{"id":"W2548281989","doi":"","title":"Goal-directed attention in late visual processing: On the scope and flexibility of feature-based attention","year":2015,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flexibility (engineering); Scope (computer science); Visual attention; Feature (linguistics); Computer science; Cognitive psychology; Psychology; Data science; Neuroscience; Perception; Economics; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00100391,0.0001901882,0.0002894143,0.0002786089,0.0002451152,0.00004487332,0.0006159547,0.0002064506,0.00001161162],"category_scores_gemma":[0.00006184435,0.0001490199,0.0001449912,0.0008867825,0.0001371316,0.0003017474,0.0000783408,0.000336857,0.00001027687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009094794,"about_ca_system_score_gemma":0.000162552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002952907,"about_ca_topic_score_gemma":0.0008742028,"domain_scores_codex":[0.9983443,0.0003553858,0.0002185491,0.0004031712,0.0005091124,0.000169481],"domain_scores_gemma":[0.998525,0.00007465723,0.0005369894,0.0003821167,0.0004318228,0.00004945388],"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":[0.00616963,0.002691495,0.004754931,0.002938599,0.0003397586,0.00003767834,0.01689065,0.002391936,0.7203589,0.006025116,0.00447588,0.2329254],"study_design_scores_gemma":[0.001548939,0.000585432,0.8651531,0.000605158,0.0001219194,0.000001887804,0.002369977,0.1257787,0.001483652,0.001789987,0.000216419,0.0003448273],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947262,0.0002246082,0.002390838,0.0009384551,0.0002726335,0.0004658812,0.000008479459,0.00008711898,0.0008857265],"genre_scores_gemma":[0.997587,0.0000412019,0.0002360049,0.00002483076,0.00001635984,0.000001054832,0.00008441017,0.000009063641,0.002000043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8603982,"threshold_uncertainty_score":0.6076856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02278740708102361,"score_gpt":0.2741477052101938,"score_spread":0.2513602981291702,"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."}}