{"id":"W4292997377","doi":"10.1177/00238309221111752","title":"Computational Modeling of an Auditory Lexical Decision Experiment Using DIANA","year":2022,"lang":"en","type":"article","venue":"Language and Speech","topic":"Phonetics and Phonology Research","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Lexical decision task; Pseudoword; Lexicon; Computer science; Speech recognition; Word recognition; Latency (audio); Word (group theory); Natural language processing; Artificial intelligence; Psychology; Linguistics; Cognition","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":[0.0007658281,0.0003100673,0.000443123,0.0003031545,0.000408936,0.0008586924,0.001612912,0.0007058191,0.004083233],"category_scores_gemma":[0.002403806,0.0003809115,0.0004481289,0.0002206932,0.0005868918,0.0008192151,0.0007533025,0.0007570909,0.0002921016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001010935,"about_ca_system_score_gemma":0.0008926624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005710992,"about_ca_topic_score_gemma":0.007002386,"domain_scores_codex":[0.9998391,0.0000587455,0.000009048988,0.00004470337,0.00002832882,0.00001999805],"domain_scores_gemma":[0.9982811,0.001410288,0.00006514307,0.00009271094,0.00008589451,0.00006491578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005050997,0.0001842432,0.003355675,0.00009546123,0.00006276527,0.0002832707,0.0003427301,0.9262707,0.01317511,0.04221544,0.001034728,0.01247479],"study_design_scores_gemma":[0.00001466763,0.00002080624,0.0001465093,0.000001203613,0.000003871832,0.00001264864,0.000007682645,0.995934,0.0006518184,0.003045606,0.0001549954,0.000006106101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5753756,0.00008503255,0.4068121,0.0009659753,0.0001053115,0.000244186,0.0004885037,0.001394887,0.01452842],"genre_scores_gemma":[0.9246519,0.00002754692,0.07273051,0.0001229799,0.000009586523,0.0002389134,0.0001005058,0.00005579993,0.002062349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005710992,"threshold_uncertainty_score":0.01365983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0460913719733166,"score_gpt":0.3910215542915912,"score_spread":0.3449301823182747,"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."}}