{"id":"W4412439205","doi":"10.1167/jov.25.9.2476","title":"Emotional gaze increases target temporal processing","year":2025,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Gaze; Psychology; Cognitive psychology; Computer science; Computer vision","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":[0.0003594289,0.00006532973,0.000135924,0.0002708868,0.00009379317,0.00007990444,0.0004432861,0.0000506185,0.000008192863],"category_scores_gemma":[0.0001257395,0.00004929485,0.0000630901,0.000327257,0.00003800084,0.0003616005,0.00009749804,0.000191529,0.000004593361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003699833,"about_ca_system_score_gemma":0.000171318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000433582,"about_ca_topic_score_gemma":6.12771e-7,"domain_scores_codex":[0.999278,0.00003998569,0.0002647787,0.0001046933,0.0002056709,0.0001068632],"domain_scores_gemma":[0.9993593,0.00004930717,0.0002186817,0.0001137515,0.000223053,0.00003588297],"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":[0.00008746531,0.001229613,0.1088241,0.0001190907,0.0000749218,0.0003002775,0.0001700544,0.0003711403,0.01630501,0.04605796,0.04047662,0.7859838],"study_design_scores_gemma":[0.00117958,0.000627394,0.8850628,0.00142002,0.00001885943,0.000360333,0.00005299588,0.02215966,0.004091823,0.06394117,0.02087339,0.0002119395],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2440019,0.0008664994,0.7489254,0.004836699,0.0003570447,0.00003041963,7.182398e-7,0.0000722714,0.0009090269],"genre_scores_gemma":[0.9184434,0.000008421378,0.08123303,0.0001522592,0.00005156166,2.448151e-7,3.678162e-7,0.000002050595,0.0001086632],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7857718,"threshold_uncertainty_score":0.2010186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01063245406676512,"score_gpt":0.2924213712361651,"score_spread":0.2817889171694,"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."}}