{"id":"W2342502044","doi":"10.1152/jn.00029.2016","title":"Tactile length contraction as Bayesian inference","year":2016,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; McMaster University","keywords":"Illusion; Percept; Stimulus (psychology); Inference; Perception; Bayesian probability; Contraction (grammar); Bayesian inference; Artificial intelligence; Psychology; Length contraction; Segmentation; Psychophysics; Computer science; Pattern recognition (psychology); Cognitive psychology; Neuroscience; Physics; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003731106,0.0001056572,0.0001983359,0.0001108858,0.00007481874,0.00001896602,0.0001945911,0.00005500997,0.0001621171],"category_scores_gemma":[0.001455894,0.00006165141,0.00009943693,0.0001137606,0.00009021252,0.0003680616,0.00003280694,0.0002188878,0.000110968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000284235,"about_ca_system_score_gemma":0.00005003053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003245882,"about_ca_topic_score_gemma":3.731613e-7,"domain_scores_codex":[0.9989505,0.000194094,0.0003106296,0.000188,0.0001678405,0.0001889696],"domain_scores_gemma":[0.9985069,0.000778667,0.0003964135,0.0001391581,0.00009014089,0.00008868798],"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":[0.0002073931,0.00005217245,0.000022838,0.000002117839,0.00000311395,0.000115814,0.00000669082,0.00005287434,0.9835123,0.00170927,0.00009673112,0.01421862],"study_design_scores_gemma":[0.003614091,0.009968224,0.0625576,0.0001186816,0.00006123646,0.004185861,0.00002619341,0.002852409,0.8129306,0.04750989,0.05555534,0.0006198162],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939991,0.000002192157,0.001502731,0.001913571,0.001748188,0.00005337569,0.000003043165,0.00001789499,0.0007599037],"genre_scores_gemma":[0.9974639,0.0002125624,0.00002394087,0.001529581,0.0003222777,0.000001026675,1.087219e-7,0.00001228143,0.0004343607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1705817,"threshold_uncertainty_score":0.2514071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0227822186046336,"score_gpt":0.2754310471531577,"score_spread":0.2526488285485241,"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."}}