{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005460145,0.0005313075,0.0006834903,0.0007456388,0.0005188308,0.001790693,0.001256647,0.001243609,0.002151438],"category_scores_gemma":[0.0286013,0.0007447433,0.0008481426,0.0004255171,0.002609677,0.003709249,0.001364256,0.001748195,0.000191297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001732668,"about_ca_system_score_gemma":0.0009577036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004721962,"about_ca_topic_score_gemma":0.002931984,"domain_scores_codex":[0.9973626,0.001409602,0.0001013454,0.0005356258,0.0004717314,0.0001191395],"domain_scores_gemma":[0.9830651,0.0141732,0.001263459,0.0006736442,0.0005795066,0.0002450448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004644295,0.0001407646,0.007353723,0.0004035499,0.000317324,0.0005711251,0.001304731,0.4244226,0.02586061,0.466457,0.001500085,0.07120399],"study_design_scores_gemma":[0.00002641334,0.00005636388,0.003174713,0.00002768572,0.0000344153,0.00009749752,0.00004291747,0.7642918,0.001271712,0.2302867,0.000646139,0.00004365622],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08737592,0.0004293818,0.903837,0.001623125,0.00006156311,0.0000664209,0.0001099637,0.0001937392,0.006302864],"genre_scores_gemma":[0.8771363,0.0003106709,0.1197567,0.0003292353,0.00008506344,0.0001208129,0.00009016979,0.0000631551,0.002107845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005460145,"threshold_uncertainty_score":0.02887636,"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."}}