{"id":"W2162517199","doi":"10.1167/10.13.11","title":"Dynamic visual information facilitates object recognition from novel viewpoints","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer vision; Observer (physics); Computer science; Artificial intelligence; Object (grammar); Viewpoints; Movement (music); Sensory cue; Motion (physics); Cognitive neuroscience of visual object recognition; Communication; Cognitive psychology; Psychology","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003810336,0.0001072928,0.0001507781,0.0002441972,0.000103406,0.0001122939,0.00011697,0.0001113977,0.001115965],"category_scores_gemma":[0.0009366442,0.00008701517,0.0001146345,0.0001640122,0.00004632465,0.001911316,0.00002283834,0.0004547504,0.001062277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003771126,"about_ca_system_score_gemma":0.00004111128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001127335,"about_ca_topic_score_gemma":0.00001560242,"domain_scores_codex":[0.9987838,0.00006883466,0.0005088378,0.0001050789,0.0004058774,0.0001275719],"domain_scores_gemma":[0.9990123,0.0002023635,0.0004042533,0.00007953672,0.0002018978,0.00009967128],"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":[0.00008574896,0.00008437211,0.0000305674,0.000006786483,0.000001906293,0.000001367993,0.0003456253,0.000005592314,0.8321781,0.000002355365,0.0001520484,0.1671055],"study_design_scores_gemma":[0.009424916,0.003422485,0.2623084,0.0009986806,0.0001352923,0.001095731,0.002230656,0.05919344,0.6031494,0.01014847,0.04665827,0.001234254],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942258,0.000006830046,0.003406452,0.0003842663,0.00126391,0.00009916985,0.00008533656,0.00002463674,0.0005036009],"genre_scores_gemma":[0.996741,0.0001669613,0.002456816,0.0004788885,0.00008457604,0.000001306568,0.00003812364,0.000006936795,0.00002538964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2622779,"threshold_uncertainty_score":0.9997972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02806036325996368,"score_gpt":0.3207234276790343,"score_spread":0.2926630644190706,"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."}}