{"id":"W1972061242","doi":"10.1142/s0218001408006648","title":"VISUAL FEATURE BINDING WITHIN THE SELECTIVE TUNING ATTENTION FRAMEWORK","year":2008,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Innovation Trust","keywords":"Percept; Computer science; Feature (linguistics); Artificial intelligence; Visual processing; Hierarchy; Information processing; Pattern recognition (psychology); Machine learning; Human–computer interaction; Neuroscience; Perception; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002926331,0.0001124217,0.0001109959,0.0001784355,0.0002675309,0.0001418975,0.0002140989,0.00006533654,0.00006407945],"category_scores_gemma":[0.0006635236,0.00008066301,0.0000918866,0.000188523,0.0001440121,0.0003482759,0.0000442446,0.0004819507,0.00005640732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004410361,"about_ca_system_score_gemma":0.0000302183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008976393,"about_ca_topic_score_gemma":0.000008104532,"domain_scores_codex":[0.9987138,0.0001339669,0.0003930097,0.0001824038,0.0004562082,0.0001206344],"domain_scores_gemma":[0.9987445,0.0003776686,0.000447827,0.000045756,0.0003252828,0.00005894257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003678151,0.0002459648,0.003587325,0.0000084536,0.00007121096,0.0003608147,0.002099947,0.0002346202,0.4776647,0.00157181,0.0001523127,0.513635],"study_design_scores_gemma":[0.00026958,0.0009211443,0.006865565,0.0007911829,0.00006837894,0.007107583,0.002708492,0.0576028,0.8217981,0.1009203,0.0003184432,0.0006283105],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9039682,0.00001411833,0.09150224,0.002279293,0.00204684,0.00007689892,0.00001500889,0.00001424957,0.00008318072],"genre_scores_gemma":[0.9978017,0.0001484143,0.0002377655,0.001123801,0.0006171414,0.000002616825,0.000005305413,0.00001014924,0.00005314521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5130067,"threshold_uncertainty_score":0.3289342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09758338814600687,"score_gpt":0.3274455576038289,"score_spread":0.229862169457822,"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."}}