  50. geom_bar(data = gd, stat = "identity", alpha = .1) +
  51. ylim(0,25) +
  52. theme_bw() +
  53. theme(legend.position="none") +
  54. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  55. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  56. labs(
  57. title = "First-Person",
  58. y = expression("SME "[FP]),
  59. x = NULL) +
  60. theme(text=element_text(size=16, family="Quicksand"))
  62. plot(SMEbyShape)
  64. ```
  66. ```{r #Retrace SME by Object}
  68. CURCI_indv <- read_csv("C:/Users/clm/Documents/CURCI-G Procrustes/Output - with scaling/Final for R/Final_CURCIG_FP_i_procrustes.csv")
  70. Shapes = "Trapezoid" # Blank, Square, or Trapezoid
  71. #CURCI_indv <- subset(CURCI_indv,Shape==Shapes) #Uncomment to subset shapes
  73. CURCI_redob <- (CURCI_indv %>%
  74. group_by(ID,Object) %>%
  75. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  77. CURCI_indvS <- subset(CURCI_indv,Shape=="Square")
  78. CURCI_indvT <- subset(CURCI_indv,Shape=="Trapezoid")
  80. gd2 <- (CURCI_indv %>%
  81. group_by(Object) %>%
  82. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  84. #Uncomment for double line graph
  85. gd2.S <- (CURCI_indvS %>%
  86. group_by(Object) %>%
  87. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  88. gd2.T <- (CURCI_indvT %>%
  89. group_by(Object) %>%
  90. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  92. ObjectbyObjectSME <- ggplot(CURCI_redob, aes(x = Object, y = ObjectSME,fill=Object,colour=Object)) +
  93. geom_point(size = .5) +
  94. #geom_line(data = gd2, stat = "identity", alpha = 1, show.legend = TRUE) +
  95. geom_line(data = gd2.S, stat = "identity", alpha = 1, show.legend = TRUE,colour="red") + #uncomment 4 double line graph
  96. geom_line(data = gd2.T, stat = "identity", alpha = 1, show.legend = TRUE,colour="blue") + #uncomment 4 double line graph
  97. #geom_bar(data = gd2, stat = "identity", alpha = .1) + #comment for double line graph
  98. theme_bw() +
  99. ylim(0,25) +
  100. theme(legend.position="none") +
  101. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  102. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  103. labs(
  104. title = "First-Person",
  105. y = expression("SME "[FP]),
  106. x = "Object Order") +
  107. theme(text=element_text(size=16, family="Quicksand"))
  109. plot(ObjectbyObjectSME)
  110. ```
  112. ```{r #Retrace SME by Even Odd}
  114. CURCI_indv <- read_csv("C:/Users/clm/Documents/CURCIBehDataindvR.csv")
  116. #Shapes = "Trapezoid" # Blank, Square, or Trapezoid
  117. #CURCI_indv <- subset(CURCI_indv,Shape==Shapes) #Uncomment to subset shapes
  119. CURCI_redob <- (CURCI_indv %>%
  120. group_by(ID,EvenOdd) %>%
  121. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  123. CURCI_indvS <- subset(CURCI_indv,Shape=="Square")
  124. CURCI_indvT <- subset(CURCI_indv,Shape=="Trapezoid")
  126. gd2 <- (CURCI_indv %>%
  127. group_by(EvenOdd) %>%
  128. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  130. SMEbyEvenOdd <- ggplot(CURCI_redob, aes(x = EvenOdd, y = ObjectSME,fill=EvenOdd,color=EvenOdd)) +
  131. geom_point(size = .5) +
  132. geom_bar(data = gd2, stat = "identity", alpha = .1) + #comment for double line graph
  133. theme_bw() +
  134. ylim(0,25) +
  135. theme(legend.position="none") +
  136. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  137. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  138. labs(
  139. title = "First-Person",
  140. y = expression("SME "[FP]),
  141. x = "Object Order") +
  142. theme(text=element_text(size=16, family="Quicksand"))
  144. plot(SMEbyEvenOdd)
  145. ```
  147. ```{r #Retrace Narrow vs. Wide}
  149. CURCI_indv <- read_csv("C:/Users/clm/Documents/CURCI-G Procrustes/Output - with scaling/Final for R/Final_CURCIG_FP_i_procrustes.csv")
  150. CURCI_indv <- subset(CURCI_indv,Shape=="Trapezoid")
  152. CURCI_redob <- (CURCI_indv %>%
  153. group_by(ID,NarWide) %>%
  154. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  156. gd <- (CURCI_indv %>%
  157. group_by(NarWide) %>%
  158. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  160. NarrowvsWide <- ggplot(CURCI_redob, aes(x = NarWide, y = ObjectSME,fill=NarWide,color=NarWide)) +
  161. geom_point(size = 2) +
  162. #geom_errorbar(aes(ymin=Mean_SME-std.error(), ymax=Mean_SME+sd), width=.2,
  163. #position=position_dodge(.9)) +
  164. geom_line(aes(group = CURCI_redob$ID), color = "black",alpha = .2 ) +
  165. geom_bar(data = gd, stat = "identity", alpha = .1) +
  166. ylim(0,25) +
  167. theme_bw() +
  168. theme(legend.position="none") +
