{"id":"W2606375199","doi":"10.1007/s00426-017-0867-5","title":"Biasing spatial attention with semantic information: an event coding approach","year":2017,"lang":"en","type":"article","venue":"Psychological Research","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cognitive psychology; Event-related potential; Coding (social sciences); Event (particle physics); Computer science; Psychology; Complex event processing; Visual attention; Information processing; Cognition; Neuroscience","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":[0.002104181,0.0005741962,0.0005083353,0.001423507,0.0003274129,0.002235933,0.001538323,0.0008803973,0.00403774],"category_scores_gemma":[0.01162406,0.0004286579,0.0007817426,0.001317342,0.001464471,0.003103809,0.001362065,0.001161166,0.0002914151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006983372,"about_ca_system_score_gemma":0.0005461932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008715205,"about_ca_topic_score_gemma":0.0007700454,"domain_scores_codex":[0.9992877,0.0002365059,0.00003727831,0.0001679963,0.0001902961,0.00008036954],"domain_scores_gemma":[0.9956934,0.002938043,0.0003312363,0.0006479498,0.0002817687,0.0001076297],"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":[0.001236068,0.0004643339,0.01080894,0.0005303225,0.0003595857,0.000200065,0.0009387267,0.0154136,0.3553851,0.3500399,0.001918139,0.2627051],"study_design_scores_gemma":[0.0001717173,0.0002406564,0.035009,0.00007304511,0.0002807332,0.000463735,0.0002658406,0.2634685,0.04773343,0.6498112,0.002385614,0.00009647207],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1549177,0.0004512995,0.8239856,0.0009586254,0.0002105381,0.0001931533,0.0003577851,0.0003008719,0.01862459],"genre_scores_gemma":[0.8696123,0.0003879233,0.1275147,0.0003549911,0.0001855852,0.0001572962,0.0002093269,0.0001566725,0.001421248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00403774,"threshold_uncertainty_score":0.0135076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6465910749973138,"score_gpt":0.5618787143313899,"score_spread":0.08471236066592391,"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."}}