{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004852518,0.0003551873,0.0002172575,0.0001241343,0.0001258671,0.0004702687,0.0004569372,0.0003566657,0.002164805],"category_scores_gemma":[0.004472781,0.0002125709,0.0002255896,0.0001142236,0.0008081853,0.0008548329,0.001128184,0.001040754,0.000193589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002503724,"about_ca_system_score_gemma":0.0001472268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003495807,"about_ca_topic_score_gemma":0.0005068555,"domain_scores_codex":[0.9996624,0.0000684128,0.00001587501,0.0001148768,0.00009878848,0.00003966848],"domain_scores_gemma":[0.9983491,0.0008674811,0.0002061781,0.0004129664,0.00008696217,0.00007735845],"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":[0.001206233,0.0002993673,0.00601948,0.0004852492,0.0001656805,0.0004475928,0.001479656,0.1685728,0.6387086,0.01492098,0.001752232,0.1659422],"study_design_scores_gemma":[0.0001008605,0.002408526,0.03506774,0.00009895658,0.0001154743,0.001502778,0.0006629899,0.6133863,0.2908151,0.04501088,0.01067868,0.0001517197],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8369296,0.0002074611,0.1593105,0.0002051245,0.0000915092,0.00005743733,0.0001247355,0.0003791396,0.002694606],"genre_scores_gemma":[0.9739595,0.00009950897,0.02449833,0.00009021114,0.00001136748,0.00002942058,0.0001684955,0.00007746013,0.001065579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002164805,"threshold_uncertainty_score":0.007242024,"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."}}