  169. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  170. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  171. labs(
  172. title = "First-Person",
  173. y = expression("SME "[FP]),
  174. x = NULL) +
  175. theme(text=element_text(size=16, family="Quicksand"))
  177. plot(NarrowvsWide)
  179. ```
  181. ```{r #Retrace Indv Object Order Comparisons}
  183. CURCI_indv <- read_csv("C:/Users/clm/Documents/CURCI-G Procrustes/Output - with scaling/Final for R/Final_CURCIG_FP_i_procrustes.csv")
  185. CURCI_indv <- subset(CURCI_indv, Object==6 | Object==4 | Object==2)
  186. CURCI_indv <- subset(CURCI_indv, NarWide != "Wide")
  188. CURCI_redob <- (CURCI_indv %>%
  189. group_by(ID,NarWide) %>%
  190. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  191. CURCI_redob$NarWide <- factor(CURCI_redob$NarWide, levels=c("Square", "Wide", "Narrow"))
  193. CURCI_redob <- subset(CURCI_redob, ObjectSME < 15)
  195. gd <- (CURCI_indv %>%
  196. group_by(NarWide) %>%
  197. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  199. indvnarwide <- ggplot(CURCI_redob, aes(x = NarWide, y = ObjectSME,fill=NarWide,color=NarWide)) +
  200. geom_point(size = 2) +
  201. #geom_errorbar(aes(min=Mean_SME-std.error(), ymax=Mean_SME+sd), width=.2,
  202. #position=position_dodge(.9)) +
  203. geom_line(aes(group = CURCI_redob$ID), color = "black",alpha = .2 ) +
  204. geom_bar(data = gd, stat = "identity", alpha = .1) +
  205. ylim(0,25) +
  206. theme_bw() +
  207. theme(legend.position="none") +
  208. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  209. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  210. labs(
  211. title = "Narrow vs. Square (First-Person)",
  212. y = expression("SME "[FP]),
  213. x = NULL) +
  214. theme(text=element_text(size=16, family="Quicksand"))
  216. plot(indvnarwide)
  217. ```
  219. ```{r #Retrace Heatmaps}
  221. Environments <- list("A1A","A1B","A2A","A2B","B1A","B1B","B2A","B2B")
  223. for (Environment in Environments)
  224. {
  226. CURCI_PTindv <- read_csv("C:/Users/clm/Documents/EXEsPT/CompletePTDataIndv.csv")
  227. CURCI_Retracekey <- read_csv("C:/Users/clm/Documents/EXEsPT/RetraceKey.csv")
  229. CURCI_Retrace <- subset(CURCI_PTindv,env==Environment)
  230. CURCI_Retracekey.env <- subset(CURCI_Retracekey,env==Environment)
  232. Loop <- c(1,2,3,4)
  233. Retraces <- c("Retrace One", "Retrace Two", "Retrace Three", "Average Retrace")
  234. RetraceX <- colnames(CURCI_Retrace)[16:19]
  235. RetraceY <- colnames(CURCI_Retrace)[20:23]
  237. for (Retrace in Loop)
  239. {
  241. png(file=paste("C:/Users/clm/Documents/EXEsPT/RetraceHeatMaps/",Environment,as.character(Retraces[Retrace]),".png",sep=""),
  242. width=600, height=600)
  244. RetraceXNow <- RetraceX[Retrace]
  245. RetraceYNow <- RetraceY[Retrace]
  247. Retraceindv.env <- ggplot(CURCI_Retrace, aes(x = .data[[RetraceXNow]], y = .data[[RetraceYNow]])) +
  248. geom_point(size = 1) +
  249. geom_point(data=CURCI_Retracekey.env, aes(x = x_cor, y = y_cor),color="red",size=2) +
  250. xlim(-40,40) +
  251. ylim(-40,40) +
  252. theme_bw() +
  253. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  254. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  255. labs(
  256. title = paste(Environment,as.character(Retraces[Retrace])),
  257. y = "Y Coordinate",
  258. x = "X Coordinate") +
  259. theme(text=element_text(size=16, family="Quicksand"))
  261. plot(Retraceindv.env)
  263. dev.off()
  265. }
  266. }
  268. ```
  270. ```{r #PT Heatmaps}
  272. Environments <- list("A1A","A1B","A2A","A2B","B1A","B1B","B2A","B2B")
  274. for (Environment in Environments)
  276. {
  278. CURCI_PTindv <- read_csv("C:/Users/clm/Documents/EXEsPT/CompletePTDataIndv.csv")
  279. CURCI_PTkey <- read_csv("C:/Users/clm/Documents/EXEsPT/PTKey.csv")
  281. CURCI_PTindv.env <- subset(CURCI_PTindv,env==Environment)
  282. CURCI_PTkey.env <- subset(CURCI_PTkey,env==Environment)
  284. png(file=paste("C:/Users/clm/Documents/EXEsPT/PTHeatMaps/",Environment,".png",sep=""),
  285. width=600, height=600)
  287. PTindv.env <- ggplot(CURCI_PTindv.env, aes(x = x_cor, y = y_cor)) +
  288. geom_point(size = 1) +
  289. geom_point(data=CURCI_PTkey.env,color="red",size=2) +
  290. xlim(-800,100) +
  291. ylim(-300,600) +
  292. theme_bw() +
  293. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  294. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  295. labs(
  296. title = Environment,
  297. y = "Y Coordinate",
  298. x = "X Coordinate") +
  299. theme(text=element_text(size=16, family="Quicksand"))
  301. plot(PTindv.env)
  303. dev.off()
  305. }
  306. ```
  308. ```{r #PT SME by Shape}
