OSCR

Long-term memory reorganization of navigational episodes.

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › Statistical analysis ↔ Analysis/OSF_BZT_retro_correlation_regression_models_171125.R, lines 264–330 · score 0.69 · 6–10000, pairwise contrasts, pairwise comparisons, cross, slopes, regression
  2. [2] § Methods › Statistical analysis ↔ Analysis/OSF_BZT_retro_ebbinghaus_simulation_171125.R, lines 130–204 · score 0.61 · Post hoc, SS, marginal, residual, sum, prediction
  3. [3] § Results › Memory of real-world navigational episodes also depends on time-independent factors ↔ Analysis/OSF_BZT_retro_generalized_additive_model_161125.R, lines 46–115 · score 0.60 · generalized additive model, zoo visitors, education, GAM, age, target placement
  4. [4] § Methods › Statistical analysis ↔ Analysis/OSF_BZT_retro_ebbinghaus_simulation_171125.R, lines 130–204 · score 0.55 · post hoc, simulated, marginal, Ebbinghaus, minutes, pairwise
  5. [5] § Results › Memory of real-world navigational episodes transforms nonlinearly across decades ↔ Analysis/OSF_BZT_retro_correlation_regression_models_171125.R, lines 264–330 · score 0.51 · regression models, Pairwise comparisons, slopes, fitting, power, zoo visit

