Few people I know actually enjoy standardized tests. Wouldn’t it be great if technology could eliminate the need for bubble-in forms and Scantron sheets? How nice would it be to simply go in and get a snapshot of your brain to find out how smart you are? Imagine walking into the test center, signing on the dotted line, getting a quick scan, and walking out with your scores in hand, helping you gain admittance into a college or land your next job. No brain-racking questions, no tricky analogies, and no obscure vocabulary. Goodbye SAT, hello functional magnetic resonance imaging (fMRI).
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In the general, there have been two types of intelligence studies: psychometric and biological. Biological approaches make use of neuroimaging techniques and examine brain function. Psychometrics focuses on mental abilities (think IQ tests). Dr. Ian Deary and associates suggest that a greater overlap of these techniques will reveal new findings. In their paper, 'Testing versus understanding human intelligence,' they state:
“The lack of overlap between these approaches means that it is unclear what the scores of intelligence tests mean in terms of fundamental biological processes of the brain.”[2]
Applying psychometric analysis techniques (IQ tests) coupled with advanced imaging has the potential to reveal the locations of “higher” cognition and neural processing. I expect that future technologies will have incredibly improved resolution, which will allow scientists to see not only what regions the brain uses but also the exact pathways that are activated for each action. By understanding which pathways are utilized for specific tasks, it may be possible to identify which genes as well as environmental factors (nutrition, education) are responsible for their development. This can lead to programs dedicated to training specific areas of the brain (several of which already exist) [5], and perhaps even drugs that foster development.
I believe it is increasingly important to consider the implications of a technology powerful enough to quickly evaluate an individual’s level of intelligence. However, before I get there, it is necessary to first explain what we can and cannot currently do. What we cannot currently do is use neuroimaging to determine how smart you are. What we can currently do is use neuroimaging to see what parts of your brain contribute to how you process information, access memories and function [3].
Localizing Intelligence
Different parts of your brain do different things and none of these brain regions work in isolation. Some regions contribute to eating, seeing, and other regions play a role in “intelligence”. The varying techniques of imaging-based testing search for different correlates of intelligence [4] (i.e., general intelligence, problem solving, learning abilities). Developments in imaging technologies have improved our ability for greater analysis, allowing for the study of both damaged and healthy brains. For example, MRI studies have found that the volume of gray matter correlates to intelligence, providing evidence for generalizations made regarding brain volume and intelligence [7]. A 2006 study of 100 postmortem brains examined the relationship between an individual’s Full Scale Wechsler Adult Intelligence Scale (WAIS) score and the volume of their brain regions. The factors they considered important to the relationship between brain size and intelligence were age, sex and hemispheric functional lateralization (They found that general verbal ability was correlated with cerebral volume in women and right-handed men. They did not find a relationship between ability and volume in with every group, however).
Additionally, PET and fMRI studies have revealed more information regarding the functionality of certain regions of the brain. By recording and interpreting the brain activity of subjects as they complete a variety of tasks, researchers are able to draw inferences based on the performance in the types of task (and thus, the type of intelligence) that calls on particular areas of the brain. This is interesting, as knowing how parts of the brain are utilized may reveal more information about the structure and hierarchy used in neural development. It also may provide interesting information regarding the pathways of neural signals throughout the nervous system. Image-based testing may allow researchers to discover why certain neurons are connected, if they are indeed aligned in a purposeful manner and consequently, how to repair such pathways when they are damaged.
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A study from Washington University in St. Louis has shed light on how our brains utilize various networks for performance with working memory tasks [1]. They described a mechanism, global connectivity, which coordinates control of other networks. In a sense, global connectivity is the CEO of your brain, insuring that all components of your system are functioning and allowing for effective control of thought and behavior. Specifically, they found that a region of the lateral prefrontal cortex (LPFC), whose activity has been found to predict working memory performance, employs global connectivity. They report that,
“critically, global connectivity in this LPFC region, involving connections both within and outside the frontoparietal network, showed a highly selective relationship with individual differences in fluid intelligence. These findings suggest LPFC is a global hub with a brainwide influence that facilitates the ability to implement control processes central to human intelligence.”
This fascinating study identified a very specific characteristic of the brain (the global connectivity of the left LPFC) that suggested investigators were accurately able to predict fluid intelligence (where fluid intelligence refers to reasoning and novel problem solving ability) [4].
