{"id":"W2043298959","doi":"10.1037/0096-1523.35.1.195","title":"Flexible visual statistical learning: Transfer across space and time.","year":2009,"lang":"en","type":"article","venue":"Journal of Experimental Psychology Human Perception & Performance","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalization; Computer science; Context (archaeology); Transfer of learning; Statistical learning; Statistical hypothesis testing; Artificial intelligence; Visual learning; Space (punctuation); Function (biology); Machine learning; Cognitive psychology; Mathematics; Psychology; Geography; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003997656,0.0001558419,0.0003141636,0.00002487091,0.0003642155,0.00005700799,0.0001258278,0.0001112707,0.002678329],"category_scores_gemma":[0.00001991638,0.00007194787,0.00009289107,0.0001227024,0.0001988979,0.0002134827,0.00001288888,0.0003351568,0.00004676062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002636902,"about_ca_system_score_gemma":0.000002658128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002403433,"about_ca_topic_score_gemma":7.425492e-7,"domain_scores_codex":[0.9986612,0.0001996129,0.0004301803,0.0002260532,0.0002169598,0.0002660408],"domain_scores_gemma":[0.9995593,0.00007296449,0.000103617,0.00003608725,0.00006642899,0.0001615858],"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":[0.0002477618,0.0002835325,0.001822549,0.000001565427,0.00001393484,0.000008071563,0.0004520261,0.000006297275,0.9364946,0.000371324,0.0003904677,0.05990782],"study_design_scores_gemma":[0.0008696346,0.009879624,0.9673051,0.00002783635,0.00003206069,0.000292162,0.001545499,0.0006087753,0.01396118,0.0004407854,0.004730675,0.0003066906],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976444,0.000139912,0.0006415207,0.0004026034,0.00006916911,0.00005512343,0.000005073922,0.00001883339,0.001023393],"genre_scores_gemma":[0.997327,0.0001203895,0.001275167,0.0005385269,0.0002739841,0.000001133946,0.00001251197,0.000001457582,0.0004498774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9654825,"threshold_uncertainty_score":0.9982334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04178499649911045,"score_gpt":0.3997596014351312,"score_spread":0.3579746049360208,"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."}}