  310. CURCI_PT <- read_csv("C:/Users/clm/Documents/CURCI-G Procrustes/Output - with scaling/Final_CURCIG_O_procrustes.csv")
  312. titled = "Overhead"
  313. #CURCI_PT <- subset(CURCI_PT,SOD>=4) #change to filter for SOD
  315. CURCI_redPT <- (CURCI_PT %>%
  316. group_by(ID,Shape) %>%
  317. #group_by(Shape) %>%
  318. summarise(Mean_SME = mean(Mean_SME, na.rm = TRUE)))
  320. gdPT <- (CURCI_PT %>%
  321. group_by(Shape) %>%
  322. summarise(Mean_SME = mean(Mean_SME, na.rm = TRUE)))
  324. PTSMEbyShape <- ggplot(CURCI_redPT, aes(x = Shape, y = Mean_SME,fill=Shape,color=Shape)) +
  325. geom_point(size = 2) +
  326. #geom_errorbar(aes(ymin=Mean_SME-std.error(), ymax=Mean_SME+sd), width=.2,
  327. #position=position_dodge(.9)) +
  328. geom_line(aes(group = CURCI_redPT$ID), color = "black",alpha = .2 ) +
  329. geom_bar(data = gdPT, stat = "identity", alpha = .1) +
  330. theme_bw() +
  331. ylim(0,400) +
  332. theme(legend.position="none") +
  333. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  334. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  335. labs(
  336. title = "Overhead",
  337. y = expression("SME "[O]),
  338. x = NULL) +
  339. theme(text=element_text(size=16, family="Quicksand"))
  341. plot(PTSMEbyShape)
  342. ```
  344. ```{r #PT SME by Object}
  346. CURCI_PTindv <- read_csv("C:/Users/clm/Documents/CURCI-G Procrustes/Output - with scaling/Final for R/Final_CURCIG_O_i_procrustes.csv")
  348. Shapes = "Trapezoid"
  349. #CURCI_PTindv <- subset(CURCI_PTindv,Shape==Shapes) #to subset the shapes
  351. CURCI_PTredob <- (CURCI_PTindv %>%
  352. group_by(ID,Object) %>%
  353. #group_by(Shape) %>%
  354. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  356. CURCI_indvPT.S <- subset(CURCI_PTindv,Shape=="Square")
  357. CURCI_indvPT.T <- subset(CURCI_PTindv,Shape=="Trapezoid")
  359. gdPT2 <- (CURCI_PTindv %>%
  360. group_by(Object) %>%
  361. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  363. gdPT2.S <- (CURCI_indvPT.S %>%
  364. group_by(Object) %>%
  365. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  366. gdPT2.T <- (CURCI_indvPT.T %>%
  367. group_by(Object) %>%
  368. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  370. PTObjectbyObjectSME <- ggplot(CURCI_PTredob, aes(x = Object, y = ObjectSME,fill=Object,color=Object)) +
  371. geom_point(size = .5) +
  372. #geom_line(data = gdPT2, stat = "identity", alpha = 1, show.legend = TRUE) +
  373. geom_line(data = gdPT2.S, stat = "identity", alpha = 1, show.legend = TRUE,color="red") +
  374. geom_line(data = gdPT2.T, stat = "identity", alpha = 1, show.legend = TRUE,color="blue") +
  375. theme_bw() +
  376. ylim(0,400) +
  377. theme(legend.position="none") +
  378. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  379. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  380. labs(
  381. title = "Overhead",
  382. y = expression("SME "[O]),
  383. x = "Object Order") +
  384. theme(text=element_text(size=16, family="Quicksand"))
  386. plot(PTObjectbyObjectSME)
  387. ```
  389. ```{r #PT SME by EvenOdd}
  391. CURCI_PTindv <- read_csv("C:/Users/clm/Documents/EXEsPT/CompletePTDataIndv.csv")
  393. #Shapes = "Square"
  394. #CURCI_PTindv <- subset(CURCI_PTindv,shape==Shapes) #to subset the shapes
  396. CURCI_PTredob <- (CURCI_PTindv %>%
  397. group_by(id,evenodd) %>%
  398. summarise(euclidian = mean(euclidian, na.rm = TRUE)))
  400. CURCI_indvPT.S <- subset(CURCI_PTindv,shape=="Square")
  401. CURCI_indvPT.T <- subset(CURCI_PTindv,shape=="Trapezoid")
  403. gdPT2 <- (CURCI_PTindv %>%
  404. group_by(evenodd) %>%
  405. summarise(euclidian = mean(euclidian, na.rm = TRUE)))
  407. PTSMEbyEvenOdd <- ggplot(CURCI_PTredob, aes(x = evenodd, y = euclidian,fill=evenodd,color=evenodd)) +
  408. geom_point(size = .5) +
  409. geom_bar(data = gdPT2, stat = "identity", alpha = .1) +
  410. theme_bw() +
  411. ylim(0,400) +
  412. theme(legend.position="none") +
  413. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  414. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  415. labs(
  416. title = "Overhead",
  417. y = expression("SME "[O]),
  418. x = "Object Order") +
  419. theme(text=element_text(size=16, family="Quicksand"))
  421. plot(PTSMEbyEvenOdd)
  422. ```
  424. ```{r #PT Narrow vs. Wide}
  426. CURCI_PTindv <- read_csv("C:/Users/clm/Documents/CURCI-G Procrustes/Output - with scaling/Final for R/Final_CURCIG_O_i_procrustes.csv")
  427. CURCI_PTindv <- subset(CURCI_PTindv,Shape=="Trapezoid")
  428. #CURCI_PTindv <- subset(CURCI_PTindv,SOD>=4)
  430. CURCI_redob <- (CURCI_PTindv %>%
  431. group_by(ID,NarWide) %>%
