Biology subjects

Rutledge, R. B.

Publications and source records attributed to Rutledge, R. B..

3 recordsLinked to original sources

bioRxiv2026

Serotonin reuptake inhibition rapidly enhances affect-reward coupling and improves mood over time

Selective serotonin reuptake inhibitors (SSRIs) are widely prescribed antidepressants, yet the mechanisms linking their rapid neurobiological effects to more gradual improvements in mood remain unclear. Computational accounts in which mood reflects the integration of reward outcome and reward prediction errors (RPEs) offer a compelling framework for examining SSRI effects, but reliance on laboratory tasks poses a major translational gap, both in timescale and ecological validity. We address this gap by examining the impact of SSRIs on affect-reward dynamics over a clinically relevant timescale using real-world rewards. Across 11 days, 66 healthy participants (n=37 receiving the SSRI citalopram) completed repeated ecological momentary assessments (EMA) of mood and goal-directed activities (up to 8 per day, totaling 3,542 surveys). By probing anticipated reward (pre-activity) and consummatory reward (post-activity), we derived real-world RPEs to test whether SSRIs amplify RPE impacts on momentary mood. Mixed-effects modelling showed that citalopram rapidly amplified the impact of reward outcome and RPEs on momentary mood, and predicted later increases in positive mood. Citalopram also selectively attenuated the impact of negative mood on expectations for future reward. These findings suggest that SSRIs modulate the bidirectional coupling between mood and reward processes in daily life, consistent with a positive feedback loop that could underpin the gradual mood improvement observed for antidepressant treatment. More broadly, our results highlight the utility of EMA as a highly sensitive real-world assay of early pharmacological effects on mood-reward dynamics.

neuroscience↗

bioRxiv2026

Dynamic emotional updating as a computational marker of well-being

Mood shapes behavior, physiology, and well-being. While mood continuously fluctuates in response to external events, it can also remain surprisingly stable, raising a fundamental question: what shapes emotional well-being, mood stability or variability? Quantifying mood along these two dimensions is challenging. We combined a closed-loop mood modulating task with computational modeling to derive quantitative markers of emotional stability and variability and map them onto the continuum from well-being to depressive symptoms. Participants (n=209) experienced adaptive reward-based mood modulation and modeling revealed that higher well-being associates with mood variability and stronger weighting of recent events, whereas depressive symptoms were associated with greater mood stability and stronger anchoring to earlier events. Results replicated in an independent test-retest dataset, which also showed that these mood updating parameters are reliable across days. Our findings identify adaptive responsiveness to recent experiences as a quantitative marker of well-being and unravel the temporal mood dynamics in health and depression.

neuroscience↗

bioRxiv2019

A neurocomputational model for intrinsic reward

Standard economic indicators provide an incomplete picture of what we value both as individuals and as a society. Furthermore, canonical macroeconomic measures, such as GDP, do not account for non-market activities (e.g., cooking, childcare) that nevertheless impact well-being. Here, we introduce a computational tool that measures the affective value of experiences (e.g., playing a musical instrument without errors). We go on to validate this tool with neural data, using fMRI to measure neural activity in male and female human subjects performing a reinforcement learning task that incorporated periodic ratings of subjective affective state. Learning performance determined level of payment (i.e., extrinsic reward). Crucially, the task also incorporated a skilled performance component (i.e., intrinsic reward) which did not influence payment. Both extrinsic and intrinsic rewards influenced affective dynamics, and their relative influence could be captured in our computational model. Individuals for whom intrinsic rewards had a greater influence on affective state than extrinsic rewards had greater ventromedial prefrontal cortex (vmPFC) activity for intrinsic than extrinsic rewards. Thus, we show that computational modelling of affective dynamics can index the subjective value of intrinsic relative to extrinsic rewards, a computational hedonometer that reflects both behavior and neural activity that quantifies the affective value of experience. SIGNIFICANCE STATEMENTTraditional economic indicators are increasingly recognized to provide an incomplete picture of what we value as a society. Standard economic approaches struggle to accurately assign values to non-market activities that nevertheless may be intrinsically rewarding, prompting a need for new tools to measure what really matters to individuals. Using a combination of neuroimaging and computational modeling, we show that despite their lack of instrumental value, intrinsic rewards influence subjective affective state and ventromedial prefrontal cortex activity. The relative degree to which extrinsic and intrinsic rewards influence affective state is predictive of their relative impacts on neural activity, confirming the utility of our approach for measuring the affective value of experiences and other non-market activities in individuals.

neuroscience↗