Paper

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The authors' code

R · 444 lines · 17 KB · no license · 2 matches

  1. # ------------------------------------------------------------------------------
  2. # :::::::::::: libraries :::::::::::::::::::::::::::::::::::::::::::::::::::::::
  3. # ------------------------------------------------------------------------------
  4. # data
  5. library(readxl)
  6. library(officer)
  7. library(tidyverse)
  8. # statistic
  9. library(effectsize)
  10. library(emmeans)
  11. library(rstatix)
  12. library(diffcor)
  13. # Model
  14. library(afex)
  15. library(splines)
  16. library(mgcv)
  18. # ------------------------------------------------------------------------------
  19. # :::::::::::::::::::::::: Read in Data ::::::::::::::::::::::::::::::::::::::::
  20. # ------------------------------------------------------------------------------
  22. # questionnaire
  23. qn_zVisit <- read_excel("C:/.../OSF_BZT_retro_zooVisit_data.xlsx", col_names = TRUE)
  24. # Target placement data
  25. data_distance <- read_excel("C:/.../OSF_BZT_retro_targetPlacement_accuracyScore_data.xlsx", col_names = TRUE)
  26. # Direction-Pointing data (without North)
  27. data_deviation <- read_excel("C:/..../OSF_BZT_retro_directionPointing_data.xlsx", col_names = TRUE)
  29. # ------------------------------------------------------------------------------
  30. # ::::::::::::::::::::: Data preparation :::::::::::::::::::::::::::::::::::::::
  31. # ------------------------------------------------------------------------------
  33. # aggregate experimental data per id and group
  34. data_dist <- data_distance %>% group_by(id,zooVisit) %>% summarise(
  35. zscore_dist = mean(z_as*-1,na.rm = TRUE), .groups = "drop")
  36. data_dist$id <- as.double(data_dist$id)
  38. data_dev <- data_deviation %>% group_by(id, zooVisit) %>% summarise(
  39. zscore_dev_b = mean(deviation_zscore_pointing_arrow, na.rm = TRUE),
  40. zscore_dev_ub = mean(deviation_zscore_pointing_video, na.rm = TRUE),
  41. .groups = "drop")
  43. # Merge datasets
  44. data_merged <- reduce(list(qn_zVisit, data_dev, data_dist), full_join, by = c("id", "zooVisit"))
  45. # select data
  46. data_visitor <- subset(data_merged, data_merged$zooVisit==1) # subset zoo-visitor
  48. # ------------------------------------------------------------------------------
  49. # :::::::::::::::::::: Pairwise Correlation & correlation matrix :::::::::::::::
  50. # ------------------------------------------------------------------------------
  52. # pairwise correlations
  53. # direction-pointing with arrow vs. direction-pointing with video
  54. cor_devb_devub <- cor.test(data_visitor$zscore_dev_b,data_visitor$zscore_dev_ub,
  55. method=c( "spearman"))
  56. # target placement vs. direction-pointing with video
  57. cor_dist_devub <- cor.test(data_visitor$zscore_dev_ub,data_visitor$zscore_dist,
  58. method=c( "spearman"))
  59. # target placement vs. direction-pointing with arrow
  60. cor_dist_devb <- cor.test(data_visitor$zscore_dev_b,data_visitor$zscore_dist,
  61. method=c( "spearman"))
  63. # Get correlation matrix
  64. # select data
  65. cor_zooVisit <- data_visitor %>%
  66. dplyr::select(zscore_dev_ub,zscore_dev_b,zscore_dist,time_last_zooVisit_days)
  68. corr.mat <- cor_mat(round(cor_zooVisit), method = "spearman",
  69. alternative = "two.sided", conf.level = 0.95)
  70. corrp.mat <- cor_pmat(round(cor_zooVisit), method = "spearman", conf.level = 0.95)
  72. print(corr.mat)
  73. print(corrp.mat)
  75. # Comparison of correlations
  76. n_obs = 104
  77. # last visit
  78. diffcor.dep(corr.mat$time_last_zooVisit_days[1], corr.mat$time_last_zooVisit_days[2],
  79. cor_devb_devub[["estimate"]][["rho"]], n_obs, cor.names = NULL,
  80. alternative = c( "two.sided"), digit = 3)
  81. diffcor.dep(corr.mat$time_last_zooVisit_days[1], corr.mat$time_last_zooVisit_days[3],
  82. cor_dist_devub[["estimate"]][["rho"]], n_obs, cor.names = NULL,
  83. alternative = c( "two.sided"), digit = 3)
  84. diffcor.dep(corr.mat$time_last_zooVisit_days[3], corr.mat$time_last_zooVisit_days[2],
  85. cor_dist_devb[["estimate"]][["rho"]], n_obs, cor.names = NULL,
  86. alternative = c( "two.sided"), digit = 3)
  88. # ------------------------------------------------------------------------------
  89. # :::::::::::::::::::::: Model memory consolidation ::::::::::::::::::::::::::::
  90. # ------------------------------------------------------------------------------
  92. # ------------------------------------------------------------------------------
  93. # Prepare data for models
