{"dataset":{"id":"61142","dataset_id":"on006012","name":"A geometric shape regularity effect in the human brain: MEG dataset","description":"This dataset contains magnetoencephalography (MEG) recordings from 20 adult participants examining the neural mechanisms underlying perception of regular geometric shapes such as hexagons, triangles, and quadrilaterals. The study investigates how geometric regularity modulates brain activity in ventral visual, intraparietal, and inferior temporal regions, and compares neural responses to predictions from convolutional neural networks and transformer vision models. A companion fMRI dataset examining the same phenomenon is shared separately.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on006012","concept_doi":"10.82901/nemar.on006012","latest_version_doi":"10.82901/nemar.on006012.v1.0.0","created_at":"2026-06-28 08:31:11","updated_at":"2026-08-19 01:02:23","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"A geometric shape regularity effect in the human brain: MEG dataset\",\n  \"description\": \"This dataset contains magnetoencephalography (MEG) recordings from 20 adult participants examining the neural mechanisms underlying perception of regular geometric shapes such as hexagons, triangles, and quadrilaterals. The study investigates how geometric regularity modulates brain activity in ventral visual, intraparietal, and inferior temporal regions, and compares neural responses to predictions from convolutional neural networks and transformer vision models. A companion fMRI dataset examining the same phenomenon is shared separately.\",\n  \"methods_description\": \"MEG data were collected from 20 adult participants during perception of simple geometric shapes. The raw data were manually organized into BIDS format and anonymized/defaced using mne_bids (mne_bids.anonymize_dataset), which shuffled participant order, changed dates, defaced anatomical images, and stripped gender information. Data were preprocessed using the mne-bids-pipeline with a custom configuration file.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Mathias Sablé-Meyer\": {},\n    \"Lucas Benjamin\": {\n      \"orcid\": \"0000-0002-9578-6039\"\n    },\n    \"Cassandra Potier Watkins\": {\n      \"orcid\": \"0000-0002-6588-0614\"\n    },\n    \"Chenxi He\": {},\n    \"Maxence Pajot\": {},\n    \"Théo Morfoisse\": {},\n    \"Fosca Al Roumi\": {\n      \"orcid\": \"0000-0001-9590-080X\"\n    },\n    \"Stanislas Dehaene\": {\n      \"orcid\": \"0000-0002-7418-8275\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"MEG\"\n    },\n    {\n      \"term\": \"geometry\"\n    },\n    {\n      \"term\": \"Visual Perception\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D014796\"\n    },\n    {\n      \"term\": \"shape perception\"\n    },\n    {\n      \"term\": \"intraparietal sulcus\"\n    },\n    {\n      \"term\": \"mathematical cognition\"\n    },\n    {\n      \"term\": \"convolutional neural networks\"\n    },\n    {\n      \"term\": \"transformer models\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on006012\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/on006012\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1101/2024.03.13.584141\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds006010\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsSupplementedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds006012.v1.0.1\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"CEA to Stanislas Dehaene\"\n    },\n    {\n      \"funder_name\": \"INSERM to Stanislas Dehaene\"\n    },\n    {\n      \"funder_name\": \"Collège de France to Stanislas Dehaene\"\n    },\n    {\n      \"funder_name\": \"FYSSEN to Mathias Sablé-Meyer\"\n    },\n    {\n      \"funder_name\": \"ERC\",\n      \"award_number\": \"ERC-ADG-2015 NeuroSyntax\",\n      \"award_title\": \"NeuroSyntax\"\n    },\n    {\n      \"funder_name\": \"CEA\"\n    },\n    {\n      \"funder_name\": \"INSERM\"\n    },\n    {\n      \"funder_name\": \"Collège de France\"\n    },\n    {\n      \"funder_name\": \"FYSSEN\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"Structural MRI Dataset\",\n  \"modalities\": [\n    \"anat\",\n    \"meg\"\n  ],\n  \"sizes\": [\n    \"76.3 GB (195 files)\"\n  ],\n  \"formats\": [\n    \".dat\",\n    \".fif\",\n    \".gz\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yml\"\n  ],\n  \"source_hash\": \"c57c07403a3d5b9237eaaffe1c3192bfe943c9bf1431a031414dec9e040173d7\"\n}","last_activity_at":"2026-06-28 08:31:11","source":"openneuro","source_id":"ds006012","subject_count":21,"modalities":"anat,meg","age_min":20,"age_max":42,"file_size":76309402467,"total_files":977,"tasks":"POGS,noise","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Mathias Sablé-Meyer, Lucas Benjamin, Cassandra Potier Watkins, Chenxi He, Maxence Pajot, Théo Morfoisse, Fosca Al Roumi, Stanislas Dehaene","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on006012-blue)](https://doi.org/10.82901/nemar.on006012)\n\n# A geometric shape regularity effect in the human brain: MEG dataset\n\nAuthors:\n\n* Mathias Sablé-Meyer*\n* Lucas