{"id":"W2402272349","doi":"","title":"A Neural Model of Human Image Categorization","year":2013,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Innovation Trust","keywords":"Categorization; Computer science; Cognition; Cognitive science; Artificial intelligence; Connectionism; Perception; Cognitive architecture; Computational model; Cognitive model; Pointer (user interface); Visual processing; Representation (politics); Visual perception; Natural language processing; Psychology; Artificial neural network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002855691,0.0003153756,0.0003213468,0.0003957351,0.0003365125,0.001156238,0.001369731,0.001451941,0.005356008],"category_scores_gemma":[0.0009826359,0.000227315,0.0006271164,0.0003410474,0.0008883892,0.001943814,0.0005900675,0.0007392893,0.001081091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007011136,"about_ca_system_score_gemma":0.0006178748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003306608,"about_ca_topic_score_gemma":0.001792998,"domain_scores_codex":[0.999891,0.000022578,0.000004258098,0.00003335543,0.00003308983,0.00001561023],"domain_scores_gemma":[0.9998608,0.00005622001,0.00001319591,0.00002068889,0.00003133743,0.00001771562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007148081,0.00007231045,0.0008230935,0.0001164723,0.00006662668,0.0002400644,0.0004648394,0.2911419,0.01569588,0.6486048,0.005191567,0.03751109],"study_design_scores_gemma":[0.00001860522,0.00003680843,0.0004578046,0.00001358215,0.00001065185,0.0001201144,0.00003670291,0.7416493,0.0005661643,0.2543382,0.002738992,0.000013134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1025185,0.001596781,0.7993947,0.006061982,0.000299243,0.0001262227,0.0005135098,0.001061324,0.08842767],"genre_scores_gemma":[0.8947532,0.0009080822,0.073173,0.0005118491,0.0001383832,0.0002300886,0.0002234715,0.00006541846,0.02999661],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005356008,"threshold_uncertainty_score":0.01791763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03776026773029518,"score_gpt":0.245965332720159,"score_spread":0.2082050649898638,"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."}}