  432. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  434. gd <- (CURCI_PTindv %>%
  435. group_by(NarWide) %>%
  436. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  438. NarrowvsWide <- ggplot(CURCI_redob, aes(x = NarWide, y = ObjectSME,fill=NarWide,color=NarWide)) +
  439. geom_point(size = 2) +
  440. #geom_errorbar(aes(ymin=Mean_SME-std.error(), ymax=Mean_SME+sd), width=.2,
  441. #position=position_dodge(.9)) +
  442. geom_line(aes(group = CURCI_redob$ID), color = "black",alpha = .2 ) +
  443. geom_bar(data = gd, stat = "identity", alpha = .1) +
  444. theme_bw() +
  445. ylim(0,400) +
  446. theme(legend.position="none") +
  447. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  448. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  449. labs(
  450. title = "Overhead",
  451. y = expression("SME "[O]),
  452. x = NULL) +
  453. theme(text=element_text(size=16, family="Quicksand"))
  455. plot(NarrowvsWide)
  457. ```
  459. ```{r #PT Indv Object Order Comparisons}
  461. CURCI_PTindv <- read_csv("C:/Users/clm/Documents/CURCI-G Procrustes/Output - with scaling/Final for R/Final_CURCIG_O_i_procrustes.csv")
  463. CURCI_PTindv <- subset(CURCI_PTindv, Object==6 | Object ==4 | Object==2)
  464. CURCI_PTindv <- subset(CURCI_PTindv, NarWide != "Wide")
  466. CURCI_redob <- (CURCI_PTindv %>%
  467. group_by(ID,NarWide) %>%
  468. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  469. CURCI_redob$NarWide <- factor(CURCI_redob$NarWide, levels=c("Square", "Wide", "Narrow"))
  471. gd <- (CURCI_PTindv %>%
  472. group_by(NarWide) %>%
  473. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  475. indvnarwide <- ggplot(CURCI_redob, aes(x = NarWide, y = ObjectSME,fill=NarWide,color=NarWide)) +
  476. geom_point(size = 2) +
  477. #geom_errorbar(aes(ymin=Mean_SME-std.error(), ymax=Mean_SME+sd), width=.2,
  478. #position=position_dodge(.9)) +
  479. geom_line(aes(group = CURCI_redob$ID), color = "black",alpha = .2 ) +
  480. geom_bar(data = gd, stat = "identity", alpha = .1) +
  481. theme_bw() +
  482. ylim(0,400) +
  483. theme(legend.position="none") +
  484. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  485. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  486. labs(
  487. title = "Narrow vs. Square (Overhead)",
  488. y = expression("SME "[O]),
  489. x = NULL) +
  490. theme(text=element_text(size=16, family="Quicksand"))
  492. plot(indvnarwide)
  493. ```
  495. ```{r #SME by Environment}
  497. CURCI_indv <- read_csv("C:/Users/clm/Documents/CURCIBehDataindvR.csv")
  498. CURCI_indv <- subset(CURCI_indv, Path_Version=="A") #change for version
  500. CURCI_redob <- (CURCI_indv %>%
  501. group_by(ID,Environment) %>%
  502. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  504. gd2 <- (CURCI_indv %>%
  505. group_by(Environment) %>%
  506. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  508. SMEbyEnv <- ggplot(CURCI_redob, aes(x = Environment, y = ObjectSME,fill=Environment,color=Environment)) +
  509. geom_point(size = .5) +
  510. geom_bar(data = gd2, stat = "identity", alpha = .1) + #comment for double line graph
  511. theme_bw() +
  512. theme(legend.position="none") +
  513. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  514. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  515. labs(
  516. title = "Version A", #change for version
  517. y = "SME",
  518. x = "Environment") +
  519. theme(text=element_text(size=16, family="Quicksand"))
  521. plot(SMEbyEnv)
  523. ```
  525. ```{r #SME by Path}
  527. CURCI_indv <- read_csv("C:/Users/clm/Documents/CURCIBehDataindvR.csv")
  529. CURCI_redob <- (CURCI_indv %>%
  530. group_by(ID,Path_Version) %>%
  531. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  533. gd2 <- (CURCI_indv %>%
  534. group_by(Path_Version) %>%
  535. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  537. SMEbyPath <- ggplot(CURCI_redob, aes(x = Path_Version, y = ObjectSME,fill=Path_Version,color=Path_Version)) +
  538. geom_point(size = .5) +
  539. geom_bar(data = gd2, stat = "identity", alpha = .1) + #comment for double line graph
  540. theme_bw() +
  541. theme(legend.position="none") +
  542. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  543. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  544. labs(
  545. y = "SME",
  546. x = "Path") +
  547. theme(text=element_text(size=16, family="Quicksand"))
  549. plot(SMEbyPath)
  551. ```
  553. ```{r #SME by Reptition}
  554. CURCI <- read_csv("C:/Users/clm/Documents/CURCIBehData2.csv")
  556. CURCI_red <- (CURCI %>%
  557. group_by(ID,Repetition) %>%
