{"id":"W3111654681","doi":"10.1016/j.visres.2020.11.007","title":"Binocular summation and efficient coding","year":2020,"lang":"en","type":"article","venue":"Vision Research","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal General Hospital","funders":"Canadian Institutes of Health Research","keywords":"Monocular; Binocular vision; Summation; Coding (social sciences); Computer science; Psychometric function; Psychophysics; Contrast (vision); Set (abstract data type); Artificial intelligence; Mathematics; Psychology; Perception; Neuroscience; Statistics","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":[0.0003611011,0.000278293,0.0003625436,0.0005821921,0.0002821629,0.0009322678,0.0005237951,0.0004439828,0.00296275],"category_scores_gemma":[0.001926092,0.0001710076,0.0002011662,0.0005749158,0.0007660486,0.002057872,0.0007526155,0.0005911841,0.000321675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004477965,"about_ca_system_score_gemma":0.0002859034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006991136,"about_ca_topic_score_gemma":0.0007370154,"domain_scores_codex":[0.9997583,0.00003498854,0.00001253702,0.00004087354,0.0001056449,0.0000476868],"domain_scores_gemma":[0.9994099,0.0002766832,0.00006107562,0.0001135342,0.00009058445,0.00004820847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003064421,0.00005399001,0.001473237,0.0001901819,0.00005061281,0.0002290556,0.00019992,0.00377868,0.2628776,0.5974373,0.002432776,0.1309702],"study_design_scores_gemma":[0.00006184356,0.0001375882,0.02605016,0.00004739278,0.00005173125,0.0009539951,0.0001099565,0.06948593,0.08887455,0.8057604,0.008420563,0.000045863],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5270994,0.007781395,0.3893394,0.002344523,0.0003096006,0.00004207768,0.0003212559,0.0005452589,0.07221711],"genre_scores_gemma":[0.9734628,0.0009087607,0.02185558,0.0001518353,0.0001226041,0.00002050417,0.0001175562,0.00007056203,0.003289839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00296275,"threshold_uncertainty_score":0.009911358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2990501521760211,"score_gpt":0.471449995773481,"score_spread":0.1723998435974599,"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."}}