{"id":"W3042157903","doi":"10.3389/fpsyg.2020.01594","title":"Modeling the Mental Lexicon as Part of Long-Term and Working Memory and Simulating Lexical Access in a Naming Task Including Semantic and Phonological Cues","year":2020,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"RWTH Aachen University","keywords":"Mental lexicon; Lexicon; Psychology; Task (project management); Cognitive psychology; Semantic memory; Computer science; Natural language processing; Cognition","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":[0.0003509368,0.0003654897,0.0002698162,0.0002924125,0.0002230894,0.0004582198,0.0008307814,0.0009936477,0.001724577],"category_scores_gemma":[0.001331175,0.0001895451,0.0005939875,0.0002354494,0.0004929747,0.0008563306,0.0003770753,0.000455127,0.0001240766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006742133,"about_ca_system_score_gemma":0.0007256424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00680757,"about_ca_topic_score_gemma":0.004261374,"domain_scores_codex":[0.9999239,0.00003201653,0.000004878826,0.00001241834,0.00001330007,0.00001337277],"domain_scores_gemma":[0.9994847,0.0003484371,0.00004853709,0.0000318491,0.00005878042,0.00002784623],"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":[0.00008908399,0.00008296163,0.001496973,0.00004631924,0.0000211943,0.0001383008,0.00008768004,0.9842709,0.00789795,0.00340991,0.00007546679,0.002383349],"study_design_scores_gemma":[0.00001068015,0.00004231613,0.0003891701,0.000002429882,0.00000834922,0.00001738632,0.00001106195,0.9973922,0.0009631088,0.00109092,0.0000688099,0.000003687242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8908379,0.0000802162,0.1037213,0.0001701713,0.00002522707,0.00008189378,0.0001645496,0.0001552372,0.00476338],"genre_scores_gemma":[0.9845834,0.00005096244,0.01415776,0.0000159973,0.00000299134,0.00009733073,0.00006968144,0.00001094162,0.001011002],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00680757,"threshold_uncertainty_score":0.01353586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.102898320586125,"score_gpt":0.3749028701215956,"score_spread":0.2720045495354706,"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."}}