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From Feedback-Learning to Semantic Memory: Can Feedback-Related Brain Activity Predict Object-Word Associations?

Code ↔ Paper

7 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 7 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Method › Analysis and ERP Amplitude Extraction ↔ Procedure.m, the whole file · a weak match · score 0.96 · preceding positive peak, 110–260 ms, 200–400 ms, negative peak, 110 ms, window
  2. [2] § Method › EEG Data Preprocessing ↔ Procedure.m, the whole file · a weak match · score 0.86 · Brain Vision Analyzer, artifact rejection, positive peaks, preprocessed, learning blocks, EEG
  3. [3] § Method › EEG Data Preprocessing ↔ create_single_trial_v1.m, lines 1–59 · score 0.70 · artifact rejection, peak latencies, learning blocks, EEG, event, ms
  4. [4] § Method › Statistical Data Analysis › ERP Main Analyses › Prediction of Association Strength Measured by N400 Amplitude ↔ R/FRN_N400.r, the whole file · a weak match · score 0.69 · FRN N400, factor FRN, removed residual, N400 amplitude, Feedback Valence, nested
  5. [5] § Method › Statistical Data Analysis › ERP Main Analyses › Prediction of Association Strength Measured by N400 Amplitude ↔ R/FRN_Recognition.R, the whole file · a weak match · score 0.69 · FRN Recognition, factor FRN, removed residual, recognition accuracy, Feedback Valence, nested
  6. [6] § Method › Analysis and ERP Amplitude Extraction ↔ create_single_trial_v1_fixedtiming_new.m, lines 1–50 · score 0.61 · 270 ms, 470 ms, segment, variable, electrode, words
  7. [7] § Method › Statistical Data Analysis › ERP Main Analyses › Prediction of Behavioral Free Recall Performance ↔ R/functions.R, lines 526–558 · score 0.61 · stepwise elimination, parsimonious model, buildmer

