{"id":"W2397286603","doi":"","title":"Perceptual Category Learning: Similarity and Differences Between Children and Adults","year":2014,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Child and Animal Learning Development","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorization; Psychology; Salience (neuroscience); Concept learning; Cognitive psychology; Perception; Similarity (geometry); Set (abstract data type); Cognition; Mental representation; Abstraction; Task (project management); Artificial intelligence; Computer science","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002188705,0.0003474783,0.0003846056,0.0001226478,0.0002847515,0.0007625375,0.0002554774,0.000221201,0.00068829],"category_scores_gemma":[0.00021738,0.0002992131,0.00007315703,0.0001477226,0.0002479912,0.0008347048,0.0003152169,0.0008505792,0.0007905504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008989121,"about_ca_system_score_gemma":0.00002417875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001484773,"about_ca_topic_score_gemma":0.000001143581,"domain_scores_codex":[0.9979833,0.0002151807,0.0003451041,0.000750522,0.0002298202,0.0004760141],"domain_scores_gemma":[0.9989753,0.000286632,0.0001150162,0.0002161408,0.0000174426,0.0003895101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007880625,0.00006137971,0.9475042,0.00001987057,0.00005677468,0.00000384225,0.0003930195,1.012877e-7,0.000005074736,0.001623973,0.0005464324,0.04970654],"study_design_scores_gemma":[0.0007491168,0.000270067,0.97134,0.00005408955,0.00002004041,0.00001934936,0.0001408691,0.000007785495,0.00001383259,0.001528821,0.02545167,0.0004043381],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9803423,0.0002962331,0.0000337087,0.0005513187,0.000063353,0.0001829368,0.0003130891,0.0003252109,0.01789189],"genre_scores_gemma":[0.997178,0.00002348011,0.0001335895,0.0003362565,0.0003029206,0.000008523587,0.0005479594,0.00005901784,0.001410321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04930221,"threshold_uncertainty_score":0.9999874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0124295510336019,"score_gpt":0.2181514573689307,"score_spread":0.2057219063353288,"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."}}