{"id":"W2973800952","doi":"10.1167/19.10.30d","title":"Diagnostic Features for Visual Object Recognition in Humans","year":2019,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Observer (physics); Computer science; Cognitive neuroscience of visual object recognition; Object (grammar); Computer vision; Pattern recognition (psychology); Perception; Viewpoints; Task (project management); Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001315282,0.0002326019,0.0002340374,0.001048799,0.0002856652,0.00123591,0.0003064502,0.0004095013,0.004107061],"category_scores_gemma":[0.008677655,0.0001735632,0.0002213938,0.000320745,0.001172157,0.001316368,0.0007336558,0.0004368882,0.0005823502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004632463,"about_ca_system_score_gemma":0.0003460858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001056281,"about_ca_topic_score_gemma":0.0007192745,"domain_scores_codex":[0.9991573,0.0001665418,0.00004203543,0.0002381916,0.000338981,0.00005691863],"domain_scores_gemma":[0.9985204,0.0004872113,0.0002977655,0.0002733962,0.0003318167,0.00008922927],"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":[0.0005999272,0.0001546808,0.04742726,0.000530481,0.00008784225,0.0005722684,0.003535288,0.002924241,0.1741964,0.09733853,0.007481864,0.6651514],"study_design_scores_gemma":[0.0001387563,0.001291149,0.462325,0.0004586468,0.0000895099,0.006582384,0.002148549,0.06729431,0.06031603,0.3278758,0.07121386,0.0002660869],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6321316,0.002921775,0.3061705,0.001895745,0.0002312146,0.0002870008,0.000532082,0.001088399,0.05474167],"genre_scores_gemma":[0.9399628,0.0002467289,0.05809774,0.0002029013,0.0000401683,0.00008034484,0.0001633237,0.00004765352,0.001158409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004107061,"threshold_uncertainty_score":0.01373947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03765932103809373,"score_gpt":0.3528450977052162,"score_spread":0.3151857766671224,"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."}}