Nick Turk-Browne Explores the Neuroscience of Learning
Nick Turk-Browne Explores the Neuroscience of Learning

Nick Turk-Browne Explores the Neuroscience of Learning

The Yale University cognitive neuroscientist studies how the brain extracts patterns from experiences.

May 1, 2019
Catherine Offord

ABOVE: © Jane Shauck Photography

When Nick Turk-Browne was a teenager, he read V.S. Ramachandran’s Phantoms in the Brain, a gift from his father, and was fascinated by the book’s message that “our experience of the world is constructed by our brain,” he says. “It blew my mind. It got me very much interested in this question of how do we experience the world, and how does our brain construct that experience?”

This fascination has been a guiding force for Turk-Browne, now a professor of cognitive neuroscience at Yale University. After graduating from the University of Toronto in 2004, he moved to Yale’s psychology department for a PhD, and “was a superstar from the beginning,” recalls psychologist Marvin Chun, one of Turk-Browne’s advisors. “He absolutely is one of the most productive and creative PhD students I’ve mentored in my entire career.”

In grad school, Turk-Browne focused on how the brain orchestrates statistical learning. Unlike episodic memory, which allows conscious recall of snippets of information such as phone numbers or birthdays, statistical learning involves the more subconscious association of events or objects, such as remembering a book with the table it’s placed on, or a person with the building they’re usually seen in. “It’s about extracting patterns, or regularities, across experiences,” explains Turk-Browne. This type of learning is critical “to generate expectations about the world, to interact, to behave in an efficient manner when confronted with new situations.”

Using behavioral experiments and functional MRI (fMRI) brain scans, Turk-Browne found that the hippocampus, an area usually linked to episodic memory, unexpectedly also showed activity associated with statistical learning, even when people weren’t paying attention to learning tasks.1 “The fact that the hippocampus was engaged in this relatively unconscious process of statistical learning was very surprising.”

In 2009, straight out of grad school, Turk-Browne started his own lab at Princeton University. “We’d never seen someone who had been that productive, not just in terms of number of papers but in terms of the substance, as a graduate student,” says Princeton psychologist Ken Norman. “It was clear that he was ready to leap right into a faculty position.”

A few years in, Turk-Browne and grad student Anna Schapiro found that, during statistical learning, the hippocampus and nearby brain regions represent objects based on how they’re temporally associated with one another—whether they’re seen together, for example—rather than on function or appearance— say, associating books with other books.2 “We think that this is a basic mechanism for how the hippocampus is able to store these sorts of regularities,” Turk-Browne says.

Using neural network models of the hippocampus, Turk-Browne, Schapiro, and Norman uncovered evidence for distinct anatomical pathways for episodic and statistical learning.3 Turk-Browne has also worked on novel tools such as BrainIAK, machine learning software developed with Intel that runs advanced analyses of fMRI data. In 2017, he returned to Yale as a professor and, as expected, he’s an “outstanding teacher and departmental citizen,” says Chun, who adds that he frequently finds himself learning from his former student. “It’s a real thrill to have him back.”

References

  1. N.B. Turk-Browne et al., “Neural evidence of statistical learning: Efficient detection of visual regularities without awareness,” J Cogn Neurosci, 21:1934–45, 2009. (Cited 315 times)
  2. A.C. Schapiro et al., “Shaping of object representations in the human medial temporal lobe based on temporal regularities,” Curr Biol, 22:P1622–27, 2012. (Cited 220 times)
  3. A.C. Schapiro et al., “Complementary learning systems within the hippocampus: A neural network modelling approach to reconciling episodic memory with statistical learning,” Philos Trans R Soc B, 372:20160049, 2017. (Cited 62 times)