University of LiègeULgFaculty of EngineeringFacSALibrary News   
Seminar : Neural mechanisms behind human decision-making: A dynamical system approach to uncover integration of return, risk, and internal biases

Abstract:

Decision-making is a complex phenomenon in which return, risk, and internal biases influence the decisions we make, much as the outcome of our decisions influences the internal biases we acquire. Unfortunately, a wide range of mental disorders, from depression to schizophrenia, can hijack the decision-making system in various ways, yet the dynamical integration of neural circuits involved in decision-making remains sparsely studied in humans. Most decision making studies involving humans identify neural correlates from functional magnetic resonance imaging data that have poor temporal resolution, and yet humans often make decisions on the order of milliseconds. To map the neural substrates of decision-making at a millisecond resolution, we exploited a unique opportunity to record from 10 humans (implanted with depth electrodes for clinical purposes) while they performed a gambling-based decision-making task. First, we constructed dynamical probabilistic models of betting decisions for each subject as a function of cognitive inputs (return and risk) and an internal-bias state that is unobserved but estimated from measured data. The models suggest that the deviation from the optimal strategy of maximizing expected reward is explained by the influence of the estimated internal-bias state for several subjects. Then, we identified brain regions whose activity modulates with cognitive inputs of our models and the estimated internal-bias state. Regions including inferior temporal gyrus, amygdala and entorhinal cortex correlate with the internal-bias state, suggesting that this internal state may carry some information about the patient’s internal-bias state during the task. These preliminary results suggest that internal-biases are likely a key component of utility functions that govern financial decisions, in addition to cognitive inputs including expected outcome and variance of outcome. They also open the door for the identification of new targets to treat decision-making problems related to mental disorders.