{"id":"W2078672439","doi":"10.1167/14.2.10","title":"Feature integration within and across visual streams occurs at different visual processing stages","year":2014,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visual processing; Computer vision; Feature (linguistics); Artificial intelligence; Segmentation; Computer science; Illusion; Object (grammar); Dorsum; Perception; Optical illusion; Process (computing); Motion perception; Motion (physics); Pattern recognition (psychology); Communication; Neuroscience; Biology; Psychology; Anatomy","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":[0.0005102506,0.0001874992,0.0002545875,0.0001046322,0.0004045054,0.0003367124,0.0001357739,0.0001263939,0.00002950129],"category_scores_gemma":[0.0003778024,0.0001223692,0.00006638664,0.0001513888,0.00009208801,0.0005577806,0.00008074461,0.0003753669,0.000007916474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007202904,"about_ca_system_score_gemma":0.00002300451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.706992e-7,"about_ca_topic_score_gemma":0.000008108269,"domain_scores_codex":[0.9984559,0.0002004698,0.0003326459,0.0002474615,0.0005540912,0.0002094013],"domain_scores_gemma":[0.9990036,0.00009285167,0.0005432178,0.00006938838,0.0001246293,0.0001662791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002044511,0.000120545,0.000398906,0.0000386152,0.000001520666,0.000005278435,0.001744542,0.00001611712,0.8339564,0.00003644873,0.0001942554,0.1632829],"study_design_scores_gemma":[0.001988801,0.004421085,0.02258163,0.0009848772,0.00002985472,0.0004279405,0.001879666,0.0539972,0.9114782,0.0007503618,0.001043443,0.0004168872],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918799,0.00006123492,0.007016954,0.0005110141,0.0003915853,0.00006787485,0.00000286465,0.00002954711,0.00003904546],"genre_scores_gemma":[0.9983603,0.00005424094,0.0003526175,0.0004626064,0.0002057361,8.048973e-7,0.000002040646,0.00001912498,0.0005425495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1628661,"threshold_uncertainty_score":0.4990069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02850561800779945,"score_gpt":0.3733444528321291,"score_spread":0.3448388348243297,"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."}}