Richard Haier is a Professor Emeritus at the University of California Irvine and is the author of the Neuroscience of Intelligence published by Cambridge University Press. Over his career he has used neuroimaging to study how brain function and structure relate to intelligence, and the ways in which “smart” brains work. He is the editor-in-chief of the journal Intelligence and the past president of the International Society for Intelligence Research. I reached out to him earlier this year to ask about his new book. What follows is an interview conducted with Quillette via email.
Thank you for taking the time to talk to Quillette Professor Haier. You’ve spent forty years studying intelligence and have compiled your knowledge into a new book accessible to the general reader called TheNeuroscience of Intelligence, which looks fascinating from its précis. Firstly, can you tell us how you became interested in intelligence research, and how you came about studying intelligence through neuroimaging?
When I started graduate school at Johns Hopkins in 1971, I was interested in social psychology and personality theories. That year Professor Julian Stanley was starting the Study of Mathematically and Scientifically Precocious Youth. I worked on his first talent search passing out pencils for 12 and 13 year old kids taking the SAT-Math exam [a standardized test used for college admission in the US]. The kids had been nominated by their math teachers as the best students in their class. Many of these kids scored as high on this test as college freshman at Hopkins. How they got this special math talent was a fundamental question and it certainly looked like something that came “naturally” since they had not yet had many math courses in school. This started my interest in individual differences in mental abilities, and intelligence was the most interesting and controversial mental ability.
It was after grad school during my first job at the National Institute of Mental Health that I learned more about genetics and how to study the brain with EEG. All these threads came together when I moved to Brown University and started my own lab to study intelligence. In the 1980s, the first neuroimaging with positron emission tomography (PET) became available and I joined my former NIMH colleagues who had moved to UC Irvine and acquired a PET scanner. I used my access to the scanner to study intelligence and brain function, including a study of math reasoning in college men and women, bringing me full circle back to the Hopkins study. Over the next 30 years, neuroimaging developed further with MRI and other technologies that I used to follow the intelligence data even deeper into the brain.
Can you remind me what the difference is between g and an IQ score?
One of the most robust, replicated findings in the entire field of psychology is that all tests of mental abilities are positively correlated with each other. This implies there is a common mental ability that accounts for these associations. This common ability is called the general factor of intelligence, abbreviated as the g-factor. Some tests require more g than others and no one test is a pure measure of g. The best estimate of the g-factor is based on combining scores from a variety of tests that tap different cognitive domains. IQ tests usually combine scores on several subtests that sample from different mental abilities so the IQ score is a good estimate of g. The g-factor is the focus of most intelligence research, especially research that aims to determine why people differ. Based on decades of compelling data (including the latest DNA analyses), many researchers, myself included, think that the g-factor is influenced mostly by genetics. That’s key because it indicates that intelligence can be modified once genetic/neurobiological mechanisms are understood. This is why neuroscience is starting to focus attention on intelligence.
Is it possible to see if someone is high in g by their brain activity on a PET scan or fMRI scan – and if so, what does it look like?
Our first PET study and many subsequent studies suggest that high intelligence is associated with more efficient brains; there are also indications that more gray matter in certain brain areas and more connections among brain areas are associated with more intelligence.
Since the first neuroimaging studies of intelligence, researchers have been trying to predict intelligence test scores from images. All such attempts had failed independent replication up to the time I was finishing the book and I explained why this was the case. However, right after I submitted my manuscript, a new study suggested this kind of prediction had succeeded. It was based on a mathematical way to assess how brain areas were connected to each other using MRI scans. Apparently, such connection patterns are stable and unique to individuals like fingerprints; and these patterns predict intelligence test scores. I was able to add this study to the book, but it is still not clear if these claims will pass independent replication. If so, there will be many questions to investigate like whether there are sex differences, and age differences that have a developmental sequence. A key question will be how such brain patterns change with learning.
I also describe new neuroscience techniques used in animals to turn neurons on and off to see how behavior changes. It may be possible to adapt some of these techniques for use in humans to study performance on mental tests experimentally instead of by correlations. This is an exciting prospect, especially for young investigators and students thinking about a career in this field.
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