{"id":"W2578208782","doi":"","title":"Simulating the Temporal Dynamics of Learning-Related Shifts in Generalization Gradients with a Single-Layer Perceptron","year":2011,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts; University of Alberta; National Science Foundation","keywords":"Generalization; Artificial intelligence; Perceptron; Machine learning; Artificial neural network; Psychology; Perception; Set (abstract data type); Computer science; Cognitive psychology; Cognitive science; Mathematics; Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007550313,0.0002707865,0.0003644537,0.000286351,0.0002385622,0.0003989692,0.0006586613,0.0007325048,0.001687233],"category_scores_gemma":[0.002947909,0.0003360754,0.0004396654,0.000344214,0.0005345497,0.0008048525,0.000489539,0.0009157996,0.0001373622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009676468,"about_ca_system_score_gemma":0.0007179028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01119864,"about_ca_topic_score_gemma":0.009875374,"domain_scores_codex":[0.999893,0.00003410227,0.000005980967,0.00001982664,0.00001650173,0.00003070942],"domain_scores_gemma":[0.9991882,0.0005414987,0.00007507105,0.00006500415,0.00006165296,0.00006850057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007280512,0.00004396092,0.001223607,0.00001281641,0.00001204357,0.00002941792,0.00002633035,0.993373,0.001648289,0.001558322,0.0001423083,0.001857065],"study_design_scores_gemma":[0.00000447971,0.000007285434,0.0001492644,7.489139e-7,0.00000121689,0.000001537031,0.000002625053,0.9991461,0.000206641,0.0004639637,0.00001465365,0.000001541464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9575643,0.00005413798,0.03969824,0.0002573963,0.00003131907,0.00002970075,0.0001310596,0.000150876,0.002083075],"genre_scores_gemma":[0.9901986,0.0000311575,0.008855012,0.00002709131,0.000002836542,0.00003509913,0.00005281239,0.00001654125,0.0007808419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01119864,"threshold_uncertainty_score":0.02226692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02922435713111043,"score_gpt":0.2114535064967378,"score_spread":0.1822291493656273,"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."}}