OSCR

Natural language processing captures memory content associated with shared neural patterns at encoding and retrieval.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches
  1. [1] § Methods › Intersubject pattern similarity analysis ↔ ISPS_slope_plots.Rmd, lines 29–71 · score 0.68 · PreC, dmPFC, vmPFC, MTL, SMC, ATL
  2. [2] § Results › Relationship between topic similarity and intersubject pattern similarity ↔ ISPS_slope_plots.Rmd, lines 29–71 · score 0.63 · PreC, dmPFC, vmPFC, MTL, SMC, ATL
  3. [3] § Results › Relationship between topic similarity and intersubject pattern similarity ↔ ISPS_slope_plots.Rmd, lines 81–121 · score 0.61 · PreC, dmPFC, vmPFC, MTL, SMC, ATL

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

R Markdown · 128 lines · 3.9 KB · no license · 3 matches

  1. ---
  2. title: "R Notebook"
  3. output: html_notebook
  4. ---
  6. # plot model estimates
  8. ```{r}
  9. library(tidyverse)
  10. library(ggseg)
  11. library(ggplot2)
  12. library(corrplot)
  13. library(dplyr)
  14. library(lme4)
  15. library(lmerTest)
  16. library(emmeans)
  17. library(sjPlot) # For extracting confidence intervals
  18. library(ggeffects)
  20. ```
  22. #encoding
  24. ## data to load
  25. ```{r}
  26. results <- read_csv("viewing_slopes_April2026.csv")
  27. ```
  29. ## plot
  30. ```{r}
  31. roi_list <-c('PMC',
  32. 'dmPFC',
  33. 'ATL',
  34. 'vmPFC',
  35. 'MTL',
  36. 'PreC',
  37. 'vLP',
  38. 'dLP',
  39. 'aLP',
  40. 'SMC')
  42. colors <- c("#63aff9","#ff0000","#ff7700","#f5c042","#2c9741","#8adA99","#003300","#3c1d8a","#a61712","#60ebeb")
  43. p<-ggplot(results,aes( y = term_beta, x = fct_relevel(file,"vmPFC","PMC","dLP","aLP","dmPFC","ATL","MTL","PreC", "vLP", "SMC"), color = file)) +
  44. geom_point(size =5) +
  45. geom_errorbar(aes(ymin =term_beta-term_se, ymax = term_beta+term_se), width = 0,linewidth=2) +
  46. geom_hline(yintercept = 0, linetype ="dashed", color = "gray40") +
  47. theme_minimal() +
  48. ylim(-0.003, .014)+
  49. scale_color_manual(values =setNames(colors, results$file))+
  50. labs(title = "Encoding ISPS",size = 40)+
  51. theme(
  52. plot.title=element_text(size = 30, hjust =0.5),
  53. plot.margin = unit(c(1,.5,.5,.5),"cm"),
  54. panel.grid.major.x = element_blank(),
  55. #panel.grid.major.y = element_blank(),
  56. panel.grid.minor.y = element_blank(),
  57. panel.border = element_blank(),
  58. axis.title.y = element_text(size = 30),
  59. axis.title.x = element_blank(),
  60. axis.line = element_line(color = "black"),
  61. axis.ticks = element_line(color = "black"),
  62. axis.text = element_text(size = 25),
  63. axis.text.x = element_text(angle=90,size = 18),
  64. legend.position = "none") +
  65. ylab("Model Estimate")
  69. ggsave('encodingISPS_topicSim_estimates_v6.png', width = 4.5,height = 6, units = "in", dpi = 300)
  71. ```
  73. # recall
  75. ## data to load
  76. ```{r}
  77. results <- read_csv("recall_slopes_April2026.csv")
  78. ```
  81. ## plot
  82. ```{r}
  83. roi_list <-c('PMC',
  84. 'dmPFC',
  85. 'ATL',
  86. 'vmPFC',
  87. 'MTL',
  88. 'PreC',
  89. 'vLP',
  90. 'dLP',
  91. 'aLP',
  92. 'SMC')
  94. colors <- c("#63aff9","#ff0000","#ff7700","#f5c042","#2c9741","#8adA99","#003300","#3c1d8a","#a61712","#60ebeb")
  95. p<-ggplot(results,aes( y = term_beta, x = fct_relevel(file,"vmPFC","PMC","dLP","aLP","dmPFC","ATL","MTL","PreC", "vLP", "SMC"), color = file)) +
  96. geom_point(size =5) +
  97. geom_errorbar(aes(ymin =term_beta-term_se, ymax = term_beta+term_se), width = 0,linewidth=2) +
  98. geom_hline(yintercept = 0, linetype ="dashed", color = "gray40") +
  99. theme_minimal() +
  100. scale_color_manual(values =setNames(colors, results$file))+
  101. labs(title = "Recall ISPS")+
  102. ylim(-0.003, .014)+
  103. theme(
  104. plot.title= element_text(size = 30, hjust = 0.5),
  105. plot.margin = unit(c(1,.5,.5,.5),"cm"),
  106. panel.grid.major.x = element_blank(),
  107. #panel.grid.major.y = element_blank(),
  108. panel.grid.minor.y = element_blank(),
  109. panel.border = element_blank(),
  110. axis.title.y = element_text(size = 30),
  111. axis.title.x = element_blank(),
  112. axis.line = element_line(color = "black"),
  113. axis.ticks = element_line(color = "black"),
  114. axis.text = element_text(size = 25),
  115. axis.text.x = element_text(angle=90,size = 18),
  116. legend.position = "none")+
  117. ylab("Model Estimate")
  119. ggsave('recallISPS_topicSim_estimates_v6.png', width = 4.5,height = 6, units = "in", dpi = 300)
  121. ```
  123. Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*.
  125. When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file).
  127. The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