  94. # ------------------------------------------------------------------------------
  96. prepare_model_data <- function(df, zscore_var, knots_vec) {
  97. df %>%
  98. dplyr::select(id, zscore = {{zscore_var}}, time_last_zooVisit_days) %>%
  99. na.omit() %>%
  100. list(knots = knots_vec)
  101. }
  103. data_list <- list(
  104. devub = prepare_model_data(data_visitor, zscore_dev_ub, c(180, 2000, 4000, 7500)),
  105. devb = prepare_model_data(data_visitor, zscore_dev_b, c(180, 1000, 2000, 6500)),
  106. dist = prepare_model_data(data_visitor, zscore_dist, c(180, 1000, 4000, 6000))
  107. )
  109. # ------------------------------------------------------------------------------
  110. # LOOCV Function
  111. # ------------------------------------------------------------------------------
  113. run_loocv_models <- function(data, knots) {
  114. models <- list(
  115. linear = function(train) lm(zscore ~ time_last_zooVisit_days, data = train),
  116. log = function(train) lm(zscore ~ log(time_last_zooVisit_days), data = train),
  117. exp = function(train) lm(log(zscore + 2) ~ time_last_zooVisit_days, data = train),
  118. power = function(train) lm(log(zscore + 2) ~ log(time_last_zooVisit_days), data = train),
  119. quadratic = function(train) lm(zscore ~ poly(time_last_zooVisit_days, 2, raw = TRUE), data = train),
  120. cubic = function(train) lm(zscore ~ poly(time_last_zooVisit_days, 3, raw = TRUE), data = train),
  121. spline = function(train) lm(zscore ~ bs(time_last_zooVisit_days, knots = knots), data = train),
  122. gam = function(train) gam(zscore ~ s(time_last_zooVisit_days), data = train)
  123. )
  125. evaluate_model <- function(model_name, model_fun) {
  126. fit_full <- tryCatch(model_fun(data), error = function(e) NULL)
  128. loocv_errors <- sapply(1:nrow(data), function(i) {
  129. test <- data[i, ]
  130. train <- data[-i, ]
  131. fit <- tryCatch(model_fun(train), error = function(e) NULL)
  132. if (is.null(fit)) return(NA)
  133. pred <- tryCatch(predict(fit, newdata = test), error = function(e) NA)
  134. if (is.na(pred)) return(NA)
  135. pred_adj <- if (model_name %in% c("exp", "power")) exp(pred) - 2 else pred
  136. (pred_adj - test$zscore)^2
  137. })
  139. mse <- mean(loocv_errors, na.rm = TRUE)
  140. rmse <- sqrt(mse)
  142. residuals <- tryCatch({
  143. full_pred <- predict(fit_full)
  144. full_pred_adj <- if (model_name %in% c("exp", "power")) exp(full_pred) - 2 else full_pred
  145. full_pred_adj - data$zscore
  146. }, error = function(e) rep(NA, nrow(data)))
  148. rse <- sqrt(mean(residuals^2, na.rm = TRUE))
  150. mod_summary <- tryCatch(summary(fit_full), error = function(e) NULL)
  152. r2 <- if (!is.null(mod_summary)) mod_summary$r.squared else NA
  153. r2_adj <- if (!is.null(mod_summary)) mod_summary$adj.r.squared else NA
  154. fstat <- if (!is.null(mod_summary) && !is.null(mod_summary$fstatistic)) mod_summary$fstatistic else NA
  155. fval <- if (!is.null(fstat)) unname(fstat["value"]) else NA
  156. df1 <- if (!is.null(fstat)) fstat["numdf"] else NA
  157. df2 <- if (!is.null(fstat)) fstat["dendf"] else NA
  158. pval <- if (!is.na(fval) && !is.na(df1) && !is.na(df2)) pf(fval, df1, df2, lower.tail = FALSE) else NA
  160. data.frame(
  161. model = model_name,
  162. AIC = round(tryCatch(AIC(fit_full), error = function(e) NA), 4),
  163. BIC = round(tryCatch(BIC(fit_full), error = function(e) NA), 4),
  164. RSE = round(rse, 4),
  165. MSE = round(mse, 4),
  166. RMSE = round(rmse, 4),
  167. R2 = if (!is.null(r2) && is.numeric(r2)) round(r2, 4) else NA,
  168. R2_adj = if (!is.null(r2_adj) && is.numeric(r2_adj)) round(r2_adj, 4) else NA,
  169. F_value = round(fval, 4),
  170. p_value = signif(pval, 4)
  171. )
  172. }
  174. results <- do.call(rbind, Map(evaluate_model, names(models), models))
  175. return(results)
  176. }
  178. # ------------------------------------------------------------------------------
  179. # run datasets
  180. # ------------------------------------------------------------------------------
  182. results_dist <- run_loocv_models(data_list$dist[[1]], data_list$dist$knots)
  183. results_dev_b <- run_loocv_models(data_list$devb[[1]], data_list$devb$knots)
  184. results_dev_ub <- run_loocv_models(data_list$devub[[1]], data_list$devub$knots)
  186. # ------------------------------------------------------------------------------
  187. # Results
  188. # ------------------------------------------------------------------------------
  190. # Combined results
  191. all_results <- rbind(results_dist, results_dev_b, results_dev_ub)
  192. print(all_results)
  194. # ------------------------------------------------------------------------------
