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Does the reading brain possess top-down connections from morphemes to letters?

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  1. [1] § Results ↔ Experiment_2/excluding_outliers.ipynb, lines 80 to 161 · score 0.69 · incorrectly answered trials, log RT, standard deviation, Filler, accuracy, RTs · Report a mismatch
  2. [2] § Results ↔ Experiment_1/analysis_of_response_times.R, the whole file · a weak match · score 0.69 · random intercepts, random slope, lme4, gram frequency, lmer, simplified · Report a mismatch
  3. [3] § Results ↔ Experiment_1/analysis_of_accuracies.R, the whole file · a weak match · score 0.68 · random intercepts, random slope, lme4, gram frequency, glmer, analyse · Report a mismatch
  4. [4] § Results ↔ Experiment_1/excluding_outliers.ipynb, lines 108 to 173 · score 0.52 · incorrectly answered trials, Filler, RTs, log, accuracy · Report a mismatch

Paper

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The authors' code

Jupyter notebook · 161 lines · 5.4 KB · no license · 1 match

  1. # %% [markdown]
  2. # # **Script for excluding outliers from our data**
  3. #
  4. # *26th Januaty 2026*
  5. #
  6. #
  7. # Content:
  8. # - checking the normality of response times (RTs)
  9. # - excluding outliers for the RTs analysis
  10. # - excluding the outliers for the analysis of accuracies
  11. #
  12. # *The same script was used when performing the post-hoc analysis where 33 word labels were excluded.*
  14. # %%
  15. import pandas as pd
  17. # %%
  18. # Importing the correct file.
  19. file_path = #name the path to your file
  20. df_all_data = pd.read_csv(file_path)
  24. # The following lines were used instead of the ones above when performing the post-hoc analysis where 33 word labels were excluded.
  25. # df_all_data_all_labels = pd.read_csv(file_path)
  27. #list_excluded_numbers = [44, 42, 46, 49, 36, 52, 47, 40, 51, 53, 39, 37, 41, 50, 17,38, 45, 8,12, 29,43, 18, 28, 14, 13, 15, 25, 32, 6, 35, 54, 2, 63]
  28. # df_all_data = df_all_data_all_labels[~df_all_data_all_labels['word_label'].isin(list_excluded_numbers)]
  31. # Code morphemes as morpheme yes = 1 and morpheme no = 0
  32. df_all_data["morpheme_code"] = df_all_data["morpheme"].apply(
  33. lambda x: 1 if x == "yes" else 0
  34. )
  37. # %%
  38. # Set dtypes
  40. df_all_data["n1"].astype(float)
  41. df_all_data["n2"].astype(float)
  42. df_all_data["n3"].astype(float)
  43. df_all_data["n4"].astype(float)
  44. df_all_data["n5"].astype(float)
  46. df_all_data["correct"].astype(int)
  47. df_all_data["correct_response"].astype(str)
  48. df_all_data["foil"].astype(str)
  49. df_all_data["morpheme"].astype(str)
  50. df_all_data["response_time"].astype(int)
  51. df_all_data["target"].astype(str)
  52. df_all_data["word"].astype(str)
  54. df_all_data["word_label"].astype(float)
  56. # %%
  57. # Check the overall accuracy per person and flag anyone whose accuracy is too low
  58. for inx, person in df_all_data.groupby('subject_ID'):
  59. accuracy = person['correct'].mean()
  60. if accuracy >= 0.60:
  61. print('Subject', inx, 'is accurate enough.')
  62. else:
  63. print('Subject', inx, 'is NOT accurate enough.')
  65. # %%
  66. # Excluding responses taht are too fast
  67. df_all_data = df_all_data[df_all_data["response_time"] >= 100]
  69. # %%
  70. # Visually checking for the normality of RTs
  71. import matplotlib.pyplot as plt
  72. import numpy as np
  74. plt.figure(figsize=(10, 6))
