This paper changed my life: Stephanie Palmer on the ties between human speech and birdsong—and her ‘informal life coach’

Answers have been edited for length and clarity.

What paper changed your life?: Birdsong and human speech: Common themes and mechanisms. A.J. Doupe and P.K. Kuhl Annual Review of Neuroscience (1999)

This review argued for the algorithmic and mechanistic ties between speech and song-learning in humans and birds. At the time, the connections between the two revealed gaps in our knowledge about how auditory feedback plays into vocal production and learning. The impact this paper has had on studies integrating feedback with motor planning in complex, sequential tasks can only begin to be estimated by its 2,000-plus citations. However, its true influence was the role it played in shaping the fields of both human and bird vocal learning by examining the large body of work that revealed how and where feedback sculpts vocal changes, even in adults.

When did you first encounter this paper?

I read this paper as I considered applying for a Sloan Fellowship postdoctoral position at the University of California, San Francisco (UCSF), after my Ph.D.

Why is this paper meaningful to you?

It inspired me to think about learning in the brain as a dynamical evolution of a complex landscape, something that was moving its own substrate as it itself changed. The idea that experience itself could shape the learning system in our brain and dynamically close the critical period for acquiring new knowledge was electrifying to me. It motivated me to make a massive jump in my career path from theoretical physics to neuroscience.

This is the first paper I read by Allison Doupe. She became one of my scientific mentors, an informal life coach in my 20s and early 30s, and a friend. She died tragically young in 2014, having made a mark on so many of us and on the field. Thinking about this paper brings up feelings of both inspiration and loss.

How did this paper change how you think about neuroscience or challenge your previous assumptions?

I had no previous in-depth experience in neuroscience research. This not only inspired me to join the field but also sparked lasting questions and research interests that I still grapple with and hope to tackle. My group studies how the brain operates in a dynamic environment, both in the lifetime of the organism and over evolutionary time. How these complementary kinds of experience shape the neural code have stuck with me throughout my career.

How did this research influence your career path?

I read this paper as I was applying for postdocs in my final year of a theoretical physics Ph.D.—with zero biology training. I had submitted applications for several traditional condensed matter theory positions and then came across this one-off, strange-sounding Sloan Fellowship postdoc position at UCSF for mathematicians and theoretical physicists interested in learning neuroscience. The Keck Center at UCSF was the hub for systems neuroscience and theoretical neuroscience in the early 2000s, and the fellowship from the Sloan Foundation was generous and bold. Still, folks in my home department reacted to this path as if I were quitting physics.

The sheer intellectual excitement I felt reading Allison’s work convinced me to apply.

Talking to her during my interview made the decision to pursue neuroscience easy. I had wanted to be a brain surgeon when I was young, so I was in awe of Allison. Her dual M.D./Ph.D. degrees suggested there might be a way to revel in the wonders of the human machine while also doing basic, foundational science. She talked with enthusiasm about how I could still do both and generously invited me into all these pursuits. In a way, this story is more about Allison and the power of her mind and personality than about any particular paper. The paper and the person cinched for me the decision to move from theoretical physics into neuroscience for my postdoc.

Is there an underappreciated aspect of this paper you think other neuroscientists should know about?

I feel this review belongs in the canon of papers that inspire deep theory work. It ties into questions about learning, generalization and the integration of priors. I think of this paper when I think about Horace Barlow’s thought pieces on efficient coding. There are obvious things that lift off immediately, but a deeper dive reveals more nuanced insights and directives: The self-organized system doesn’t start from scratch; capacity is a fluid concept; learning landscapes change as learning progresses and are never fully cemented.