{"dataset":{"id":"57134","dataset_id":"on004483","name":"ABSeqMEG","description":"This dataset contains MEG recordings from a study investigating how the human brain compresses regular binary sound sequences in working memory, testing the language of thought hypothesis. Participants listened to hierarchically structured sequences of two sounds varying in complexity, quantified via minimal description length, while occasional deviant sounds probed their internalized knowledge of sequence structure. The study aimed to characterize how brain activity relates to sequence complexity and predictive processing.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on004483","concept_doi":"10.82901/nemar.on004483","latest_version_doi":"10.82901/nemar.on004483.v1.0.0","created_at":"2026-06-24 11:31:42","updated_at":"2026-08-19 13:00:25","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"ABSeqMEG\",\n  \"description\": \"This dataset contains MEG recordings from a study investigating how the human brain compresses regular binary sound sequences in working memory, testing the language of thought hypothesis. Participants listened to hierarchically structured sequences of two sounds varying in complexity, quantified via minimal description length, while occasional deviant sounds probed their internalized knowledge of sequence structure. The study aimed to characterize how brain activity relates to sequence complexity and predictive processing.\",\n  \"methods_description\": \"Brain activity was recorded using magneto-encephalography (MEG) while participants listened to hierarchies of 16-item sequences composed of two sounds, varying in complexity based on minimal description length. Occasional deviant sounds were introduced to probe participants' knowledge of the sequence structure.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Samuel Planton*\": {},\n    \"Fosca Al Roumi*\": {},\n    \"Liping Wang\": {},\n    \"Stanislas Dehaene\": {\n      \"orcid\": \"0000-0002-7418-8275\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"MEG\"\n    },\n    {\n      \"term\": \"working memory\"\n    },\n    {\n      \"term\": \"Auditory Perception\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D001307\"\n    },\n    {\n      \"term\": \"sequence learning\"\n    },\n    {\n      \"term\": \"predictive coding\"\n    },\n    {\n      \"term\": \"language of thought\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on004483\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1101/2022.10.15.512361\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsSupplementTo\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds004483\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsVersionOf\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds004483.v1.0.0\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/on004483\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"European Research Council\",\n      \"award_number\": \"695403\",\n      \"award_title\": \"NeuroSyntax\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"MEG Dataset\",\n  \"modalities\": [\n    \"meg\"\n  ],\n  \"sizes\": [\n    \"25.1 GB (266 files)\"\n  ],\n  \"formats\": [\n    \".dat\",\n    \".fif\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yml\"\n  ],\n  \"source_hash\": \"8634723ca4a72c14f53e090b314ac37d836e777192d2ab9daca58196aaa2c612\"\n}","last_activity_at":"2026-06-24 11:31:42","source":"openneuro","source_id":"ds004483","subject_count":19,"modalities":"meg","age_min":18,"age_max":35,"file_size":25146613737,"total_files":1388,"tasks":"abseq","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Samuel Planton*, Fosca Al Roumi*, Liping Wang, Stanislas Dehaene","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on004483-blue)](https://doi.org/10.82901/nemar.on004483)\n\nThis dataset contains the MEG data from the article entitled Compression of binary sound sequences in human working memory https://www.biorxiv.org/content/10.1101/2022.10.15.512361v1 \n\nAccording to the language of thought hypothesis, regular sequences are compressed in human working memory using recursive loops akin to a mental program that predicts future items. We tested this theory by probing working memory for 16-item sequences made of two sounds. We recorded brain activity with functional MRI and magneto-encephalography (MEG) while participants listened to a hierarchy of sequences of variable complexity, whose minimal description required transition probabilities, chunking, or nested structures. Occasional deviant sounds probed the participants’ knowledge of the sequence. We predicted that task difficulty and brain activity would be proportional to minimal description length (MDL) in our formal language. Furthermore, activity should increase with MDL for learned sequences, and decrease with MDL for deviants. These predictions were upheld in both fMRI and MEG, indicating that sequence predictions are highly dependent on sequence structure and become weaker and delayed as complexity increases. The proposed language recruited bilateral superior temporal, precentral, anterior intraparietal and cerebellar cortices. These regions overlapped extensively with a localizer for mathematical calculation, and much less with spoken or written language processing. We propose that these areas collectively encode regular sequences as repetitions with variations and their recursive composition into nested structures.","bids_version":"1.6.0","sessions_count":null,"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":13359244584,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":282,"zarr_failure_count":282,"zarr_deterministic":1,"zarr_failed_at":"2026-08-06 19:07:38","num_dataset_citations":3,"num_datapaper_citations":2,"n_channels":null,"electrode_system":null,"has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":25111746303,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":null,"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 13:00:13\",\"metadata_updated_at\":\"2026-08-19 13:00:24\",\"archive_checked_at\":\"2026-06-24 11:50:52\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-24 11:41:42\",\"citations_updated_at\":\"2026-09-08 03:00:50\",\"channel_montage_checked_at\":null,\"hed_checked_at\":\"2026-06-30 04:59:43\",\"data_checked_at\":\"2026-08-10 03:00:50\",\"availability_report_at\":\"2026-07-23 01:19:06\",\"recording_stats_at\":null,\"signal_defaults_at\":\"2026-09-02 12:13:44\"}","participants":19,"num_citations":5,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"23.42 GB","zarr_data_failures":{"count":282,"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}}