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Phase similarity between similar objects indicates representational merging across retrieval training but not sleep.

Code ↔ Paper

16 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 16 matches
  1. [1] § Materials and Methods › Data analysis › Representational change analyses › Source localisation ↔ scripts/phase_paper/dnap_class_32_source_reconstruction.py, lines 220–301 · score 0.88 · unit noise gain, rank deficiency, LCMV filters, beamforming, covariance, inverse
  2. [2] § Materials and Methods › Data analysis › EEG preprocessing ↔ scripts/sigma_paper/04_dnap_sigma_sleep-yasa.py, lines 46–82 · score 0.85 · linked mastoid, pre processed, miscellaneous, bandwidth, resampled, E1
  3. [3] § Materials and Methods › Data analysis › Representational change analyses › Linear modelling ↔ scripts/phase_paper/dnap_class_15_L-IR-DR_models.Rmd, lines 599–722 · score 0.83 · 0–400 ms, 400–700 ms, Treatment contrast, delta, beta, model
  4. [4] § Results › Only the retrieval training intervention induces further representational merging of similar objects in the alpha band › Intervention shift linear mixed-effects modelling ↔ scripts/phase_paper/dnap_class_15_L-IR-DR_models.Rmd, lines 487–597 · score 0.82 · 0–400 ms, 400–700 ms, Treatment contrast, delta, beta, fitted
  5. [5] § Materials and Methods › Data analysis › EEG preprocessing ↔ scripts/phase_paper/dnap_class_01_preproc.py, lines 441–519 · score 0.81 · AutoReject, bandwidth, resampled, ECG, EMG1, EMG2
  6. [6] § Results › Encoding-driven representational merging of similar objects in theta-band phase › Encoding shift linear mixed-effects modelling ↔ scripts/phase_paper/dnap_class_15_L-IR-DR_models.Rmd, lines 599–722 · score 0.77 · 0–400 ms, 400–700 ms, bounds, delta, beta, interaction
  7. [7] § Results › Item-level behavioural accuracy to similar-lure objects is predicted by representational merging in the retrieval training intervention ↔ scripts/phase_paper/dnap_class_21_IR-DR_models_items.Rmd, lines 91–133 · score 0.71 · correctly classified, lure accuracy, 400–700 ms, CI, bins, predicted
  8. [8] § Materials and Methods › Data analysis › Behaviour modelling ↔ scripts/phase_paper/dnap_class_15_L-IR-DR_models.Rmd, lines 111–183 · score 0.65 · treatment contrast, lme4, delayed recognition accuracy, outliers, modelling, confidence
  9. [9] § Materials and Methods › Data analysis › EEG phase similarity ↔ scripts/phase_paper/dnap_class_02_read-in_complex.py, lines 173–232 · score 0.62 · 2–30 Hz, 100–700 ms, Morlet, epoch, 100 ms, EEG
  10. [10] § Materials and Methods › Data analysis › EEG phase similarity ↔ scripts/phase_paper/dnap_class_23_read-in_complex_avg.py, lines 170–234 · score 0.62 · 2–30 Hz, 100–700 ms, Morlet, epoch, 100 ms, EEG
  11. [11] § Materials and Methods › Data analysis › Representational change analyses › Source localisation ↔ scripts/phase_paper/dnap_class_32_source_reconstruction.py, lines 220–301 · score 0.62 · inverse solution, LCMV filter, sensor, occipital, phase
  12. [12] § Materials and Methods › Data analysis › Representational change analyses › Source localisation ↔ scripts/phase_paper/dnap_class_33_phase_itpc_source.py, lines 339–381 · score 0.59 · Harvard Oxford atlas, MNI, space, brain, filter, phase
  13. [13] § Materials and Methods › Data analysis › Representational change analyses › Cluster-based permutation tests ↔ scripts/phase_paper/dnap_class_04_phase_same-sim.py, lines 709–772 · score 0.52 · mne.stats.spatio_temporal_cluster_test, tailed, sanity, permutation, phase
  14. [14] § Materials and Methods › Data analysis › Representational change analyses › Cluster-based permutation tests ↔ scripts/phase_paper/dnap_class_24_phase_same-rand_avg.py, lines 523–561 · score 0.52 · mne.stats.spatio_temporal_cluster_test, tailed, sanity, permutation, phase
  15. [15] § Results › Encoding-driven representational merging of similar objects in theta-band phase › Encoding shift source localisation ↔ scripts/phase_paper/dnap_class_36_phase_L-to-IR_graphs_source.py, lines 263–306 · score 0.52 · Harvard Oxford, glass brain, atlas, cluster
  16. [16] § Results › Encoding-driven representational merging of similar objects in theta-band phase › Encoding shift source localisation ↔ scripts/phase_paper/dnap_class_33_phase_itpc_source.py, lines 339–381 · score 0.52 · Harvard Oxford, glass brain, atlas

Paper

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The authors' code

R Markdown · 722 lines · 27 KB · no license · 4 matches

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It can be read at the source: scripts/phase_paper/dnap_class_15_L-IR-DR_models.Rmd.

