Brain Awareness Week: Mini Q&A with William R. Uttal
Our second Brain Awareness Week (March 11-17) Q&A is with William R. Uttal, author of Reliability in Cognitive Neuroscience: A Meta-Meta-Analysis. William is Professor Emeritus (Engineering) at Arizona State University and Professor Emeritus (Psychology) at the University of Michigan. He is also the author of many other books, including Mind and Brain and The New Phrenology.
Why did you become critical of brain imaging-cognitive correlational studies?
As a physiological psychologist by training, I had been interested in the problem of brain localization from my graduate school days. In the 1990s, with the rapid expansion of fMRI methods (based as they are on the postulate of localization), I became concerned about the conceptual, statistical, and, most of all, the empirical discrepancies in the field. Alternative interpretations of what these responses mean exist and I set out to explore them. In recent years, evidence has been accumulating that supports the doubts many of us now have about both the validity and reliability of the cognitive applications of these wonderful machines.
Have your research topics and/or methods changed over the course of your career?
Of course! As technology, the findings of cognitive neurosciences, and interests have evolved, everyone has to respond or be left behind.
I have done “wet” neurophysiology, “dry” psychophysics, and something that might be called philosophy of science. However, it is also important to maintain an anchor to the past so that we can appreciate some of the difficulties encountered when trying to explain the relation between mind and brain—the main task of cognitive neuroscience.
What kind of changes do you think we need to make in brain science education?
There are three main changes that I feel would help cognitive neuroscience mature into a more robust science. First, there should be some additional training in logical analysis, critical thinking, and a cluster of topics that might be called philosophy of science. Second, training in mathematics must be expanded so that students have the intellectual as well as technological tools to understand the limits of this science. Third, we must teach students entering this field to appreciate the extreme complexity of both cognitive processing and brain organization and the possibility that the problem may never be completely solved.