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Statistics in Motion: Does the Infant Motor System Predict Actions Based on Their Transitional Probability?

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 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Data Analysis › Analysis ↔ StatisticalAnalysis/Mu_cluster_comparison.R, lines 24–82 · score 0.70 · small cluster, central cluster, AIC, linear, mu, models
  2. [2] § Data Analysis › EEG Processing ↔ EEG/ChannelRejectionSummary.m, the whole file · a weak match · score 0.69 · FieldTrip, interpolated channels, SD, rejection, occipital, EEG

Paper

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

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The authors' code

R · 82 lines · 2.8 KB · no license · 1 match

  2. ####### Libraries ---------------------------------------------------------------
  4. library(lme4)
  5. library(lmerTest)
  6. library(easystats)
  7. library(tidyverse)
  10. ####### Set and read data ---------------------------------------------------------------
  12. find_project_dir <- function(path = getwd()) {
  13. path <- normalizePath(path, winslash = "/", mustWork = TRUE)
  14. repeat {
  15. if (file.exists(file.path(path, "README.md")) && dir.exists(file.path(path, "StatisticalAnalysis"))) {
  16. return(path)
  17. }
  18. parent <- dirname(path)
  19. if (identical(parent, path)) stop("Could not find Statistics-in-Motion project directory.")
  20. path <- parent
  21. }
  22. }
  24. project_dir <- find_project_dir()
  25. data_dir <- normalizePath(file.path(project_dir, "..", "..", "Data", "ProcessedBids"), winslash = "/", mustWork = TRUE)
  26. results_dir <- file.path(project_dir, "Results")
  28. dir.create(results_dir, recursive = TRUE, showWarnings = FALSE)
  30. files = list.files(data_dir, pattern = "*DFmu.csv", recursive = TRUE, full.names = TRUE)
  32. df = map_dfr(files, read_csv,show_col_types = FALSE)
  35. # Make Probabilities more understandable
  36. df$Probability = df$Trial
  37. df[df['Probability']==1,'Probability'] = 25
  38. df[df['Probability']==2,'Probability'] = 50
  39. df[df['Probability']==3,'Probability'] = 75
  40. df[df['Probability']==4,'Probability'] = 100
  42. df$Probability = factor(df$Probability) # categorical for anova
  45. ####### Multiple clusters ---------------------------------------------------------------
  47. ## Clusters
  48. Cluster.s = c('C3' , 'Cz', 'C4')
  49. Cluster.c = c('FC1' , 'CP1', 'Cz', 'FC2', 'CP2')
  50. Cluster.l = c('FC1' , 'CP1', 'C3', 'FC5', 'CP5')
  51. Cluster.r = c('FC2', 'CP2', 'C4', 'FC6', 'CP6')
  54. df.s = df[df$Channels %in% Cluster.s,] # small cluster
  56. df.c = df[df$Channels %in% Cluster.c,] # big central cluster
  58. df.l = df[df$Channels %in% Cluster.l,] # big left cluster
  60. df.r = df[df$Channels %in% Cluster.r,] # big right cluster
  63. ####### Run Linear Models ---------------------------------------------------------------
  65. ### The models
  67. `C4, Cz, C3` = lmer(Power ~Probability +(1|Id/Channels),data=df.s)
  68. `FC1, C3, CP1, Cz, FC2, C4, CP2` = lmer(Power ~Probability +(1|Id/Channels),data=df.c)
  69. `FC1, CP1, C3, FC5, CP5` = lmer(Power ~Probability + (1|Id/Channels),data=df.l)
  70. `FC2, CP2, C4, FC6, CP6` = lmer(Power ~Probability +(1|Id/Channels),data=df.r)
  72. comp = as.data.frame(compare_performance(`C4, Cz, C3`,`FC1, C3, CP1, Cz, FC2, C4, CP2`,`FC1, CP1, C3, FC5, CP5`,`FC2, CP2, C4, FC6, CP6`, metrics ='AIC'))
  74. ggplot(comp, aes(x=Name, y= AIC, fill= Name))+
  75. geom_bar(stat = 'identity')+
  76. scale_x_discrete('Clusters')+
  77. scale_fill_colorhex_d()+
  78. theme_minimal(base_size = 20)+
  79. theme(legend.position = "none")
  81. ggsave(file.path(results_dir, "Mu_Comparison.png"),
  82. device = "png", width = 40, height = 25, units = "cm",dpi=500)

