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Neural tracking of surprisal in Spanish-English bilingual children during naturalistic heritage language listening.

Code ↔ Paper

2 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 2 matches
  1. [1] § Method › Analysis › Exploratory ROI analysis ↔ Data and Analysis Scripts/ROI Analysis/brain-behav.R, lines 1–81 · score 0.69 · brms, English listening, Spanish listening, coefficients, intercept, covariance
  2. [2] § Method › Analysis › Exploratory ROI analysis ↔ Data and Analysis Scripts/No Disorders Analysis N=83/brain-behav_NoDisorders.R, lines 1–81 · score 0.68 · brms, English listening, Spanish listening, coefficients, intercept, covariance

Paper

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

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The authors' code

R · 91 lines · 2.5 KB · no license · 1 match

  1. ## Juanito brain-behavior correlations (ROI analysis)
  3. #setwd()
  4. getwd()
  5. library(tidyverse)
  6. library(GGally)
  8. d <- read_csv('Juanito_ROI_2025.11.18.csv') #this was created by joining beta values with proficiency data
  10. d |>
  11. select(-...1, -id, -var) |>
  12. ggpairs()
  14. summary(lm(beta ~ age, data=d))$coefficients
  15. summary(lm(beta ~ EN_TNLcomp, data=d))$coefficients
  16. summary(lm(beta ~ SP_TNLcomp, data=d))$coefficients
  17. summary(lm(beta ~ EN_SRF_raw, data=d))$coefficients
  18. summary(lm(beta ~ SP_SRF_raw, data=d))$coefficients
  21. library(brms)
  23. my_priors <- c(prior(normal(0, 1), class='Intercept'),
  24. prior(normal(0, 2.5), class='b'))
  26. # Following the method of including error in
  27. # the dependent measure described here:
  29. # m_names <- c('EN_TNLcomp',
  30. # 'SP_TNLcomp',
  31. # 'EN_SRF_raw',
  32. # 'SP_SRF_raw')
  34. m_names <- c('English listening',
  35. 'Spanish listening',
  36. 'English reading',
  37. 'Spanish reading')
  39. m2 <- brm(beta | se(var, sigma=TRUE) ~ EN_TNLcomp + age, #age as covariate since using raw scores in all measures
  40. data=d |> drop_na(),
  41. prior = my_priors,
  42. backend='cmdstanr')
  43. check_hmc_diagnostics(m2$fit)
  45. m3 <- brm(beta | se(var, sigma=TRUE) ~ SP_TNLcomp + age,
  46. data=d |> drop_na(),
  47. prior = my_priors,
  48. backend='cmdstanr')
  49. check_hmc_diagnostics(m3$fit)
  51. m4 <- brm(beta | se(var, sigma=TRUE) ~ EN_SRF_raw + age,
  52. data=d |> drop_na(),
  53. prior = my_priors,
  54. backend='cmdstanr')
  55. check_hmc_diagnostics(m4$fit)
  57. m5 <- brm(beta | se(var, sigma=TRUE) ~ SP_SRF_raw + age ,
  58. data=d |> drop_na(),
  59. prior = my_priors,
  60. backend='cmdstanr')
  61. check_hmc_diagnostics(m5$fit)
  63. coef_sum <-
  64. bind_rows(posterior_summary(m2)[2,],
  65. posterior_summary(m3)[2,],
  66. posterior_summary(m4)[2,],
  67. posterior_summary(m5)[2,]
  68. ) |>
  69. mutate(model = m_names)
  71. coef_sum |>
  72. ggplot(aes(x=model,
  73. y=Estimate,
  74. ymin=Q2.5,
  75. ymax=Q97.5)) +
  76. geom_hline(yintercept=0, linetype=2, col='darkgrey') +
  77. geom_errorbar(width=0.1) +
  78. geom_point(col='darkblue', size=3) +
  79. geom_point(col='lightblue') +
  80. coord_flip() +
  81. theme_classic()
  83. #ggsave('ROIscatterplots.png', height=2, width=4)
  85. loo_compare(
  86. add_criterion(m2, 'loo'),
  87. add_criterion(m3, 'loo'),
  88. add_criterion(m4, 'loo'),
  89. add_criterion(m5, 'loo'),
  90. model_names = m_names
  91. ) # no reliable differences in fit

