{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3406","dataset_id":"ds005594","associated_paper_doi":null,"authors":["Jack E. Taylor","Rasmus Sinn","Cosimo Iaia","Christian J. Fiebach"],"bids_version":"1.7.0","contact_info":["Jack E. Taylor"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds005594.v1.0.3","datatypes":["eeg"],"demographics":{"subjects_count":16,"ages":[20,25,26,24,21,20,24,26,20,26,24,20,20,21,27,25],"age_min":20,"age_max":27,"age_mean":23.0625,"species":null,"sex_distribution":{"f":12,"m":4},"handedness_distribution":{"r":16}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005594","osf_url":null,"github_url":null,"paper_url":null},"funding":["Goethe University Fokus"],"ingestion_fingerprint":"2f0545c6c13b46a0596c10bc385214457395fe5a2833a22a7b0a0b814c737e0e","license":"CC0","n_contributing_labs":null,"name":"Alphabetic Decision Task (Arial Light Font)","readme":"Generated from raw data by MNE-BIDS (Appelhoff et al., 2019) and custom code to join to behavioural data, stimulus information, and metadata.\n## Notes on the Data\nFor full details on this dataset, see our preprint: Taylor et al. (2024) https://doi.org/10.1101/2024.11.11.622929\nGeneral notes:\n* An issue during recording meant that sub-05 completed the first block without data being saved. The experiment was restarted from the beginning for this participant. This participant was not included in our analyses, but the data are included in this dataset. They are also identified with the `recording_restarted` field in `participants.tsv`.\n* A separate issue during recording meant that EEG data for some trials were lost for `sub-01`, though enough trials were recorded in total to meet our criteria for inclusion in the analysis. The raw data comprised two separate recordings. In this dataset, the two recordings are concatenated end-to-end into one file. The point at which the files are joined is marked with a boundary event. This participant is identified with the `recording_interrupted` field in `participants.tsv`.\n* During the course of the experiment, we identified an issue with the wiring in one splitter box, which meant that voltages from channels FT7 and FC3 were swapped in the raw recorded data. We elected to keep the wiring as it was for the duration of the experiment, and then swapped the data from the two channels in the code that generated this BIDS dataset. This means that this issue has been corrected in this BIDS version of the data.\n* \"BAD\" periods (MNE term) for key presses and break periods are included in the events files.\n* Recording dates/times have been anonymised by shifting all recordings backwards in time by a constant number of days (same constant for all participants). This obscures information that may be used to identify participants, but preserves time-of-day information, and the relative times elapsed between different recordings.\n## References\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":"Christian J. Fiebach","sessions":[],"size_bytes":11695588974,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["alphabeticdecision"],"timestamps":{"digested_at":"2026-04-22T12:28:38.615487+00:00","dataset_created_at":"2024-10-24T11:03:30.698Z","dataset_modified_at":"2024-11-12T09:42:30.000Z"},"total_files":16,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005594","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"24d48e31bf92c2c1","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Perception"],"confidence":{"pathology":0.7,"modality":0.7,"type":0.6},"reasoning":{"few_shot_analysis":"Most similar few-shot by paradigm/stimulus is the schizophrenia-spectrum visual discrimination dataset (Meta-rdk), labeled Modality=Visual and Type=Perception, where participants make a forced-choice judgment about a visual stimulus (\"We used a visual discrimination task...\"). This guides the convention that a non-value-based forced-choice about sensory input maps to Type=Perception (not Decision-making). Another relevant convention is that when no clinical recruitment is described, datasets are labeled Pathology=Healthy (e.g., multiple few-shot Healthy datasets with typical young-adult demographics and no diagnosis language).","metadata_analysis":"Key metadata facts:\n1) Task name strongly implies visual text stimuli: title is \"Alphabetic Decision Task (Arial Light Font)\" (Arial is a visual font).\n2) Participant description contains no diagnosis/clinical recruitment language: \"Subjects: 16; ... Age range: 20-27\".\n3) Dataset processing notes and reference to a preprint do not mention a patient group or intervention: \"Generated from raw data by MNE-BIDS...\" and \"For full details on this dataset, see our preprint\".\nThese support a healthy, lab-based cognitive task with visually presented letter/word stimuli; however, the metadata provided does not explicitly spell out the stimulus presentation modality beyond the font reference.","paper_abstract_analysis":"No useful paper information. (Only a preprint link is provided; no abstract text is included in the metadata snippet.)","evidence_alignment_check":"Pathology:\n- Metadata says: \"Subjects: 16... Age range: 20-27\" with no mention of any diagnosis or patient recruitment.\n- Few-shot pattern suggests: absent explicit clinical recruitment language, label as Healthy.\n- Alignment: ALIGN.\n\nModality:\n- Metadata says: \"Alphabetic Decision Task (Arial Light Font)\" (font implies visual text).\n- Few-shot pattern suggests: tasks involving visual stimuli/visual discrimination are labeled Visual.\n- Alignment: ALIGN (but note modality is inferred rather than explicitly stated as 'visual stimuli presented').\n\nType:\n- Metadata says: \"Alphabetic Decision Task\" (a decision/judgment about alphabetic/letter stimuli; details not provided).\n- Few-shot pattern suggests: forced-choice judgments about sensory stimuli (e.g., visual discrimination) map to Perception rather than Decision-making (which is reserved for value-based/choice-policy aims).\n- Alignment: ALIGN (but task goal/construct is under-specified, so Type relies on convention-based inference).","decision_summary":"Top-2 candidates (with head-to-head selection):\n\nPathology:\n1) Healthy — Evidence: no disorder terms anywhere; demographics only: \"Subjects: 16... Age range: 20-27\".\n2) Unknown — Would apply if recruitment/diagnosis info were missing and there were hints of a clinical study; none are present.\nWinner: Healthy. Alignment: aligns with few-shot convention.\nConfidence basis: 1 strong explicit snippet showing only typical demographics and no clinical language.\n\nModality:\n1) Visual — Evidence: \"Arial Light Font\" strongly implies visually presented letters/words.\n2) Unknown — Because the provided metadata does not explicitly describe stimulus presentation (screen vs auditory spelling, etc.).\nWinner: Visual. Alignment: aligns with few-shot convention for visually driven discrimination/judgment tasks.\nConfidence basis: 1 explicit cue (font) + contextual inference.\n\nType:\n1) Perception — Evidence: task is a judgment/decision about alphabetic/letter stimuli (sensory/recognition/discrimination-like), and few-shot convention maps such forced-choice sensory judgments to Perception.\n2) Decision-making — Plausible because the word 'Decision' is in the task name, but there is no evidence of value-based choice, reinforcement, or policy learning as primary aim.\nWinner: Perception. Alignment: aligns with few-shot convention.\nConfidence basis: task-label inference only (no explicit construct description), so confidence kept moderate."}},"nemar_citation_count":1,"computed_title":"Alphabetic Decision Task (Arial Light Font)","nchans_counts":[{"val":66,"count":16}],"sfreq_counts":[{"val":1000.0,"count":16}],"stats_computed_at":"2026-04-22T23:16:00.310675+00:00","source_url":"https://openneuro.org/datasets/ds005594","total_duration_s":46561.904,"canonical_name":null,"name_confidence":0.67,"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":"Taylor2024"}}