  558. summarise(Mean_SME = mean(Mean_SME, na.rm = TRUE)))
  560. gd <- (CURCI %>%
  561. group_by(Repetition) %>%
  562. summarise(Mean_SME = mean(Mean_SME, na.rm = TRUE)))
  564. SMEbyRepetition <- ggplot(CURCI_red, aes(x = Repetition, y = Mean_SME,fill=Repetition,color=Repetition)) +
  565. geom_point() +
  566. geom_line(aes(group = CURCI_red$ID), color = "black",alpha = .2 ) +
  567. geom_bar(data = gd, stat = "identity", alpha = .1) +
  568. theme_bw() +
  569. theme(legend.position="none") +
  570. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  571. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  572. labs(
  573. y = "SME",
  574. x = "Repetition") +
  575. theme(text=element_text(size=16, family="Quicksand"))
  577. plot(SMEbyRepetition)
  578. ```
  580. ```{r #Retrace SME by SOD}
  581. CURCI <- read_csv("C:/Users/clm/Documents/CURCI-G Procrustes/Output - with scaling/Final_CURCIG_FP_procrustes.csv")
  582. CURCI$Shape = factor(CURCI$Shape)
  584. #Shapes = "Low" # Blank, Square, or Trapezoid
  585. #CURCI <- subset(CURCI,Shape==Shapes) #Uncomment to subset shapes
  586. #CURCI <- subset(CURCI,SOD<4)
  588. CURCI_red <- (CURCI %>%
  589. group_by(ID,SOD) %>%
  590. #group_by(Shape) %>%
  591. summarise(Mean_SME = mean(Mean_SME, na.rm = TRUE)))
  593. SMEbySOD <- ggplot(CURCI_red, aes(x = SOD, y = Mean_SME,fill=SOD,color=SOD)) +
  594. geom_point() +
  595. geom_smooth(method=lm) +
  596. #geom_errorbar(aes(ymin=Mean_SME-std.error(), ymax=Mean_SME+sd), width=.2,
  597. #position=position_dodge(.9)) +
  598. theme_bw() +
  599. theme(legend.position="none") +
  600. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  601. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  602. ylim(0,15) +
  603. labs(
  604. title = "First-Person",
  605. y = expression("SME "[FP]),
  606. x = "SOD") +
  607. theme(text=element_text(size=16, family="Quicksand"))
  609. plot(SMEbySOD)
  610. ```
  612. ```{r #PT SME by SOD}
  613. CURCI_PT <- read_csv("C:/Users/clm/Documents/CURCI-G Procrustes/Output - with scaling/Final_CURCIG_O_procrustes.csv")
  615. #titled = "All Low SOD Trapezoid" # Blank, Square, or Trapezoid
  616. #CURCI_PT <- subset(CURCI_PT,shape=="Square") #Uncomment to subset shapes
  617. #CURCI_PT <- subset(CURCI_PT,SOD<4) #Uncomment to subset SOD
  619. CURCI_redPT <- (CURCI_PT %>%
  620. group_by(ID,SOD) %>%
  621. #group_by(Shape) %>%
  622. summarise(Mean_SME = mean(Mean_SME, na.rm = TRUE)))
  624. PTSMEbySOD <- ggplot(CURCI_redPT, aes(x = SOD, y = Mean_SME,fill=SOD,color=SOD)) +
  625. geom_point() +
  626. geom_smooth(method=lm) +
  627. #geom_errorbar(aes(ymin=Mean_SME-std.error(), ymax=Mean_SME+sd), width=.2,
  628. #position=position_dodge(.9)) +
  629. theme_bw() +
  630. ylim(0,250) +
  631. theme(legend.position="none") +
  632. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  633. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  634. labs(
  635. title = "Overhead",
  636. y = expression("SME "[O]),
  637. x = "SOD") +
  638. theme(text=element_text(size=16, family="Quicksand"))
  640. plot(PTSMEbySOD)
  641. ```
  643. ```{r #Retrace Narrow vs. Wide BOXPLOT}
  645. CURCI_indv <- read_csv("C:/Users/clm/Documents/CURCIBehDataindvR.csv")
  646. CURCI_indv <- subset(CURCI_indv,Shape=="Trapezoid")
  648. CURCI_redob <- (CURCI_indv %>%
  649. group_by(ID,NarWide) %>%
  650. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  652. gd <- (CURCI_redob %>%
  653. group_by(NarWide) %>%
  654. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  656. gdstde <- (CURCI_redob %>%
  657. group_by(NarWide) %>%
  658. summarise(stde = std.error(ObjectSME, na.rm = TRUE)))
  660. NarrowvsWide <- ggplot(CURCI_redob, aes(x = NarWide, y = ObjectSME,fill=NarWide,color=NarWide)) +
  661. geom_boxplot(alpha = .35) +
  662. ylim(0,25) +
  663. theme_bw() +
  664. theme(legend.position="none") +
  665. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  666. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  667. labs(
  668. title = "First-Person",
  669. y = expression("SME "[FP]),
  670. x = NULL) +
  671. theme(text=element_text(size=16, family="Quicksand"))
  673. plot(NarrowvsWide)
  675. #rev = subset(CURCI_redob,NarWide=="Narrow")
  676. #rev = subset(CURCI_redob,NarWide=="Wide")
  678. ```
  680. ```{r #PT Narrow vs. Wide BOXPLOT}
  682. CURCI_PTindv <- read_csv("C:/Users/clm/Documents/Other/EXEsPT/CompletePTDataIndv.csv")
  683. CURCI_PTindv <- subset(CURCI_PTindv,Shape=="Trapezoid")
  684. #CURCI_PTindv <- subset(CURCI_PTindv,SOD>=4)
  686. CURCI_redob <- (CURCI_PTindv %>%
  687. group_by(ID,NarWide) %>%
