{"id":"W2983774488","doi":"10.1101/578633","title":"Constrained sampling from deep generative image models reveals mechanisms of human target detection","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; York University","keywords":"Artificial intelligence; Computer science; Normalization (sociology); Pattern recognition (psychology); Convolutional neural network; Discriminative model; Generative model; Deep learning; Filter bank; Human visual system model; Computer vision; Generative grammar; Image (mathematics); Filter (signal processing)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007372954,0.0005382434,0.0007271436,0.0003929396,0.0002557359,0.0003546554,0.001029806,0.0005351916,0.00003990277],"category_scores_gemma":[0.00008152483,0.0005976177,0.0002716916,0.0005130917,0.0001003104,0.0006564708,0.0006837695,0.000653393,0.0000441783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002381175,"about_ca_system_score_gemma":0.0002254302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001146381,"about_ca_topic_score_gemma":0.000005245175,"domain_scores_codex":[0.9964642,0.0003023833,0.0008643654,0.001337624,0.0005749207,0.0004565168],"domain_scores_gemma":[0.9966111,0.00006553886,0.000895876,0.001450656,0.0007852591,0.0001915354],"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":[0.000007872423,0.00008131375,0.0000425579,0.000100587,0.00009555111,0.000005482746,0.00003679697,0.001948154,0.9846866,0.01298304,0.000005903071,0.000006174781],"study_design_scores_gemma":[0.0004208382,0.0001201656,0.002047931,0.0001852251,0.00004541265,1.769485e-8,0.000006842902,0.1723996,0.8213599,0.002803382,0.000009181323,0.0006014403],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2378487,0.0001026446,0.7594649,0.00003820369,0.001451074,0.00057804,0.0001140629,0.0003872176,0.00001510804],"genre_scores_gemma":[0.830315,0.00001706922,0.1692641,0.00009991227,0.0001601697,0.00008389426,8.54852e-7,0.00005540674,0.000003579867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5924663,"threshold_uncertainty_score":0.9996475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02777242487097663,"score_gpt":0.2546581065955379,"score_spread":0.2268856817245613,"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."}}