{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00004383133,0.0001812708,0.0001655907,0.0001494843,0.0001201702,0.0007460592,0.0002721311,0.00009514121,0.002264281],"category_scores_gemma":[0.000199492,0.0001663994,0.0001112717,0.0003228135,0.0001255448,0.006405855,0.0001026159,0.0002269192,0.004367095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001336043,"about_ca_system_score_gemma":0.00002977726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003671192,"about_ca_topic_score_gemma":2.647199e-7,"domain_scores_codex":[0.9986848,0.00007133897,0.0003548419,0.000336793,0.0002809545,0.0002712925],"domain_scores_gemma":[0.9993776,0.00005726524,0.0001247301,0.0002235582,0.00004164482,0.0001751843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001919122,0.0001876412,0.005666411,0.00004979399,0.000002626918,0.000002281715,0.00004807795,0.00008602533,0.9845945,0.002098776,0.001638656,0.005606058],"study_design_scores_gemma":[0.001175269,0.0002308914,0.00555838,0.00006517546,0.00001669774,0.00001824047,0.00006065859,0.08137862,0.8295373,0.07910265,0.002037419,0.0008187468],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9685605,0.000004426439,0.0007665154,0.0004011593,0.0000495942,0.0003145362,0.0009229889,0.0002708305,0.02870944],"genre_scores_gemma":[0.9970866,0.000006623497,0.0002919713,0.0007180571,0.00004289843,0.00002870483,0.0003382297,0.00004646734,0.001440462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1550572,"threshold_uncertainty_score":0.9986478,"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."}}