{"id":"W1990098832","doi":"10.3389/fnbeh.2011.00091","title":"Neural basis of feature-based contextual effects on visual search behavior","year":2012,"lang":"en","type":"article","venue":"Frontiers in Behavioral Neuroscience","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Ontario Ministry of Research and Innovation; Canadian Institutes of Health Research","keywords":"Feature (linguistics); Basis (linear algebra); Visual search; Computer science; Artificial intelligence; Pattern recognition (psychology); Cognitive psychology; Psychology; Neuroscience; Mathematics","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":[0.00009151599,0.0001285918,0.0001503903,0.0001365177,0.0001014064,0.0001686758,0.0001150453,0.0001673419,0.0004289279],"category_scores_gemma":[0.0006125409,0.0001173135,0.0001262387,0.00006551178,0.0002897934,0.0001579686,0.0003033133,0.0002021913,0.00004169468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000216631,"about_ca_system_score_gemma":0.0001472827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001123779,"about_ca_topic_score_gemma":0.002569742,"domain_scores_codex":[0.9999415,0.000007559634,0.000003317209,0.00001837079,0.0000124511,0.00001686029],"domain_scores_gemma":[0.9997969,0.00005254088,0.00005725302,0.00002836477,0.00002496888,0.0000400747],"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":[0.0001527887,0.00001284971,0.008570988,0.00002418148,0.00001080177,0.00009371307,0.00007145118,0.0003335169,0.9844087,0.000144987,0.00002759745,0.006148539],"study_design_scores_gemma":[0.00001458194,0.0003037135,0.896381,0.00001275286,0.00003530185,0.0002978408,0.0001016067,0.006751915,0.09498101,0.0007246425,0.000380514,0.00001505671],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986812,0.00009179834,0.0008562558,0.00001779169,0.000001686183,0.000002812492,0.00002297087,0.0000128225,0.0003126898],"genre_scores_gemma":[0.9991959,0.00005399671,0.0005822843,0.00001036071,0.000002135049,0.000005989396,0.00003055925,0.000004585779,0.0001142592],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001123779,"threshold_uncertainty_score":0.002234519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1147354079133851,"score_gpt":0.3959733973841346,"score_spread":0.2812379894707495,"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."}}