Benjamin\n* Cassandra Potier Watkins\n* Chenxi He\n* Maxence Pajot\n* Théo Morfoisse\n* Fosca Al Roumi\n* Stanislas Dehaene\n\n*Corresponding author: [mathias.sable-meyer@ucl.ac.uk](mailto:mathias.sable-meyer@ucl.ac.uk)\n\n## Abstract\n\nThe perception and production of regular geometric shapes is a characteristic trait of human cultures since prehistory, whose neural mechanisms are unknown. Behavioral studies suggest that humans are attuned to discrete regularities such as symmetries and parallelism, and rely on their combinations to encode regular geometric shapes in a compressed form. To identify the relevant brain systems and their dynamics, we collected functional MRI and magnetoencephalography data in both adults and six-year-olds during the perception of simple shapes such as hexagons, triangles and quadrilaterals. The results revealed that geometric shapes, relative to other visual categories, induce a hypoactivation of ventral visual areas and an overactivation of the intraparietal and inferior temporal regions also involved in mathematical processing, whose activation is modulated by geometric regularity. While convolutional neural networks captured the early visual activity evoked by geometric shapes, they failed to account for subsequent dorsal parietal and prefrontal signals, which could only be captured by discrete geometric features or by more advanced transformer models of vision. We propose that the perception of abstract geometric regularities engages an additional symbolic mode of visual perception.\n\n## Notes about this dataset\n\nWe separately share the fMRI dataset at [https://openneuro.org/datasets/ds006010](https://openneuro.org/datasets/ds006010). Below are some notes about the\nMEG dataset of N=20 participants:\n\n* The code for the analyses associated to\n  [https://doi.org/10.1101/2024.03.13.584141](https://doi.org/10.1101/2024.03.13.584141)\n  are provided at\n  [https://github.com/mathias-sm/AGeometricShapeRegularityEffectHumanBrain](https://github.com/mathias-sm/AGeometricShapeRegularityEffectHumanBrain).  \n  However, these analyses have been performed on pre-processed data _without_\n  this defacing steps. I am not publishing this raw data, but should there be\n  discrepancies or problems coming from the defacing, I have a copy of the following\n  information, which I may ask for permission to share in specific cases:\n    1. The original data\n    2. The seed used for the anonymization procedure\n    3. The shuffling information.  \n* Anonymization (including defacing of the `anat` folder) has been performed\n  using the following command:  \n  `python -c 'import mne_bids; mne_bids.anonymize_dataset(\"<input>\", \"<output>\", random_state=<number>, daysback=<number>)'`  \n  This has shuffled the participant order, changed the dates, defaced the\n  anatomy, and stripped gender information from the dataset.\n* The data was pre-processed with the configuration file provided at\n  [https://github.com/mathias-sm/AGeometricShapeRegularityEffectHumanBrain/blob/main/MEG/POGS_MEG_config.py](https://github.com/mathias-sm/AGeometricShapeRegularityEffectHumanBrain/blob/main/MEG/POGS_MEG_config.py)\n  for `mne-bids-pipeline` with the development version at the time,\n  `bce60a79241731bdd03fccffa6cf315a35b33ab2` on\n  [https://github.com/mne-tools/mne-bids-pipeline/](https://github.com/mne-tools/mne-bids-pipeline/)\n","bids_version":"1.6.0","sessions_count":15,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"failed","zarr_converted_at":null,"zarr_store_count":null,"zarr_index_etag":null,"zarr_source_commit":null,"archive_status":"ready","archive_size":40153484881,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":173,"zarr_failure_count":173,"zarr_deterministic":0,"zarr_failed_at":"2026-08-26 21:28:47","num_dataset_citations":0,"num_datapaper_citations":13,"n_channels":null,"electrode_system":null,"has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":76300231982,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":0,"total_recording_duration":null,"recording_duration_min":null,"recording_duration_max":null,"recording_count":null,"recordings_unavailable":null,"recordings_measured":null,"channel_count_min":null,"channel_count_max":null,"sampling_frequency":null,"power_line_frequency":null,"eeg_reference":null,"placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-19 01:02:11\",\"metadata_updated_at\":\"2026-08-19 01:02:21\",\"archive_checked_at\":\"2026-06-28 09:08:51\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-28 08:41:25\",\"citations_updated_at\":\"2026-09-08 03:00:49\",\"channel_montage_checked_at\":null,\"hed_checked_at\":\"2026-06-30 05:27:02\",\"data_checked_at\":\"2026-08-25 03:00:20\",\"availability_report_at\":\"2026-07-23 01:27:57\",\"recording_stats_at\":null,\"signal_defaults_at\":\"2026-09-02 12:42:36\"}","participants":21,"num_citations":13,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"71.07 GB","zarr_data_failures":{"count":173,"detail_ref":"zarr/index.json","compacted_by":"migration_0074"},"zarr_index_url":null,"attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}