{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007430292,0.0001474645,0.0003829981,0.001201239,0.0002318913,0.001132427,0.0002319736,0.0004447211,0.00252551],"category_scores_gemma":[0.005140053,0.000174675,0.0002122841,0.0005126484,0.0006489006,0.001345846,0.0007072247,0.0004612601,0.0009487356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002156065,"about_ca_system_score_gemma":0.0001657401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001499315,"about_ca_topic_score_gemma":0.001646064,"domain_scores_codex":[0.9994973,0.00004445281,0.00005680312,0.0001380339,0.0001717391,0.00009168551],"domain_scores_gemma":[0.997638,0.0006265455,0.0006423171,0.0001718262,0.0005052734,0.0004160433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007977565,0.0003408312,0.9021049,0.0001204436,0.0000625479,0.0006361197,0.02048779,0.0001045341,0.01621787,0.0008070809,0.001257642,0.05706241],"study_design_scores_gemma":[0.00001341542,0.0004415139,0.9912999,0.00001884085,0.00001261013,0.001009075,0.00401027,0.0001448561,0.001106687,0.0005268985,0.001402193,0.0000136826],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977914,0.0003165369,0.0001059437,0.00003839153,0.000006858375,0.00000696836,0.0001391144,0.000008767195,0.00158603],"genre_scores_gemma":[0.9989311,0.000155809,0.0001575878,0.00003030849,0.000006024847,0.000009810818,0.0002031247,0.00000517159,0.0005010222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00252551,"threshold_uncertainty_score":0.00844866,"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."}}