{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3353","dataset_id":"ds004483","associated_paper_doi":null,"authors":["Samuel Planton*","Fosca Al Roumi*","Liping Wang","Stanislas Dehaene"],"bids_version":"1.6.0","contact_info":["Fosca Al Roumi"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004483.v1.0.0","datatypes":["meg"],"demographics":{"subjects_count":19,"ages":[21,29,35,24,18,28,27,22,22,34,24,20,29,26],"age_min":18,"age_max":35,"age_mean":25.642857142857142,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004483","osf_url":null,"github_url":null,"paper_url":null},"funding":["European Research Council (ERC, https://erc.europa.eu/) grant to S.D. (NeuroSyntax, ID: 695403)."],"ingestion_fingerprint":"fbe157c42d34040528258d449fb817396a79a7b6dd1bf7274d41bf2739620376","license":"CC0","n_contributing_labs":null,"name":"ABSeqMEG","readme":"This 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\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.","recording_modality":["meg"],"senior_author":"Stanislas Dehaene","sessions":[],"size_bytes":25146610011,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["abseq"],"timestamps":{"digested_at":"2026-04-22T12:26:40.767859+00:00","dataset_created_at":"2023-02-08T23:22:49.117Z","dataset_modified_at":"2023-02-13T14:53:45.000Z"},"total_files":282,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004483","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"a6342a99a6378bbb","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:40:24.151524+00:00"},"tags":{"pathology":["Healthy"],"modality":["Auditory"],"type":["Memory"],"confidence":{"pathology":0.6,"modality":0.85,"type":0.85},"reasoning":{"few_shot_analysis":"Most similar few-shot example by paradigm/construct is the healthy auditory digit-span dataset labeled as Modality=Auditory and Type=Memory, because it is an explicit working-memory paradigm with auditory item presentation and uses EEG/MEG responses to memorized sequences. By convention, when the primary stated aim is working memory (rather than simple sensory detection), EEGDash Type maps to Memory even if the task includes deviants/oddball-like probes.","metadata_analysis":"Key population/task/stimulus facts from the provided README: (1) Working memory focus: \"Compression of binary sound sequences in human working memory\" and \"We tested this theory by probing working memory for 16-item sequences made of two sounds.\" (2) Auditory stimulus: \"16-item sequences made of two sounds\" and \"participants listened to a hierarchy of sequences\" and \"Occasional deviant sounds\". (3) No clinical recruitment is described; only general experimental aims and recordings: \"We recorded brain activity with functional MRI and magneto-encephalography (MEG) while participants listened...\"","paper_abstract_analysis":"No useful paper information beyond what is already included in the README text (which reads like an abstract/summary).","evidence_alignment_check":"Pathology: Metadata says nothing about a patient group (no diagnoses or clinical recruitment described); few-shot conventions indicate that absent explicit clinical terms, label as Healthy. ALIGN (no conflict).\nModality: Metadata explicitly says \"sound sequences\" / \"two sounds\" / \"participants listened\"; few-shot conventions map sound-listening paradigms to Auditory. ALIGN.\nType: Metadata explicitly emphasizes \"working memory\" repeatedly and probes memory for structured sequences; few-shot conventions map working-memory tasks (e.g., digit span) to Type=Memory even when there are deviant probes. ALIGN.","decision_summary":"Top-2 candidates per category:\n- Pathology: (1) Healthy — supported by lack of any explicit diagnosis/recruitment term and generic participant description; (2) Unknown — possible because participants are not explicitly stated as healthy controls. Winner: Healthy (convention: default to Healthy when no pathology is indicated). Confidence=0.6 because there are 0 explicit participant health-status quotes.\n- Modality: (1) Auditory — \"binary sound sequences\", \"two sounds\", \"participants listened\", \"deviant sounds\"; (2) Multisensory — fMRI+MEG are mentioned but those are recording modalities, not stimulus channels. Winner: Auditory. Confidence=0.85 based on multiple explicit stimulus quotes.\n- Type: (1) Memory — \"human working memory\", \"probing working memory\"; (2) Perception — could be argued due to listening and deviant detection, but the stated research aim is WM compression/prediction. Winner: Memory. Confidence=0.85 with repeated explicit working-memory framing."}},"nemar_citation_count":2,"computed_title":"ABSeqMEG","nchans_counts":[{"val":396,"count":263}],"sfreq_counts":[{"val":250.0,"count":263}],"stats_computed_at":"2026-04-21T23:17:03.729957+00:00","total_duration_s":60058.932,"canonical_name":null,"name_confidence":0.98,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Planton2023"}}