  688. summarise(euclidian = mean(euclidian, na.rm = TRUE)))
  690. gd <- (CURCI_redob %>%
  691. group_by(NarWide) %>%
  692. summarise(euclidian = mean(euclidian, na.rm = TRUE)))
  694. gdstde <- (CURCI_redob %>%
  695. group_by(NarWide) %>%
  696. summarise(stde = std.error(euclidian, na.rm = TRUE)))
  698. NarrowvsWide <- ggplot(CURCI_redob, aes(x = NarWide, y = euclidian,fill=NarWide,color=NarWide)) +
  699. geom_boxplot(alpha= .35) +
  700. theme_bw() +
  701. ylim(0,400) +
  702. theme(legend.position="none") +
  703. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  704. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  705. labs(
  706. title = "Overhead",
  707. y = expression("SME "[O]),
  708. x = NULL) +
  709. theme(text=element_text(size=16, family="Quicksand"))
  711. plot(NarrowvsWide)
  713. ptIQR = subset(CURCI_redob,NarWide=="Narrow")
  714. IQR(ptIQR$euclidian, na.rm = TRUE)
  716. ```
  718. ```{r #Retrace Indv Object Order Comparisons BOXPLOT}
  720. CURCI_indv <- read_csv("C:/Users/clm/Documents/CURCIBehDataindvR.csv")
  722. CURCI_indv <- subset(CURCI_indv, Object==6 | Object==4 | Object==2)
  723. CURCI_indv <- subset(CURCI_indv, NarWide != "Wide")
  725. CURCI_redob <- (CURCI_indv %>%
  726. group_by(ID,Shape) %>%
  727. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  729. CURCI_redob <- subset(CURCI_redob, ObjectSME < 15)
  731. gd <- (CURCI_redob %>%
  732. group_by(Shape) %>%
  733. summarise(ObjectSME = mean(ObjectSME, na.rm = TRUE)))
  735. gdstde <- (CURCI_redob %>%
  736. group_by(Shape) %>%
  737. summarise(stde = std.error(ObjectSME, na.rm = TRUE)))
  739. indvnarwide <- ggplot(CURCI_redob, aes(x = Shape, y = ObjectSME,fill=Shape,color=Shape)) +
  740. geom_boxplot(alpha = .35) +
  741. ylim(0,25) +
  742. theme_bw() +
  743. theme(legend.position="none") +
  744. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  745. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  746. labs(
  747. title = "Wide vs. Square (First-Person)",
  748. y = expression("SME "[FP]),
  749. x = NULL) +
  750. theme(text=element_text(size=16, family="Quicksand"))
  752. plot(indvnarwide)
  753. ```
  755. ```{r #PT Indv Object Order Comparisons BOXPLOT}
  757. CURCI_PTindv <- read_csv("C:/Users/clm/Documents/Other/EXEsPT/CompletePTDataIndv.csv")
  759. CURCI_PTindv <- subset(CURCI_PTindv, Object==6 | Object ==4 | Object==2)
  760. CURCI_PTindv <- subset(CURCI_PTindv, NarWide != "Wide")
  762. CURCI_redob <- (CURCI_PTindv %>%
  763. group_by(ID,Shape) %>%
  764. summarise(euclidian = mean(euclidian, na.rm = TRUE)))
  766. gd <- (CURCI_PTindv %>%
  767. group_by(Shape) %>%
  768. summarise(euclidian = mean(euclidian, na.rm = TRUE)))
  770. gdstde <- (CURCI_redob %>%
  771. group_by(Shape) %>%
  772. summarise(stde = std.error(euclidian, na.rm = TRUE)))
  774. indvnarwide <- ggplot(CURCI_redob, aes(x = Shape, y = euclidian,fill=Shape,color=Shape)) +
  775. geom_boxplot(alpha = .35) +
  776. theme_bw() +
  777. ylim(0,400) +
  778. theme(legend.position="none") +
  779. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  780. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  781. labs(
  782. title = "Wide vs. Square (Overhead)",
  783. y = expression("SME "[O]),
  784. x = NULL) +
  785. theme(text=element_text(size=16, family="Quicksand"))
  787. plot(indvnarwide)
  788. ```
  790. ```{r #PT SME by Shape BOXPLOT}
  792. CURCI_PT <- read_csv("C:/Users/clm/Documents/Other/EXEsPT/CompletePTData.csv")
  794. titled = "Overhead"
  795. CURCI_PT <- subset(CURCI_PT,SOD<4.6) #change to filter for SOD
  797. CURCI_redPT <- (CURCI_PT %>%
  798. group_by(ID,Shape) %>%
  799. #group_by(Shape) %>%
  800. summarise(euclidian = mean(euclidian, na.rm = TRUE)))
  802. gd <- (CURCI_redPT %>%
  803. group_by(Shape) %>%
  804. summarise(euclidian = mean(euclidian, na.rm = TRUE)))
  806. gdstde <- (CURCI_redPT %>%
  807. group_by(Shape) %>%
  808. summarise(stde = std.error(euclidian, na.rm = TRUE)))
  810. PTSMEbyShape <- ggplot(CURCI_redPT, aes(x = Shape, y = euclidian,fill=Shape,color=Shape)) +
  811. geom_boxplot(alpha = .35) +
  812. theme_bw() +
  813. ylim(0,400) +
  814. theme(legend.position="none") +
  815. theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  816. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  817. labs(
  818. title = "Low SOD (Overhead)",
  819. y = expression("SME "[O]),
  820. x = NULL) +
  821. theme(text=element_text(size=16, family="Quicksand"))
  823. plot(PTSMEbyShape)
  825. IQR = subset(CURCI_redPT,Shape=="Square")
  826. IQR(IQR$euclidian, na.rm = TRUE)
  827. ```