ISPS_slope_plots.Rmd at commit e86dfaf, no license · at the source

Overview

Authors: June-Kyo Kim1, Joshua Koh2, Charan Ranganath3, Alexander J Barnett2,4
ORCID iDs: Charan Ranganath, Alexander J Barnett
  1. University of Toronto, Department of Psychology, Toronto, Canada
  2. McGill University, Department of Neurology & Neurosurgery, Montreal, Canada
  3. University of California, Davis, Center for Neuroscience, Davis, USA
  4. McGill University, McConnell Brain Imaging Centre, Montreal, Canada
Institutions: University of Toronto (Canada); McGill University (Canada); University of California, Davis (United States)
Journal: Communications psychology, volume 4, issue 1, article 124
Dates: received 9 September 2025; accepted 22 May 2026; published online 4 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44271-026-00481-0 · PMID 42243459 · PMCID PMC13547101 · OpenAlex W7163588681
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, fMRI & imaging
Keywords: Human behaviour, Cognitive neuroscience
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: United States Department of Defense | United States Navy | Office of Naval Research (ONR) (N00014-17-1-2961); Fonds de Recherche du Québec - Santé (Fonds de la recherche en sante du Quebec) (CB - 366768); Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada) (RGPIN-2023-05010)
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

People can experience the same event yet form distinct memories shaped by individual interpretations. Prior research shows that multivariate activity patterns in the Default Mode Network (DMN) are correlated across individuals during shared experiences, suggesting a role in representing high-level event features. However, it remains unclear whether these shared neural patterns reflect similarity in subsequent memory content. Here, we examined whether memory similarity correlates with intersubject spatial patterns in the DMN using a pre-existing dataset. Twenty-four individuals watched and recounted two cartoon movies during fMRI scanning. Using topic modeling, we transformed verbal recall into vectors of latent topics to quantify memory similarity across participants. We found that greater similarity in recalled content was associated with stronger shared activation patterns at encoding and retrieval, particularly in the posterior medial, medial prefrontal and anterior temporal cortices. These findings highlight the utility of natural language processing tools in linking memory representations to brain activity and underscore the DMN’s role in encoding, interpreting, and recalling complex event features.

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 3 matches between paragraphs and lines of code.

ajbarn/recall_similarity_ISPS

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e86dfaf6dd6a2df4e9b2b94d32edd70978aab285, 6 May 2026
Languages: R (2)
Size: 19 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: emmeans (2 files), ggplot2 (2 files), ggseg (2 files), tidyverse (2 files), lme4 (1 file), lmerTest (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/ajbarn/recall_similarity_ISPS

Code availability

The code used to create figures is available via GitHub (https://github.com/ajbarn/recall_similarity_ISPS).

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:

  • 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;
  • 3 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 Statement

The data used to create figures is available via GitHub (https://github.com/ajbarn/recall_similarity_ISPS).

The code used to create figures is available via GitHub (https://github.com/ajbarn/recall_similarity_ISPS).

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, 4 authors, 2 keywords, 3 funders, 55 references.

