{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3388","dataset_id":"ds004809","associated_paper_doi":null,"authors":["Haydn G. Herrema","Michael J. 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The experiment consists of participants studying a list of words, presented visually one at a time, completing simple arithmetic problems that function as a distractor, and then freely recalling the words from the just-presented list in any order.  The data was collected at clinical sites across the country as part of a collaboration with the Computational Memory Lab at the University of Pennsylvania.\nUnique to this paradigm is the semantic construction of the word lists.  Each word comes from one of 25 semantic categories, and each list of 12 items contains 6 pairs of same-category words from 3 different categories.  This means that each list has 4 words from 3 semantic categories, and in each half of the list there will be 1 pair of words from each category.  For example, if a list contains words from categories A, B, and C, a possible list construction would be:\n**A1 - A2 - B1 - B2 - C1 - C2 - A3 - A4 - C3 - C4 - B3 - B4**\n#### To Note\n* The iEEG recordings are labeled either \"monopolar\" or \"bipolar.\"  The monopolar recordings are referenced (typically a mastoid reference), but should always be re-referenced before analysis.  The bipolar recordings are referenced according to a paired scheme indicated by the accompanying bipolar channels tables.\n* Each subject has a unique montage of electrode locations.  MNI and Talairach coordinates are provided when available, along with brain region annotations.\n* Recordings were made on multiple different systems, so we have done the scaling to provide all voltage values in V.\n#### Contact\nFor questions or inquiries, please contact sas-kahana-sysadmin@sas.upenn.edu.","recording_modality":["ieeg"],"senior_author":"Michael J. Kahana","sessions":["0","1","10","2","3","4","5","6","7","8"],"size_bytes":512365835522,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["catFR1"],"timestamps":{"digested_at":"2026-04-22T12:26:52.775478+00:00","dataset_created_at":"2023-10-19T14:46:21.581Z","dataset_modified_at":"2024-04-23T00:23:08.000Z"},"total_files":889,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004809","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"0189913d7982aa5b","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Epilepsy"],"modality":["Visual"],"type":["Memory"],"confidence":{"pathology":0.65,"modality":0.85,"type":0.85},"reasoning":{"few_shot_analysis":"Most similar few-shot by cognitive construct is the digit span dataset (Healthy / Auditory / Memory), which shows the convention that list-learning/working-memory paradigms are labeled Type=Memory based on the study goal (remembering items), not on motor responses. For modality, the schizophrenia visual discrimination example and the digit span example show that Modality is assigned from the stimulus channel (visual dots vs auditory digits). For pathology inference, the pediatric epilepsy example illustrates that clinically acquired electrophysiology datasets are labeled Epilepsy when the recruited population is epilepsy patients; however, in the current metadata epilepsy is not explicitly stated, so this can only be used as a convention guide, not a hard fact.","metadata_analysis":"Key task facts: (1) Memory paradigm: \"participants studying a list of words, presented visually one at a time ... and then freely recalling the words from the just-presented list in any order.\" (2) Delay/distractor component: \"completing simple arithmetic problems that function as a distractor.\" Key recording/population context: (3) Intracranial recordings: \"behavioral events and intracranial electrophysiological recordings.\" (4) Clinical acquisition setting: \"The data was collected at clinical sites across the country.\"","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says \"intracranial electrophysiological recordings\" and collected at \"clinical sites\" but does not name a diagnosis. Few-shot pattern suggests iEEG at clinical sites commonly implies epilepsy monitoring cohorts (as in the epilepsy example), but this is not explicitly confirmed here. => Partial alignment (clinical setting aligns), but diagnosis is inferred, not stated.\n\nModality: Metadata says words are \"presented visually\". Few-shot convention assigns Modality by stimulus channel (e.g., visual discrimination -> Visual; digit span with auditory digits -> Auditory). => Align.