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Action information is integrated into entorhinal representations of conceptual space and is reflected in eye movements.

Code ↔ Paper

14 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 14 matches
  1. [1] § Method details › Functional data preprocessing ↔ neural_data_analyses/6_cluster_correction.py, lines 75–132 · score 0.74 · MNI152NLin6Asym, MNI space, volume, FSL, resampled, affines
  2. [2] § Method details › Statistical analyses › Analysis of eyetracking data. ↔ eyetracking_data_analyses/2_eye_exploration_allsubs.ipynb, lines 290–337 · score 0.73 · 500–2500 ms, noise, window, onset, median, eyes
  3. [3] § Results › Entorhinal pattern similarities scale with afforded action similarity ↔ neural_data_analyses/8_roi_based_rsa.ipynb, lines 14–55 · score 0.72 · hippocampus left, affordance magnitude, hippocampus right, link distance, cortex, entorhinal
  4. [4] § Method details › Functional data preprocessing ↔ neural_data_analyses/4_run_glm_single_subject.py, lines 114–138 · score 0.71 · probabilistic masks, minimize, FWHM, slice, affines, confounding
  5. [5] § Method details › Anatomical data preprocessing ↔ neural_data_analyses/2_fmriprep_acthippo.py, lines 146–232 · score 0.68 · MNI152NLin2009cAsym, MNI152NLin6Asym, workflow, Nipype, sh, BIDS
  6. [6] § Method details › Statistical analyses › Representational similarity analysis. ↔ neural_data_analyses/10_single_subject_searchlight_partials.py, lines 44–81 · score 0.62 · spearman rank correlation, tie corrected, neural
  7. [7] § Method details › Statistical analyses › Representational similarity analysis. ↔ eyetracking_data_analyses/1_eye_create_events.py, lines 255–340 · score 0.62 · stick function, button presses, probe trial, fixation, regressors, stimulus
  8. [8] § Method details › Statistical analyses › Representational similarity analysis. ↔ neural_data_analyses/3_create_events.py, lines 255–329 · score 0.62 · stick function, button presses, probe trial, fixation, regressors, stimulus
  9. [9] § Method details › Statistical analyses › Representational similarity analysis. ↔ neural_data_analyses/8_roi_based_rsa.ipynb, lines 153–190 · score 0.61 · spearman rank correlation, tie corrected, neural
  10. [10] § Method details › MRI Data Acquisition ↔ neural_data_analyses/1_dicom_to_bids.py, lines 175–206 · score 0.56 · anatomical scan, MP2RAGE
  11. [11] § Results › Entorhinal pattern similarities scale with afforded action similarity ↔ eyetracking_data_analyses/5_deepmreye_eye_comparisons.ipynb, lines 231–292 · score 0.54 · gaze lateralization, DeepMReye, Predicted gaze, correlated, eyetracker
  12. [12] § Method details › Statistical analyses › Analysis of eyetracking data. ↔ eyetracking_data_analyses/3_directional_stat_allsubs.ipynb, lines 1–66 · score 0.52 · Gaussian kernel, window, smoothed, eyetracking, gaze, eyes
  13. [13] § Method details › Anatomical data preprocessing ↔ neural_data_analyses/6_cluster_correction.py, lines 75–132 · score 0.52 · MNI152NLin6Asym, Volume, FSL, GM, T1w, Brain
  14. [14] § Method details › Statistical analyses › DeepMReye. ↔ eyetracking_data_analyses/5_deepmreye_eye_comparisons.ipynb, lines 231–292 · score 0.50 · gaze lateralization, DeepMReye, predicting