Mu_cluster_comparison.R at commit 015ccdf, no license · at the source

Overview

Authors: Tommaso Ghilardi1,2, Marlene Meyer2, Claire D. Monroy3, Sarah A. Gerson4, Sabine Hunnius2
ORCID iDs: Tommaso Ghilardi, Marlene Meyer, Claire D. Monroy, Sarah A. Gerson, Sabine Hunnius
  1. Centre for Brain and Cognitive Development Birkbeck University of London London UK
  2. Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen Nijmegen the Netherlands
  3. School of Psychology Keele University Keele UK
  4. School of Psychology Cardiff University Cardiff UK
Institutions: Radboud University Nijmegen (Netherlands); Donders Institute for Brain, Cognition and Behaviour (Netherlands); Birkbeck, University of London (United Kingdom); Keele University (United Kingdom); Cardiff University (United Kingdom)
Journal: Infancy : the official journal of the International Society on Infant Studies, volume 31, issue 4, article e70114
Dates: received 11 March 2025; accepted 16 July 2026; published online 10 August 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1111/infa.70114 · PMID 42576376 · PMCID PMC13457992 · OpenAlex W4321330255
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.31234/osf.io/wyhqx
Status: code verified
Categories: EEG (modality), human (organism), developmental (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing
Keywords: action prediction, infants' action understanding, mu rhythm, predictive processing, statistical learning, transitional probability
MeSH: Child Development*, Learning*, Motor Activity*, Electroencephalography, Female, Humans, Infant, Male, Probability (* major topic)
Topic: Child and Animal Learning Development (Developmental and Educational Psychology, Psychology), according to OpenAlex
Funding: Horizon 2020 Marie Skłodowska (765298)
Citations: cited by 1 paper (Europe PMC); 70 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

TommasoGhilardi/Statistics-in-Motion

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 015ccdf9eabed322a00ac01179ae60bae34cb977, 29 July 2026
Languages: MATLAB (13), R (5)
Size: 23 files, 18 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (9 files), easystats (5 files), tidyverse (5 files), cowplot (4 files), brms (3 files), lme4 (3 files), lmerTest (3 files), Signal Processing Toolbox (1 file), Stan (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
19 files
At the source: github.com/TommasoGhilardi/Statistics-in-Motion

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

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 18 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);
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Data

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

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1111/infa.70114.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 9 MeSH terms, 1 funder, 69 references.

Cite

This paper

Ghilardi, T., Meyer, M., Monroy, C. D., Gerson, S. A., & Hunnius, S. (2026). Statistics in Motion: Does the Infant Motor System Predict Actions Based on Their Transitional Probability? Infancy : the official journal of the International Society on Infant Studies, 31(4), e70114. https://doi.org/10.1111/infa.70114

BibTeX

@article{ghilardi2026statistics,
author = {Ghilardi, Tommaso and Meyer, Marlene and Monroy, Claire D. and Gerson, Sarah A. and Hunnius, Sabine},
title = {{Statistics in Motion: Does the Infant Motor System Predict Actions Based on Their Transitional Probability?}},
journal = {Infancy : the official journal of the International Society on Infant Studies},
year = {2026},
month = jul,
volume = {31},
number = {4},
pages = {e70114},
publisher = {Wiley},
issn = {1525-0008},
doi = {10.1111/infa.70114},
url = {https://doi.org/10.1111/infa.70114},
pmid = {42576376},
pmcid = {PMC13457992}
}

RIS

TY - JOUR
AU - Ghilardi, Tommaso
AU - Meyer, Marlene
AU - Monroy, Claire D.
AU - Gerson, Sarah A.
AU - Hunnius, Sabine
TI - Statistics in Motion: Does the Infant Motor System Predict Actions Based on Their Transitional Probability?
T2 - Infancy : the official journal of the International Society on Infant Studies
J2 - Infancy
PY - 2026
DA - 2026/07/01
VL - 31
IS - 4
SP - e70114
SN - 1525-0008
PB - Wiley
DO - 10.1111/infa.70114
UR - https://doi.org/10.1111/infa.70114
LA - en
ER -

CSL-JSON

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