  195. # ::::::::::: Get best model according to AIC ::::::::::::::::::::::::::::::::::
  196. # ------------------------------------------------------------------------------
  198. # Best model AIC
  199. best_aic_dist <- results_dist[which.min(results_dist$AIC), ]
  200. best_aic_dev_b <- results_dev_b[which.min(results_dev_b$AIC), ]
  201. best_aic_dev_ub <- results_dev_ub[which.min(results_dev_ub$AIC), ]
  203. print(best_aic_dist)
  204. print(best_aic_dev_b)
  205. print(best_aic_dev_ub)
  207. # ------------------------------------------------------------------------------
  208. # Addition: generalize additive model (for R2 and test statistic)
  209. # ------------------------------------------------------------------------------
  211. gam_devub.fit <- gam((data_visitor$zscore_dev_ub) ~ s(data_visitor$time_last_zooVisit_days))
  212. summary(gam_devub.fit)
  213. print(gam_devub.fit)
  215. gam_devb.fit <- gam((data_visitor$zscore_dev_b) ~ s(data_visitor$time_last_zooVisit_days))
  216. summary(gam_devb.fit)
  217. print(gam_devb.fit)
  219. gam_dist.fit <- gam((data_visitor$zscore_dist) ~ s(data_visitor$time_last_zooVisit_days))
  220. summary(gam_dist.fit)
  221. print(gam_dist.fit)
  223. # ------------------------------------------------------------------------------
  224. # :::::::::::::::: Task comparison per model :::::::::::::::::::::::::::::::::::
  225. # ------------------------------------------------------------------------------
  227. # Reshape data for comparison
  228. data_visitor_response <- pivot_longer(data_visitor,
  229. cols = c('zscore_dist', 'zscore_dev_ub', 'zscore_dev_b'),
  230. names_to = 'task',
  231. values_to = 'response')
  233. # Prepare the data for modeling
  234. data_visitor_allTasks <- data_visitor_response %>% dplyr::select( id, response,
  235. task, time_last_zooVisit_days)
  236. data_visitor_allTasks <- na.omit(data_visitor_allTasks)
  238. # ------------------------------------------------------------------------------
  239. # Fit models
  240. # ------------------------------------------------------------------------------
  242. # Power-transformed model
  243. power_all.fit <- lm(log(response + 2) ~ task * log(time_last_zooVisit_days), data = data_visitor_allTasks)
  244. anova_power <- anova(power_all.fit)
  245. omega_squared(power_all.fit, partial = FALSE)
  246. anova_power
  248. # ------------------------------------------------------------------------------
  249. # Trends per task
  250. # ------------------------------------------------------------------------------
  252. decay_rates <- emtrends( power_all.fit,specs = ~ task,var = "log(time_last_zooVisit_days)")
  253. decay_sum <- summary(decay_rates)
  255. decay_sum <- decay_sum %>%
  256. mutate(
  257. estimate = `log(time_last_zooVisit_days).trend`,
  258. t_value = estimate / SE,
  259. p_value = 2 * pt(abs(t_value), df, lower.tail = FALSE),
  260. r_effect = sqrt((t_value^2) / (t_value^2 + df))
  261. )
  262. decay_sum
  264. # ------------------------------------------------------------------------------
  265. # Comparison of slopes
  266. # ------------------------------------------------------------------------------
  268. # compare pairs of regression curves
  269. emtrends_power <- pairs(emtrends(power_all.fit,specs=pairwise~task,var="time_last_zooVisit_days"))
  271. # Pairwise comparison of slopes
  272. pairs_slopes <- pairs(emtrends_power)
  273. print(pairs_slopes)
  276. # ------------------------------------------------------------------------------
  277. # ::::::::::::: pairwise contrasts between tasks :::::::::::::::::::::::::::::::
  278. # ------------------------------------------------------------------------------
  280. # Define time grid (every 10 days from 6 to 10,000)
  281. time_points <- seq(6, 10000, by = 10)
  283. # Define levels
  284. tasks <- levels(data_visitor_allTasks$task)
  286. # Data with timepoint and task
  287. newdata <- expand.grid(
  288. time_last_zooVisit_days = time_points,
  289. task = tasks
  290. )
  292. newdata$log_time <- log(newdata$time_last_zooVisit_days)
  294. # Emmeans per combination
  295. emm_list <- emmeans(
  296. object = power_all.fit,
  297. specs = ~ task | time_last_zooVisit_days,
  298. at = list(time_last_zooVisit_days = time_points)
  299. )
  301. # Pairwise contrasts per timepoint
  302. contrast_results <- summary(pairs(emm_list), infer = TRUE)
  304. # Prepare dataframe and extract timepoints
  305. all_results <- as.data.frame(contrast_results) %>%
  306. mutate(
  307. time_last_zooVisit_days = as.numeric(as.character(time_last_zooVisit_days))
  308. )
