{"id":"W2095580536","doi":"10.1007/s11031-011-9206-0","title":"Is there an advantage for recognizing multi-modal emotional stimuli?","year":2011,"lang":"en","type":"article","venue":"Motivation and Emotion","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":144,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Deutscher Akademischer Austauschdienst","keywords":"Psychology; Emotional prosody; Prosody; Modality (human–computer interaction); Cognitive psychology; Perception; Modal; Facial expression; Emotion perception; Semantics (computer science); Speech recognition; Communication; Computer science; Artificial intelligence","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":[0.001047247,0.0002509828,0.0004926144,0.0003070462,0.0002378835,0.001144959,0.0006572109,0.001154116,0.009243715],"category_scores_gemma":[0.004827861,0.0002778306,0.0004128176,0.0001373768,0.0005096673,0.002616655,0.00085531,0.0008523726,0.0008853429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000146872,"about_ca_system_score_gemma":0.0001720629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003055464,"about_ca_topic_score_gemma":0.0003800441,"domain_scores_codex":[0.9996268,0.00004555448,0.0000167432,0.000123503,0.0001120206,0.00007529173],"domain_scores_gemma":[0.9985682,0.0005754566,0.0002036187,0.0002859231,0.0001558945,0.0002107647],"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":[0.001793307,0.0005388598,0.04452655,0.0004843404,0.0002398262,0.0003163923,0.0002784666,0.0001743775,0.5959987,0.009294813,0.002955039,0.3433992],"study_design_scores_gemma":[0.0001855868,0.001638157,0.8355941,0.000235202,0.0003153005,0.004892332,0.000906645,0.006849348,0.09416019,0.04056938,0.01455196,0.0001017499],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9570304,0.003133221,0.01332242,0.00486088,0.0005308106,0.00004510484,0.0002404377,0.0001421207,0.02069451],"genre_scores_gemma":[0.9854255,0.0009585987,0.0081659,0.001210123,0.0003213517,0.00004787899,0.0001649767,0.00009359192,0.003612051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009243715,"threshold_uncertainty_score":0.03092337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2791765896467844,"score_gpt":0.34426206253953,"score_spread":0.06508547289274558,"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."}}