Paper

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

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The authors' code

MATLAB · 84 lines · 4.8 KB · no license · 2 matches

  1. %This is the general procedure used for extracting single trial segments
  2. %for later statistical analyses
  4. clear
  6. %Step 1: Read in average data for all conditions and participants
  7. % Step 1.1: define conditions and names
  8. conditions = {'Positive','Negative'};
  9. condition_names = {'Positive','Negative'};
  10. % Step 1.2: define subject exclusions and place of subject no in logfiles
  11. exclusions = {'/07','/15','/19','/21','/22','/25','/31','/43','/45','/47','/51','/56','/65','/77'};
  12. subject_index = (1:2);
  13. % Step 1.3: Import Data
  14. Restructure_Matrices('Export',conditions,exclusions,subject_index)
  15. % Step 1.4: Create pooled electrodes
  16. create_pooled({'Fz','FCz','Cz'},conditions);
  17. create_pooled({'P7','P8'},conditions);
  18. create_pooled({'Fz','Cz','Pz'},conditions);
  20. %Step 2: Get the most negative peak and the preceding positive peak in the
  21. %defined time windows (FzFCzCz: 200-400 ms for negative peak, 100 - negpeak
  22. %for positive peak; P7P8: 110-260 ms for negative peak, 30 - negpeak for
  23. %positive peak
  24. getpeaktopeak(conditions,condition_names,{'FzFCzCz','P7P8'},{200,110},{400,260},{100,30},200,false);
  26. %Step 3: Check Single Subject Averages
  27. % Open the Script sserps. Check for each participant if the peak lies in
  28. % the respective areas. Compare for a few participants if the negative
  29. % peak found by the previous function (see negativepeak table) matches
  31. %Step 4: we use a Brain Vision Analyzer Macro to export operation infos for
  32. %the artifact rejection, that includes which segments are removed. This
  33. %information is imported here:
  34. opinfo2('Export')
  36. %Step 5: Import behavioral data. For further analysis, it is important that
  37. %they include the exact same factor names, trial numbers and subject codes.
  38. read_pres_logs_casino('Logfiles_clean',exclusions)
  39. % remove problematic trials with erroneous logfile information
  40. rows = (~strcmp(all_data.Subject,'48') | all_data.TrialNumber ~= 1112);
  41. all_data = all_data(rows,:);
  42. rows = (~strcmp(all_data.Subject,'48') | all_data.TrialNumber ~= 1480);
  43. all_data = all_data(rows,:);
  45. %Step 6: Combine opinfo-table and behavioral data
  46. bw_analysis2
  47. bw_analysis3
  48. %Step 7: Adapt the behavioral data table so that it can be combined with
  49. %EEG data
  50. restructure_beh
  51. restructure_beh_priming
  53. %these functions read in the single trial data and then export the
  54. %respective timeslots. I have a general function that does peak-to-peak
  55. %measures and a "fixedtiming" function that creates an area average across
  56. %a timespan (of course, for this function to work you do not need any of
  57. %the average-data preprocessing above). In this analysis I wanted both
  58. %measures, so I combined them.
  59. %Step 8: Import single trial segment, extract single trial values and
  60. %combine behavioral and EEG data for FRN and N170
  61. create_single_trial_v1('Export',exclusions)
  62. %add P300 measures to the dataset
  63. create_single_trial_fixedtiming_P300('Export',exclusions)
  64. dependentvariables_st_v1_learning_blocks = outerjoin(dependentvariables_st_v1_learning_blocks,dependentvariables_st_area_P300,'MergeKeys',true);
  66. %Step 9: Import single trial segment, extract single trial values and
  67. %combine behavioral and EEG data for N400
  68. create_single_trial_v1_fixedtiming_new('Export',exclusions)
  69. %Step 10: Combine priming and learning data
  70. convert_learning_blocks()
  71. dependentvariables_st_area_priming.Properties.VariableNames = {'Subject','PrimingCondition','PrimingSegnum','PrimingElectrodes','areaarea250500','area270470','area500700','object','FeedbackTiming','PrimingAccuracy'};
  72. dependentvariables_st_v1_learning_blocks_longformat.FeedbackTiming = string(dependentvariables_st_v1_learning_blocks_longformat.FeedbackTiming);
  73. dependentvariables_st_area_priming.FeedbackTiming = string(dependentvariables_st_area_priming.FeedbackTiming);
  74. dependentvariables_st_v1_learning_blocks_longformat.PrimingAccuracy = string(dependentvariables_st_v1_learning_blocks_longformat.PrimingAccuracy);
  75. dependentvariables_st_area_priming.PrimingAccuracy = string(dependentvariables_st_area_priming.PrimingAccuracy);
  76. dependentvariables_st_v1_learning_blocks_longformat.PrimingElectrodes = string(dependentvariables_st_v1_learning_blocks_longformat.PrimingElectrodes);
  77. dependentvariables_st_area_priming.PrimingElectrodes = string(dependentvariables_st_area_priming.PrimingElectrodes);
  78. dependentvariables_st_v1_learning_blocks_longformat.PrimingCondition = string(dependentvariables_st_v1_learning_blocks_longformat.PrimingCondition);
  79. dependentvariables_st_area_priming.PrimingCondition = string(dependentvariables_st_area_priming.PrimingCondition);
  80. alldata = outerjoin(dependentvariables_st_v1_learning_blocks_longformat,dependentvariables_st_area_priming,'MergeKeys',true);
  82. %Step 11: Export tables used for statistical analysis
  83. rewritetable(dependentvariables_st_v1_learning_blocks,'learning_alldata.csv')
  84. rewritetable(alldata,'completedata_r.csv')

Procedure.m, no license · at the source

Overview

Authors: Christine Albrecht1, Laura Bechtold1, Marta Ghio1, Christian Bellebaum1
ORCID iDs: Christine Albrecht, Laura Bechtold
  1. Faculty of Mathematics and Natural Sciences, Institute of Experimental Psychology Heinrich Heine University Düsseldorf Düsseldorf Germany
Institutions: Heinrich Heine University Düsseldorf (Germany)
Journal: Psychophysiology, volume 63, issue 5, article e70306
Dates: received 5 August 2025; accepted 2 April 2026; published online 6 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/psyp.70306 · PMID 42089872 · PMCID PMC13148314 · OpenAlex W7160411006
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Statistics, Physiology & signal measures
Keywords: feedback learning, FRN, N170, N400, semantic memory
MeSH: Association Learning*, Cerebral Cortex*, Evoked Potentials*, Feedback, Psychological*, Mental Recall*, Recognition, Psychology*, Adolescent, Adult, Electroencephalography, Female, Humans, Male, Semantics, Young Adult (* major topic)
Topic: Memory Processes and Influences (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (467460456)
Citations: not cited yet (Europe PMC); 96 references in the paper

