{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005766077,0.0004171461,0.0006171548,0.0006180403,0.0002828077,0.001337914,0.0003958443,0.0005214198,0.002304189],"category_scores_gemma":[0.002853964,0.0004241543,0.0006757292,0.0003942972,0.000454747,0.00191962,0.0007431832,0.0008920085,0.0002960644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005356595,"about_ca_system_score_gemma":0.000431871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00128801,"about_ca_topic_score_gemma":0.001292289,"domain_scores_codex":[0.9994696,0.00004421916,0.00002940114,0.0001222715,0.0002185887,0.0001158871],"domain_scores_gemma":[0.9989035,0.0003821341,0.0001890388,0.0001022209,0.0002438371,0.0001791125],"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":[0.0007390982,0.00008672826,0.004171083,0.00007400511,0.00003467914,0.00007841914,0.0001620677,0.000567052,0.9622732,0.001187512,0.0001430144,0.03048314],"study_design_scores_gemma":[0.0001769358,0.001826193,0.4213982,0.00006231453,0.0002263901,0.0006238307,0.0004974532,0.03881645,0.5259184,0.008213435,0.002115657,0.0001246764],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814426,0.0002683151,0.01539178,0.00006902387,0.00003452653,0.00003623447,0.00006046099,0.0001457222,0.002551223],"genre_scores_gemma":[0.9848985,0.0002079045,0.01303359,0.00005523936,0.00002076104,0.00004734435,0.0001845417,0.00008831939,0.001463734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002304189,"threshold_uncertainty_score":0.007708311,"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."}}