{"id":"W2078962922","doi":"10.1162/jocn_a_00565","title":"Different Neural Networks Are Involved in Audiovisual Speech Perception Depending on the Context","year":2014,"lang":"en","type":"article","venue":"Journal of Cognitive Neuroscience","topic":"Multisensory perception and integration","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"Centre National de la Recherche Scientifique","keywords":"Perception; Psychology; Neurocomputational speech processing; Percept; Speech perception; Context (archaeology); Syllable; Speech recognition; Motor theory of speech perception; Sensory system; Speech production; Cognitive psychology; Communication; Computer science; Neuroscience","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":[0.0006400574,0.0001432878,0.0002063446,0.0001948251,0.0001415669,0.00008196454,0.0002198595,0.00006591142,0.0005674378],"category_scores_gemma":[0.001062197,0.00008809286,0.0001065669,0.0002025245,0.0001803969,0.0001944033,0.00002167939,0.0006382392,0.00003900158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005710338,"about_ca_system_score_gemma":0.0000089011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008928396,"about_ca_topic_score_gemma":0.00008072804,"domain_scores_codex":[0.998022,0.0007718832,0.0003921219,0.000222441,0.0003579409,0.0002336415],"domain_scores_gemma":[0.9986003,0.0006229253,0.0004069023,0.0001053235,0.0001782727,0.00008627115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002681638,0.001983355,0.351144,0.00001289105,0.00002528813,0.0002522208,0.01161588,0.001066493,0.219257,0.003952686,0.004812171,0.4031964],"study_design_scores_gemma":[0.00080117,0.0006953504,0.9588554,0.0001255783,0.000009891597,0.00007501508,0.005779753,0.03316816,0.0002582936,0.00002493419,0.0001077898,0.00009867929],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910768,0.000007809982,0.005086847,0.0006883551,0.001721015,0.0001647738,0.00000201446,0.000009445843,0.001242922],"genre_scores_gemma":[0.9941716,0.00002051758,0.000004332875,0.005303936,0.0002980228,0.000005314162,7.824535e-7,0.00001014872,0.0001853336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6077114,"threshold_uncertainty_score":0.6213046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08236488083103045,"score_gpt":0.3583524659435245,"score_spread":0.275987585112494,"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."}}