Scientific American Mind
By: Richard J. Haier
A new neuroscience of intelligence is revealing that not all brains work in the same way
- Brain structure and metabolic efficiency may underlie individual differences in intelligence, and imaging research is pinpointing which regions are key players.
- Smart brains work in many different ways. Women and men who have the same IQ show different underlying brain architectures.
- The latest research suggests that an individual’s pattern of gray and white matter might underlie his or her specific cognitive strengths and weaknesses
We all know someone who is not as smart as we are—and someone who is smarter. At the same time, we all know people who are better or worse than we are in a particular area or task, say, remembering facts or performing rapid mental math calculations. These variations in abilities and talents presumably arise from differences among our brains, and many studies have linked certain very specific tasks with cerebral activity in localized areas. Answers about how the brain as a whole integrates activity among areas, however, have proved elusive. Just what does a “smart” brain look like?
Now, for the first time, intelligence researchers are beginning to put together a bigger picture. Imaging studies are uncovering clues to how neural structure and function give rise to individual differences in intelligence. The results so far are confirming a view many experts have had for decades: not all brains work in the same way. People with the same IQ may solve a problem with equal speed and accuracy, using a different combination of brain areas. [For more on IQ and intelligence, see “Rational and Irrational Thought: The Thinking That IQ Tests Miss,” by Keith E. Stanovich]
Men and women show group average differences on neuroimaging measures, as do older and younger groups, even at the same level of intelligence. But newer studies are demonstrating that individual differences in brain structure and function, as they relate to intelligence, are key—and the latest studies have exposed only the tip of the iceberg. These studies hint at a new definition of intelligence, based on the size of certain brain areas and the efficiency of information flow among them. Even more tantalizing, brain scans soon may be able to reveal an individual’s aptitude for certain academic subjects or jobs, enabling accurate and useful education and career counseling. As we learn more about intelligence, we will better understand how to help individuals fulfill or perhaps enhance their intellectual potential and success.
For 100 years intelligence research relied on pencil-and-paper testing for metrics such as IQ. Psychologists used statistical methods to characterize the different components of intelligence and how they change over people’s lifetimes. They determined that virtually all tests of mental ability, irrespective of content, are positively related to one another—that is, those who score high on one test tend to score high on the others. This fact implies that all tests share a common factor, which was dubbed g, a general factor of intelligence. The g factor is a powerful predictor of success and is the focus of many studies. [For more on g, see “Solving the IQ Puzzle,” by James R. Flynn; Scientific American Mind, October/November 2007.]
In addition to the g factor, psychologists also have established other primary components of intelligence, including spatial, numerical and verbal factors, reasoning abilities known as fluid intelligence, and knowledge of factual information, called crystallized intelligence. But the brain mechanisms and structures underlying g and the other factors could not be inferred from test scores or even individuals with brain damage and thus remained hidden.
The advent of neuroscience techniques about 20 years ago finally offered a way forward. New methods, particularly neuroimaging, now allow a different approach to defining intelligence based on physical properties of the brain. In 1988 my colleagues and I at the University of California, Irvine, conducted one of the first studies to use such techniques. Using positron-emission tomography (PET), which produces images of metabolism in the brain by detecting the amount of low-level radioactive glucose used by neurons as they fire, we traced the brain’s energy use while a small sample of volunteers solved nonverbal abstract reasoning problems on a test called the Raven’s Advanced Progressive Matrices.
This test is known to be a good indicator of g, so we were hoping to answer the question of where general intelligence arises in the brain by determining which areas showed increased activation while solving the test problems. To our surprise, greater energy use (that is, increased glucose metabolism) was associated with poorer test performance. Smarter people were using less energy to solve the problems—their brains were more efficient.
The next obvious question was whether energy efficiency can arise through practice. In 1992 we used PET before and after subjects learned the computer game Tetris (a fast paced visuospatial puzzle), and we found less energy use in several brain areas after 50 days of practice and increased skill. The data suggest that over time the brain learns what areas are not necessary for better performance, and activity in those areas diminishes—leading to greater overall efficiency. Moreover, the individuals in the study with high g showed more brain efficiency after practice than the people with lower g.
