{"id":"W4362634407","doi":"10.31219/osf.io/4cu98","title":"How bad becomes good: A neurocomputational model of flexible affect valuation","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Affect (linguistics); Valuation (finance); Psychology; Value (mathematics); Cognitive psychology; Social psychology; Computer science; Communication; Economics; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008362732,0.0003235876,0.0003185324,0.0005254932,0.0002810532,0.002296155,0.000736237,0.0005447041,0.002871819],"category_scores_gemma":[0.003656616,0.000270787,0.0006526933,0.0003859223,0.001353123,0.002622356,0.0006910961,0.001105681,0.0004305959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008843451,"about_ca_system_score_gemma":0.0004393384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001574145,"about_ca_topic_score_gemma":0.001235358,"domain_scores_codex":[0.9996973,0.00009725019,0.00001004289,0.000115065,0.00004149599,0.00003894687],"domain_scores_gemma":[0.9991766,0.0004104679,0.0001448574,0.0001119874,0.00007513047,0.00008091985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005868535,0.0003655959,0.04567241,0.0001764123,0.0002712444,0.0007243709,0.003038408,0.1283064,0.04299252,0.6526879,0.001917252,0.1232607],"study_design_scores_gemma":[0.00003759584,0.0001218954,0.02616557,0.00002574219,0.0000402761,0.0004236105,0.0002414858,0.3909057,0.002165958,0.5783731,0.001454563,0.00004444459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4519763,0.0006025322,0.5140792,0.004082239,0.00008123452,0.0001005205,0.0002604075,0.0001885167,0.02862918],"genre_scores_gemma":[0.9623299,0.0001782287,0.03509996,0.0001639994,0.00002627077,0.00007248711,0.00006290952,0.00002661778,0.002039741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002871819,"threshold_uncertainty_score":0.009607196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4839604181539778,"score_gpt":0.4416812754107131,"score_spread":0.04227914274326472,"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."}}