Abstract

This study investigated the neural mechanisms underlying feedback‐based learning of novel‐object‐novel‐word associations, focusing on how feedback‐locked event‐related potentials acquired during learning relate to subsequent memory performance and acquired association strength. Specifically, we examined whether amplitudes of the feedback‐related negativity (FRN) and N170 components after immediate or delayed feedback predicted not only free recall and recognition performance, but also N400 priming effects exerted by the novel objects on the novel words as a neural correlate of the strength of the associations acquired through feedback‐based learning. Sixty‐six healthy young adults learned novel associations receiving either immediate or delayed deterministic feedback, followed by free recall tests and a newly introduced primed recognition task to measure N400 priming effects. Results showed that FRN amplitudes during learning were associated with recognition performance and predicted frontal N400 priming effects, suggesting a link to procedural, automatically retrieved memories. In contrast, N170 amplitudes were related not only to recognition, but also free recall and to general facilitation of semantic retrieval and integration processes, reflected in reduced N400 amplitudes, indicating a role in declarative memory and familiarity‐driven processing facilitation. Overall, feedback‐based learning elicited robust N400 priming effects, reflecting successful associative integration. However, no consistent effects of feedback timing or valence were observed, likely due to learning strategies adopted based on the deterministic nature of feedback and the anticipation of the memory tasks after learning. These findings highlight distinct contributions of feedback‐related ERP components to different forms of memory representations, linking general mechanisms of feedback‐based learning to the resulting representations.

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 7 matches between paragraphs and lines of code.

OSF s36y2

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (21), R (20)
Size: 331 files, 41 scripts
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), easystats (2 files), car (1 file), ggplot2 (1 file), ggpubr (1 file), lme4 (1 file), lmerTest (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
41 files
At the source: doi.org/10.17605/OSF.IO/S36Y2

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

Tracing map

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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;
  • 41 scripts, each with its path and the digest of its content;
  • 7 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 that support the findings of this study and all analysis scripts are openly accessible through the Open Science Framework at https://doi.org/10.17605/OSF.IO/S36Y2.

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 → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 14 MeSH terms, 1 funder, 92 references.

Cite

This paper

Albrecht, C., Bechtold, L., Ghio, M., & Bellebaum, C. (2026). From Feedback-Learning to Semantic Memory: Can Feedback-Related Brain Activity Predict Object-Word Associations? Psychophysiology, 63(5), e70306. https://doi.org/10.1111/psyp.70306

BibTeX

@article{albrecht2026feedback,
author = {Albrecht, Christine and Bechtold, Laura and Ghio, Marta and Bellebaum, Christian},
title = {{From Feedback-Learning to Semantic Memory: Can Feedback-Related Brain Activity Predict Object-Word Associations?}},
journal = {Psychophysiology},
year = {2026},
month = may,
volume = {63},
number = {5},
pages = {e70306},
publisher = {Wiley},
issn = {0048-5772},
doi = {10.1111/psyp.70306},
url = {https://doi.org/10.1111/psyp.70306},
pmid = {42089872},
pmcid = {PMC13148314}
}

RIS

TY - JOUR
AU - Albrecht, Christine
AU - Bechtold, Laura
AU - Ghio, Marta
AU - Bellebaum, Christian
TI - From Feedback-Learning to Semantic Memory: Can Feedback-Related Brain Activity Predict Object-Word Associations?
T2 - Psychophysiology
J2 - Psychophysiology
PY - 2026
DA - 2026/05/01
VL - 63
IS - 5
SP - e70306
SN - 0048-5772
PB - Wiley
DO - 10.1111/psyp.70306
UR - https://doi.org/10.1111/psyp.70306
LA - en
ER -

CSL-JSON

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