{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a335d","dataset_id":"ds004521","associated_paper_doi":null,"authors":["Edward Ester","Paige Pytel"],"bids_version":"1.8.0","contact_info":["Edward Ester"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004521.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":34,"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/ds004521","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"c09ffa22c9b6fbf363cb6ce02a2d09339ed672a2c581ed600786c46ca955241f","license":"CC0","n_contributing_labs":null,"name":"Changes in behavioral priority influence the accessibility of working memory content - Experiment 1","readme":"Preprocessed data from Experiment 1 of Ester & Pytel \"Changes in behavioral priority influence the accessibility of working memory content\". Analytic scripts for this project can be found on OSF: https://osf.io/gtd5f/. Note that to analyze the BIDS data, you'll need to modify the analysis scripts to read in the BIDS .set files rather than the expected .mat files. See the OSF wiki for more information","recording_modality":["eeg"],"senior_author":"Paige Pytel","sessions":[],"size_bytes":11470005753,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["Postcues"],"timestamps":{"digested_at":"2026-04-22T12:26:42.035134+00:00","dataset_created_at":"2023-03-05T01:39:56.268Z","dataset_modified_at":"2023-03-07T18:36:19.000Z"},"total_files":34,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004521","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","task-Postcues_events.json"]},"nemar_citation_count":3,"computed_title":"Changes in behavioral priority influence the accessibility of working memory content - Experiment 1","nchans_counts":[{"val":62,"count":34}],"sfreq_counts":[{"val":250.0,"count":34}],"stats_computed_at":"2026-04-22T23:16:00.307831+00:00","tags":{"pathology":["Healthy"],"modality":["Unknown"],"type":["Memory"],"confidence":{"pathology":0.6,"modality":0.5,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot example by research construct is the digit span working memory dataset (Healthy / Auditory / Memory). It establishes the convention that datasets centered on working memory capacity/maintenance/accessibility should be labeled Type=Memory, regardless of the specific task mechanics. However, unlike the digit span example, this dataset’s metadata does not explicitly state the stimulus channel (auditory vs visual), so modality cannot be transferred by analogy and must rely on this dataset’s own metadata (which is sparse).","metadata_analysis":"Key metadata facts available are limited to title/task name and a brief README:\n1) Title explicitly frames the study around working memory: \"Changes in behavioral priority influence the accessibility of working memory content - Experiment 1\".\n2) README repeats this working-memory focus: \"Preprocessed data from Experiment 1 of Ester & Pytel \\\"Changes in behavioral priority influence the accessibility of working memory content\\\".\" \n3) Task label is given but without stimulus description: tasks: [\"Postcues\"].\nNo metadata text specifies participant diagnosis/clinical recruitment, nor does it specify whether stimuli are visual, auditory, tactile, etc.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: no diagnosis/clinical recruitment is mentioned (e.g., only \"Subjects: 34\").\n- Few-shot pattern suggests: cognitive-task datasets without explicit clinical recruitment are labeled Healthy.\n- Alignment: ALIGN (no conflict). Final follows the convention.\n\nModality:\n- Metadata says: no explicit sensory modality; only task name \"Postcues\" is provided.\n- Few-shot pattern suggests: modality should reflect stimulus channel, but cannot be inferred reliably without explicit stimulus description.\n- Alignment: INSUFFICIENT EVIDENCE (neither confirms nor conflicts). Final uses Unknown due to missing facts.\n\nType:\n- Metadata says: working memory is the explicit focus (\"accessibility of working memory content\").\n- Few-shot pattern suggests: working-memory paradigms map to Type=Memory.\n- Alignment: ALIGN. Final follows both metadata and convention.","decision_summary":"Top-2 candidate labels with head-to-head selection:\n\nPathology:\n- Candidate 1: Healthy — Evidence: no clinical population mentioned; only \"Subjects: 34\".\n- Candidate 2: Unknown — Evidence: participants’ health status not explicitly stated.\nDecision: Healthy wins because the dataset is a standard cognitive experiment with no stated clinical recruitment (convention in few-shot examples). Evidence alignment: aligned. Confidence=0.6 (contextual inference; no explicit 'healthy' quote).\n\nModality:\n- Candidate 1: Unknown — Evidence: no stimulus modality described; only task name \"Postcues\".\n- Candidate 2: Visual — Evidence: 'post-cue' working memory paradigms are often visual, but this is not stated here.\nDecision: Unknown wins because modality cannot be determined from provided metadata. Evidence alignment: insufficient evidence. Confidence=0.5 (multiple plausible modalities; no direct quotes).\n\nType:\n- Candidate 1: Memory — Evidence quotes: title includes \"working memory content\"; README repeats \"accessibility of working memory content\".\n- Candidate 2: Attention — Evidence: \"behavioral priority\" could relate to attentional prioritization, but working memory is the explicit construct.\nDecision: Memory wins because working memory is named as the primary focus in both title and README. Evidence alignment: aligned. Confidence=0.8 (2 explicit working-memory quotes + strong few-shot convention mapping WM studies to Memory)."}},"total_duration_s":204.0,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"a2d8a3cd2f32be59","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"canonical_name":null,"name_confidence":0.88,"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":"Ester2023_Changes_behavioral"}}