{"id":"W4402905719","doi":"10.1167/jov.24.10.1280","title":"Decoding Contextual Effects in Vision: A Cross-Species Behavioral Approach","year":2024,"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":"York University","funders":"","keywords":"Decoding methods; Psychology; Cognitive psychology; Computer science; Cognitive science; Communication; Algorithm","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":[0.001155602,0.0004313345,0.0003915897,0.0007207536,0.0003802086,0.0007165622,0.0004894732,0.0006374527,0.001816529],"category_scores_gemma":[0.00403634,0.0003062655,0.0003074759,0.0002466612,0.0007882756,0.000866163,0.001282828,0.0006183992,0.0002124157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003859659,"about_ca_system_score_gemma":0.0002393838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002915537,"about_ca_topic_score_gemma":0.004264003,"domain_scores_codex":[0.9993395,0.0001777535,0.00002969282,0.0002899969,0.0001094942,0.00005354845],"domain_scores_gemma":[0.9988192,0.0003188284,0.0002311878,0.0002749204,0.0002259403,0.0001298176],"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":[0.0005080156,0.0006854506,0.08331308,0.0003521551,0.000328119,0.0001426125,0.00281509,0.002132402,0.830519,0.003167657,0.0005246795,0.07551183],"study_design_scores_gemma":[0.00002052931,0.001870916,0.8932661,0.00008122194,0.0002824023,0.000761734,0.001583279,0.01967686,0.07148459,0.006357134,0.004494392,0.0001207661],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9800273,0.0004649383,0.01566449,0.0001067399,0.00002691094,0.00005534624,0.0001417414,0.0001032107,0.003409448],"genre_scores_gemma":[0.9841008,0.0002215878,0.01436439,0.0001524695,0.00001563118,0.00007381039,0.0001863218,0.00005193557,0.0008332172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002915537,"threshold_uncertainty_score":0.006111443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08423840440954068,"score_gpt":0.4190029280580415,"score_spread":0.3347645236485008,"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."}}