{"id":"W2767038265","doi":"10.1017/s1366728917000566","title":"Individual differences predict ERP signatures of second language learning of novel grammatical rules","year":2017,"lang":"en","type":"article","venue":"Bilingualism Language and Cognition","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"P600; Grammatical gender; Psychology; Linguistics; Agreement; Similarity (geometry); Computer science; First language; Event-related potential; Natural language processing; Artificial intelligence; Cognition; Noun","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":[0.0004561952,0.0001919487,0.0002047592,0.0001856671,0.00008526524,0.0003758416,0.000110769,0.0002903933,0.001691484],"category_scores_gemma":[0.002843133,0.0001496631,0.0001035435,0.0001108485,0.0002866684,0.000268575,0.0002645974,0.0003732069,0.000225345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001140074,"about_ca_system_score_gemma":0.0001156794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008287663,"about_ca_topic_score_gemma":0.001477438,"domain_scores_codex":[0.999885,0.00002254916,0.000006359677,0.00004874578,0.00002024323,0.00001715139],"domain_scores_gemma":[0.9986024,0.0007830382,0.0003084166,0.00008215077,0.0001130241,0.0001109689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0009916107,0.000280026,0.3170868,0.00009274611,0.00009072802,0.0005354115,0.002440985,0.0003970123,0.6554207,0.0003403245,0.0002156652,0.02210811],"study_design_scores_gemma":[0.00001595542,0.0002324841,0.9914101,0.000003089297,0.00001450958,0.0003928687,0.0002388465,0.0006384575,0.00658583,0.0003124029,0.0001471509,0.000008401561],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992664,0.00001228444,0.0002413717,0.00001599437,0.000001372773,0.000003564031,0.0000433772,0.000005436407,0.0004101452],"genre_scores_gemma":[0.9992417,0.00001612532,0.0003216189,0.00001991354,0.000002457181,0.000007427414,0.00006340599,0.000009489431,0.0003178655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001691484,"threshold_uncertainty_score":0.005658627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03539020957374098,"score_gpt":0.2972510095637834,"score_spread":0.2618607999900424,"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."}}