{"id":"W2921979765","doi":"10.1016/j.neuron.2019.04.014","title":"Rapid Invariant Encoding of Scene Layout in Human OPA","year":2019,"lang":"en","type":"article","venue":"Neuron","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":81,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"European Research Council; Aalto-Yliopisto; Academy of Finland; British Academy","keywords":"Functional magnetic resonance imaging; Magnetoencephalography; Computer vision; Artificial intelligence; Computer science; Visual cortex; Invariant (physics); Decoding methods; Pattern recognition (psychology); Psychology; Neuroscience; Mathematics; Electroencephalography","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":[0.0001228073,0.0001928311,0.000202194,0.0003185415,0.0001691612,0.0008545935,0.0002560052,0.0003005281,0.003613611],"category_scores_gemma":[0.001683877,0.0002207579,0.0001887886,0.0002705235,0.0003378147,0.0008208613,0.0006254802,0.0004940752,0.0004996181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002537898,"about_ca_system_score_gemma":0.0002871069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001538877,"about_ca_topic_score_gemma":0.001729433,"domain_scores_codex":[0.9999418,0.000007630838,0.000002051299,0.00001473708,0.0000157557,0.00001803843],"domain_scores_gemma":[0.9997316,0.00008024071,0.00003727424,0.00005621575,0.0000452933,0.0000493234],"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":[0.0003955285,0.00004409766,0.003656724,0.0001389658,0.00002329159,0.0003939572,0.0003399078,0.004250353,0.8510658,0.0149027,0.002255502,0.1225331],"study_design_scores_gemma":[0.0001129077,0.0005805154,0.5073286,0.0001268926,0.0001133455,0.003666833,0.0008713476,0.1585672,0.2376491,0.07897054,0.01189389,0.0001188601],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8808071,0.0007430855,0.1014629,0.0003639609,0.0002004439,0.00004350856,0.0007559516,0.0006055594,0.01501744],"genre_scores_gemma":[0.986172,0.0001966764,0.01061336,0.00007623714,0.00002402507,0.00001285594,0.0002336452,0.0001414712,0.002529803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003613611,"threshold_uncertainty_score":0.01208878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08304940579911864,"score_gpt":0.3241437226740184,"score_spread":0.2410943168748998,"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."}}