  310. # Filter for significant contrasts
  311. significant_results <- all_results %>%
  312. filter(p.value < 0.05)
  314. # For each contrast, identify the earliest day from which it remains significant
  315. significant_summary <- significant_results %>%
  316. group_by(contrast) %>%
  317. summarise(
  318. first_signif_day = min(time_last_zooVisit_days),
  319. last_signif_day = max(time_last_zooVisit_days)
  320. )
  322. print(significant_summary)
  324. # Output
  325. print(significant_results[, c("time_last_zooVisit_days", "contrast", "estimate", "p.value")])
  328. # ------------------------------------------------------------------------------
  329. # Crossing mean of control group (zoo-naive)
  330. # ------------------------------------------------------------------------------
  332. # Mean & SD (Attention: lower value => upper SD)
  333. calculate_mean_and_upper_sd <- function(data, zscore_col, group_var) {
  334. mean_val <- mean(data[[zscore_col]][data[[group_var]] == 2], na.rm = TRUE)
  335. sd_val <- sd(data[[zscore_col]][data[[group_var]] == 2], na.rm = TRUE)
  336. upper_sd_val <- mean_val - sd_val # Attention
  337. return(list(mean = mean_val, upper_sd = upper_sd_val))
  338. }
  340. # z-scores
  341. zscore_vars <- c("zscore_dev_ub", "zscore_dev_b", "zscore_dist")
  342. result_list <- list()
  344. # run function
  345. for (zscore_var in zscore_vars) {
  346. result_list[[zscore_var]] <- calculate_mean_and_upper_sd(data_merged, zscore_var, "zooVisit")
  347. }
  349. # ------------------------------------------------------------------------------
  350. # Power-Regressions per z-score
  351. # ------------------------------------------------------------------------------
  353. calculate_time_values <- function(data, zscore_col, mean_value, upper_sd_value) {
  355. # Power Regression
  356. power_fit <- lm(log(get(zscore_col) + 2) ~ log(time_last_zooVisit_days), data = data)
  358. # Coefficients
  359. beta_0 <- coef(power_fit)[1]
  360. beta_1 <- coef(power_fit)[2]
  362. # How many days to reach mean of control group
  363. time_for_mean <- exp((log(mean_value + 2) - beta_0) / beta_1)
  365. # How many days to reach SD of control group
  366. time_for_upper_sd <- exp((log(upper_sd_value + 2) - beta_0) / beta_1)
  368. # years
  369. time_for_mean_years <- time_for_mean / 365
  370. time_for_upper_sd_years <- time_for_upper_sd / 365
  372. # Return list of results
  373. return(list(
  374. beta_0 = beta_0,
  375. beta_1 = beta_1,
  376. time_for_mean_days = time_for_mean,
  377. time_for_upper_sd_days = time_for_upper_sd,
  378. time_for_mean_years = time_for_mean_years,
  379. time_for_upper_sd_years = time_for_upper_sd_years
  380. ))
  381. }
  383. # calculation per z-score
  384. time_values_list <- list()
  386. # run datasets
  387. for (zscore_var in zscore_vars) {
  388. time_values_list[[zscore_var]] <- calculate_time_values(data_visitor, zscore_var,
  389. result_list[[zscore_var]]$mean,
  390. result_list[[zscore_var]]$upper_sd)
  391. }
  393. # Results
  394. time_values_list
  396. # ------------------------------------------------------------------------------
  397. # Get values for three decades per equation/ z-score
  398. # ------------------------------------------------------------------------------
  400. # get list of timepoints
  401. timepoint_list <- list(time_last_zooVisit_days = seq(0.1, 365 * 30, by = 365 / 4))
  403. # get list of variables
  404. zscore_columns <- c("zscore_dev_ub", "zscore_dev_b", "zscore_dist")
  406. # table of results
  407. result_table <- data.frame(
  408. time_last_zooVisit_days = timepoint_list$time_last_zooVisit_days,
  409. zscore_dev_ub = NA,
  410. zscore_dev_b = NA,
  411. zscore_dist = NA
  412. )
  414. # get results per z-score
  415. for (i in 1:length(zscore_columns)) {
  416. zscore_col <- zscore_columns[i]
  418. # Fit model
  419. power.fit <- lm(log(get(zscore_col) + 2) ~ log(time_last_zooVisit_days), data = data_visitor)
  421. # calculate estimated means
  422. emmeans_result <- emmeans(power.fit, ~ time_last_zooVisit_days, at = timepoint_list)
  424. # get mean and upper and lower CL
  425. summary_result <- summary(emmeans_result)
  427. # table
  428. result_table[[zscore_col]] <- paste(
  429. round(summary_result$emmean, 3),
  430. "(",
  431. round(summary_result$lower.CL, 3),
  432. "-",
  433. round(summary_result$upper.CL, 3),
  434. ")"
  435. )
  436. }
  438. # prepare word document
  439. doc <- read_docx()
  441. doc <- doc %>%
  442. body_add_table(value = result_table, style = "table_template")
  444. print(doc, target = "OSF_BZT_retro_zscore_results_30years.docx")

