{"id":"W2006836935","doi":"10.1016/j.neunet.2005.06.015","title":"Modelling divided visual attention with a winner-take-all network","year":2005,"lang":"en","type":"article","venue":"Neural Networks","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Attractor; Mechanism (biology); Unitary state; Cognitive psychology; Artificial neural network; Visual attention; Artificial intelligence; Working memory; Psychology; Cognition; Neuroscience","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001961208,0.0002733317,0.0002222343,0.0000558819,0.0003849473,0.0002208195,0.0002425351,0.0001446837,0.0001843588],"category_scores_gemma":[0.00001633042,0.0002229818,0.0000877607,0.0004518511,0.00008063205,0.000478923,0.00006221183,0.0004507557,0.00009687487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003865356,"about_ca_system_score_gemma":0.00001447093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006830414,"about_ca_topic_score_gemma":0.00001192865,"domain_scores_codex":[0.9978732,0.0001583178,0.0003005607,0.000594837,0.000396543,0.0006765056],"domain_scores_gemma":[0.9993182,0.00009109162,0.0001482918,0.0002125084,0.0000431731,0.0001866636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001279121,0.00006661974,0.00009610787,0.000006317957,0.000003713304,0.00001124082,0.00007086449,0.9715714,0.005233817,0.0004627337,0.0007954211,0.0215538],"study_design_scores_gemma":[0.0005343969,0.0002500855,0.00009977021,0.00004800134,0.00002248278,0.0000504817,0.00001322738,0.9951773,0.0006118041,0.0002160403,0.002674877,0.0003014983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5999565,0.0001009732,0.3962023,0.0007972643,0.0005898692,0.0003072788,0.000001493284,0.0005095057,0.001534835],"genre_scores_gemma":[0.9900441,0.00005119527,0.001564679,0.005830814,0.001607123,0.00002117038,0.00001217217,0.00005395383,0.0008147999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3946376,"threshold_uncertainty_score":0.9092936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06013840906220345,"score_gpt":0.3010529157499217,"score_spread":0.2409145066877182,"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."}}