{"id":"W4416593280","doi":"10.1038/s44271-025-00343-1","title":"Value computations underpin flexible emotion expression","year":2025,"lang":"en","type":"article","venue":"Communications Psychology","topic":"Emotions and Moral Behavior","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto; Social Sciences and Humanities Research Council of Canada; Canada Research Chairs; Government of Canada; Government of Ontario","keywords":"Anticipation (artificial intelligence); Normative; Happiness; Anger; Emotion classification; Expression (computer science); Value (mathematics); Reputation; Emotion work","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002684473,0.0001603644,0.0002022363,0.0004293994,0.0005660267,0.00003287624,0.001083608,0.0002312739,0.0008256458],"category_scores_gemma":[0.00003418921,0.0001703336,0.0001030919,0.0007800604,0.0003520166,0.00009225492,0.0002804343,0.0003946852,0.0009009417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005947209,"about_ca_system_score_gemma":0.00004839579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007092906,"about_ca_topic_score_gemma":0.00002203054,"domain_scores_codex":[0.9982779,0.0005446916,0.0004342689,0.0003814954,0.00007613366,0.0002854876],"domain_scores_gemma":[0.9963782,0.0002211103,0.0001170508,0.003084512,0.0001314503,0.00006761812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00002944582,0.001386688,0.004018761,0.000007849838,0.00005570162,0.000001826572,0.0007868469,0.000045396,0.004072471,0.8374636,0.0972967,0.05483476],"study_design_scores_gemma":[0.002938041,0.0002069527,0.5476656,0.0002179452,0.0001645572,0.00005631492,0.001833991,0.0004167559,0.0004629164,0.06650226,0.3790153,0.0005194105],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05336642,0.003504865,0.2592193,0.02551586,0.002823998,0.0008027465,0.00003049029,0.0006842675,0.654052],"genre_scores_gemma":[0.9627438,0.0002143739,0.02129892,0.00257324,0.00003725573,0.0002731959,0.000215973,0.00002277994,0.01262044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9093774,"threshold_uncertainty_score":0.999877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1142512748348444,"score_gpt":0.4808210653267133,"score_spread":0.3665697904918689,"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."}}