{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a344d","dataset_id":"ds006446","associated_paper_doi":null,"authors":["Isaac Kinley","Sue Becker"],"bids_version":"1.7.0","contact_info":["Isaac Kinley"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds006446.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":29,"ages":[18,18,18,18,18,21,17,18,18,18,17,22,19,18,19,18,18,18,21,21,22,18,18,18,18,18,19,22,22],"age_min":17,"age_max":22,"age_mean":18.896551724137932,"species":null,"sex_distribution":{"f":25,"m":4},"handedness_distribution":{"r":27,"l":2}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds006446","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"ce7f209fc37739471fe642028b8d77cac07faf63513ae8f513ed37156b8ea516","license":"CC0","n_contributing_labs":null,"name":"Cueing the future to reduce temporal discounting","readme":"EEG study of episodic future thinking and delay discounting, to be described in a forthcoming paper. Briefly, participants described a series of future events and were then cued to think about these events as they made intertemporal choices. They were also asked how vivid their mental imagery of these events was.\nReferences\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\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8","recording_modality":["eeg"],"senior_author":"Sue Becker","sessions":[],"size_bytes":17285482171,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["itc"],"timestamps":{"digested_at":"2026-04-22T12:29:27.764108+00:00","dataset_created_at":"2025-07-07T03:26:37.180Z","dataset_modified_at":"2025-07-07T11:34:38.000Z"},"total_files":29,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006446","raw_key":"dataset_description.json","dep_keys":["CHANGES","README.txt","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"f2052bb539726eb9","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Decision-making"],"confidence":{"pathology":0.8,"modality":0.65,"type":0.85},"reasoning":{"few_shot_analysis":"Most similar few-shot examples by paradigm/purpose are the decision-making tasks involving choices and valuation. In particular: (1) the few-shot example \"EEG: Three armed bandit gambling task\" is labeled Type= \"Affect\" but its README emphasizes reward processing and feedback; it demonstrates the convention that choice+feedback tasks can map to higher-level constructs depending on stated aim. (2) the few-shot example \"EEG: Reinforcement Learning in Parkinson's\" is labeled Type=\"Decision-making\" and involves explicit choices and outcomes; this guides mapping intertemporal choice/discounting to Decision-making when the construct is valuation/choice policy. For Pathology, multiple few-shot examples show that when no disorder recruitment is stated (e.g., healthy college students), Pathology is labeled \"Healthy\".","metadata_analysis":"Key metadata facts: (1) Title explicitly states the construct: \"Cueing the future to reduce temporal discounting\". (2) README states: \"EEG study of episodic future thinking and delay discounting\" and that participants \"made intertemporal choices\". (3) README adds an imagery-related measure: \"They were also asked how vivid their mental imagery of these events was.\" (4) Participants metadata provides demographics only (no diagnosis): \"Subjects: 29; ... Age range: 17-22\".","paper_abstract_analysis":"No useful paper information. (Forthcoming paper mentioned; no abstract provided.)","evidence_alignment_check":"Pathology: Metadata says nothing about a clinical group and only provides demographics (e.g., \"Subjects: 29\"; \"Age range: 17-22\"). Few-shot pattern suggests labeling normative, non-clinical recruitment as \"Healthy\". ALIGN.\n\nModality: Metadata does not explicitly specify stimulus channel (no mention of sounds/tactile stimulation), but the described procedure is cue-driven episodic future thinking during choices (\"were then cued to think about these events as they made intertemporal choices\"), which conventionally is implemented via on-screen textual/visual cues in EEG tasks. Few-shot conventions: choice tasks with presented cues typically map to Visual unless explicitly auditory/tactile. PARTIAL ALIGN but relies on inference due to lack of explicit modality description.\n\nType: Metadata explicitly states delay discounting and intertemporal choice (\"delay discounting\"; \"made intertemporal choices\"; \"temporal discounting\"). Few-shot convention maps valuation/choice constructs (reinforcement learning / gambling-like choice paradigms) to \"Decision-making\" when decision policy/value is central. ALIGN.","decision_summary":"Top-2 candidates (with head-to-head selection):\n\nPathology:\n1) Healthy — Evidence: no disorder mentioned; only demographics: \"Subjects: 29\" and \"Age range: 17-22\"; study described as cognitive/decision task in participants.\n2) Unknown — Could be considered if recruitment criteria were missing.\nDecision: Healthy wins because metadata indicates a standard non-clinical participant sample and contains no clinical recruitment language.\n\nModality:\n1) Visual — Evidence (inferred): participants were \"cued\" while making choices; such cues/choice options are typically visually presented in lab EEG; no alternative sensory modality is described.\n2) Other — Candidate because \"episodic future thinking\" and \"mental imagery\" could be treated as internally generated rather than stimulus-driven.\nDecision: Visual wins narrowly because the task depends on external cueing during choice, most plausibly via visual presentation; however, modality is not explicitly stated.\n\nType:\n1) Decision-making — Evidence: \"delay discounting\"; \"temporal discounting\"; \"made intertemporal choices\".\n2) Memory — Candidate because it involves \"episodic future thinking\" and rating vividness of future-event imagery.\nDecision: Decision-making wins because the primary behavioral paradigm is intertemporal choice/discounting, with episodic future thinking used as a manipulation to affect choice.\n\nConfidence justifications:\n- Pathology (0.8): supported by 2+ metadata facts showing demographics only and no clinical recruitment (\"Subjects: 29\"; \"Age range: 17-22\").\n- Modality (0.65): relies on contextual inference (cueing and choice likely visually presented) with no direct explicit statement of stimulus modality.\n- Type (0.85): supported by multiple explicit construct phrases (\"temporal discounting\"; \"delay discounting\"; \"made intertemporal choices\") and strong few-shot convention match to decision-making tasks."}},"computed_title":"Cueing the future to reduce temporal discounting","nchans_counts":[{"val":65,"count":29}],"sfreq_counts":[{"val":2048.0,"count":29}],"stats_computed_at":"2026-04-22T23:16:00.311591+00:00","total_duration_s":64906.98583984375,"canonical_name":null,"name_confidence":0.55,"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":"Kinley2025"}}