Neuroeconomics Laboratory 1.74

The principle focus of our laboratory is the study of neuroeconomics. An emerging field, neuroeconomics, is an integration of the principles of neuroscience, psychology, and economic theory with a purpose to explore and explain human decision making and learning. In our laboratory, we use neuroimaging techniques such as event related brain potentials and functional magnetic resonance imaging in conjunction with mathematical modeling to improve our understanding of the neural processes and computations that underlie human decision making and learning.

Our research is focused in two principle streams – one exploring the theoretical aspects of decision making and learning and the other exploring the functional capabilities of the human learning system. More specifically, our first research stream is targeted at assessing the validity of traditional computational learning and decision making models by comparing the output of these models with the actual neural responses measured in humans during task performance. For instance, since the advent of the Rescorla-Wagner learning rule, reinforcement learning theory has posited that human learning occurs when there is a discrepancy between the predicted and actual magnitude of a reward. Importantly, these “prediction errors” are used to update the reward predictions – or values – associated with different response options. In this manner, we, as humans, can learn the response options that will allow us to maximize reward payouts, and thus make better and more effective decisions. So, to validate the theoretical predictions made by the Rescorla-Wagner learning rule (and other more modern approaches such as the Method of Temporal Differences), we record neural data while people perform various learning and decision making tasks and compare these data with the predictions made by computational models. As such, we can use the neural data to improve the theoretical models, and we can use the theoretical models to make predictions with regard to what we will see in the neural data.

Our second research stream is focused on examining the role of the human reinforcement learning system in various types of research. Specifically, a recent theory posits that the anterior cingulate cortex, basal ganglia, and midbrain dopamine system comprise a reinforcement learning system, predicated on learning rules such as Rescorla Wagner, within medial-frontal cortex tasked with the optimization of behaviour. Our interest is to assess the types of learning that the medial frontal system is involved in. Previous work in our laboratory has demonstrated that the medial-frontal learning system appears to play a role in cognitive learning, motor learning, language acquisition, and the acquisition of perceptual expertise.

In addition to these two research streams, our laboratory continues to do behavioural research in motor learning and control, specifically looking at the role of the dorsal and ventral visual streams in the online control of movement.