{"id":"W4318756262","doi":"10.3389/fnhum.2023.982849","title":"Motor imagery training to improve language processing: What are the arguments?","year":2023,"lang":"en","type":"review","venue":"Frontiers in Human Neuroscience","topic":"Action Observation and Synchronization","field":"Psychology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Agence Nationale de la Recherche","keywords":"Conceptualization; Motor imagery; Action (physics); Computer science; Psychology; Situated; Cognitive psychology; Cognitive science; Human–computer interaction; Neuroscience; Artificial intelligence; Brain–computer interface; Electroencephalography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005693795,0.0003086828,0.0006475786,0.0004846112,0.0003644908,0.00050182,0.0008972463,0.0001634021,0.00004984044],"category_scores_gemma":[0.0002786414,0.0002353361,0.0001432813,0.001506466,0.0001815983,0.0005040659,0.0001196835,0.0004996654,0.00004734745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001788012,"about_ca_system_score_gemma":0.0001221665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005939857,"about_ca_topic_score_gemma":0.00000467034,"domain_scores_codex":[0.9974818,0.0002756039,0.0005878994,0.0008407481,0.0003554334,0.0004585316],"domain_scores_gemma":[0.9987373,0.00005813453,0.0004998699,0.0005646465,0.00004039165,0.00009969073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002791806,0.00004366475,0.00008508165,0.000745438,0.000006677886,0.00005538821,0.003864388,0.00001185743,0.00001347908,0.00007385296,0.01002683,0.9850705],"study_design_scores_gemma":[0.0001468806,0.00005715635,0.0004814779,0.002624011,0.00004953316,0.00001122991,0.009479463,0.0003337253,7.614761e-7,0.00003796801,0.9864028,0.0003750077],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001122364,0.9685486,0.01296364,0.0002575954,0.01515722,0.002035889,0.00003787069,0.0002617256,0.0006252538],"genre_scores_gemma":[0.0005664018,0.9472713,0.0003549395,0.003169537,0.0007387036,0.001316285,0.00005843035,0.0001967296,0.04632761],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9846956,"threshold_uncertainty_score":0.9596728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1315207781292926,"score_gpt":0.399745220654196,"score_spread":0.2682244425249034,"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."}}