{"id":"W1991313482","doi":"10.3758/s13414-014-0686-3","title":"Setting semantics: conceptual set can determine the physical properties that capture attention","year":2014,"lang":"en","type":"article","venue":"Attention Perception & Psychophysics","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Embodied cognition; Perception; Cognitive psychology; Set (abstract data type); Stimulus (psychology); Meaning (existential); Psychology; Semantics (computer science); Computer science; Artificial intelligence","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":[0.001918041,0.000823281,0.0008897429,0.001618066,0.001136382,0.006121681,0.001872326,0.00171557,0.01065453],"category_scores_gemma":[0.01834777,0.0008450159,0.001665474,0.001011567,0.003342855,0.01738241,0.002926264,0.002531959,0.001082913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001129572,"about_ca_system_score_gemma":0.0007607569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008551495,"about_ca_topic_score_gemma":0.0005831353,"domain_scores_codex":[0.9982887,0.0004142075,0.0001161076,0.0006198733,0.0003867014,0.0001744235],"domain_scores_gemma":[0.9948291,0.002289613,0.0005334674,0.001081275,0.000700218,0.0005663679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000594229,0.0002395765,0.006097272,0.0004481486,0.000202557,0.0003889204,0.002130452,0.007368674,0.06057322,0.8575733,0.00218415,0.06219952],"study_design_scores_gemma":[0.00005303925,0.0001165642,0.005569017,0.00004912567,0.00008189443,0.0002696946,0.0004246533,0.03646547,0.006528023,0.9481512,0.002221223,0.00007027115],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2698833,0.0005770864,0.6871006,0.001577121,0.000362362,0.0002131535,0.0007951403,0.001411226,0.03807996],"genre_scores_gemma":[0.9553438,0.0001078458,0.04227925,0.0002289238,0.00005952014,0.0001057326,0.0003817856,0.0003649571,0.001128083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01065453,"threshold_uncertainty_score":0.03564298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07215331773472648,"score_gpt":0.3105499288464701,"score_spread":0.2383966111117437,"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."}}