{"doi":"10.1101/2023.03.17.533213","title":"Valence-partitioned learning signals drive choice behavior and phenomenal subjective experience in humans","abstract":"SUMMARY How the human brain generates conscious phenomenal experience is a fundamental problem. In particular, it is unknown how variable and dynamic changes in subjective affect are driven by interactions with objective phenomena. We hypothesize a neurocomputational mechanism that generates valence-specific learning signals associated with ‘what it is like’ to be rewarded or punished. Our hypothesized model maintains a partition between appetitive and aversive information while generating independent and parallel reward and punishment learning signals. This valence-partitioned reinforcement learning (VPRL) model and its associated learning signals are shown to predict dynamic changes in 1) human choice behavior, 2) phenomenal subjective experience, and 3) BOLD-imaging responses that implicate a network of regions that process appetitive and aversive information that converge on the ventral striatum and ventromedial prefrontal cortex during moments of introspection. Our results demonstrate the utility of valence-partitioned reinforcement learning as a neurocomputational basis for investigating mechanisms that may drive conscious experience. Highlights TD-Reinforcement Learning (RL) theory interprets punishments relative to rewards. Environmentally, appetitive and aversive events are statistically independent. Valence-partitioned RL (VPRL) processes reward and punishment independently. We show VPRL better accounts for human choice behavior and associated BOLD activity. VPRL signals predict dynamic changes in human subjective experience.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":393065,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.953,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":920629,"name":"Angela Jiang","orcid":"0009-0004-5150-4628","position":1,"is_corresponding":false},{"id":493456,"name":"Rachel Jones","orcid":"0000-0003-1826-5560","position":2,"is_corresponding":false},{"id":920628,"name":"Jonathan D. Trattner","orcid":"0000-0002-1097-7603","position":3,"is_corresponding":false},{"id":811244,"name":"Kenneth T. Kishida","orcid":"0000-0002-7394-8922","position":4,"is_corresponding":false},{"id":724473,"name":"L. Paul Sands","orcid":"0009-0003-1552-5504","position":0,"is_corresponding":true}],"reference_count":69,"raw_metadata":null,"created_at":"2026-07-19T01:19:01.355412Z","pmid":"36993384","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}