Niloufar Shahdoust

The paper uses deep reinforcement learning to model risk-adaptive decision-making strategies and identifies low-dimensional neural dynamics associated with high-performing strategies in humans.

The paper investigates neural representations of time during decision-making under risk using human intracranial electro-physiology and a temporal convolution network to predict response timing.

This study shows that an asymmetric reinforcement learning model better explains human decision-making under risk than symmetric models.

This study shows that aperiodic exponents from intracranial EEG can differentiate depression symptom severity and correlate with specific brain regions and networks.