{"id":"W3084180635","doi":"10.1101/2020.09.10.291674","title":"Visual perceptual learning generalizes to untrained effectors","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Perception; Task (project management); Cognitive psychology; Visual perception; Effector; Neuroscience; Visual search; Psychology; Perceptual learning; Computer science; Contrast (vision); Artificial intelligence; Biology","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":[0.000618357,0.0003986801,0.0003816241,0.0002785826,0.0001355782,0.0007266984,0.0008676837,0.0005552002,0.002106979],"category_scores_gemma":[0.003522518,0.0002797833,0.0003056407,0.00009189753,0.001059325,0.0008135988,0.001073394,0.001359558,0.0005085073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003289394,"about_ca_system_score_gemma":0.0002410836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004215122,"about_ca_topic_score_gemma":0.0002826796,"domain_scores_codex":[0.9994824,0.0000593996,0.00003308748,0.0001772004,0.0001718165,0.00007603275],"domain_scores_gemma":[0.9978817,0.0004932664,0.0004460783,0.0007319096,0.0002228014,0.0002241613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001570596,0.0002992141,0.002809228,0.00006997014,0.00001981512,0.0001038751,0.0001238666,0.00222955,0.9733554,0.0006969832,0.0001185343,0.02001647],"study_design_scores_gemma":[0.00006124285,0.002228138,0.1139957,0.00004208959,0.00003165318,0.0005756038,0.0001519715,0.03642981,0.8376769,0.007120867,0.00162962,0.00005645357],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865583,0.00008589842,0.01014114,0.00007799852,0.00002728122,0.00001804408,0.00003340219,0.0002088193,0.002849109],"genre_scores_gemma":[0.997099,0.00003445951,0.001618241,0.00006819228,0.000005971005,0.00001072744,0.00004974136,0.00004934886,0.001064268],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002106979,"threshold_uncertainty_score":0.007048488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03988631878370645,"score_gpt":0.2853788806709546,"score_spread":0.2454925618872482,"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."}}