CURCI Figures General Script.Rmd at commit c44cb8e, no license · at the source

Overview

Authors: Jaida Long1,2, Estibaliz Herrera1,2, Yiran Li3, Felipe Oliveira1, Rida Ahmed4, Sana Hussain1, Camille Rivera5, Kristin Liquori1, Thackery Brown1,2
ORCID iDs: Jaida Long, Thackery Brown
  1. Georgia Institute of Technology,Atlanta, GA USA
  2. Center for Research and Education in Navigation, Atlanta, GA USA
  3. University of Waterloo,Waterloo, ON Canada
  4. Georgia State University,Atlanta, GA USA
  5. Albizu University,San Juan, Puerto Rico USA
Institutions: Georgia Institute of Technology (United States); University of Waterloo (Canada); Georgia State University (United States); Carlos Albizu University (Puerto Rico)
Journal: Nature communications, volume 17, issue 1, article 8400
Dates: received 26 June 2025; accepted 18 June 2026; published online 7 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-75129-y · PMID 42414280 · PMCID PMC13473551 · OpenAlex W7167607308
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Statistics
Keywords: Human behaviour, Spatial memory
MeSH: Cognition*, Environment*, Learning*, Spatial Memory*, Spatial Navigation*, Animals, Entorhinal Cortex, Grid Cells, Humans, Space Perception (* major topic)
Topic: Spatial Cognition and Navigation (Automotive Engineering, Engineering), according to OpenAlex
Funding: National Institute on Aging (1R01AG073250-01A1); Shurl and Kay Curci Foundation
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

