How brain cells predict rewards during impulsive choices
The role of distributional reinforcement learning in human neurons during impulsive choices
['FUNDING_R01'] · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · NIH-11256746
This project will record brain activity from neurosurgery patients doing decision-making tasks to learn how neurons predict a range of possible rewards during impulsive choices.
Quick facts
| Phase | ['FUNDING_R01'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH (nih funded) |
| Locations | 1 site (SALT LAKE CITY, UNITED STATES) |
| Trial ID | NIH-11256746 on ClinicalTrials.gov |
What this research studies
If you take part, researchers will record electrical activity from neurons during routine neurosurgery while you play decision-making games such as the Balloon Analog Risk Task and a probabilistic reversal learning task. They will focus on brain areas involved in decision-making — orbitofrontal cortex, anterior cingulate cortex, and mesial temporal structures — to look for neural signals that represent a range of possible future rewards (distributional reinforcement codes). The team will analyze how these signals change over time as people make impulsive versus cautious choices. Results aim to explain how these brain codes contribute to impulsive behavior and to inform future work on impulse control and substance use disorders.
Who could benefit from this research
Good fit: Ideal participants are neurosurgical patients who will have intracranial recordings as part of their clinical care and are able to perform computer-based decision tasks.
Not a fit: People who are not undergoing neurosurgery, cannot have electrodes placed, or cannot perform the tasks are unlikely to be eligible and would not directly benefit from participation.
Why it matters
Potential benefit: If successful, this work could reveal brain mechanisms behind impulsive choices and help guide future treatments for impulse control and substance use disorders.
How similar studies have performed: Previous animal and AI research has shown distributional reinforcement approaches can reveal detailed reward signals, and past human intracranial recording studies have linked neural activity to decision-making, but applying distributional reinforcement learning to human neurons is a new direction.
Where this research is happening
SALT LAKE CITY, UNITED STATES
- UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH — SALT LAKE CITY, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: SMITH, ELLIOT H — UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- Study coordinator: SMITH, ELLIOT H
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.