{"id":"W1786853006","doi":"10.1037/xhp0000139","title":"The role of spatial frequency in expert object recognition.","year":2015,"lang":"en","type":"article","venue":"Journal of Experimental Psychology Human Perception & Performance","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Army Research Institute for the Behavioral and Social Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Sparrow; Cognitive neuroscience of visual object recognition; Object (grammar); Identification (biology); Artificial intelligence; Range (aeronautics); Finch; Perception; Computer science; Pattern recognition (psychology); Psychology; Communication; Computer vision; Ecology; Biology","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":[0.001069243,0.0002876327,0.0001899576,0.0006698399,0.0001556191,0.0006594087,0.0003673944,0.0005249983,0.001879418],"category_scores_gemma":[0.008464132,0.0002140589,0.0001747543,0.0002258447,0.0004512439,0.001455949,0.0006018531,0.0002702067,0.0003716987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002432758,"about_ca_system_score_gemma":0.0002357567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001935664,"about_ca_topic_score_gemma":0.00182387,"domain_scores_codex":[0.9994712,0.00008788311,0.00003968814,0.0001551977,0.0001984468,0.00004746828],"domain_scores_gemma":[0.9940163,0.003463376,0.001013152,0.0005193202,0.0005215225,0.0004663879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001391715,0.0003650243,0.265444,0.0006041875,0.0001243957,0.0004016467,0.001086942,0.003168625,0.3069713,0.001801756,0.0007984834,0.4178419],"study_design_scores_gemma":[0.00002070432,0.0008113907,0.9658279,0.00007130401,0.00004033525,0.001178741,0.0003189918,0.0107754,0.01580912,0.003591166,0.001511251,0.00004359891],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788739,0.001359024,0.0122878,0.0001379844,0.00002945172,0.0000231373,0.0000718612,0.00004656077,0.007170314],"genre_scores_gemma":[0.993461,0.0003678679,0.005129842,0.000076007,0.00002194331,0.00001061711,0.00006350386,0.00001162365,0.0008575741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001935664,"threshold_uncertainty_score":0.006287277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09168778753016951,"score_gpt":0.3732074293052206,"score_spread":0.2815196417750511,"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."}}