{"id":"W3132305315","doi":"10.1016/j.cognition.2021.104632","title":"Multiple-image arrays in face matching tasks with and without memory","year":2021,"lang":"en","type":"article","venue":"Cognition","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph-Humber","funders":"","keywords":"Matching (statistics); Face (sociological concept); Abstraction; Artificial intelligence; Psychology; Perception; Facial recognition system; Image (mathematics); Representation (politics); Computer science; Computer vision; Face perception; Pattern recognition (psychology); Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003105799,0.0009921291,0.001590413,0.00102856,0.0004532665,0.002680558,0.001886042,0.002000771,0.01081579],"category_scores_gemma":[0.03151321,0.0006847173,0.000605899,0.0005310719,0.0009072493,0.00614448,0.001654367,0.00156686,0.001864185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000637173,"about_ca_system_score_gemma":0.0007385883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001689834,"about_ca_topic_score_gemma":0.001349921,"domain_scores_codex":[0.9980828,0.0003551567,0.0002224244,0.0005713241,0.0005711086,0.0001972718],"domain_scores_gemma":[0.9872571,0.008953024,0.001010695,0.001922717,0.000358642,0.0004978009],"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":[0.1056139,0.01563757,0.04041498,0.001505837,0.0008486899,0.0006961046,0.003511903,0.007615502,0.5727891,0.007569003,0.004419175,0.2393782],"study_design_scores_gemma":[0.007724337,0.02075014,0.6846766,0.0004083618,0.001407525,0.003471566,0.001988022,0.07887729,0.116122,0.07239316,0.01147191,0.0007091039],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879712,0.0004604368,0.003437272,0.0001172449,0.0002731182,0.0001744146,0.0004318543,0.000112124,0.00702227],"genre_scores_gemma":[0.9869075,0.0002637081,0.003318895,0.0003561284,0.0001435859,0.0003343887,0.0007531513,0.0002164753,0.007706204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01081579,"threshold_uncertainty_score":0.03618246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04168066043726373,"score_gpt":0.2855568163792802,"score_spread":0.2438761559420164,"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."}}