{"id":"W4413312139","doi":"10.1073/pnas.2513768122","title":"Efficient neural encoding as revealed by bilingualism","year":2025,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Neuroscience and Music Perception","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; McGill University; Centre for Research on Brain Language and Music","funders":"Fonds de recherche du Québec; Centre for Research on Brain, Language and Music","keywords":"Computer science; Neuroscience of multilingualism; Encoding (memory); Cognition; Artificial neural network; Artificial intelligence; Natural language processing; Cognitive science; Psychology; Neuroscience","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":[0.0002576731,0.0001609158,0.0001859211,0.0001656589,0.000130316,0.0005116333,0.000151856,0.0002487475,0.001010392],"category_scores_gemma":[0.001437705,0.000186381,0.0001625142,0.0001096023,0.0005179521,0.000560653,0.0004424097,0.0003368484,0.0001213603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003546652,"about_ca_system_score_gemma":0.0003703665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002363179,"about_ca_topic_score_gemma":0.003831392,"domain_scores_codex":[0.9999069,0.00002859185,0.000005086848,0.0000244203,0.00001649425,0.00001845263],"domain_scores_gemma":[0.9997441,0.00009691024,0.00004792571,0.00005441437,0.0000272169,0.00002944794],"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":[0.0007924295,0.00008713271,0.05918688,0.0002153921,0.0001454391,0.001981126,0.002050009,0.1332789,0.6504517,0.08137764,0.0008161116,0.06961729],"study_design_scores_gemma":[0.00006656344,0.000256467,0.1208936,0.00003036985,0.00007447346,0.001483162,0.0004583749,0.6605912,0.06163191,0.1517867,0.002645661,0.00008143197],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9761297,0.0000887101,0.0180032,0.0001849258,0.00000810608,0.00000427648,0.00007584681,0.0001279207,0.005377211],"genre_scores_gemma":[0.9983669,0.00003055306,0.001341637,0.000006629953,0.000001392326,0.000001920103,0.00002122714,0.00001036164,0.0002193513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002363179,"threshold_uncertainty_score":0.004698873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05445009908516051,"score_gpt":0.3494142429953549,"score_spread":0.2949641439101944,"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."}}