{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a343b","dataset_id":"ds006159","associated_paper_doi":null,"authors":["Mateo Leganes-Fonteneau"],"bids_version":"1.7.0","contact_info":["Mateo Leganes Fonteneau","Paradeisios Alexandros BOULAKIS"],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds006159.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":61,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds006159","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"20fe5259c661819e7be98e7318e34eaddc4c26d3ee0961eba15e33fb609c6da3","license":"CC0","n_contributing_labs":null,"name":"Implicit Learning EEG (BioSemi)","readme":null,"recording_modality":["eeg"],"senior_author":"Mateo Leganes-Fonteneau","sessions":[],"size_bytes":15404183606,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["implicitlearn"],"timestamps":{"digested_at":"2026-04-22T12:29:10.239699+00:00","dataset_created_at":"2025-04-23T10:01:37.563Z","dataset_modified_at":"2025-05-21T14:05:01.000Z"},"total_files":61,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006159","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"e2dbbd8c5084f9df","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Unknown"],"type":["Learning"],"confidence":{"pathology":0.6,"modality":0.4,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot by research purpose is the example titled \"EEG: Probabilistic Learning with Affective Feedback: Exp #2\" (Pathology=Healthy, Modality=Visual, Type=Learning). That example shows the convention that when the experiment is explicitly framed as a learning paradigm (reinforcement/probabilistic learning), the catalog Type label should be \"Learning\". Here the dataset title/task name explicitly indicate implicit learning, which aligns with that convention. However, unlike the few-shot learning example, this dataset’s metadata does not specify the sensory modality (e.g., visual/auditory) or the health status of participants, so those labels cannot be confidently inferred from few-shot conventions alone.","metadata_analysis":"Key available metadata is very sparse. Relevant quotes:\n1) Title indicates the construct: \"Implicit Learning EEG (BioSemi)\".\n2) Task list provides only a task name: \"implicitlearn\".\n3) Participants field provides only a count and no clinical descriptors: \"Subjects: 61\".\nThere are no quoted details about stimulus type (visual/auditory/etc.), trial structure, or participant recruitment criteria/diagnosis.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: only \"Subjects: 61\" with no disorder/diagnosis mentioned.\n- Few-shot pattern suggests: many cognitive-task datasets without clinical language are labeled \"Healthy\" (e.g., probabilistic learning example), but this is a convention and not a stated fact here.\n- Alignment: PARTIAL/UNCERTAIN (no explicit metadata fact). Decision relies on contextual inference.\n\nModality:\n- Metadata says: no stimulus channel described; only \"Implicit Learning\" and \"implicitlearn\" are given.\n- Few-shot pattern suggests: many learning tasks are visual (e.g., probabilistic learning example is Visual), but this dataset provides no confirming details.\n- Alignment: CONFLICT/INSUFFICIENT (few-shot suggests Visual, but metadata provides no evidence). Metadata insufficiency forces \"Unknown\".\n\nType:\n- Metadata says: \"Implicit Learning\" (title) and task \"implicitlearn\".\n- Few-shot pattern suggests: learning paradigms map to Type=\"Learning\" (probabilistic learning example).\n- Alignment: ALIGNS (both metadata wording and few-shot convention point to Learning).","decision_summary":"Top-2 candidates (with head-to-head comparison):\n\n1) Pathology\n- Candidate A: Healthy\n  Evidence: absence of any clinical recruitment terms plus generic participant count (\"Subjects: 61\") suggests a normative cohort typical of cognitive EEG studies.\n- Candidate B: Unknown\n  Evidence: no explicit statement like \"healthy participants\" or exclusion criteria.\n  Decision: Healthy narrowly over Unknown because metadata contains no indication of a clinical population and the dataset appears to be a standard cognitive implicit-learning EEG experiment; however this is inference-only.\n  Evidence alignment status: Uncertain (no explicit metadata quote asserting health).\n\n2) Modality\n- Candidate A: Visual\n  Evidence: few-shot learning paradigms commonly use visual stimuli (e.g., probabilistic learning example labeled Visual), and implicit learning tasks often use visual sequences.\n- Candidate B: Unknown\n  Evidence: no metadata quotes describing stimuli (no mention of screen, images, tones, etc.).\n  Decision: Unknown over Visual because there is zero explicit modality evidence in metadata.\n  Evidence alignment status: Conflict/insufficient; metadata lacks required facts.\n\n3) Type\n- Candidate A: Learning\n  Evidence quotes: \"Implicit Learning EEG (BioSemi)\" and task name \"implicitlearn\".\n- Candidate B: Other\n  Evidence: without paradigm details, could in principle reflect attention/memory/decision components.\n  Decision: Learning over Other because the dataset is explicitly labeled as implicit learning in the title/task.\n  Evidence alignment status: Aligns with few-shot learning labeling convention.\n\nConfidence justification:\n- Pathology=0.6 because it is contextual inference only (no explicit \"healthy\" quote).\n- Modality=0.4 because there is no modality evidence and multiple modalities remain plausible.\n- Type=0.7 because there is at least one explicit metadata cue (title/task name) indicating a learning focus, though task details are missing."}},"computed_title":"Implicit Learning EEG (BioSemi)","nchans_counts":[{"val":73,"count":14}],"sfreq_counts":[{"val":1024.0,"count":14}],"stats_computed_at":"2026-04-22T23:16:00.311373+00:00","total_duration_s":null,"canonical_name":null,"name_confidence":0.34,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"LeganesFonteneau2025"}}