Paper

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The authors' code

Python · 242 lines · 8.1 KB · no license · 2 matches

  2. import sys
  3. #prepend_str = sys.argv[1]
  4. prepend_str = ''
  6. #contrast_s = ['modulationState', 'modulationAffordance','modulationAffordanceMagnitude','modulationNumericalDifference','modulationMagnitude','stimulus',
  7. # 'visSame', 'postProbe']
  9. append_str = ''
  10. smoothing_ = '5'
  11. #prepend_str = '1A_'
  13. contrast_s = ['scalarcode']
  15. for contrast_ in contrast_s:
  17. print('='*50)
  18. print(f'Running contrast {contrast_}')
  19. print('='*50)
  20. # TO DO: convert files to MNI space before running this!!!
  22. # =============================================================================
  23. import os
  24. import json
  25. from glob import glob
  26. import pandas as pd
  27. import numpy as np
  29. from nilearn.glm.second_level import SecondLevelModel
  30. import nibabel as nib
  31. import matplotlib.pyplot as plt
  32. from scipy.stats import norm
  33. from nilearn import plotting, image
  36. dataset_dir = '/data/pt_02747/action_hippo/data/'
  37. data_dir = '/data/pt_02747/action_hippo/data/derivatives/'
  38. output_dir_first = '/data/pt_02747/action_hippo/data/derivatives/first_level/nilearn_glm/'
  39. output_dir_second = '/data/pt_02747/action_hippo/data/derivatives/second_level/nilearn_glm/'
  40. working_dir = '/data/pt_02747/action_hippo/data/derivatives/working_dir/'
  42. # get all subjects
  43. subs = [os.path.basename(f) for f in glob(data_dir + 'sub*')]
  44. subs = [sub for sub in subs if '.' not in sub]
  46. # get run labels
  47. runs = ['run-0' + str(i) for i in range(1,9)]
  50. mask_gm = '/data/pt_02747/tpl-MNI152NLin6Asym_res-25_label-GM_03.nii.gz'
  54. from nilearn.glm.second_level import non_parametric_inference
  57. models = []
  58. count = 0
  59. for sub in subs:
  60. # get filename
  61. display_orient = 'z'
  62. file_ = output_dir_first + f'{sub}/{sub}_smoothing-{smoothing_}_space-MNI152NLin6Asym{append_str}_contrast-{contrast_}_stat-t_statmap.nii.gz'
  63. # extract nifti file from filenames
  64. try:
  65. nifti_ = nib.load(file_)
  66. except:
  67. print(f'file {file_} not found')
  68. continue
  69. # append to list
  70. models.append(nifti_)
  71. count += 1
  72. #print('models are: ',models)
  75. # =============================================================================
  77. second_level_input = models
  78. design_matrix = pd.DataFrame(
  79. [1] * len(second_level_input),
  80. columns=[contrast_],
  81. )
  83. # =============================================================================
  84. # create a mask for small volume correction
  85. # =============================================================================
  87. # to add a mask in we add mask_img=mask_img to SecondLevelModel()
  88. # in this case let's use a random HC one and see how it looks
  90. # really we need to map our MPM map from T1w to MNI space
  91. # and then use that as a mask
  92. # in this case we can just use the MNI152NLin6Asym brain
  94. # load juelich atlas in MNI space
  95. '''juelich_atlas_1mm_file = '/data/pt_02747/action_hippo/juelich_atlas/MPMs_FSL/JulichBrainAtlas_3.0_areas_MPM_b_N10_MNI152NLin6Asym.nii.gz'
  96. # load mask
  97. juelich_atlas_1mm = nib.load(juelich_atlas_1mm_file)
  98. # downsample to 2mm
  99. from nilearn.image import resample_to_img
  100. # take one of our maps as a reference
  101. ref_img = second_level_input[0]
  102. # resample to 2.5mm
  103. juelich_atlas_2mm = resample_to_img(juelich_atlas_1mm_file, ref_img, interpolation='nearest')
  104. # for MTL atlas, take voxels with values of ... *drumroll* 23, 24, 25, 26, 27 28 and 1023 to 1028, inclusive
  105. # get mask
  106. mtl_values = [23, 24, 25, 26, 27, 28, 1023, 1024, 1025, 1026, 1027, 1028] # HPC and EC
  107. #mtl_values = [25, 1025] # EC
  108. #mtl_values = [74, 75, 1074, 1075] # mPFC
  109. mtl_values = [1134, 1135, 1136]
  110. # check if voxel values are in mtl_values, create binary map
  111. mask = np.isin(juelich_atlas_2mm.get_fdata(), mtl_values)
  112. # turn True and False into 1 and 0
  113. mask = mask.astype(int)
  114. # convert to nifti
  115. mask_nii = nib.Nifti1Image(mask, affine=juelich_atlas_2mm.affine, header=juelich_atlas_2mm.header)
  116. # plot
  117. plotting.plot_roi(mask_nii, title='Mask')'''
  118. # =============================================================================
  119. # use parametric inference
  120. # =============================================================================
  123. from nilearn.glm.second_level import SecondLevelModel
  125. second_level_model = SecondLevelModel(
  126. #mask_img = mask_nii
  127. mask_img = mask_gm
  128. )
  129. second_level_model = second_level_model.fit(
  130. second_level_input,
  131. design_matrix=design_matrix,
  132. )
  134. # apply correction using bonferroni correction
  135. from nilearn.image import get_data, math_img
  137. zmap = second_level_model.compute_contrast(output_type="z_score")
  138. nib.save(zmap, output_dir_second + f'{prepend_str}zmap_{contrast_}_twosided.nii.gz')
  141. p_val = second_level_model.compute_contrast(output_type="p_value")
  145. # =============================================================================
  146. # get accurate GM mask, as template includes voxels which are excluded in subject
  147. # specific GM masks
  148. # =============================================================================
  150. # non-paametric inference function is strange
  151. # take the mask only where the pval map is not 0.5, as this accounts for our GM mask
  152. # looking a bit different in T1w space and MNI space
  153. # (not quite sure why the MNI space one includes the brainstem)
  155. # intersect mask with pval map
  156. mask_mult = math_img('img1 * img2', img1=mask_gm, img2=p_val)
  158. # filter to take value which are not 0.5
  159. mask_mult_data = mask_mult.get_fdata()
  160. mask_mult_data[mask_mult_data == 0.5] = 0
  161. mask_mult_data[mask_mult_data != 0] = 1
  163. # turn into nifti image
  164. mask_mult_nii = nib.Nifti1Image(mask_mult_data, affine=mask_mult.affine, header=mask_mult.header)
  166. # plot
  167. plotting.plot_roi(mask_mult_nii, title='Mask')
  169. mask_gm = mask_mult_nii
  171. # =============================================================================
  173. #n_voxels = np.sum(get_data(second_level_model.masker_.mask_img_))
  174. n_voxels = np.sum(get_data(mask_gm))
  176. # plot second_level_model.masker_.mask_img_
  177. plotting.plot_roi(second_level_model.masker_.mask_img_, title='Mask')
  181. # save pval map
  182. nib.save(p_val, output_dir_second + f'{prepend_str}pval_{contrast_}_onesided.nii.gz')
  184. # Correcting the p-values for multiple testing and taking negative logarithm
  185. neg_log_pval = math_img(
  186. f"-np.log10(np.minimum(1, img * {str(n_voxels)}))",
  187. img=p_val,
  188. )
  189. print('this many voxels: ' , n_voxels)
  191. # save z_map
  192. nib.save(neg_log_pval, output_dir_second + f'{prepend_str}logp_bonferroni_{contrast_}_onesided.nii.gz')
  194. print('corrected logp map saved')
  196. # =============================================================================
  197. # use non-parametric inference
  198. # =============================================================================
  200. print('Running non-parametric inference')
  202. out_dict = non_parametric_inference(
  203. second_level_input,
  204. design_matrix=design_matrix,
  205. mask=mask_gm,
  206. model_intercept=True,
  207. n_perm=10000, # 500 for the sake of time. Ideally, this should be 10,000.
  208. two_sided_test=True,
  209. n_jobs=-1,
  210. verbose=1,
  211. tfce=True
  212. )
  214. # =============================================================================
  215. # save output
  217. # create subfolder
  219. tfce = out_dict['tfce']
  220. #t = out_dict['t']
  221. logp_max_tfce = out_dict['logp_max_tfce']
  222. #logp_max_t = out_dict['logp_max_t']
  224. # save tfce
  225. nib.save(tfce, output_dir_second + f'{prepend_str}tfce_{contrast_}_twosided.nii.gz')
  227. print('tfce saved')
  229. # save t
  230. #nib.save(t, output_dir_second + f'{prepend_str}t_{contrast_}_onesided.nii.gz')
  232. #print('t saved')
  234. # save logp_max_tfce
  235. nib.save(logp_max_tfce, output_dir_second + f'{prepend_str}logp_max_tfce_{contrast_}_twosided.nii.gz')
  237. print('logp_max_tfce saved')
  239. # save logp_max_t
  240. #nib.save(logp_max_t, output_dir_second + f'{prepend_str}logp_max_t_{contrast_}_onesided.nii.gz')
  242. #print('logp_max_t saved')

