The Role of Uncertainty in Intelligence - Department of Psychology

Intelligence is one of the oldest constructs in scientific psychology (Spearman, 1904), and relates to some of the best-replicated and strongest empirical effects (Deary, 2012). Yet, despite this long history, numerous questions remain, including around fundamental issues like how to define intelligence (van der Maas, Kan, & Borsboom, 2014; or whether to even try! -- Jensen, 1998, Chapter 3), which factors comprise it (Bartholomew, Deary, & Lawn, 2009; Detterman, Petersen, & Frey, 2016; Major, Johnson, & Deary, 2012; van der Maas et al., 2006), and how it relates to the brain (Haier, 2016).

A major goal of our research is to improve understanding of intelligence and its neural basis. We approach this goal through studies that combine experimental, neuropsychological, and physiological techniques. Most of the studies relating to this line of research center around two key premises:

First, we take a view of the brain that emphasizes the fundamentally dynamic nature of behavior and neural activity. In light of that, we typically investigate more momentary aspects of brain-behavior relations using EEG and ERP methods, which are well-suited to characterize neural variation that occurs at that timescale. At the same time, we have also pursued, MRI/fMRI, and TMS when the scientific questions have called for it, and we are hoping to employ these methods in the future.

Second, we have tentatively defined intelligence as “the capacity to respond appropriately despite uncertainty.” In addition to being relatively general, we think a definition along these lines may be useful in helping to shift the emphasis away from particular constructs in defining intelligence (e.g., working memory, processing speed) and more towards a focus on the specific information processing demands that are relevant to particular tasks. In this way, we hope to develop a cumulative picture of how intelligence as a very general and overarching construct relates to neural activity in specific circuits under particular conditions.

Within that context, we have pursued a series of studies examining the role uncertainty in explaining within- and between-subject variation in cognitive performance, and its potential as a unifying concept in the neuroscience of intelligence. Overall, these studies have found that various forms of uncertainty seem to be crucial for explaining the ways in which neural activity relates to cognitive functioning, such that greater resilience to uncertainty (in the form of higher neural activation) seems to consistently predict higher ability (Euler, McKinney, Schryver, & Okabe, 2017; Euler, Niermeyer, & Suchy, 2016; Euler, Weisend, Jung, Thoma, & Yeo, 2015). Most recently, a review paper from our lab (Euler, 2018) sought to connect this general uncertainty-based framework to Predictive Processing theories of brain functioning (e.g., Clark, 2013), which are gaining traction in broader neuroscience.

Current Projects

One of the major ongoing projects in the lab seeks to test a group of predictions from the uncertainty framework, specifically related to ERP-IQ effects, and the role that top-down oscillatory processes may play in driving task-IQ correlations. Over the long term, we believe the uncertainty framework has implications for understanding intellectual development, and especially how exposure to different environments (e.g., growing up in a less advantaged environment) may result in differential uncertainty for some individuals in the context of cognitive assessment and research on brain-IQ relationships.

Recent lab papers on the intelligence and uncertainty framework

Euler, M. J. (2018). Intelligence and Uncertainty: Implications of Hierarchical Predictive Processing for the Neuroscience of Cognitive Ability. Neurosci Biobehavioural Reviews, 94, 93–112.

Euler, M. J., *McKinney, T. L., *Schryver, H. M., & *Okabe, H. (2017). ERP correlates of the decision time-IQ relationship: The role of complexity in task- and brain-IQ effects. Intelligence, 65, 1–10.

Euler, M. J., *Niermeyer, M. A., & Suchy, Y. (2016). Neurocognitive and neurophysiological correlates of motor planning during familiar and novel contexts. Neuropsychology, 30 (1), 109–119.

Euler, M. J., Weisend, M. P., Jung, R. E., Thoma, R. J., & Yeo, R. A. (2015). Reliable activation to novel stimuli predicts higher fluid intelligence. NeuroImage, 114, 311–319.