\n\nType: Metadata says the experiment involves studying and \"freely recalling\" word lists after a distractor, i.e., episodic free recall. Few-shot convention labels item retention/recall paradigms as Memory (digit span example). => Align.","decision_summary":"Top-2 candidates per category:\n\nPathology:\n1) Epilepsy — Evidence: \"intracranial electrophysiological recordings\" + \"collected at clinical sites\" strongly suggests an implanted iEEG clinical cohort typical of epilepsy monitoring; large multi-site iEEG memory datasets commonly come from epilepsy patients.\n2) Unknown — Evidence: no explicit diagnostic label (e.g., \"epilepsy\") appears in the provided metadata.\nDecision: Epilepsy (inferred from iEEG + clinical sites; not explicitly stated). Confidence moderated due to lack of explicit diagnosis.\n\nModality:\n1) Visual — Evidence: \"words, presented visually one at a time\".\n2) Other — Would apply if modality were mixed/unclear, but it is explicit.\nDecision: Visual.\n\nType:\n1) Memory — Evidence: \"studying a list of words\" and \"freely recalling the words\"; distractor-delayed free recall is a classic episodic memory paradigm.\n2) Attention — Could be argued due to distractor arithmetic, but the primary construct is recall of word lists.\nDecision: Memory.\n\nConfidence justification (quotes/features): Modality and Type each have direct task-description quotes; Pathology relies on clinical/iEEG context quotes without explicit diagnosis."}},"nemar_citation_count":1,"computed_title":"Categorized Free Recall: Delayed Free Recall of Word Lists Organized by Semantic Categories","nchans_counts":[{"val":126,"count":70},{"val":124,"count":30},{"val":108,"count":26},{"val":125,"count":20},{"val":128,"count":19},{"val":139,"count":19},{"val":88,"count":17},{"val":120,"count":16},{"val":127,"count":16},{"val":145,"count":15},{"val":148,"count":15},{"val":131,"count":15},{"val":116,"count":15},{"val":112,"count":14},{"val":64,"count":14},{"val":196,"count":14},{"val":179,"count":13},{"val":142,"count":13},{"val":110,"count":13},{"val":155,"count":12},{"val":118,"count":12},{"val":251,"count":11},{"val":90,"count":11},{"val":133,"count":11},{"val":159,"count":11},{"val":114,"count":11},{"val":121,"count":11},{"val":94,"count":10},{"val":178,"count":10},{"val":186,"count":10},{"val":113,"count":10},{"val":92,"count":10},{"val":105,"count":9},{"val":158,"count":9},{"val":115,"count":9},{"val":198,"count":9},{"val":152,"count":9},{"val":200,"count":8},{"val":183,"count":8},{"val":156,"count":8},{"val":247,"count":8},{"val":104,"count":8},{"val":212,"count":7},{"val":166,"count":7},{"val":122,"count":7},{"val":106,"count":7},{"val":98,"count":7},{"val":68,"count":7},{"val":241,"count":6},{"val":184,"count":6},{"val":78,"count":6},{"val":109,"count":6},{"val":76,"count":6},{"val":150,"count":6},{"val":240,"count":6},{"val":100,"count":6},{"val":250,"count":5},{"val":56,"count":5},{"val":165,"count":5},{"val":154,"count":5},{"val":208,"count":5},{"val":168,"count":5},{"val":189,"count":4},{"val":185,"count":4},{"val":238,"count":4},{"val":134,"count":4},{"val":173,"count":4},{"val":89,"count":4},{"val":219,"count":4},{"val":192,"count":4},{"val":70,"count":4},{"val":180,"count":4},{"val":164,"count":4},{"val":97,"count":4},{"val":175,"count":4},{"val":224,"count":4},{"val":72,"count":4},{"val":141,"count":4},{"val":188,"count":4},{"val":160,"count":3},{"val":130,"count":3},{"val":95,"count":3},{"val":167,"count":3},{"val":220,"count":3},{"val":209,"count":3},{"val":83,"count":3},{"val":60,"count":3},{"val":162,"count":3},{"val":46,"count":3},{"val":140,"count":3},{"val":207,"count":3},{"val":229,"count":3},{"val":123,"count":2},{"val":151,"count":2},{"val":119,"count":2},{"val":53,"count":2},{"val":67,"count":2},{"val":93,"count":2},{"val":96,"count":2},{"val":177,"count":2},{"val":172,"count":2},{"val":132,"count":2},{"val":161,"count":2},{"val":187,"count":2},{"val":176,"count":2},{"val":193,"count":2},{"val":203,"count":2},{"val":84,"count":2},{"val":169,"count":2},{"val":80,"count":1},{"val":14,"count":1},{"val":239,"count":1},{"val":26,"count":1},{"val":202,"count":1},{"val":86,"count":1},{"val":102,"count":1},{"val":37,"count":1},{"val":143,"count":1},{"val":36,"count":1},{"val":107,"count":1},{"val":182,"count":1},{"val":62,"count":1},{"val":136,"count":1},{"val":111,"count":1},{"val":52,"count":1},{"val":243,"count":1},{"val":206,"count":1},{"val":218,"count":1},{"val":163,"count":1},{"val":99,"count":1},{"val":213,"count":1},{"val":75,"count":1},{"val":50,"count":1},{"val":146,"count":1},{"val":153,"count":1},{"val":16,"count":1},{"val":85,"count":1},{"val":63,"count":1}],"sfreq_counts":[{"val":1000.0,"count":766},{"val":500.0,"count":93},{"val":1600.0,"count":10},{"val":999.0,"count":8},{"val":1023.999,"count":6},{"val":1024.0,"count":4},{"val":499.7071,"count":2}],"stats_computed_at":"2026-04-22T23:16:00.308381+00:00","total_duration_s":2071088.7582694134,"canonical_name":null,"name_confidence":0.66,"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":"Herrema2023_Categorized_Free_Recall"}}