6_cluster_correction.py at commit 0f22b84, no license · at the source

Overview

Authors: Alexander Eperon1,2, Christian F. Doeller2,3, Stephanie Theves2,4, Roberto Bottini1
ORCID iDs: Alexander Eperon, Roberto Bottini
  1. Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
  2. Department of Psychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
  3. Kavli Institute for Systems Neuroscience, The Egil and Pauline Braathen and Fred Kavli Center for Cortical Microcircuits, Jebsen Center for Alzheimer’s Disease, Norwegian University of Science and Technology, Trondheim, Norway
  4. Minerva Research Group Neural Codes of Intelligence, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
Institutions: University of Trento (Italy); Max Planck Institute for Human Cognitive and Brain Sciences (Germany); Norwegian University of Science and Technology (Norway); Kavli Institute for Systems Neuroscience (Norway); Max Planck Institute for Empirical Aesthetics (Germany)
Journal: PLoS biology, volume 24, issue 4, article e3003755
Dates: received 21 August 2025; accepted 27 March 2026; published online 13 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003755 · PMID 41973704 · PMCID PMC13089867 · OpenAlex W7154054508
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
MeSH: Entorhinal Cortex*, Eye Movements*, Space Perception*, Cognition, Female, Hippocampus, Humans, Male, Memory, Reaction Time, Young Adult (* major topic)
Journal subjects: Biology and Life Sciences, Anatomy, Brain, Cerebral Cortex, Entorhinal Cortex, Medicine and Health Sciences, Physiology, Sensory Physiology, Visual System, Eye Movements, Neuroscience, Sensory Systems, Brain Mapping, Functional Magnetic Resonance Imaging, Diagnostic Medicine, Diagnostic Radiology, Magnetic Resonance Imaging, Research and Analysis Methods, Imaging Techniques, Radiology and Imaging, Neuroimaging, Cognitive Science, Cognitive Psychology, Learning, Psychology, Social Sciences, Learning and Memory, Perception, Sensory Perception, Vision, Cognitive Neuroscience, Reaction Time, Computer and Information Sciences, Software Engineering, Preprocessing, Engineering and Technology, Hippocampus
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Max-Planck-Gesellschaft (Max Planck Society) (Minerva Fast Track Fellowship); Kavli Foundation; Italian Ministry of University and Research (MUR-FARE, MODGET R18WJMSNZF); HORIZON EUROPE European Research Council (ERC-StG, NOAM 804422, ATCOM 10112565)
Citations: not cited yet (Europe PMC); 124 references in the paper
Research resources: RRID:SCR_001362, 97 RRID:SCR_002502, RRID:SCR_002823, distributed with ANTs 2.3.3 [100 RRID:SCR_004757, RRID:SCR_005927, RRID:SCR_008796, 95 RRID:SCR_016216

