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March 15, 2013

Brain Awareness Week: Mini Q&A with James V. Stone

Posted by: Katie Heasley

Our final Brain Awareness Week Q&A is with James V. Stone, author of  Vision and Brain: How We Perceive the World. James is a Reader in the Psychology Department at Sheffield University in England.     

 

What sparked your interest in vision science and the brain?

First, brains. If I did not know I had one, I simply would not believe this mass of nerve cells that is my brain could give rise to complex behaviours, learning, and consciousness. How does it do it? Why does it do it in the particular way that it does? Could it be done any other way, using other biological or computational components? Most importantly, is there a small set of general computational principles that underpin brain function?

Second, vision. Humans are basically visual animals. So it makes sense to use vision as a vehicle for studying general principles of brain function. Not that other senses, like audition, are unimportant, of course. If I was a bat then I would probably be trying to find out about the brain by studying echo-location, and saying, "not that other senses, like vision, are unimportant ...".  

 

How have your research topics and/or methods changed over the course of your career?

I used to be fascinated by the particular mechanisms that underpin brain function, like how/when/where particular brain regions become activated. More recently, I have become interested not only in how the brain works, but why it works in the way that it does (given constraints implicit in the laws of physics and in evolution). In other words, it is important to consider the computational reasons for the brain seeing a particular illusion, and not just the physiological mechanisms that seem to give rise to the illusion.

 

What kinds of changes do you think we need to make in brain science education?

I think it is not enough to teach students that the brain is complex, we need to describe how complex (and we can [kind of] do that, using Shannon's information theory). It is not enough to tell students that the the brain learns, we need to describe precisely how it learns, and why it learns some things better than others (and that is being done, using ideas based on Bayesian analysis). It is not enough to teach students that the brain is like a computer, we need to describe what sort of computations it does (and that is being done too, using computational modelling). So, we can take these true, but vague, statements, and turn them into rigorous, well-defined questions, most of which have rigorous, well-defined answers. 

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