Neurocognition of Decision Making


Our everyday lives involve frequent situations where we must make judgments based on noisy or incomplete sensory information – for example deciding whether crossing the street on a rainy morning, in poor visibility, is safe. How do we process and combine this incoming sensory information to form a decision? In this scenario, being able to additionally rely on an internal estimate of whether our perceptual judgment is accurate – that is to rely on a sense of confidence – can further facilitate adaptive behaviour and decision making.


Similarly, on a daily basis we need to make a host of preference-based (also referred to as value-based) decisions – for example deciding which piece of clothing to put on in the morning. In many of these instances the amount of sensory information remains unchanged but the subjective value we assign to the different options changes. How to we weigh the pros and cons of the various alternatives? More generally how do we combine different sources of probabilistic information to make decisions that are more likely to lead to a rewarding outcome?

Naturally, reinforcement-guided learning (that is, our ability to learn through trial and error) is pertinent in these situations as well. How does our prior experience with the available options help us make better predictions and ultimately better choices in the future?

These scenarios are representative of some of the main neuroscientific questions our lab is currently involved with. To address these questions we have devised a multimodal approach (see below) which allows us to expose the brain networks involved in human decision making as well as the mechanistic details of the underlying neural computations.


Multimodal Neuroimaging Approach


Our general research approach relies heavily on the fusion of two major disciplines: cognitive neuroscience and engineering. Cognitive neuroscience provides the foundation upon which the critical hypotheses about how the brain works to support behavior are framed. Engineering on the other hand lends itself to finding new and more sophisticated ways to collect, analyze and ultimately decode the behavioral and neural data. The computational techniques used in our lab are motivated by classical problems in signal processing, machine learning and statistical pattern recognition.

Our ultimate goal is to go beyond mere “brain mapping” and to start looking for distributed neural representations and deciphering how information flow through a “network” can lead to changes in behavior. One way we tackle this goal is through simultaneous EEG/fMRI experiments, which have the potential of simultaneously providing high-spatial and high-temporal resolution information about neural function. Importantly, linking fMRI brain activations with temporally specific EEG components would help infer the causal interactions of the underlying network that would have otherwise been difficult to discern with either modality alone.

In addition, we capitalize on the power of computational models to describe behavior and use the models’ predictions to inform the analysis of our neuroimaging data (e.g., EEG/MEG, fMRI, TMS). Crucially, model-based neuroimaging has the potential of providing a mechanist account of the neural processes under consideration by identifying when and where the various model parameters, which instantiate the underlying neural computations, are implemented in the brain.

Finally, we design multivariate data analysis techniques, to take advantage of the distributed nature (both in space and time) of the brain signals of interest in order to extract and ultimately exploit inter-trial and inter-subject response variability. We then use these techniques in combination with neuroimaging to identify distributed neural representations of interest and to uncover latent brain states that would have likely remained unobserved with more conventional (e.g., univariate) analysis tools. For more details visit our Publications page for manuscripts representative of this approach.

Multimodal Neuroimaging Approach