Valence-partitioned learning signals drive choice behavior and phenomenal subjective experience in humans.

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.

Computational reinforcement learning, reward (and punishment), and dopamine in psychiatric disorders.

In the DSM-5, psychiatric diagnoses are made based on self-reported symptoms and clinician-identified signs. Though helpful in choosing potential interventions based on the available regimens, this conceptualization of psychiatric diseases can limit basic science investigation into their underlying causes. The reward prediction error (RPE) hypothesis of dopamine neuron function posits that phasic dopamine signals encode the difference between the rewards a person expects and experiences. The computational framework from which this hypothesis was derived, temporal difference reinforcement learning (TDRL), is largely focused on reward processing rather than punishment learning. Many psychiatric disorders are characterized by aberrant behaviors, expectations, reward processing, and hypothesized dopaminergic signaling, but also characterized by suffering and the inability to change one's behavior despite negative consequences. In this review, we provide an overview of the RPE theory of phasic dopamine neuron activity and review the gains that have been made through the use of computational reinforcement learning theory as a framework for understanding changes in reward processing. The relative dearth of explicit accounts of punishment learning in computational reinforcement learning theory and its application in neuroscience is highlighted as a significant gap in current computational psychiatric research. Four disorders comprise the main focus of this review: two disorders of traditionally hypothesized hyperdopaminergic function, addiction and schizophrenia, followed by two disorders of traditionally hypothesized hypodopaminergic function, depression and post-traumatic stress disorder (PTSD). Insights gained from a reward processing based reinforcement learning framework about underlying dopaminergic mechanisms and the role of punishment learning (when available) are explored in each disorder. Concluding remarks focus on the future directions required to characterize neuropsychiatric disorders with a hypothesized cause of underlying dopaminergic transmission.