Nicholas Turk-Browne

Nick Turk-Browne
Professor
320 Peretsman Scully Hall
Ph.D., Yale University
Curriculum Vitæ (202.27 KB)
Summary

The goal of cognitive psychology and neuroscience is to understand the mind and brain, and in practice this means studying specific components of cognition, such as attention, perception, learning, and memory. Often, though, these components are studied in isolation, and we risk missing the forest for the trees. The overarching theme of our research is that cognitive processes are inherently interactive, and that exploring their behavioral and neural interactions can be an especially effective way to understand how they work. This philosophy has led us in several directions; here are three examples:

Statistical Learning — Our world is largely stable: we repeatedly encounter the same people, places, and things, and over time these objects tend to show up in the same environments and in predictable sequences. Statistical learning involves the automatic and incidental detection and representation of these regularities. For example, we effortlessly learn the locations of objects in a room, the boundaries between words in a language, and the sequence of landmarks on the way home. Using behavioral, fMRI, computational, and patient studies, we have made progress in understanding how statistical learning works, how it is supported in the brain, and how it influences other cognitive processes.

Repetition Attenuation — Statistical learning is a fairly complex type of learning that develops over time. We have also studied other forms of learning and memory, including perhaps the most basic and immediate type of learning: neural changes that occur from a single perceptual experience. This learning is reflected in repetition attenuation: the fact that the brain habituates and responds less to repeated things than to novel things (also known as repetition suppression or fMRI-adaptation). We have explored repetition attenuation as an important neural consequence of the interaction between perception and memory, and have exploited it to understand the nature of visual representations, as well as how these representations compete to be processed.

Background Fluctuations — Beyond examining specific phenomena like statistical learning and repetition attenuation, insights about cognition might be gleaned from studying spontaneous fluctuations in brain activity that occur in the background of ongoing tasks. Consider an analogy from cosmology: only by removing the bright light from stars and galaxies can faint cosmic background radiation be detected in space, but studying this radiation revolutionized our understanding of the origins of the universe. With more modest goals, we have used background brain states to identify functional circuits in the brain that support attention and learning, to predict variations in behavior that are otherwise hard to explain, and to improve cognitive abilities via neurofeedback.

Other research in our lab, treating the mind and brain as integrated systems, explores topics as varied as prediction, action, drawing, and forgetting.

Representative Publications

deBettencourt, M. T., Cohen, J. D., Lee, R. F., Norman, K. A., & Turk-Browne, N. B. (2015). Closed-loop training of attention with real-time brain imaging. Nature Neuroscience, 18, 470-475.

Kim, G., Lewis-Peacock, J. A., Norman, K. A., & Turk-Browne, N. B. (2014). Pruning of memories by context-based prediction error. Proceedings of the National Academy of Sciences, 111, 8997-9002.

Schapiro, A. C., Gregory, E., Landau, B., McCloskey, M., & Turk-Browne, N. B. (2014). The necessity of the medial temporal lobe for statistical learning. Journal of Cognitive Neuroscience, 26, 1736-1747.

Turk-Browne, N. B. (2013). Functional interactions as big data in the human brain. Science, 342, 580-584.

Shohamy, D., & Turk-Browne, N.B. (2013). Mechanisms for widespread hippocampal involvement in cognition. Journal of Experimental Psychology: General, 142, 1159-1170.