OSF_BZT_retro_correlation_regression_models_171125.R, no license · at the source

Overview

Authors: Deetje Iggena1,2,3, Thereza Schmelter4, Patrizia M. Maier1,2, Khaled Reguieg4, Carsten Finke1,2, Kristian Hildebrand4, Christoph J. Ploner1
ORCID iDs: Deetje Iggena, Patrizia M. Maier, Carsten Finke, Kristian Hildebrand, Christoph J. Ploner
  1. Department of Neurology, Charité-Universitätsmedizin Berlin,Berlin, Germany
  2. Berlin School of Mind and Brain, Humboldt-Universität zu Berlin,Berlin, Germany
  3. Berlin Institute of Health at Charité-Universitätsmedizin Berlin,Berlin, Germany
  4. Berlin University of Applied Sciences,Berlin, Germany
Institutions: Charité - Universitätsmedizin Berlin (Germany); Humboldt-Universität zu Berlin (Germany); Berlin Institute of Health at Charité - Universitätsmedizin Berlin (Germany); Berliner Hochschule für Technik (Germany)
Journal: Nature human behaviour, volume 10, issue 7, pages 1327-1339
Dates: received 10 April 2025; accepted 9 April 2026; published online 18 May 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41562-026-02472-x · PMID 42151554 · PMCID PMC13388105 · OpenAlex W4409125915
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.04.01.646566
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Statistics, Machine learning, Preprocessing, Connectivity, Physiology & signal measures
Keywords: Cognitive neuroscience, Spatial memory, Human behaviour, Consolidation, Long-term memory
MeSH: Memory, Long-Term*, Spatial Memory*, Spatial Navigation*, Humans (* major topic)
Topic: Intelligent Tutoring Systems and Adaptive Learning (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: Berlin Institute of Health at Charité; Deutsche Forschungsgemeinschaft (DFG) (327654276 – SFB 1315); Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie (Federal Ministry for Education, Science, Research and Technology) (16SV8171)
Citations: not cited yet (Europe PMC); 44 references in the paper