  75. plt.hist(df_all_data["response_time"], bins=200, color="skyblue", edgecolor="black")
  77. # Performing a log transformation on the response times, as the data was not normally distributed.
  78. df_all_data["log_response_time"] = np.log(df_all_data["response_time"])
  80. # %% [markdown]
  81. # ## Excluding outliers for the RTs analysis and Analysis of Accuracies
  82. #
  83. # - calculating the mean log RT and standard deviation (SD) per person based on correctly answered morpheme present (morpheme == 'yes') and morpheme absent (morpheme == 'no') trials
  84. # - removing trials with log RT more than 2.5 SDs away from the mean
  85. # - using the same upper and lower cut-off values for the exclusion of outliers for the incorrectly answered trials (analysis of accuracies)
  87. # %%
  88. df_correctly_answered_trials_without_outliers = pd.DataFrame()
  89. df_accuracies_trials_without_outliers = pd.DataFrame()
  91. # Removing outliers based on mean log RT and SD per person
  92. for unique_number, df_one_subject_data in df_all_data.groupby("subject_ID"):
  94. df_correct_answers = df_one_subject_data[
  95. (
  96. (df_one_subject_data["correct"] == 1)
  97. & (df_one_subject_data["morpheme"] != "filler")
  99. )
  100. ]
  102. time_mean = df_correct_answers["log_response_time"].mean()
  103. std = df_correct_answers["log_response_time"].std()
  105. df_without_outliers_one_subject_morphemes = df_correct_answers[
  106. ((df_correct_answers["log_response_time"]) > (time_mean - (2.5 * std)))
  107. & ((df_correct_answers["log_response_time"]) < (time_mean + (2.5 * std)))
  108. ]
  110. # Calculating and cut-off values per participant to exclude incorrect trials
  111. lower_boundary = time_mean - (2.5 * std)
  112. upper_boundary = time_mean + (2.5 * std)
  114. df_correctly_answered_trials_without_outliers = pd.concat(
  115. [
  116. df_correctly_answered_trials_without_outliers,
  117. df_without_outliers_one_subject_morphemes,
  118. ],
  119. ignore_index=True,
  120. )
  122. # Incorrect and correct trials
  123. df_incorrect_answers = df_one_subject_data[
  124. (df_one_subject_data["response_time"] < 5000) # Excluding timed-out responses
  125. & (df_one_subject_data["morpheme"] != "filler")
  127. ]
  129. df_without_outliers_one_subject_accuracies = df_incorrect_answers[
  130. ((df_incorrect_answers["log_response_time"]) > lower_boundary)
  131. & ((df_incorrect_answers["log_response_time"]) < upper_boundary)
  132. ]
  134. df_accuracies_trials_without_outliers = pd.concat(
  135. [
  136. df_accuracies_trials_without_outliers,
  137. df_without_outliers_one_subject_accuracies,
  138. ]
  139. )
  142. df_correctly_answered_trials_without_outliers["overall_frequency"] = (
  143. df_correctly_answered_trials_without_outliers["n2"]
  144. + df_correctly_answered_trials_without_outliers["n3"]
  145. + df_correctly_answered_trials_without_outliers["n4"]
  146. + df_correctly_answered_trials_without_outliers["n5"]
  147. ) / 4
  148. df_accuracies_trials_without_outliers["overall_frequency"] = (
  149. df_accuracies_trials_without_outliers["n2"]
  150. + df_accuracies_trials_without_outliers["n3"]
  151. + df_accuracies_trials_without_outliers["n4"]
  152. + df_accuracies_trials_without_outliers["n5"]
  153. ) / 4
  155. df_correctly_answered_trials_without_outliers.to_csv(
  156. "correctly_answered_trials_without_outliers_morphemes.csv"
  157. )
  159. df_accuracies_trials_without_outliers.to_csv(
  160. "acuracies_trials_without_outliers_morphemes.csv"
  161. )