brain-behav.R, no license · at the source

Overview

Authors: Lauren K Salig1,2, Chi-Lin Yu3,4, Molly Leachman2, Nivedhitha Dondati Purushotham5, Valeria Ortiz-Villalobos2, Zahira Flores-Gaona2, Viviana Vélez Negrón2, Carla Girardini2, Hsin-Yuan Fang2, Alisa Baron6, Lisa M Bedore7, James R Booth8, Elizabeth D Peña9, Teresa Satterfield10, Jonathan R Brennan11, Ioulia Kovelman2,12
ORCID iDs: Lauren K Salig, Molly Leachman, Carla Girardini, Lisa M Bedore
  1. The College of New Jersey, Department of Psychology, 2000 Pennington Rd., Ewing, NJ 08628, USA
  2. University of Michigan, Department of Psychology, 530 Church St, Ann Arbor, MI 48109, USA
  3. University of North Carolina at Chapel Hill, Department of Psychology and Neuroscience, USA
  4. Oklahoma State University, Department of Psychology, 403 Psychology Building, Stillwater, OK 74078-3064, USA
  5. University of Michigan, Department of Computer Science and Engineering, 2260 Hayward Street, Ann Arbor, MI 48109, USA
  6. University of Rhode Island, Department of Communicative Disorders, 75 Briar Lane, Kingston, RI, USA
  7. Temple University, Department of Communication Sciences and Disorders, Paley Hall, Philadelphia, PA 19122, USA
  8. Vanderbilt University, Department of Psychology and Human Development, 230 Appleton Place, Nashville, TN 37203-5721, USA
  9. University of California, School of Education, 3200 Education Bldg, Irvine, CA 92697, USA
  10. University of Michigan, Department of Romance Languages & Literatures, 812 E Washington St, Ann Arbor, MI 48104, USA
  11. University of Michigan, Department of Linguistics, 812 E Washington St, Ann Arbor, MI 48104, USA
  12. The University of Texas at Dallas, Department of Speech, Language, and Hearing, USA
Institutions: University of Michigan (United States); The College of New Jersey (United States); University of North Carolina at Chapel Hill (United States); Oklahoma State University (United States); University of Rhode Island (United States); Temple University (United States); Vanderbilt University (United States); University of California, Irvine (United States); The University of Texas at Dallas (United States)
Journal: Brain and language, volume 281, article 105833
Dates: published online 29 August 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.bandl.2026.105833 · PMID 42667920 · PMCID PMC13592032 · OpenAlex W7117314432
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.2139/ssrn.5964042
Status: code verified
Categories: fNIRS (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Preprocessing, fMRI & imaging
Keywords: Bilingual, fNIRS, Computational, Surprisal, Language development, Naturalistic listening, Heritage language
MeSH: Brain*, Comprehension*, Language Development*, Multilingualism*, Speech Perception*, Brain Mapping, Child, Female, Humans, Language, Male, Spectroscopy, Near-Infrared (* major topic)
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NICHD NIH HHS (T32 HD007109, R01 HD109224, R01 HD111637)
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Our understanding of the neurobiology of language development is imprecise, particularly for bilingual children in their heritage language, since much research focuses on monolingual children or societal majority languages. In this study, we use a novel computational approach to quantifying linguistic predictions with fNIRS neuroimaging to examine bilingual children’s (N = 88, ages 7–12) brain activity as they listen to a naturalistic story in their heritage language of Spanish. The children demonstrated successful neural tracking of word predictability in their heritage language, suggesting they can form rich linguistic representations in Spanish. There were some trends for reading and listening comprehension abilities in English and Spanish to modulate neural tracking of predictability in a Spanish story, but no effects were reliable enough to interpret with confidence. Overall, this first use of a predictability-based computational, naturalistic listening comprehension approach with bilingual children demonstrates the feasibility of this approach and its utility for studying language development.

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 2 matches between paragraphs and lines of code.

OSF fxzpk

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (7), R (5)
Size: 68 files, 12 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), brms (4 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
12 files
At the source: osf.io/fxzpk/overview

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.

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;
  • 12 scripts, each with its path and the digest of its content;
  • 2 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

Data and analysis scripts are available on OSF: https://osf.io/fxzpk/overview

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 → Elsevier BV
  • Authors: added Lisa M Bedore (0000-0002-1973-3939); removed Lisa M Bedore

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 16 authors, 7 keywords, 12 MeSH terms, 1 funder, 39 references.

Cite

This paper

Salig, L. K., Yu, C.-L., Leachman, M., Purushotham, N. D., Ortiz-Villalobos, V., Flores-Gaona, Z., Negrón, V. V., Girardini, C., Fang, H.-Y., Baron, A., Bedore, L. M., Booth, J. R., Peña, E. D., Satterfield, T., Brennan, J. R., & Kovelman, I. (2026). Neural tracking of surprisal in Spanish-English bilingual children during naturalistic heritage language listening. Brain and language, 281, 105833. https://doi.org/10.1016/j.bandl.2026.105833

BibTeX

@article{salig2026neural,
author = {Salig, Lauren K and Yu, Chi-Lin and Leachman, Molly and Purushotham, Nivedhitha Dondati and Ortiz-Villalobos, Valeria and Flores-Gaona, Zahira and Negrón, Viviana Vélez and Girardini, Carla and Fang, Hsin-Yuan and Baron, Alisa and Bedore, Lisa M and Booth, James R and Peña, Elizabeth D and Satterfield, Teresa and Brennan, Jonathan R and Kovelman, Ioulia},
title = {{Neural tracking of surprisal in Spanish-English bilingual children during naturalistic heritage language listening}},
journal = {Brain and language},
year = {2026},
month = aug,
volume = {281},
pages = {105833},
publisher = {Elsevier BV},
issn = {0093-934X},
doi = {10.1016/j.bandl.2026.105833},
url = {https://doi.org/10.1016/j.bandl.2026.105833},
pmid = {42667920},
pmcid = {PMC13592032}
}

RIS

TY - JOUR
AU - Salig, Lauren K
AU - Yu, Chi-Lin
AU - Leachman, Molly
AU - Purushotham, Nivedhitha Dondati
AU - Ortiz-Villalobos, Valeria
AU - Flores-Gaona, Zahira
AU - Negrón, Viviana Vélez
AU - Girardini, Carla
AU - Fang, Hsin-Yuan
AU - Baron, Alisa
AU - Bedore, Lisa M
AU - Booth, James R
AU - Peña, Elizabeth D
AU - Satterfield, Teresa
AU - Brennan, Jonathan R
AU - Kovelman, Ioulia
TI - Neural tracking of surprisal in Spanish-English bilingual children during naturalistic heritage language listening
T2 - Brain and language
J2 - Brain Lang
PY - 2026
DA - 2026/08/29
VL - 281
SP - 105833
SN - 0093-934X
PB - Elsevier BV
DO - 10.1016/j.bandl.2026.105833
UR - https://doi.org/10.1016/j.bandl.2026.105833
LA - en
ER -

CSL-JSON

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