{"id":"W3031153019","doi":"10.1101/2020.05.28.122036","title":"Motion opponency examined throughout visual cortex with multivariate pattern analysis of fMRI data","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Flicker; Visual cortex; Artificial intelligence; Noise (video); Computer science; Computer vision; Neuroscience; Motion perception; Motion detection; Visual processing; Motion (physics); Psychology","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":[0.0003189472,0.000235913,0.0001848218,0.000667989,0.0001316506,0.0003190852,0.0001171214,0.0001626303,0.001364274],"category_scores_gemma":[0.001491009,0.00009705412,0.0002554544,0.0004121106,0.0003018573,0.0002839402,0.0001910477,0.0002960779,0.00009535697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001778087,"about_ca_system_score_gemma":0.0001914267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001119136,"about_ca_topic_score_gemma":0.001615761,"domain_scores_codex":[0.9999056,0.00002107589,0.000005317114,0.00002886701,0.00002103619,0.00001809948],"domain_scores_gemma":[0.999643,0.0001641855,0.00008313555,0.00004595522,0.00003544148,0.00002826519],"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":[0.0005019697,0.00007879762,0.01195739,0.0001305441,0.0001072249,0.0001974926,0.0001563952,0.003161905,0.9143642,0.000980664,0.0002653148,0.06809802],"study_design_scores_gemma":[0.00003333364,0.0006815124,0.7323454,0.00003739622,0.0001573688,0.001443474,0.0001975084,0.08087097,0.1765471,0.0061374,0.001480614,0.00006787325],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9329144,0.0001964449,0.06470664,0.00009102401,0.00001393789,0.00006379377,0.0002973778,0.0002204737,0.001495925],"genre_scores_gemma":[0.9833347,0.0000849245,0.0161718,0.00001843827,0.00001126514,0.00003117605,0.00009348941,0.000023957,0.0002302478],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001364274,"threshold_uncertainty_score":0.004563928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08364112620635492,"score_gpt":0.3194546175059175,"score_spread":0.2358134912995626,"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."}}