Potential Issues
We are learning more about the brain and the biological bases for intelligence every day. We have expanded our understanding of memory, cognitive thought and neural computation [2,6]. Our understanding of imaging techniques and what we can learn from them continues to grow. It may very well be possible to someday use neuroimaging to evaluate an individual’s intelligence. It is becoming more widely accepted that a neurobiological basis for intelligence exists (at least for reasoning and problem-solving) [4]. At the same time, the success of these intelligence studies presents ethical issues. Gray et al. pose the question, “Is it ever ethical to assess population-group (racial or ethnic) differences in intelligence?” While little variation has been found between racial groups, the public perception of intelligence studies has been negatively impacted by concerns of racism [4]. It is important to consider the consequences of studies that investigate intelligence differences in population-groups (racial, ethnic, and socioeconomic status). Gray states that it is not necessary to consider race when exploring the neurobiological bases of intelligence. The majority of variation occurs within a racial group and not between them. However, if a study were to investigate race and intelligence, Gray states that it will be necessary to have consent as well as active support from the target groups (i.e., financial support).
There are in fact studies that have investigated test score differences associated with race. Claude Steele, Ph.D., a professor of social psychology at Stanford University, discussed the test performance differences of white and black Americans. In his interview with PBS, Dr. Steele explained that a serious gap in test scores exists between whites and blacks, citing 100-point differences on the verbal and quantitative sections of the SAT. This is a concern for policy makers who are responsible for maintaining a standard level of education, as these low scores may help identify areas that need improvement. However, the negative effect of these findings is due to something Dr. Steele refers to as “stereotype threat.” He explains that when a salient negative stereotype about a group that you identify with may apply (i.e., lower SAT scores), when you are in that test situation the prospect of matching the stereotype can be distracting and upsetting. This stereotype threat impacts test taking, undermining test performance.
When considering the neurobiological bases of intelligence, it may be harder to escape these stereotypes. It may someday become known which genes code for higher intelligence, and thus higher test scores. Already, we have begun studying what regions of the brain relate to intelligence test performance. The next step will be discovering which genes code for the development of those regions, then connecting the dots from genes to intelligence. Furthermore, an understanding of the environmental and social influence that control the activation of these genes (and thus development) will be needed. Future generations may have stereotype threats that are based on genes rather than groups. When you know that you carry the genes for a certain level of intelligence, you may find yourself doubtful of your ability to perform above the level predicted by your genome and fall short of your potential.
There is a lot to be learned regarding how our genes relate to intelligence and by understanding how different gene pools code for their “smarts,” our understanding could grow immensely. However, as I mentioned earlier, suggesting that one group is genetically hard-coded to be smarter than another will have enormous implications. Science and research are going to continue pushing this ethical boundary and I predict that we eventually will be able to find many links between genes and intelligence. It is both beneficial and crucial to discuss how genes and intelligence should be studied now, before the research takes place. We have a unique opportunity where ethics can lay the guidelines before this technology emerges.
There are exciting possibilities that neuroimaging intelligence tests may bring. On the one hand, we can learn so much about the brain, how we think and how we can make it better. On the other hand, it may drive ethnic and racial groups, as well as socio-economic groups, farther apart. So we really have to ask ourselves, is that the price we have to pay to not fill out another Scantron sheet?
Want to Cite This Post?
Craig, E. The Future of Intelligence Testing. The Neuroethics Blog. Retrieved on from, http://www.theneuroethicsblog.com/2012/12/the-future-of-intelligence-testing.html
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http://www.medicaldaily.com/articles/11216/20120801/intelligence-mri-iq-test-brain.htm
http://www.psychologytoday.com/blog/finding-the-next-einstein/201202/could-brain-imaging-replace-the-sat
http://en.wikipedia.org/wiki/Neuroimaging_intelligence_testing
References
1. Cole, M. W., Yarkoni, T., Repovs, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. The Journal of neuroscience : the official journal of the Society for Neuroscience, 32(26), 8988–99. doi:10.1523/JNEUROSCI.0536-12.2012
2. Deary, I. J., & Caryl, P. G. (1997). Neuroscience and human intelligence differences. Trends in neurosciences, 20(8), 365–71. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9246731
3. Duncan, J. (2000). A Neural Basis for General Intelligence. Science, 289(5478), 457–460. doi:10.1126/science.289.5478.457
4. Gray, J. R., & Thompson, P. M. (2004). Neurobiology of intelligence: science and ethics. Nature reviews. Neuroscience, 5(6), 471–82. doi:10.1038/nrn1405
5. Hackman, D. a, Farah, M. J., & Meaney, M. J. (2010). Socioeconomic status and the brain: mechanistic insights from human and animal research. Nature reviews. Neuroscience, 11(9), 651–9. doi:10.1038/nrn2897
6. Prabhakaran, V., Rypma, B., & Gabrieli, J. D. E. (2001). Neural substrates of mathematical reasoning: A functional magnetic resonance imaging study of neocortical activation during performance of the necessary arithmetic operations test. Neuropsychology, 15(1), 115–127. doi:10.1037//0894-4105.15.1.115
7. Witelson, S. F., Beresh, H., & Kigar, D. L. (2006). Intelligence and brain size in 100 postmortem brains: sex, lateralization and age factors. Brain : a journal of neurology, 129(Pt 2), 386–98. doi:10.1093/brain/awh69
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