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Structure Learning
structure learning

Structure Learning

Although there has been a wealth research on causal inference, much of it has focused on only one particular form of causality, for instance stimulus A leads to outcome B but not out outcome C. While this is a simple and powerful representation for many studies of causality,  many situations in the real-world are much more complex. Many systems come in richer, more complex forms, such as hierarchies (e.g. social groups), clustered environments (e.g. foraging patches), tree structures (e.g. sequential events) and even cyclic events (e.g. seasonal events), and making inferences in these systems may require more complex computations.  

Despite recent progress, there is still little understanding of the neuronal mechanisms that underlie updating, inference, and generalization in structured environments.  Such an understanding is central to any mechanistic description of human cognition and behavior in the real world. The importance of this is underlined by the fact that abnormal learning and generalization are likely to be core impairments in certain expressions of anxiety disorders and psychosis. Shedding light on the associated neural mechanisms thus holds promise for generating novel insights into the neural basis of these devastating disorders.

Our lab uses combinations of behavioral, eye-tracking, neuroimaging and physiological measurements to look at how the brain forms and updates such representations of complex casual structures in the environment. One line of research looks at the computations and networks involved in inferring hierarchical relationships between these relationships and make meaningful stimulus outcome associations at particular levels of the hierarchy. For example, imagine you walk up to a bush, eat one of the ripe purplish berries and learn that it tastes great. Does this mean that all purplish berries are good? Can this outcome be generalized to all berries regardless of color? Maybe anything that you can pick off a plant will be delicious. Then what if you eat the red berries on the next bush and it makes you sick. Our lab seeks to understand how your brain represents a taxon of berries, forming relevant and meaningful associations at each level of the hierarchy (e.g. blueberries are good and red berries are bad, but they're still both berries which are ultimately good).