(*indicates graduate student co-authors)

Theoretical Issues in the Neuroscience of Intelligence

In addition to our primary focus on the uncertainty framework, there are a number of other interesting, unresolved questions in the neuroscience of intelligence that come up in the context of our lab. Below is a partial list of questions that graduate or undergraduate researchers in the lab might want to pursue:

How is it that greater neural activity predicts higher ability under many conditions, but shows the opposite relationship in others? Under what circumstances does the Neural Efficiency Hypothesis hold (Basten, Stelzel, & Fiebach, 2013; Dunst et al., 2014; Haier et al., 1988; Neubauer & Fink, 2009)? What are the principles that govern these effects?

How is it that intelligence appears to be a unitary capacity (e.g., as implied by psychometric g) and yet emerges through the action of numerous sub-networks with distinct functional roles?

To what extent is intelligence most related to variability within a single domain-general neural network (i.e., Parieto-Frontal Integration Theory; P-FIT; Jung & Haier, 2007)? To what degree do different profiles of cognitive strengths and weaknesses reflect variation in more domain-specific brain functions?

What role does the default-mode network play in cognitive performance and overall ability?

What is the role of learning in intellectual development?

References and Further Reading

Bartholomew, D. J., Deary, I. J., & Lawn, M. (2009). A new lease of life for Thomson’s bonds model of intelligence. Psychological Review, 116(3), 567–579.

Basten, U., Stelzel, C., & Fiebach, C. J. (2013). Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network. Intelligence, 41(5), 517–528.

Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. The Behavioral and Brain Sciences, 36(3), 181–204. https://doi.org/10.1017/S0140525X12000477

Deary, I. J. (2012). Intelligence. Annual Review of Psychology, 63(1), 453–482. https://doi.org/10.1146/annurev-psych-120710-100353

Detterman, D. K., Petersen, E., & Frey, M. C. (2016). Process Overlap and System Theory: A Simulation of, Comment on, and Integration of Kovacs and Conway. Psychological Inquiry, 27(3), 200–204. https://doi.org/10.1080/1047840X.2016.1181514

Dunst, B., Benedek, M., Jauk, E., Bergner, S., Koschutnig, K., Sommer, M., … Neubauer, A. C. (2014). Neural efficiency as a function of task demands. Intelligence, 42(0), 22–30. https://doi.org/http://dx.doi.org/10.1016/j.intell.2013.09.005

Haier, R. J. (2016). The neuroscience of intelligence. Cambridge University Press.

Haier, R. J., Siegel Jr, B. V, Nuechterlein, K. H., Hazlett, E., Wu, J. C., Paek, J., … Buchsbaum, M. S. (1988). Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence, 12(2), 199–217. https://doi.org/10.1016/0160-2896(88)90016-5

Jensen, A. R. (1998). The g factor: The science of mental ability. Westport, CT: Praeger.

Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135–187. https://doi.org/10.1017/S0140525X07001185

Major, J. T., Johnson, W., & Deary, I. J. (2012). Comparing models of intelligence in Project TALENT: The VPR model fits better than the CHC and extended Gf–Gc models. Intelligence, 40(6), 543–559. https://doi.org/10.1016/j.intell.2012.07.006

Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience & Biobehavioral Reviews, 33(7), 1004–1023. https://doi.org/10.1016/j.neubiorev.2009.04.001

Spearman, C. (1904). “General Intelligence,” Objectively Determined and Measured. The American Journal of Psychology, 15(2), 201–292. https://doi.org/10.2307/1412107

van der Maas, H. L. J., Dolan, C. V, Grasman, R. P. P. P., Wicherts, J. M., Huizenga, H. M., & Raijmakers, M. E. J. (2006). A dynamical model of general intelligence: the positive manifold of intelligence by mutualism. Psychological Review, 113(4), 842.

van der Maas, H. L. J., Kan, K.-J., & Borsboom, D. (2014). Intelligence Is What the Intelligence Test Measures. Seriously. Journal of Intelligence, 2(1), 12–15. https://doi.org/10.3390/jintelligence2010012