{"id":"W2030648978","doi":"10.1037/h0087326","title":"Multiple object tracking and attentional processing.","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":184,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Visual processing; Psychology; Visual search; Visual field; Set (abstract data type); Object (grammar); N2pc; Perception; Visual perception; Eye movement; Computer vision; Cognitive psychology; Search engine indexing; Zoom; Process (computing); Eye tracking; Artificial intelligence; Communication; Computer science; Neuroscience; Lens (geology)","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":[0.001816423,0.0003322797,0.0003945666,0.0008641173,0.0003403886,0.001563721,0.0006576853,0.0008716663,0.003381703],"category_scores_gemma":[0.00909492,0.0002891683,0.0003526715,0.0009523483,0.001210052,0.003437083,0.0008887499,0.0006116061,0.0004635268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00123251,"about_ca_system_score_gemma":0.0006847056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005050127,"about_ca_topic_score_gemma":0.003340006,"domain_scores_codex":[0.9992256,0.0001321506,0.0000387535,0.0002375609,0.0002844133,0.00008151855],"domain_scores_gemma":[0.9970497,0.00154717,0.0007569079,0.0002111644,0.0002887323,0.0001463899],"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":[0.001287388,0.0002892942,0.04981841,0.001946698,0.0002361869,0.0009104169,0.002301886,0.008334327,0.2410793,0.1771334,0.002873978,0.5137888],"study_design_scores_gemma":[0.0001855752,0.0009749822,0.5874932,0.0005086994,0.0002599315,0.00259976,0.0009645238,0.04560373,0.04532054,0.2942643,0.02159088,0.0002338972],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6715178,0.06385721,0.1766395,0.003852522,0.0006830869,0.0002094959,0.000465652,0.000430889,0.08234373],"genre_scores_gemma":[0.9667873,0.007211485,0.01933726,0.000446012,0.0001380583,0.0001048008,0.0001553699,0.00004957074,0.005770223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005050127,"threshold_uncertainty_score":0.0113129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1306623666968856,"score_gpt":0.363682530363108,"score_spread":0.2330201636662224,"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."}}