{"id":"W4402830990","doi":"10.1126/sciadv.adl1776","title":"Contrastive learning explains the emergence and function of visual category-selective regions","year":2024,"lang":"en","type":"article","venue":"Science Advances","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Neural coding; Computer science; Artificial intelligence; Coding (social sciences); Functional magnetic resonance imaging; Visual cortex; Representation (politics); Pattern recognition (psychology); Psychology; Cognitive psychology; Neuroscience; Mathematics","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":[0.0002515321,0.0001844295,0.0001783126,0.0002206537,0.0001330202,0.0003678597,0.0005217633,0.0003702671,0.0005958462],"category_scores_gemma":[0.001086348,0.0001285595,0.0002841021,0.0001021234,0.0008366785,0.0007750312,0.0004400489,0.0005667396,0.0001201264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003923252,"about_ca_system_score_gemma":0.0001765636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001022142,"about_ca_topic_score_gemma":0.001097362,"domain_scores_codex":[0.999938,0.00001201285,0.000001685498,0.00002144878,0.00001376848,0.00001303609],"domain_scores_gemma":[0.9997204,0.0001295974,0.00003927626,0.0000596514,0.00003070292,0.00002021042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003318061,0.00006731792,0.01946183,0.0001598299,0.0001090934,0.0004650606,0.0004906734,0.4149403,0.1808329,0.2505386,0.001341857,0.1312607],"study_design_scores_gemma":[0.00001093186,0.00005267037,0.005779604,0.000006787066,0.00001371247,0.0001670118,0.00002705938,0.8378181,0.01138347,0.1440256,0.0007042383,0.0000108092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5669859,0.0004336651,0.4249787,0.0006323781,0.00002507859,0.00001534474,0.000065106,0.0002012439,0.006662542],"genre_scores_gemma":[0.9920112,0.00005866271,0.007333133,0.00002672392,0.0000096819,0.000005502866,0.00001499116,0.00001256841,0.0005274546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001022142,"threshold_uncertainty_score":0.002846479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03285454021408718,"score_gpt":0.3296656165291084,"score_spread":0.2968110763150212,"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."}}