Cite

This paper

Kim, J.-K., Koh, J., Ranganath, C., & Barnett, A. J. (2026). Natural language processing captures memory content associated with shared neural patterns at encoding and retrieval. Communications psychology, 4(1), 124. https://doi.org/10.1038/s44271-026-00481-0

BibTeX

@article{kim2026natural,
author = {Kim, June-Kyo and Koh, Joshua and Ranganath, Charan and Barnett, Alexander J},
title = {{Natural language processing captures memory content associated with shared neural patterns at encoding and retrieval}},
journal = {Communications psychology},
year = {2026},
month = jun,
volume = {4},
number = {1},
pages = {124},
publisher = {Nature Publishing Group},
issn = {2731-9121},
doi = {10.1038/s44271-026-00481-0},
url = {https://doi.org/10.1038/s44271-026-00481-0},
pmid = {42243459},
pmcid = {PMC13547101}
}

RIS

TY - JOUR
AU - Kim, June-Kyo
AU - Koh, Joshua
AU - Ranganath, Charan
AU - Barnett, Alexander J
TI - Natural language processing captures memory content associated with shared neural patterns at encoding and retrieval
T2 - Communications psychology
J2 - Commun Psychol
PY - 2026
DA - 2026/06/04
VL - 4
IS - 1
SP - 124
SN - 2731-9121
PB - Nature Publishing Group
DO - 10.1038/s44271-026-00481-0
UR - https://doi.org/10.1038/s44271-026-00481-0
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s44271-026-00481-0",
"type": "article-journal",
"title": "Natural language processing captures memory content associated with shared neural patterns at encoding and retrieval",
"container-title": "Communications psychology",
"author": [
{
"family": "Kim",
"given": "June-Kyo"
},
{
"family": "Koh",
"given": "Joshua"
},
{
"family": "Ranganath",
"given": "Charan"
},
{
"family": "Barnett",
"given": "Alexander J"
}
],
"container-title-short": "Commun Psychol",
"volume": "4",
"issue": "1",
"page": "124",
"DOI": "10.1038/s44271-026-00481-0",
"PMID": "42243459",
"PMCID": "PMC13547101",
"ISSN": "2731-9121",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s44271-026-00481-0",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
4
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1073/pnas.2512071123 [code]
Narrative "twist" shifts within-individual neural representations of dissociable story features.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: lmerTest, lme4, tidyverse, cognitive, 9 references
[2] doi:10.1371/journal.pbio.3003886 [code]
Shared memories of event details in the human brain are altered by misinformation and test expectations.
Journal: PLoS biology
In common: lme4, cognitive, 7 references
[3] doi:10.1038/s41467-026-71428-6 [code]
Binding items to contexts through conjunctive neural representations with the method of loci.
Journal: Nature communications
In common: lmerTest, lme4, ggplot2, 1 other tool, 4 references
[4] doi:10.1038/s41467-026-71151-2 [code]
Common and distinct neural correlates of social interaction processing and theory of mind in narratives.
Journal: Nature communications
In common: cognitive, 7 references
[5] doi:10.1038/s41398-026-04010-9 [code]
Bullying victimization and brain development: a longitudinal structural magnetic resonance imaging study from adolescence to early adulthood.
Journal: Translational psychiatry
In common: ggseg, emmeans, lmerTest, 3 other tools
[6] doi:10.1073/pnas.2530482123 [code]
The detection of episodic memory in others biases social choice.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: lmerTest, lme4, ggplot2, 1 other tool, cognitive, 2 references
[7] doi:10.1002/hbm.70605 [code]
BrainEnrich: Revealing Biological Insights for Imaging-Derived Phenotypes Through Transcriptomic Enrichment.
Journal: Human brain mapping
In common: ggseg, emmeans, lmerTest, 2 other tools, 1 reference
[8] doi:10.1162/imag.a.1235 [code]
Intracranial volume: To adjust or not to adjust? It is not a matter of if, but how.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: ggseg, lmerTest, lme4, 2 other tools, 1 reference
[9] doi:10.1371/journal.pbio.3003666 [code]
Emotion regulation success involves systematic gradient-based reconfigurations of large-scale activation patterns in the human brain.
Journal: PLoS biology
In common: emmeans, lmerTest, lme4, 2 other tools, cognitive, 1 reference
[10] doi:10.1038/s41467-026-73914-3 [code]
Cortical reinstatement of causally related events sparks narrative insights by updating neural representation patterns.
Journal: Nature communications
In common: lmerTest, lme4, cognitive, 3 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.