Rebecca J. Rabinovich

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.

This study uses human brain recordings to show that creative thinking is associated with distinct neural states in the default mode and dorsal attention networks.

This study investigates the spatio-temporal electrophysiological patterns of the default mode network (DMN) during creativity and mind wandering, revealing distinct dynamics and causal roles in creative thinking.

This study shows that reinforcement learning changes neuronal activity in mouse primary somatosensory cortex, enhancing time encoding and responses to temporal surprise.