Abstract

The hippocampal-entorhinal system represents relations between states in spatial and nonspatial cognitive maps. Critical to understanding how these memory representations are used for cognition is to determine whether the actions underlying state transitions are incorporated in entorhinal cognitive maps. Participants learned to transition between states using different actions, operationalized as mathematical operations. We found that the entorhinal cortex represented the afforded actions across the states. This action representation was not explained by other properties of the task space, such as link distance between the states or reaction times. Furthermore, gaze behavior reflected the direction of afforded actions in the horizontal axis, and the strength of this lateralization predicted both performance and entorhinal pattern similarities, suggesting a link between gaze behavior and neurocognitive mechanisms for navigating conceptual spaces. In sum, this study provides first evidence for the integration of action information into ocular and entorhinal representations of conceptual spaces, suggesting that these may not just map out experiences, but provide information about how to explore knowledge.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 14 matches between paragraphs and lines of code.

Zenodo 19209884

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 6 files
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (25 files), pandas (20 files), Matplotlib (18 files), NiBabel (15 files), SciPy (14 files), seaborn (13 files), Nilearn (11 files), ANTs (3 files), Nipype (3 files), Dcm2Bids (1 file), MNE-Python (1 file), statsmodels (1 file), TensorFlow (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
34 files
At the source: doi.org/10.5281/zenodo.19209884

alexeperon/action-hippo

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 0f22b84b91af3f9bf1b9387301052c6a282a4904, 24 March 2026
Languages: Python (27), Jupyter (7)
Size: 39 files, 34 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, 7 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (25 files), pandas (20 files), Matplotlib (18 files), NiBabel (15 files), SciPy (14 files), seaborn (13 files), Nilearn (11 files), ANTs (3 files), Nipype (3 files), Dcm2Bids (1 file), MNE-Python (1 file), statsmodels (1 file), TensorFlow (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
35 files
At the source: github.com/alexeperon/action-hippo

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

Tracing map

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What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 68 scripts, each with its path and the digest of its content;
  • 14 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

The data and code underlying our results are now publicly available on Zenodo: https://doi.org/10.5281/zenodo.19209884. The code is available on Zenodo, and can be accessed directly from Github at: https://github.com/alexeperon/action-hippo.

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

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 11 MeSH terms, 4 funders, 117 references, 7 RRIDs.

Cite

This paper

Eperon, A., Doeller, C. F., Theves, S., & Bottini, R. (2026). Action information is integrated into entorhinal representations of conceptual space and is reflected in eye movements. PLoS biology, 24(4), e3003755. https://doi.org/10.1371/journal.pbio.3003755

BibTeX

@article{eperon2026action,
author = {Eperon, Alexander and Doeller, Christian F. and Theves, Stephanie and Bottini, Roberto},
title = {{Action information is integrated into entorhinal representations of conceptual space and is reflected in eye movements}},
journal = {PLoS biology},
year = {2026},
month = apr,
volume = {24},
number = {4},
pages = {e3003755},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003755},
url = {https://doi.org/10.1371/journal.pbio.3003755},
pmid = {41973704},
pmcid = {PMC13089867}
}

RIS

TY - JOUR
AU - Eperon, Alexander
AU - Doeller, Christian F.
AU - Theves, Stephanie
AU - Bottini, Roberto
TI - Action information is integrated into entorhinal representations of conceptual space and is reflected in eye movements
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/04/13
VL - 24
IS - 4
SP - e3003755
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003755
UR - https://doi.org/10.1371/journal.pbio.3003755
LA - en
ER -

CSL-JSON

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