Decoding the neural correlates of dynamic decision-making in humans

This research project will combine computational modeling, machine learning (ML) algorithms and whole-brain neural recordings (magnetoencephalography, MEG) to shed light on how the mechanisms underlying dynamic decision making are implemented in the human brain. Specifically, we will use the statistical framework of information theory to characterize inter-areal neural coupling and the direction of information flow when weighing sensory evidence and committing to a specific choice during dynamic decision-making. By providing a quantitative link between the behavioral and neural dynamics subserving how decisions are continuously formed in the brain, this project will contribute to expose mechanisms that are likely to figure prominently in human cognition, in health and disease.

Faculty Supervisor:

Karim Jerbi

Student:

Partner:

Institut de Neurosciences de la Timone

Discipline:

Life Sciences

Sector:

Life Sciences (not health); Biotechnology; Other; Artificial Intelligence

University:

Université de Montréal

Program: