One of the hallmarks of human intelligence is our ability to flexibly adapt our thinking and behavior based on the overall context of a situation, a trait known as "cognitive flexibility." Among the simplest behavioral tasks that require cognitive flexibility are visual "target switching" paradigms that involve viewing the same visual stimuli in the context of searching for different targets. As an extension, "task switching" paradigms often involve searching for a target based on one stimulus property (e.g., match the shape regardless of color) followed by searching for a different property (e.g., match the color regardless of shape). While interactions between brain areas that lie in the visual, temporal and frontal lobes are thought to play important roles in flexibly switching between targets and tasks, the specific neural mechanisms that allow for cognitive flexibility remain little-understood. Understanding these neural mechanisms has proven to be a considerable challenge, at least in part because the neural activity in the brain areas involved reflect heterogeneous and difficult-to-understand mixtures of different types of information. With support from the National Science Foundation, Dr. Nicole Rust and colleagues will record neural signals as subjects perform target and task switching paradigms and they will then use newly-developed computational data analysis techniques to tease apart the neural mechanisms that the brain uses to flexibly switch between targets and tasks.

An array of disorders including obsessive compulsive disorder and autism have been linked to deficits in the mechanisms underlying cognitive flexibility, thus developing a basic understanding of how these mechanisms function is likely to be important for developing treatments to address the disorders that arise when these mechanisms go awry. Additionally, a basic understanding of the neural mechanisms underlying cognitive flexibility has the potential to benefit the robotics and computer vision communities interested in constructing artificial systems that can flexibly switch between targets and tasks to assist humans. Motivated by the notion that the experience of scientific discovery is one that cannot be fathomed through classroom experiences alone, the results of this project will also be incorporated into an undergraduate educational course focused on analyzing neural data. Finally, Dr. Rust will participate in the big data effort by making the data available to support other coordinated NSF efforts that aim to make use of real data in the teaching of STEM related courses and to enable participation in discovery science by those who would otherwise have no access to such data.

National Science Foundation (NSF)
Division of Behavioral and Cognitive Sciences (BCS)
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Akaysha Tang
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University of Pennsylvania
United States
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