{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001802562,0.0002067226,0.001008543,0.0002046182,0.00002167549,0.00001160149,0.0001352196,0.0001002163,0.0004030299],"category_scores_gemma":[0.00008967616,0.0001596358,0.0002864767,0.0006252531,0.00003554774,0.0001261365,0.00002568065,0.0002199482,0.0001077895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004564081,"about_ca_system_score_gemma":0.000126854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001528384,"about_ca_topic_score_gemma":0.000005433895,"domain_scores_codex":[0.9982193,0.000243613,0.000750096,0.0004047618,0.0002447036,0.0001374894],"domain_scores_gemma":[0.9991395,0.0003518672,0.0002968497,0.0001521719,0.00002807862,0.00003149797],"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":[0.000002006288,0.00004163422,2.178009e-8,0.01752568,0.000002247998,0.000005920815,0.000001401396,0.00007612254,8.714513e-7,0.0004436424,0.0002083731,0.9816921],"study_design_scores_gemma":[0.0002542378,0.0001328196,0.000001145713,0.146174,0.0002644862,0.0002285563,7.57229e-7,0.01719533,0.000003010681,0.01943118,0.8157083,0.0006062094],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000005492449,0.9878432,0.0001241939,0.00002136159,0.0001071521,0.0008945424,0.0001002454,0.00002754488,0.01087628],"genre_scores_gemma":[0.0000159817,0.9987139,0.0001231139,0.0005859783,0.00001812863,0.00005781828,0.0001753844,0.00001644006,0.000293276],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9810859,"threshold_uncertainty_score":0.6509761,"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."}}