{"id":"W4285151415","doi":"10.1109/tmech.2022.3175774","title":"Eye-Gaze Metrics for Cognitive Load Detection on a Driving Simulator","year":2022,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pupillary response; Driving simulator; Workload; Cognitive load; Pupillometry; Gaze; Computer science; Eye tracking; Simulation; Pupil; Cognition; Human–computer interaction; Computer vision; Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003875097,0.0005066296,0.0003042621,0.001340292,0.0002548382,0.000404891,0.0002183855,0.0004062043,0.001804325],"category_scores_gemma":[0.001969865,0.0001084917,0.0002312243,0.0007993006,0.0001214486,0.0004538618,0.0003985296,0.0002585718,0.0003708143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000249056,"about_ca_system_score_gemma":0.0002953759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004327441,"about_ca_topic_score_gemma":0.006547675,"domain_scores_codex":[0.9996024,0.00009709538,0.00003006638,0.00008291341,0.0001535436,0.00003405935],"domain_scores_gemma":[0.9990945,0.000256431,0.0001339667,0.0000584093,0.0004038583,0.00005287207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001883354,0.0008058301,0.272601,0.001008106,0.0003656918,0.000385313,0.001634492,0.01299005,0.4207231,0.0007349827,0.004548437,0.2823196],"study_design_scores_gemma":[0.00005627476,0.001791586,0.878338,0.00007118799,0.0001733851,0.0007175768,0.0007777272,0.06260566,0.05223503,0.0003768524,0.002751932,0.0001049041],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729502,0.0005041784,0.02222279,0.0000578679,0.00003001426,0.0001300871,0.001417153,0.000388824,0.002298817],"genre_scores_gemma":[0.9877396,0.0002906787,0.01021363,0.00002774656,0.0000161045,0.0001138487,0.0009373627,0.0000279127,0.0006330805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004327441,"threshold_uncertainty_score":0.008604527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02961674801557811,"score_gpt":0.352734313547218,"score_spread":0.3231175655316398,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}