excluding_outliers.ipynb, no license · at the source

Overview

Authors: Maša Mlinarič1, Jonathan Grainger2, Elisabeth Beyersmann3, Joshua Snell1
  1. Vrije Universiteit Amsterdam, Amsterdam, Netherlands
  2. Centre Nationale de Recherche Scientifique, Paris, France
  3. Macquarie University, Sydney, Australia
Institutions: Vrije Universiteit Amsterdam (Netherlands); Centre National de la Recherche Scientifique (France); Macquarie University (Australia)
Journal: Psychonomic bulletin & review, volume 33, issue 8, article 268
Dates: received 25 September 2025; accepted 12 October 2026; published online 27 October 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3758/s13423-026-03037-9 · PMID 42945029 · PMCID PMC13714072 · OpenAlex W7223820265
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: human (organism), cognitive (subfield)
Keywords: Reading, Morphological processing, Orthographic processing
MeSH: Brain*, Pattern Recognition, Visual*, Psycholinguistics*, Reading*, Adult, Female, Humans, Male, Young Adult (* major topic)
Topic: Reading and Literacy Development (Developmental and Educational Psychology, Psychology), according to OpenAlex
Funding: European Research Council (ERC101164084); NWO Vidi (https://doi.org/10.61686/NBBFT35796)
Citations: not cited yet (Europe PMC); 50 references in the paper

Abstract

No psycholinguist will contradict the importance of morphemes in language processing. But as a cognitive construct, the morpheme and its operations are not fully mapped out yet. Here, we investigated whether morphemes provide top-down support for their constituent letters: a so-called morpheme superiority effect. In two experiments (N = 100 per experiment), French participants identified single letters in nonword strings that either comprised a French affix (e.g., ‘o’ in ‘cojuste’) or not (‘o’ in ‘sojuste’). In Experiment 1 participants provided free-recall responses by typing letters seen at postcued locations. Experiment 2 was a Reicher–Wheeler (i.e., two-alternative forced-choice) version of the same task. Neither experiment evidenced better identification of target letters embedded in affixes than nonaffixes. On the other hand, we did observe an impact of the frequencies of embedding N-grams (i.e., letter chunks). Hence, while the brain learns and represents letter combinations, we did not observe evidence for morpheme representations partaking in the brain’s feedback loop. Findings are discussed in light of the recent PONG model of word recognition.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.3758/s13423-026-03037-9.

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

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OSF 8z43h

License: none: the authors keep all their rights
State: the link answers, verified on 30 October 2026
Evidence: files inventoried
Languages: R (4), Jupyter (2)
Size: 14 files, 6 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: license file, 2 notebooks
Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: lme4 (4 files), Matplotlib (2 files), NumPy (2 files), pandas (2 files)
Availability: 1 check, the latest on 30 October 2026: the link answers (HTTP 200)
  • 30 October 2026: the link answers (HTTP 200)
7 files
At the source: osf.io/8z43h/

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

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Data Availability Statement

All data and scripts are openly available online (https://osf.io/8z43h/).

All data and scripts are openly available online (https://osf.io/8z43h/).

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

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Version 1, 30 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 3 keywords, 9 MeSH terms, 2 funders, 36 references.

Cite

This paper

Mlinarič, M., Grainger, J., Beyersmann, E., & Snell, J. (2026). Does the reading brain possess top-down connections from morphemes to letters? Psychonomic bulletin & review, 33(8), 268. https://doi.org/10.3758/s13423-026-03037-9

BibTeX

@article{mlinaric2026does,
author = {Mlinarič, Maša and Grainger, Jonathan and Beyersmann, Elisabeth and Snell, Joshua},
title = {{Does the reading brain possess top-down connections from morphemes to letters?}},
journal = {Psychonomic bulletin \& review},
year = {2026},
month = oct,
volume = {33},
number = {8},
pages = {268},
publisher = {Springer Science+Business Media},
issn = {1069-9384},
doi = {10.3758/s13423-026-03037-9},
url = {https://doi.org/10.3758/s13423-026-03037-9},
pmid = {42945029},
pmcid = {PMC13714072}
}

RIS

TY - JOUR
AU - Mlinarič, Maša
AU - Grainger, Jonathan
AU - Beyersmann, Elisabeth
AU - Snell, Joshua
TI - Does the reading brain possess top-down connections from morphemes to letters?
T2 - Psychonomic bulletin & review
J2 - Psychon Bull Rev
PY - 2026
DA - 2026/10/27
VL - 33
IS - 8
SP - 268
SN - 1069-9384
PB - Springer Science+Business Media
DO - 10.3758/s13423-026-03037-9
UR - https://doi.org/10.3758/s13423-026-03037-9
LA - en
ER -

CSL-JSON

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