{"id":"W2134777860","doi":"10.21236/ada458109","title":"Computational Models of Object Recognition in Cortex: A Review","year":2000,"lang":"en","type":"review","venue":"","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Office of Naval Research; National Science Foundation","keywords":"Categorization; Cognitive neuroscience of visual object recognition; Feed forward; Computer science; Computational model; Object (grammar); Identification (biology); Artificial intelligence; Focus (optics); Visual cortex; Computational neuroscience; Pattern recognition (psychology); Cognitive science; Machine learning; Neuroscience; Psychology; Biology","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":[0.001012156,0.001595392,0.001910671,0.002476654,0.0003855173,0.002465639,0.004126464,0.002252391,0.004203225],"category_scores_gemma":[0.002261394,0.000884078,0.001009128,0.003250555,0.001717995,0.00504932,0.001102537,0.001639378,0.003350459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001379325,"about_ca_system_score_gemma":0.001473544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002812779,"about_ca_topic_score_gemma":0.001746376,"domain_scores_codex":[0.9996578,0.00007619208,0.00003653763,0.00008294774,0.000119683,0.00002692344],"domain_scores_gemma":[0.9991472,0.0005755325,0.0000481635,0.00005721892,0.0001446964,0.00002720848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001427965,0.00009045678,0.001209168,0.01216973,0.0003007762,0.0005082111,0.000417461,0.03626965,0.002172468,0.1522173,0.04785129,0.7466506],"study_design_scores_gemma":[0.0000462376,0.000112806,0.002208522,0.003980142,0.0002240087,0.001859554,0.0001930069,0.02938594,0.00105671,0.425992,0.5348231,0.0001179631],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001728809,0.9347091,0.05273554,0.001905285,0.0003959604,0.00003270403,0.0002123011,0.0003188649,0.007961307],"genre_scores_gemma":[0.01908043,0.9550139,0.0212087,0.0006101415,0.0008187395,0.0001059219,0.0005006599,0.00007542859,0.002586082],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004203225,"threshold_uncertainty_score":0.01406121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1333072443658415,"score_gpt":0.3385767094784487,"score_spread":0.2052694651126072,"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."}}