{"id":"W4399274489","doi":"10.1101/2024.05.27.596127","title":"The Impact of Scene Context on Visual Object Recognition: Comparing Humans, Monkeys, and Computational Models","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Canadian Institutes of Health Research; Simons Foundation Autism Research Initiative; National Institutes of Health; Fondation Bertarelli","keywords":"Context (archaeology); Computer science; Artificial intelligence; Object (grammar); Cognitive neuroscience of visual object recognition; Computer vision; Computational model; Pattern recognition (psychology); Geography","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":[0.0008683693,0.0002617912,0.0004057186,0.000518258,0.0002087342,0.001211313,0.000345485,0.0003926206,0.0007254225],"category_scores_gemma":[0.003325521,0.0002393429,0.000368316,0.0002491446,0.001106651,0.001186321,0.0006349467,0.0004274778,0.0001849725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003645124,"about_ca_system_score_gemma":0.0002628165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002809349,"about_ca_topic_score_gemma":0.002514944,"domain_scores_codex":[0.9997001,0.0001099389,0.000009825818,0.0001207187,0.00003837681,0.00002118332],"domain_scores_gemma":[0.9992965,0.0003739563,0.00009239944,0.0001469783,0.00004249407,0.00004756792],"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":[0.001422723,0.0001916821,0.1985606,0.0009500712,0.001355471,0.0007260784,0.004070517,0.2530594,0.2211685,0.06073109,0.002433052,0.2553307],"study_design_scores_gemma":[0.00004348858,0.0004925064,0.2570095,0.0001227816,0.0002772655,0.0006262767,0.0009186732,0.5732993,0.02097449,0.1417104,0.004419037,0.0001063435],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9686752,0.002643956,0.02194098,0.0009685475,0.00002540687,0.000006794878,0.0001294302,0.0001322075,0.005477405],"genre_scores_gemma":[0.9957553,0.00057917,0.003268692,0.00008960125,0.0000154494,0.000007903132,0.00006776577,0.00004327548,0.0001728294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002809349,"threshold_uncertainty_score":0.005585968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06602139107335701,"score_gpt":0.2999993982740257,"score_spread":0.2339780072006687,"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."}}