{"id":"W2945042682","doi":"10.1016/j.tics.2019.04.005","title":"Affect and Decision Making: Insights and Predictions from Computational Models","year":2019,"lang":"en","type":"review","venue":"Trends in Cognitive Sciences","topic":"Mental Health Research Topics","field":"Psychology","cited_by":78,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Institute of Mental Health","keywords":"Ambiguity; Computational model; CLARITY; Affect (linguistics); Field (mathematics); Management science; Conceptual model; Computer science; Psychology; Cognitive science; Data science; Artificial intelligence; Communication","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":[0.0005102576,0.0002134225,0.0006025604,0.0008802521,0.0002013276,0.00007986396,0.0001921186,0.000181264,0.0004357911],"category_scores_gemma":[0.00006608316,0.000161007,0.00005538331,0.0007847255,0.0006272625,0.0001830866,0.0001711353,0.0003759803,0.0000446281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005986914,"about_ca_system_score_gemma":0.0001433497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008704542,"about_ca_topic_score_gemma":0.0001270022,"domain_scores_codex":[0.9977706,0.0003790811,0.0003568462,0.0007712763,0.0004275893,0.0002945966],"domain_scores_gemma":[0.9958172,0.003785191,0.0001482414,0.0001087328,0.00004075767,0.000099812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001182089,0.00004353176,0.0001117731,0.0002669061,0.00002148907,0.00001029637,0.0005141987,0.000007958747,1.95638e-9,0.001334425,0.0001355339,0.9975421],"study_design_scores_gemma":[0.007342692,0.004634874,0.06803136,0.1480455,0.001308479,0.0004112803,0.004338705,0.07090317,1.512739e-7,0.3908519,0.3009437,0.003188135],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001356127,0.9746863,0.001125628,0.00001706107,0.0004489802,0.0004709351,0.0003217065,0.00001752253,0.0215558],"genre_scores_gemma":[0.02014328,0.9774939,0.001590403,0.00005852503,0.00009909771,0.0001259483,0.0001252533,0.0000150801,0.0003485356],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.994354,"threshold_uncertainty_score":0.6565674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.585627689190419,"score_gpt":0.6108267600937582,"score_spread":0.02519907090333928,"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."}}