{"id":"W2752713396","doi":"10.1167/17.10.504","title":"Ruling out task difficulty in the context-generalization of texture perceptual learning","year":2017,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial intelligence; Perception; Context effect; Context (archaeology); Computer vision; Generalization; Computer science; Perceptual learning; Pattern recognition (psychology); Cognitive psychology; Psychology; Mathematics; Geometry; Neuroscience; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001023411,0.00006668169,0.0001409405,0.00009370977,0.0002608278,0.0002485579,0.0007400012,0.00004829709,0.00001463383],"category_scores_gemma":[0.0003841026,0.00004242996,0.00007872147,0.00006697484,0.00004020637,0.0006041082,0.00007483312,0.0003230564,0.000005367104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002024916,"about_ca_system_score_gemma":0.00003369776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008983551,"about_ca_topic_score_gemma":0.00001333899,"domain_scores_codex":[0.9988824,0.0001919183,0.0003406901,0.00009065012,0.0003937467,0.000100656],"domain_scores_gemma":[0.9987873,0.0000942761,0.0007266069,0.0002041328,0.0001528835,0.00003478618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001209106,0.0003253011,0.02261752,0.00003861545,0.00003757622,0.0001048138,0.1366306,0.04392482,0.1011226,0.01173864,0.002541072,0.6807975],"study_design_scores_gemma":[0.002275705,0.0007113275,0.6886107,0.0004921937,0.00001766461,0.0001454974,0.009378906,0.2809535,0.0005181218,0.0004588207,0.01620308,0.0002345614],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6790407,0.0002014142,0.3170519,0.001832933,0.0004610665,0.00006726608,2.8113e-7,0.000009503735,0.001334905],"genre_scores_gemma":[0.996538,0.00004789954,0.003051435,0.0001237729,0.0001032653,2.630457e-7,6.749242e-7,0.000003961339,0.0001306683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6805629,"threshold_uncertainty_score":0.2396849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02771521976879825,"score_gpt":0.3111856686829511,"score_spread":0.2834704489141528,"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."}}