{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3392","dataset_id":"ds004844","associated_paper_doi":null,"authors":["Jason S. Metcalfe","Victor Paul","Benamin Haynes","Corey Atwater","Amar Marathe","Gregory Gremillion","Kim Drnec","William Nothwang","Justin R. Estepp","Margaret Bowers","Jamie Lukos","Tony Johnson","Mike Dunkel","Stephen Gordon","Jon Touryan","Kevin King"],"bids_version":"1.8.0","contact_info":["Kevin King"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004844.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":17,"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/ds004844","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"ec4c40adf85731bd75570e33a70a5edc3bf24881c5003f9761f58bbbf80d9674","license":"CC0","n_contributing_labs":null,"name":"T22","readme":"TX22 dataset: Predicting and influencing trust-based decisions about control authority hand-off and take-over during simulated, semi-automated driving in a leader-follower paradigm.Vehicle survivability is critically important in todays military. Significant DoD investments have focused on developing and integrating autonomous vehicle technologies to mitigate the effects of human error and thus enhance surviability and mission effectiveness. In a previous experiment (SANDR designation: ARL_TX20), we explored how a human operators acceptance and use of advanced technology is influenced by their trust and related factors, like subjective workload and automation reliability. Nevertheless, more critical than measuring and achieving a certain level of trust is the need for a capability to resolve observed (or predicted) discrepancies between trust and trustworthiness that will undermine effective joint system performance. Using the same paradigm as we developed for our previous experiment (ARL_TX20), here we explore our ability to (a) make accurate real-time predictions of instances where intervention is necessary and (b) use those predictions to provide feedback to the driver that is intended to support active \"trust management\" by influencing the trust-based decisions of the driver.","recording_modality":["eeg"],"senior_author":"Kevin King","sessions":["CA","MM","NA","UA"],"size_bytes":23976121518,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["Drive"],"timestamps":{"digested_at":"2026-04-22T12:27:03.048261+00:00","dataset_created_at":"2023-11-13T19:09:08.656Z","dataset_modified_at":"2023-11-13T19:23:59.000Z"},"total_files":68,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004844","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.tsv","task-Drive_events.json"]},"nemar_citation_count":0,"computed_title":"T22","nchans_counts":[{"val":72,"count":68}],"sfreq_counts":[{"val":1024.0,"count":68}],"stats_computed_at":"2026-04-22T23:16:00.308524+00:00","tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Decision-making"],"confidence":{"pathology":0.6,"modality":0.6,"type":0.9},"reasoning":{"few_shot_analysis":"Closest few-shot conventions are the datasets involving value/choice behavior: (1) “EEG: Reinforcement Learning in Parkinson's” (Type=Decision-making) and (2) “EEG: Three armed bandit gambling task” (decision/choice with feedback; though labeled Affect in the example, it demonstrates that choice/feedback paradigms are mapped to higher-level constructs rather than motor responses). By convention, when the metadata emphasizes choices/policy (here: trust-based authority handoff/takeover decisions), the appropriate Type label is Decision-making rather than Motor or Attention. For Modality, many decision tasks in examples are screen-based (Visual); a driving simulator paradigm is typically visually driven even if it includes other channels.","metadata_analysis":"Key quoted metadata facts:\n- Readme: “Predicting and influencing trust-based decisions about control authority hand-off and take-over during simulated, semi-automated driving in a leader-follower paradigm.”\n- Readme: “we explored how a human operators acceptance and use of advanced technology is influenced by their trust and related factors”\n- Readme: “provide feedback to the driver that is intended to support active \\\"trust management\\\" by influencing the trust-based decisions of the driver.”\n- Tasks: [\"Drive\"]\n- Participants: “Subjects: 17” (no diagnosis/clinical recruitment described).","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: only “Subjects: 17” with no clinical descriptors; nothing about patients/diagnoses.\n- Few-shot pattern suggests: when no disorder recruitment is described, label as Healthy.\n- Alignment: ALIGN (metadata absence of pathology is consistent with a normative cohort).\n\nModality:\n- Metadata says: task is “Drive” in “simulated, semi-automated driving” (no explicit stimulus channel like auditory/visual specified).\n- Few-shot pattern suggests: simulator/interactive tasks with on-screen content are typically labeled Visual unless explicitly stated as multisensory.\n- Alignment: PARTIAL (visual is inferred rather than explicitly stated).\n\nType:\n- Metadata says: explicitly frames the study around “trust-based decisions” and influencing those decisions via feedback.\n- Few-shot pattern suggests: paradigms primarily about choices/acceptance/use and decision policies map to Decision-making.\n- Alignment: ALIGN (both metadata framing and few-shot conventions point to Decision-making).","decision_summary":"Top-2 candidates per category with head-to-head comparison:\n\nPathology:\n1) Healthy — Evidence: no clinical recruitment described (only “Subjects: 17”); task is applied human factors/driving trust, not a clinical cohort.\n2) Unknown — Evidence: metadata does not explicitly say “healthy volunteers/controls”.\nDecision: Healthy wins because the dataset provides no indication of any disorder-based recruitment, matching the catalog convention to treat unspecified populations as Healthy. (Alignment: aligns with few-shot convention.)\n\nModality:\n1) Visual — Evidence: “simulated…driving” and task “Drive” strongly implies a visually presented driving scene as the dominant stimulus.\n2) Multisensory/Other — Evidence: driving could include audio/haptics, but none is described.\nDecision: Visual wins as the most likely dominant stimulus channel for a driving simulator, but with reduced confidence due to lack of explicit stimulus description. (Alignment: partial.)\n\nType:\n1) Decision-making — Evidence quotes: “trust-based decisions about control authority hand-off and take-over”; “acceptance and use… influenced by their trust”; “influencing the trust-based decisions of the driver.”\n2) Attention — Evidence: driving can involve sustained attention demands, but the stated research purpose centers on trust/authority decisions.\nDecision: Decision-making wins because the primary construct is explicitly decision/trust/authority handoff rather than attention per se. Confidence is high due to multiple explicit metadata statements."}},"total_duration_s":76508.0,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"be6537ea7f9ea970","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"author_year":"Metcalfe2023_T22","canonical_name":null}}