Researchers hypothesize that geometric irregularities can impede spatial memory by distorting neural metrics of space provided by entorhinal grid cells. However, irregularly-shaped geometries could also contribute orientation information that benefits spatial memory. Moreover, serial order effects could counteract or amplify geometry effects during route navigation. Our study directly tests how effects of environment geometry (trapezoid and square) and series in route navigation interact to influence spatial memory in a virtual navigation task. We uncover effects of environment geometry predicted by grid cell models and serial order effects predicted by path integration models. Critically, our results demonstrate that these effects combine in an additive manner, such that location memory toward the end of a route in regions with the largest geometric irregularities (trapezoid) yield the worst spatial memory. Additionally, self-report measures of navigational ability are associated with increased sensitivity to geometric irregularities, emphasizing the essential role of individual differences in navigation.

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

Repositories

Its files are read in the Code ↔ Paper reader above.

MAPLabgroup/CURCI-G

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c44cb8ec579cfcc854d26a9c1a33a5bfd01e7c79, 5 May 2026
Languages: R (1)
Size: 5 files, 1 script
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: afex (1 file), car (1 file), ggplot2 (1 file), reshape2 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/MAPLabgroup/CURCI-G

Zenodo 20045218

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: afex (1 file), car (1 file), ggplot2 (1 file), reshape2 (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:

Code availability

This study used code for data organization, data analysis, and figure creation. All code and programming scripts that support the findings of this study are available on GitHub (10.5281/zenodo.2004521863): github.com/MAPLabgroup/CURCI-G.

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

Tracing map

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

What the map holds:

  • 2 repositories 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;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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

Data availability

All display items presented in the main manuscript and supplementary information can be reproduced from material shared on GitHub (10.5281/zenodo.2004521863): github.com/MAPLabgroup/CURCI-G. The material in the repository includes all data, including processed and raw data, in addition to any programming scripts used.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 keywords, 10 MeSH terms, 2 funders, 60 references.

Cite

This paper

Long, J., Herrera, E., Li, Y., Oliveira, F., Ahmed, R., Hussain, S., Rivera, C., Liquori, K., & Brown, T. (2026). Environment geometry alters sequential route learning and its integration into cognitive maps. Nature communications, 17(1), 8400. https://doi.org/10.1038/s41467-026-75129-y

BibTeX

@article{long2026environment,
author = {Long, Jaida and Herrera, Estibaliz and Li, Yiran and Oliveira, Felipe and Ahmed, Rida and Hussain, Sana and Rivera, Camille and Liquori, Kristin and Brown, Thackery},
title = {{Environment geometry alters sequential route learning and its integration into cognitive maps}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {8400},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75129-y},
url = {https://doi.org/10.1038/s41467-026-75129-y},
pmid = {42414280},
pmcid = {PMC13473551}
}

RIS

TY - JOUR
AU - Long, Jaida
AU - Herrera, Estibaliz
AU - Li, Yiran
AU - Oliveira, Felipe
AU - Ahmed, Rida
AU - Hussain, Sana
AU - Rivera, Camille
AU - Liquori, Kristin
AU - Brown, Thackery
TI - Environment geometry alters sequential route learning and its integration into cognitive maps
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/07
VL - 17
IS - 1
SP - 8400
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75129-y
UR - https://doi.org/10.1038/s41467-026-75129-y
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-75129-y",
"type": "article-journal",
"title": "Environment geometry alters sequential route learning and its integration into cognitive maps",
"container-title": "Nature communications",
"author": [
{
"family": "Long",
"given": "Jaida"
},
{
"family": "Herrera",
"given": "Estibaliz"
},
{
"family": "Li",
"given": "Yiran"
},
{
"family": "Oliveira",
"given": "Felipe"
},
{
"family": "Ahmed",
"given": "Rida"
},
{
"family": "Hussain",
"given": "Sana"
},
{
"family": "Rivera",
"given": "Camille"
},
{
"family": "Liquori",
"given": "Kristin"
},
{
"family": "Brown",
"given": "Thackery"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "8400",
"DOI": "10.1038/s41467-026-75129-y",
"PMID": "42414280",
"PMCID": "PMC13473551",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-75129-y",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
7
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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[9] doi:10.1192/bjp.2026.10664 [code]
Early effects of a novel 5-HT&lt;sub&gt;4&lt;/sub&gt;R agonist (PF-04995274) and the SSRI citalopram on emotional cognition in unmedicated depression: RESTAND study.
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[10] doi:10.1038/s41467-026-70289-3 [code]
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