Valuation Mechanisms that Shape the Interaction of Phasic and Tonic Dopamine

PROJECT SUMMARY Dopamine (DA) is a critical neuromodulator for mood, motivation, and reward-based learning. Dysregulated DA dynamics across brain regions and timescales contribute to psychiatric and substance use disorders, yet how DA timescales mediate the relationship between learning and motivation remains unresolved. Striatal DA release is documented to fluctuate on fast “phasic” and slower “tonic” timescales, hypothesized to regulate learning and motivation, respectively. Prevailing reinforcement learning (RL) theories suggest that phasic DA transients encode reward prediction errors (RPEs), which diffuse and accumulate into tonic DA levels to regulate motivational engagement. This interpretation assumes that subjective values driving motivational engagement are reactive to recent reward exposure. However, alternative theories propose that tonic DA may arise independently from phasic DA, reflecting proactive effort allocation based on internal calculations rather than recent reward exposure. Resolving these conflicting views is essential for understanding DA’s role in motivation and its dysfunction in psychopathology. This proposal aims to define the circuit and computational mechanisms linking phasic and tonic DA to flexible motivational engagement. Using a novel broadband DA measurement technique we recently developed, I will analyze how DA dynamics across timescales interact to support reactive-vs-proactive motivational flexibility. Aim 1 will assess how these valuation processes influence the relationship between phasic and tonic DA. Aim 2 will identify circuit mechanisms regulating how phasic DA signals accumulate into tonic levels under varying task demands of reactive-vs-proactive value learning. These studies will reconcile longstanding theoretical debates on DA function, providing a mechanistic and computational framework for understanding how DA shapes motivation and learning across timescales. The insights gained from these experiments will inform targeted interventions for circuit-specific vulnerabilities in addiction, depression, and psychosis.