INESC-ID   Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento em Lisboa
technology from seed


Knowledge Discovery and Bioinformatics
Inesc-ID Lisboa

Integrated Neurocomputational and Empirical Studies of Learning and Cognition

06/29/2012 - 11:00
06/29/2012 - 12:00

My program of research focuses on the integrated use of neurocomputational models and empirical techniques – most notably brain imaging – to investigate the neural bases of learning and cognition in healthy subjects and the disruption of these processes in psychiatric disorders. This talk will present three examples in which this integrated approach has been crucial to arrive at novel insights that would be out of reach for more classical approaches. In the first example, I will use a standard reinforcement-learning model to show that learning to avoid negative outcomes depends on an internally generated reward signal. The firing of dopamine neurons in the brain represents reward signals, so this model made new predictions concerning patterns of dopamine release during avoidance learning that we have confirmed experimentally. In the second example, we have used model-based functional magnetic resonance imaging (fMRI) – a technique that fits a computational model to behavior and fMRI data – to identify the neural substrates of habit learning in humans. We found that learning a habit depends on the engagement of a specific brain region; in fact, we were able to distinguish participants who learned the habit from those who did not based on whether or not they engaged this region. In the third and final example, using a neurocomputational model in which we manipulated neurotransmitter levels, we found that reduced levels of a specific neurotransmitter produce the behavioral deficits that characterize attention-deficit/hyperactivity disorder (a disorder characterized by inattention, hyperactivity, and impulsivity). The model made several new predictions concerning abnormal patterns of brain connectivity in patients with this disorder, which we have confirmed using fMRI. These examples illustrate the power of using neurocomputational models in tight integration with empirical techniques to advance our understanding of the neural bases of high-level cognitive processes in healthy subjects and to elucidate how these processes go awry in psychiatric disorders.