{"id":"W4410632802","doi":"10.1088/2634-4386/addc90","title":"Wandering around: a bioinspired approach to visual attention through object motion sensitivity","year":2025,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sensitivity (control systems); Computer vision; Object (grammar); Motion (physics); Visual attention; Artificial intelligence; Computer science; Cognitive psychology; Psychology; Engineering; Neuroscience; Perception","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":[0.0003332426,0.0001750405,0.0001823813,0.0002158328,0.0002386262,0.0002329363,0.0001195783,0.00005862527,1.915434e-7],"category_scores_gemma":[0.00006102669,0.0001912587,0.0000603401,0.0007313045,0.0000132224,0.0002536359,0.0002227333,0.0001885674,0.000004534109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004259604,"about_ca_system_score_gemma":0.00001307121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003794881,"about_ca_topic_score_gemma":0.000001386244,"domain_scores_codex":[0.9987742,0.00006625601,0.000225615,0.0004929648,0.0001660914,0.0002748333],"domain_scores_gemma":[0.9995889,0.00006146642,0.00004132799,0.0001943553,0.00004560275,0.00006833624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003113032,0.000516849,0.004633519,0.0008594724,0.0001062649,0.00006110842,0.002137744,0.3163977,0.4456507,0.03149314,0.0001188276,0.1979936],"study_design_scores_gemma":[0.0002447748,0.00005914196,0.02809632,0.00009370991,0.000007490651,0.00007124429,0.00003197111,0.9697776,0.0012536,0.00005045333,0.0001396874,0.00017394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4526359,0.00001470303,0.546082,0.0001135401,0.0004822305,0.00009190474,2.110897e-7,0.0003466325,0.0002329826],"genre_scores_gemma":[0.9870392,0.000004696325,0.01265471,0.0001875642,0.00006930708,0.000004710831,0.000002392951,0.00001024364,0.00002717748],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.65338,"threshold_uncertainty_score":0.7799305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0203339267509768,"score_gpt":0.2494724945119773,"score_spread":0.2291385677610005,"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."}}