{"id":"W2892627196","doi":"10.1167/18.10.266","title":"Automatic prospective and retrospective activation of object representations during statistical learning","year":2018,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Object (grammar); Artificial intelligence; Representation (politics); Sequence (biology); Gaze; Psychology; Computer science; Statistical learning; Communication; Task (project management); Computer vision; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004990498,0.0002327891,0.0002767758,0.000250182,0.000168099,0.0006740887,0.0003600302,0.00028636,0.001417734],"category_scores_gemma":[0.00357522,0.0003693272,0.0002032157,0.0001981196,0.000370985,0.001012322,0.0007834499,0.0004253768,0.0002098369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002196579,"about_ca_system_score_gemma":0.0002436016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004710897,"about_ca_topic_score_gemma":0.0005251782,"domain_scores_codex":[0.9995763,0.00006827577,0.00001844891,0.0001689651,0.000106464,0.00006162439],"domain_scores_gemma":[0.9985722,0.0005557835,0.0003352588,0.0002609838,0.0001350111,0.0001407616],"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":[0.0006962228,0.00009286741,0.02263996,0.000126274,0.00005467798,0.0002013586,0.0009970639,0.0008879854,0.8802161,0.001197097,0.0003179683,0.09257244],"study_design_scores_gemma":[0.00009996686,0.001514566,0.7139148,0.000063165,0.0001459798,0.001113493,0.0007085302,0.04081302,0.2297298,0.008926138,0.002881474,0.00008907617],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788855,0.0001936286,0.01857245,0.00006390549,0.00002312223,0.0000243241,0.00007779445,0.0001554626,0.002003848],"genre_scores_gemma":[0.9936021,0.00008201152,0.005437075,0.00002229212,0.000009881009,0.00002083323,0.00009763242,0.00004042021,0.0006878703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001417734,"threshold_uncertainty_score":0.004742742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02811238299319472,"score_gpt":0.3694878775143873,"score_spread":0.3413754945211925,"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."}}