Abstract

During navigation, the brain builds representations of self-motion and of environmental information for future action. The classic view suggests that these representations consolidate and eventually stabilize. However, there are no data on their fate at extended memory delays. Here we investigated memory of real-world navigational episodes across memory delays of up to three decades. We show that memory of the spatial aspects of these episodes do not achieve a stable state but rather continue to transform for many years. Our data suggest that at any given point in time, spatial memory of navigational episodes is a changing combination of episode-independent schematic information and several interacting spatial representations directly related to a navigational episode, which may show distinct temporal trajectories. Consistent with recent accounts of memory reorganization, we further show that neither current theories of systems consolidation nor classic models of forgetting fully explain spatial memory performance at extended memory delays.

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

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OSF sb65k

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Tools: tidyverse (6 files), easystats (4 files), emmeans (4 files), rstatix (3 files), mgcv (2 files), afex (1 file), cowplot (1 file), ggplot2 (1 file), Statistics and Machine Learning Toolbox (1 file)
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Reproduced under the paper's license (CC BY), from the paper cited above.

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  • 8 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • 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

The data and R code of this study are available via OSF at https://osf.io/sb65k/.

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 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 4 MeSH terms, 3 funders, 42 references.

Cite

This paper

Iggena, D., Schmelter, T., Maier, P. M., Reguieg, K., Finke, C., Hildebrand, K., & Ploner, C. J. (2026). Long-term memory reorganization of navigational episodes. Nature human behaviour, 10(7), 1327-1339. https://doi.org/10.1038/s41562-026-02472-x

BibTeX

@article{iggena2026long,
author = {Iggena, Deetje and Schmelter, Thereza and Maier, Patrizia M. and Reguieg, Khaled and Finke, Carsten and Hildebrand, Kristian and Ploner, Christoph J.},
title = {{Long-term memory reorganization of navigational episodes}},
journal = {Nature human behaviour},
year = {2026},
month = may,
volume = {10},
number = {7},
pages = {1327--1339},
publisher = {Nature Portfolio},
issn = {2397-3374},
doi = {10.1038/s41562-026-02472-x},
url = {https://doi.org/10.1038/s41562-026-02472-x},
pmid = {42151554},
pmcid = {PMC13388105}
}

RIS

TY - JOUR
AU - Iggena, Deetje
AU - Schmelter, Thereza
AU - Maier, Patrizia M.
AU - Reguieg, Khaled
AU - Finke, Carsten
AU - Hildebrand, Kristian
AU - Ploner, Christoph J.
TI - Long-term memory reorganization of navigational episodes
T2 - Nature human behaviour
J2 - Nat Hum Behav
PY - 2026
DA - 2026/05/18
VL - 10
IS - 7
SP - 1327
EP - 1339
SN - 2397-3374
PB - Nature Portfolio
DO - 10.1038/s41562-026-02472-x
UR - https://doi.org/10.1038/s41562-026-02472-x
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41562-026-02472-x",
"type": "article-journal",
"title": "Long-term memory reorganization of navigational episodes",
"container-title": "Nature human behaviour",
"author": [
{
"family": "Iggena",
"given": "Deetje"
},
{
"family": "Schmelter",
"given": "Thereza"
},
{
"family": "Maier",
"given": "Patrizia M."
},
{
"family": "Reguieg",
"given": "Khaled"
},
{
"family": "Finke",
"given": "Carsten"
},
{
"family": "Hildebrand",
"given": "Kristian"
},
{
"family": "Ploner",
"given": "Christoph J."
}
],
"container-title-short": "Nat Hum Behav",
"volume": "10",
"issue": "7",
"page": "1327-1339",
"DOI": "10.1038/s41562-026-02472-x",
"PMID": "42151554",
"PMCID": "PMC13388105",
"ISSN": "2397-3374",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41562-026-02472-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
18
]
]
}
}

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

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