{"id":"W3096303321","doi":"10.1167/jov.20.11.236","title":"Expectations alter representations during object categorization","year":2020,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Categorization, perception, and language","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Categorization; Object (grammar); Focus (optics); Representation (politics); Variance (accounting); Artificial intelligence; Pattern recognition (psychology); Psychology; Cognitive neuroscience of visual object recognition; Cognitive psychology; Image (mathematics); Moment (physics); Computer science; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005956066,0.0002369258,0.0002520298,0.0002505477,0.0001779855,0.001005181,0.0002760444,0.0005421219,0.002037724],"category_scores_gemma":[0.004162997,0.0002576046,0.0002491451,0.0001306577,0.0002967154,0.0009428037,0.0006358357,0.0004461127,0.0003292239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003273629,"about_ca_system_score_gemma":0.0002892119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000812894,"about_ca_topic_score_gemma":0.0007174074,"domain_scores_codex":[0.9994308,0.00007436734,0.00004693511,0.0001531114,0.0002025922,0.00009217044],"domain_scores_gemma":[0.9986389,0.0005047164,0.0002958948,0.0002139345,0.0002037514,0.0001426231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0007615965,0.00007570116,0.01525384,0.00009261453,0.00003193139,0.000154646,0.0009337582,0.0009915273,0.9330552,0.00137694,0.0003478493,0.04692436],"study_design_scores_gemma":[0.00005771569,0.00180154,0.6794945,0.00006172933,0.0001297536,0.0007504558,0.0009389447,0.02908676,0.2750386,0.008752675,0.003778371,0.0001090555],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855656,0.0002124191,0.01080013,0.00009117259,0.00003022849,0.0000183425,0.0000572289,0.0001452489,0.003079719],"genre_scores_gemma":[0.9964465,0.00006920565,0.002454984,0.00004472008,0.00001056338,0.00001235599,0.00009365087,0.00004202105,0.0008259711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002037724,"threshold_uncertainty_score":0.006816924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02064113396449819,"score_gpt":0.3480820806557103,"score_spread":0.3274409466912122,"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."}}