{"id":"W4312132414","doi":"10.31234/osf.io/bsdjr","title":"Brief category learning distorts perceptual space for complex scenes","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Categorization; Perception; Space (punctuation); Categorical variable; Categorical perception; Artificial intelligence; Scene statistics; Generative grammar; Computer science; Feature (linguistics); Task (project management); Phenomenon; Cognitive psychology; Psychology; Machine learning; Linguistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001193244,0.000234065,0.0002356643,0.00007380707,0.0002666322,0.0001009297,0.0003082988,0.0001225837,0.0009109772],"category_scores_gemma":[0.00001483273,0.0002638691,0.0001241329,0.00006914592,0.00004417169,0.00003895357,0.0003812268,0.0006005776,0.000007012873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001619289,"about_ca_system_score_gemma":0.00004264709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001464258,"about_ca_topic_score_gemma":0.00001303236,"domain_scores_codex":[0.9990916,0.00001307381,0.0002030282,0.0003284719,0.0001227801,0.0002411095],"domain_scores_gemma":[0.999488,0.0000359164,0.0000508017,0.0003232179,0.00004962489,0.00005241595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000232751,0.0001989695,0.001031936,0.004643939,0.0002176657,0.000007517349,0.003947888,0.4159346,0.01494518,0.02515975,0.4573215,0.07656772],"study_design_scores_gemma":[0.0001048925,0.00002176067,0.0003607592,0.00003113868,0.00004393515,0.000006050588,0.0004029929,0.2429717,0.0005809828,0.003477161,0.7514106,0.0005880751],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003463868,0.0004447138,0.9603866,0.000330389,0.0002422662,0.0006901014,0.00006018853,0.003408786,0.03097307],"genre_scores_gemma":[0.8551614,0.0001303419,0.1345498,0.00007686092,0.0001755532,0.001522122,0.0009113429,0.0001497612,0.007322755],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8516976,"threshold_uncertainty_score":0.9999813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03536920004975395,"score_gpt":0.284515766166861,"score_spread":0.249146566117107,"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."}}