{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a338f","dataset_id":"ds004841","associated_paper_doi":null,"authors":["Gabriella Larkin","James A. Davis","Victor Paul","Marcel Cannon","Chris Manteuffel","Ben Brewster","Tony Johnson","Mike Dunkel","Stephen Gordon","Kevin King"],"bids_version":"1.8.0","contact_info":["Kevin King"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004841.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":20,"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/ds004841","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"36c2f6bc26af925d89de780b69f533f34efb3f6593c67f9e45c3529895a8c8c3","license":"CC0","n_contributing_labs":null,"name":"TX14","readme":"TX14 dataset: Perform a local situational awareness task while maintaining supervisory control of a semi-autonomous vehicle.\nThis Army’s transition to a leaner, more agile and rapidly-deployable force requires the advent of autonomous technologies and systems, and more reliance on computers and machines. This move from traditional warfare to FCS represents a shift in the human role, as well. Technological advancement has made it so that the role of the user has been transformed from active controller to system monitor and manager, intervening only in the case of a problem. As such, the soldier’s dependency on robotics technologies, tele-operations, indirect driving and autonomy is expected to increase significantly. Additionally, although semi-autonomous driving technologies have proven beneficial in aggregate measures of local area awareness (i.e., target/threat detection) and vehicle control, it is important to understand the situational trade-offs between local area awareness and vehicle control, as situational trade-offs provide the basis for developing dynamic task allocation within Crewstations.","recording_modality":["eeg"],"senior_author":"Kevin King","sessions":["VehicleMotionOnly","VehicleWithNoise"],"size_bytes":7846933953,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["DriveOnMission"],"timestamps":{"digested_at":"2026-04-22T12:27:02.318748+00:00","dataset_created_at":"2023-11-13T17:40:25.216Z","dataset_modified_at":"2023-11-13T17:59:04.000Z"},"total_files":147,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004841","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.tsv","task-DriveOnMission_events.json"]},"nemar_citation_count":0,"computed_title":"TX14","nchans_counts":[{"val":70,"count":147}],"sfreq_counts":[{"val":256.0,"count":147}],"stats_computed_at":"2026-04-22T23:16:00.308488+00:00","tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.7,"modality":0.6,"type":0.7},"reasoning":{"few_shot_analysis":"Closest few-shot convention match is the visual cognitive-control/monitoring style tasks labeled as Attention (e.g., the DPX cognitive control task example labeled Type=Attention with visually presented cues/probes). TX14 similarly emphasizes monitoring/management and target/threat detection under supervisory control, which fits the catalog’s use of Attention for sustained monitoring and vigilance-like demands. A weaker alternative analogy is the motor movement/imagery example (Type=Motor), but TX14’s description focuses more on supervisory control and situational awareness than on studying movement execution/imagery per se.","metadata_analysis":"Key task facts from metadata:\n- Task purpose: \"Perform a local situational awareness task while maintaining supervisory control of a semi-autonomous vehicle.\" (readme)\n- Monitoring/manager role: \"the role of the user has been transformed from active controller to system monitor and manager\" (readme)\n- Perception/awareness target: \"local area awareness (i.e., target/threat detection) and vehicle control\" (readme)\n- Participants: only \"Subjects: 20\" (participants_overview) with no diagnosis/clinical recruitment mentioned.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: only \"Subjects: 20\" and no mention of any disorder/diagnosis (participants_overview/readme).\n- Few-shot pattern suggests: when no clinical population is described, label as Healthy (seen across multiple healthy task datasets).\n- Alignment: ALIGN (no clinical recruitment described).\n\nModality:\n- Metadata says: supervisory control/monitoring and situational awareness, including \"target/threat detection\" (readme), but does not explicitly name stimulus modality.\n- Few-shot pattern suggests: similar monitoring/detection tasks are typically Visual when they involve supervisory control interfaces and detection (as in many attention/control paradigms).\n- Alignment: PARTIAL (metadata implies detection/monitoring but does not explicitly state visual; inference required).\n\nType:\n- Metadata says: \"situational awareness\", \"system monitor and manager\", and trade-offs involving \"target/threat detection\" (readme).\n- Few-shot pattern suggests: monitoring/vigilance and cognitive control demands are labeled Attention (e.g., DPX example).\n- Alignment: ALIGN (conceptual focus is attention/situational awareness rather than explicit learning/memory/motor execution).","decision_summary":"Top-2 candidates per category and selection:\n\nPathology:\n1) Healthy — Evidence: no disorder/diagnosis mentioned; only \"Subjects: 20\" and task context is operational/human factors (\"Perform a local situational awareness task...\").\n2) Unknown — Could be used because demographics/health screening are not explicitly stated.\nWinner: Healthy (standard catalog convention when no clinical recruitment is indicated).\n\nModality:\n1) Visual — Evidence: task includes \"target/threat detection\" and supervisory monitoring/management of a vehicle, which strongly implies interaction with a visual crewstation/interface; no competing sensory channel described.\n2) Multisensory — Possible in driving/supervisory control settings, but metadata does not mention auditory/tactile cues.\nWinner: Visual (best-supported by task description; multisensory is speculative).\n\nType:\n1) Attention — Evidence: \"local situational awareness\", \"system monitor and manager\", and \"target/threat detection\" emphasize sustained monitoring/awareness.\n2) Decision-making — Could apply because supervisory control involves choices/interventions, but decision policy/value learning is not stated as the primary aim.\nWinner: Attention (situational awareness/monitoring is the central construct described).\n\nConfidence justification (by available explicit evidence):\n- Pathology: based on lack of any clinical terms plus clear task/operational framing (no recruitment pathology stated).\n- Modality: inferred (no explicit 'visual stimuli' phrase), hence lower confidence.\n- Type: multiple explicit situational awareness/monitoring phrases point to Attention, though still not a canonical lab task label."}},"source_url":"https://openneuro.org/datasets/ds004841","total_duration_s":102404.0,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"946c28aab8d527be","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"canonical_name":null,"name_confidence":0.46,"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":"Larkin2023_TX14"}}