{"id":"W2466723879","doi":"10.1152/jn.00282.2016","title":"Representing multiple object weights: competing priors and sensorimotor memories","year":2016,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Prior probability; Object (grammar); Psychology; Lift (data mining); Illusion; Cognitive psychology; Cognition; Computer science; Artificial intelligence; Communication; Machine learning; Neuroscience; Bayesian probability","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":[0.0007834507,0.0003143031,0.0002834871,0.000326868,0.0001787002,0.0009174742,0.0005055392,0.0004940517,0.001391363],"category_scores_gemma":[0.008360778,0.0003608715,0.0002219425,0.0001998244,0.0005817988,0.001685475,0.00103739,0.0006316478,0.000136499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002608071,"about_ca_system_score_gemma":0.0002494013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001094985,"about_ca_topic_score_gemma":0.001059118,"domain_scores_codex":[0.9995831,0.00006952864,0.00003259774,0.00012124,0.0001471033,0.0000463461],"domain_scores_gemma":[0.9978729,0.0008362459,0.0005465913,0.0004295548,0.0001571975,0.0001575255],"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":[0.001773379,0.0005164282,0.06289992,0.000282592,0.0001796223,0.0005654156,0.003465435,0.006761395,0.7139407,0.004351995,0.0004621249,0.2048009],"study_design_scores_gemma":[0.0001535611,0.001614628,0.8281168,0.00008817905,0.0002125209,0.001230835,0.001542003,0.05314425,0.08660286,0.02513595,0.001995339,0.0001630681],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898678,0.0001294858,0.008225284,0.00006232945,0.00001221402,0.0000173731,0.00002731628,0.00003341229,0.001624802],"genre_scores_gemma":[0.9964857,0.00006734037,0.002975088,0.00002374457,0.000005575761,0.00001522798,0.00004223072,0.00001123155,0.0003738585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001391363,"threshold_uncertainty_score":0.004654527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0260722704545585,"score_gpt":0.2512245612851043,"score_spread":0.2251522908305458,"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."}}