Whether it’s foraging for food or attracting a waiter's attention, movement is the only way we have of interacting with the world. The brain makes these actions possible by taking visual and other sensory signals and using them to determine future actions. Scientists working to understand how this process works have taken on the task of reverse-engineering the human brain.
Engineering Professor Daniel Wolpert of Cambridge University examines computational models that allow scientists to describe and predict how the brain solves problems related to action. When existing models fall short, Wolpert creates new ones.
In this way, Wolpert has changed our understanding of how the brain works, said Karl Friston, professor and neuroscientist at University College London.
“Wolpert uses computational theory and beautifully crafted, simple behavioral experiments to get at the fundamental principles governing how we move and coordinate our actions,” Friston said.
This groundbreaking work earned Wolpert the 2010 Golden Brain Award from the Berkeley, California-based Minerva Foundation. The award was presented to Wolpert in a private ceremony during the 41st annual meeting of the Society for Neuroscience held in Washington, D.C. in November, 2011.
Among Wolpert’s contributions are findings that resulted in a paradigm shift in the field. Combining theoretical and behavioral work he has shown that our brains represent information about the world as probabilities and processes such information in a mathematically predictable way.
“It turns out the brain behaves in a very statistical manner,” Wolpert said. “We’ve shown that this is a very powerful framework for understanding the brain.”
For example, when we try to estimate where a ball may bounce while playing tennis, our brains effortlessly combine our prior knowledge of how likely it is that our opponent will place the ball at different locations on the court with visual information of the approaching ball. As our visual system is not perfect, our brains combine these two sources of information to make an optimal estimate of the location of the bounce.
Wolpert has shown that, when faced with these kinds of tasks, the brain reliably makes such optimal estimates before selecting the best action.
To test these and other theories in the lab, Wolpert and his colleagues use virtual reality and robotic interfaces to tightly control the experiences of the adult humans that are part of their experiments. “We can control what people see and feel and, in this way, we can tease apart which theories hold true.”
Currently, Wolpert is working to unite two fields of neuroscience: that which studies decision-making and that which deals with motor control. “We want to better understand how people link decisions with what they are going to do,” he explained.
Wolpert’s ultimate goal is both to understand normal function of the brain and apply this understanding to brain disorders. “Five percent of the population suffers from diseases that affect movement,” he explained. “The hope is that we will not only understand what goes wrong in disease, but how to design better mechanisms for rehabilitation.”