Overview

Authors: Hayley Bree Caldwell1, Alex Chatburn1, Kurt Lushington1, Simon Hanslmayr2, Sebastian Michelmann3
ORCID iDs: Hayley Bree Caldwell, Alex Chatburn, Kurt Lushington, Simon Hanslmayr, Sebastian Michelmann
  1. School of Psychology, Adelaide University, Adelaide, South Australia, Australia
  2. School of Psychology and Neuroscience, Centre for Neurotechnology, University of Glasgow, Glasgow, United Kingdom
  3. Department of Psychology, New York University, New York City, NY, United States
Institutions: Adelaide University (Australia); The University of Adelaide (Australia); University of Glasgow (United Kingdom); New York University (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1321
Dates: received 23 February 2026; accepted 6 July 2026; published online 31 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1321 · PMID 42549201 · PMCID PMC13430988 · OpenAlex W7167899992
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Spectral & time-frequency
Keywords: memory consolidation, retrieval training, sleep, EEG phase, representational change, gist extraction
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 120 references in the paper

Abstract

Retrieval training (i.e., cued recall) is theorised to induce rapid memory consolidation, similarly to sleep. Across consolidation, related neural representations become increasingly similar; yet, representational change has never been directly compared between sleep and retrieval training to test similarities in their underlying mechanisms. In this study, 30 subjects (27F, 18–34, M = 22.17) completed 4 separate sessions in which they (1) learnt object–word pairs, followed by (2) immediate recognition testing, (3) one of four 120-min interventions (retrieval training, restudy, sleep, or wake), and (4) delayed recognition testing. We compared EEG phase similarity between similar and different objects to assess the time, frequency, and anatomical distribution of representational similarity across encoding (learning to immediate recognition), and each intervention (immediate to delayed recognition). We hypothesised that EEG phase patterns for similar objects would become more similar (i.e., representational merging) across retrieval training and sleep interventions, and predict a greater endorsement of similar-object lures. We found increased representational similarity between similar objects across encoding in the theta-band and occipital sources. Crucially, additional representational merging was only observed across the retrieval training intervention, in the alpha-band and parieto-occipital sources. Despite retrieval training leading to reduced performance in discriminating similar-objects lures, greater representational merging across retrieval training predicted greater discrimination of similar-object lures. Together, these findings suggest that sleep and retrieval training induce different memory transformations across equivalent timescales. Retrieval training may generally provoke rapid gist extraction, with greater neocortical integration supporting episodic discrimination. Conversely, sleep may selectively maintain short-term task-relevant episodic and semantic details.

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 16 matches between paragraphs and lines of code.

OSF mygh9

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (41), R (10)
Size: 166 files, 51 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Holds: README, 10 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: MNE-Python (41 files), Matplotlib (39 files), NumPy (37 files), SciPy (32 files), car (10 files), easystats (10 files), emmeans (10 files), lme4 (10 files), tidyverse (10 files), pandas (8 files), seaborn (5 files), autoreject (4 files), ggpubr (4 files), Nilearn (4 files), NiBabel (3 files), rstatix (3 files), lmerTest (2 files), broom (1 file), Pingouin (1 file), Plotly (1 file), scikit-learn (1 file), YASA (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
52 files, to read at the source

This repository has no license: its authors keep all rights. Read it at the source.

At the source: doi.org/10.17605/OSF.IO/MYGH9

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;
  • 51 scripts, each with its path and the digest of its content;
  • 16 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 and Code Availability

The data and code used to produce all reported analyses are publicly available on Open Science Framework: https://doi.org/10.17605/OSF.IO/MYGH9. Access to the full dataset collected is available upon request by contacting the corresponding author.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 118 references.

Cite

This paper

Caldwell, H. B., Chatburn, A., Lushington, K., Hanslmayr, S., & Michelmann, S. (2026). Phase similarity between similar objects indicates representational merging across retrieval training but not sleep. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1321. https://doi.org/10.1162/imag.a.1321

BibTeX

@article{caldwell2026phase,
author = {Caldwell, Hayley Bree and Chatburn, Alex and Lushington, Kurt and Hanslmayr, Simon and Michelmann, Sebastian},
title = {{Phase similarity between similar objects indicates representational merging across retrieval training but not sleep}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jul,
volume = {4},
pages = {IMAG.a.1321},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1321},
url = {https://doi.org/10.1162/imag.a.1321},
pmid = {42549201},
pmcid = {PMC13430988}
}

RIS

TY - JOUR
AU - Caldwell, Hayley Bree
AU - Chatburn, Alex
AU - Lushington, Kurt
AU - Hanslmayr, Simon
AU - Michelmann, Sebastian
TI - Phase similarity between similar objects indicates representational merging across retrieval training but not sleep
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/07/31
VL - 4
SP - IMAG.a.1321
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1321
UR - https://doi.org/10.1162/imag.a.1321
LA - en
ER -

CSL-JSON

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