{"id":"W4245016735","doi":"10.1167/11.11.678","title":"Eye Movement in Face Change Detection Task","year":2011,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Fixation (population genetics); Eye movement; Psychology; Perception; Eye tracking; Computer vision; Change detection; Ocular dominance; Face (sociological concept); Artificial intelligence; Cognitive psychology; Communication; Computer science; Neuroscience; Visual cortex; Population; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003330829,0.00005145423,0.00009605034,0.0002318915,0.00002756937,0.00001499185,0.0003095683,0.00004642842,0.000004829298],"category_scores_gemma":[0.00002032805,0.00004004006,0.00003737488,0.0002226065,0.00001312094,0.0003138835,0.00006727141,0.0001693917,0.00001128684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004771923,"about_ca_system_score_gemma":0.000008911187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002344594,"about_ca_topic_score_gemma":0.00001304456,"domain_scores_codex":[0.9994326,0.00003112251,0.0001993398,0.00008679256,0.000144343,0.0001058508],"domain_scores_gemma":[0.9995989,0.00001020561,0.0001805659,0.0001253907,0.00005477733,0.00003012278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00003119887,0.000319115,0.01282974,0.000007646993,0.00001018658,0.0001252015,0.001905636,0.000006858028,0.06594814,0.001526548,0.00008043195,0.9172093],"study_design_scores_gemma":[0.0003808268,0.0008586016,0.9657151,0.00008175552,0.000002731659,0.00002135315,0.00005919653,0.001988873,0.02568716,0.004441698,0.0006905358,0.00007216541],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7637668,0.00016278,0.2346853,0.0006492754,0.0004412768,0.00004823034,1.386794e-7,0.00002858431,0.0002175995],"genre_scores_gemma":[0.9931774,0.00003117795,0.006633845,0.0001058683,0.00003404038,0.000001074377,2.330965e-8,0.000002447332,0.00001408296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9528853,"threshold_uncertainty_score":0.1632786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03826758901358732,"score_gpt":0.2828752492884561,"score_spread":0.2446076602748688,"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."}}