{"success":true,"database":"eegdash","data":{"_id":"6953f4239276ef1ee07a32e8","dataset_id":"ds003703","associated_paper_doi":null,"authors":["Evgenii Kalenkovich","Anna Shestakova","Nina Kazanina"],"bids_version":"1.4.0","contact_info":["Zhenya Kalenkovich"],"contributing_labs":null,"data_processed":true,"dataset_doi":"10.18112/openneuro.ds003703.v1.0.0","datatypes":["meg"],"demographics":{"subjects_count":34,"ages":[22,20,27,35,20,21,23,27,20,38,20,19,19,20,23,18,29,18,19,27,23,18,20,21,23,18,22,27,26,20,21,18,22,23],"age_min":18,"age_max":38,"age_mean":22.558823529411764,"species":null,"sex_distribution":{"f":21,"m":13},"handedness_distribution":{"r":34}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds003703","osf_url":null,"github_url":null,"paper_url":null},"funding":["The study has been funded by the International Laboratory for Social Neuroscience of the Institute for Cognitive Neuroscience HSE, RF Government grant # 075-15-2019-1930."],"ingestion_fingerprint":"42e42ace13c3a442910d19875b9efcdd3569023aed61f9b5f9c72edec32305ce","license":"CC0","n_contributing_labs":null,"name":"Frequency Tagging of Syntactic Structure or Lexical Properties","readme":"References\n----------\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896\nNiso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. http://doi.org/10.1038/sdata.2018.110","recording_modality":["meg"],"senior_author":"Nina Kazanina","sessions":[],"size_bytes":99158117602,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["listeningToSpeech","rest"],"timestamps":{"digested_at":"2026-04-22T12:25:39.759122+00:00","dataset_created_at":"2021-06-18T03:26:19.091Z","dataset_modified_at":"2021-06-23T07:41:36.000Z"},"total_files":102,"storage":{"backend":"s3","base":"s3://openneuro.org/ds003703","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv","task-listeningToSpeech_events.json"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"250b34c9bb123bed","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Auditory"],"type":["Perception"],"confidence":{"pathology":0.6,"modality":0.85,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot example by stimulus modality/paradigm is: “Subcortical responses to music and speech…” (Healthy + Auditory + Perception). That example treats passive/attentive listening to complex sounds (speech/music) as Auditory modality and Perception type. This dataset also centers on listening to speech (syntactic/lexical manipulation), so the same convention supports labeling Modality=Auditory and Type=Perception rather than, e.g., Motor or Memory.\nA secondary relevant convention is the “digit span task” example (Healthy + Auditory + Memory), which clarifies that Auditory tasks become Memory only when explicit memorization/recall is central. Here the task label is “listeningToSpeech” with a linguistics focus, not working-memory recall, pushing away from Memory.","metadata_analysis":"Key metadata supporting labels:\n1) Title indicates speech/language stimulus and goal: “Frequency Tagging of Syntactic Structure or Lexical Properties”.\n2) Tasks list includes an explicit listening task: tasks: [“listeningToSpeech”, “rest”].\n3) Participants overview gives a typical healthy adult sample without any diagnosis fields: “Subjects: 34; … Age range: 18-38; Handedness: {'r': 34}”.\nNo metadata snippet mentions any clinical recruitment/diagnosis (e.g., patients, disorder groups).","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: participants are described only demographically (“Subjects: 34… Age range: 18-38”) with no disorder/patient language.\n- Few-shot pattern suggests: when no clinical group is described and sample is typical adults, label as Healthy.\n- Alignment: ALIGN (no conflict).\n\nModality:\n- Metadata says: task is “listeningToSpeech” and title is about “Syntactic Structure or Lexical Properties” (speech/language).\n- Few-shot pattern suggests: speech/music listening datasets are labeled Auditory.\n- Alignment: ALIGN.\n\nType:\n- Metadata says: focus is syntactic structure/lexical properties during “listeningToSpeech”.\n- Few-shot pattern suggests: listening-based stimulus processing is categorized as Perception unless explicit memory/decision/reward/motor aims dominate.\n- Alignment: ALIGN (though language/syntax is not a dedicated Type label, so it maps by convention to Perception vs Other).","decision_summary":"Top-2 candidates with head-to-head selection:\n\nPathology:\n1) Healthy (selected) — Evidence: no clinical recruitment terms; only demographics: “Subjects: 34… Age range: 18-38”. Matches few-shot convention where non-clinical samples are Healthy.\n2) Unknown — Would be chosen if metadata were insufficient to infer health status, but here typical adult demographics plus absence of pathology is a standard cue.\nAlignment status: aligned. Confidence evidence: absence of any disorder mention + demographic-only participant description.\n\nModality:\n1) Auditory (selected) — Evidence: tasks include “listeningToSpeech”; title concerns linguistic (speech) properties.\n2) Resting State — because tasks also include “rest”, but the dataset’s stated focus in the title is speech/syntax frequency-tagging, indicating the listening task is primary.\nAlignment status: aligned. Confidence evidence: two explicit metadata cues (“listeningToSpeech”; title about syntactic/lexical properties of speech) plus strong few-shot analog (speech listening example).\n\nType:\n1) Perception (selected) — Evidence: listening-to-speech paradigm aimed at processing syntactic/lexical properties; aligns with few-shot convention mapping stimulus processing to Perception.\n2) Other — plausible because syntax/linguistics is not explicitly represented in the Type label set; however, within allowed labels, Perception best captures auditory speech processing.\nAlignment status: aligned. Confidence evidence: title + task name indicate perceptual/language processing, but no further task details/abstract, so not maximal confidence."}},"nemar_citation_count":1,"computed_title":"Frequency Tagging of Syntactic Structure or Lexical Properties","nchans_counts":[{"val":314,"count":102}],"sfreq_counts":[{"val":1000.0,"count":102}],"stats_computed_at":"2026-04-22T23:16:00.222425+00:00","total_duration_s":78537.898,"canonical_name":null,"name_confidence":0.55,"name_meta":{"suggested_at":"2026-04-14T10:18:35.342Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Kalenkovich2021"}}