Professor Russ Poldrack’s lab is seeking a new full-time Research Coordinator. The RC will work closely with and report directly to Dr. Patrick...

Nature Reviews Psychology - Understanding the link between anhedonia and reward processing in adolescence can enhance mental health interventions specifically for young people. In this Review, Ma...

Gut-brain vagal signals gate dopamine ensembles, reshaping reward and motivation beyond a brain-centric view.

The Reward Positivity (RewP) is an electroencephalogram (EEG) feature that emerges following performance feedback and is commonly understood to index both positive and negative reward-prediction error (RPE+ and RPE−, respectively) signals. In contrast to this dominant perspective, we argue that the RewP is an independent EEG feature that selectively responds to positive RPE and is superimposed on a common background signal. We further propose that the RewP signals a goal prediction error: it is elicited by abstract signals instead of by hedonic 'rewards'. This goal prediction error appears to be produced by a critic-like architecture that is associated with the actor–critic framework in reinforcement learning. This perspective emphasizes the role of the RewP in goal attainment and cognitive control as opposed to being a simple indicator of reward receipt.

Where do objective functions come from? How do we select what goals to pursue? Human intelligence is adept at synthesizing new objective functions on the fly. How does this work, and can we endow arti...

This study shows that non-invasive ultrasound to the human nucleus accumbens can modulate deep brain activity and enhance reward-guided learning, offering a potential alternative to invasive neuromodu...

Nature Human Behaviour - In this study, Collins proposes an alternative dual-process (working memory and habit) model of reinforcement learning in humans.

A core strength of computational psychiatry is its focus on theory-driven research, in which cognitive processes are precisely quantified using computational models that formalize specific theoretical mechanisms. However, the data used in these studies often come from traditional laboratory-based cognitive tasks, which have unclear ecological validity. In this review we propose that the same theoretical frameworks and computational models can be applied to real-world data such as experience sampling, passive data, and digital-behavior data (e.g., online activity such as on social media). In turn, modeling real-world data can benefit from a theory-driven computational approach to move from purely predictive to explanatory power. We illustrate these points using emerging studies and discuss the challenges and opportunities of using real-world data in computational psychiatry.

Human exploration is driven by two distinct neural mechanisms, a valence-independent rate signal and a valence-dependent global noise signal.

Individual dopaminergic neurons encode future rewards over distinct temporal horizons.

Reinforcement learning task-based behavioral and computational measures displayed low test–retest reliability at the individual level. Also in contrast to self-assessed personality measures, behaviora...

The brain and body are necessarily connected. Here the authors show that brain blood flow and electrical activity are coupled with systemic physiological changes in the body.