{"id":"W1975385207","doi":"10.1093/cercor/bhv081","title":"Conjunctive Coding of Complex Object Features","year":2015,"lang":"en","type":"article","venue":"Cerebral Cortex","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Ontario Brain Institute; Western University; University of Toronto","funders":"Canadian Institutes of Health Research; University of Toronto; James S. McDonnell Foundation","keywords":"Perirhinal cortex; Computer science; Artificial intelligence; Coding (social sciences); Object (grammar); Percept; Visual Objects; Cognitive neuroscience of visual object recognition; Pattern recognition (psychology); Communication; Neuroscience; Psychology; Perception; Temporal lobe; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001702343,0.0001345725,0.0001918305,0.0002596356,0.0001568383,0.0005945321,0.0003079961,0.0002067966,0.001575063],"category_scores_gemma":[0.001407616,0.0001839157,0.0001688149,0.0003288795,0.000498054,0.0007600019,0.0007544908,0.0003313162,0.000135695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003029786,"about_ca_system_score_gemma":0.0001856356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00118644,"about_ca_topic_score_gemma":0.001815128,"domain_scores_codex":[0.9998628,0.0000120457,0.000006505954,0.0000523325,0.00004162244,0.00002470971],"domain_scores_gemma":[0.9996235,0.0001444465,0.00007034033,0.00006732127,0.00006161908,0.00003279113],"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":[0.0001775789,0.00002645308,0.004482612,0.00006340356,0.00002286755,0.0002284041,0.000239999,0.001289945,0.9380599,0.006292582,0.0002219579,0.04889435],"study_design_scores_gemma":[0.0000743644,0.0003565538,0.4178737,0.00003888715,0.0001118497,0.001892426,0.000520412,0.1109415,0.4126429,0.0502384,0.00523679,0.00007228702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9418163,0.0002597919,0.04852247,0.0001301943,0.00003765642,0.0000234635,0.0001466172,0.0001546558,0.008908908],"genre_scores_gemma":[0.9899355,0.00007665766,0.009096492,0.00002325501,0.00001234612,0.000009080843,0.00005175058,0.00002453643,0.0007703289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001575063,"threshold_uncertainty_score":0.005269051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07239921339940794,"score_gpt":0.2854957598493932,"score_spread":0.2130965464499853,"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."}}