{"id":"W2019698643","doi":"10.4018/jssci.2012040103","title":"Cognitive Computational Models of Emotions and Affective Behaviors","year":2012,"lang":"en","type":"article","venue":"International Journal of Software Science and Computational Intelligence","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computational model; Computer science; Cognition; Multidisciplinary approach; Cognitive computing; Subconscious; Affective computing; Cognitive model; Rational analysis; Cognitive science; Consciousness; Computational intelligence; Cognitive psychology; Cognitive robotics; Artificial intelligence; Process (computing); Psychology; Embodied cognition","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":[0.001262772,0.0001448408,0.0002002115,0.0005341175,0.0001955958,0.0001621452,0.0006985082,0.00004222854,0.000006911173],"category_scores_gemma":[0.0005332083,0.0001342794,0.00006760729,0.0005811458,0.0007825613,0.002043975,0.0003848575,0.000210735,0.000002617899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007646175,"about_ca_system_score_gemma":0.0003654324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001124127,"about_ca_topic_score_gemma":7.972234e-7,"domain_scores_codex":[0.9976718,0.00006467797,0.0004989136,0.000249362,0.001276518,0.0002387448],"domain_scores_gemma":[0.993383,0.001146974,0.0004533472,0.0000688471,0.004729448,0.0002183541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006768874,0.0005166839,0.01679044,0.00001663837,0.000151264,0.00002150497,0.005866901,0.1655855,0.00008540527,0.1458086,0.00007399719,0.6650154],"study_design_scores_gemma":[0.0006291587,0.0004830241,0.1525288,0.0005976224,0.00005602744,0.001681077,0.0009922705,0.5814433,0.001094477,0.2600093,0.00003629523,0.0004487909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1955829,0.0004797028,0.8029576,0.0001948837,0.0005750472,0.00007092473,0.00001060865,0.00001771917,0.0001106156],"genre_scores_gemma":[0.9173944,0.00006049436,0.08221296,0.0002027739,0.0001143727,0.000001901362,0.000002647237,0.0000050085,0.000005460347],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7218115,"threshold_uncertainty_score":0.5475754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03477370738432911,"score_gpt":0.3263262400272099,"score_spread":0.2915525326428808,"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."}}