{"id":"W2767994764","doi":"","title":"Conceptual Hierarchies Arise from the Dynamics of Learning and Processing: Insights from a Flat Attractor Network","year":2006,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Child and Animal Learning Development","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Superordinate goals; Artificial intelligence; Semantics (computer science); Categorical variable; Priming (agriculture); Metric (unit); Natural language processing; Dynamics (music); Similarity (geometry); Concept learning; Cognitive science; Computer science; Cognitive psychology; Psychology; Social psychology; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.00008234992,0.0003225652,0.0003736166,0.00005579322,0.0003013502,0.0006614517,0.0003413669,0.0001920116,0.0005639312],"category_scores_gemma":[0.00009760221,0.0002361636,0.0001112215,0.0002916665,0.0004211513,0.0008729714,0.0002368182,0.0008689291,0.0002707461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002069893,"about_ca_system_score_gemma":0.00007436147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007067966,"about_ca_topic_score_gemma":0.00002353548,"domain_scores_codex":[0.9980637,0.000186829,0.0005283517,0.0005467476,0.0002815842,0.0003927569],"domain_scores_gemma":[0.9986096,0.0007113478,0.0002882235,0.0002296293,0.0000287384,0.0001324774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002924755,0.0001184908,0.9641018,0.000008818357,0.00009802389,0.00003037241,0.0008201128,0.00002379142,0.00009856842,0.02024116,0.002086828,0.01207959],"study_design_scores_gemma":[0.0008842061,0.0001140694,0.6906876,0.0002166469,0.00004070055,0.000004797571,0.0008400544,0.0002063907,0.0000996778,0.02248727,0.2839505,0.000468069],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9747363,0.004432193,0.0001764847,0.000581527,0.0001514241,0.0001910058,0.0008984429,0.0002170414,0.01861559],"genre_scores_gemma":[0.994469,0.0000182524,0.0006989285,0.0002298573,0.0006280806,0.00001171055,0.002084524,0.00007180055,0.001787847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2818637,"threshold_uncertainty_score":0.963047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01098048500490974,"score_gpt":0.215671983643795,"score_spread":0.2046914986388853,"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."}}