Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-24T10:05:03.540Z Has data issue: false hasContentIssue false

5 - Evaluating the Weight of the Evidence

Cognitive Neuroscience Theories of Intelligence

from Part II - Theories, Models, and Hypotheses

Published online by Cambridge University Press:  11 June 2021

By
Matthew J. Euler  and
Ty L. McKinney
Edited by
Aron K. Barbey ,
Sherif Karama  and
Richard J. Haier
Aron K. Barbey
Affiliation:
University of Illinois, Urbana-Champaign
Sherif Karama
Affiliation:
McGill University, Montréal
Richard J. Haier
Affiliation:
University of California, Irvine
Get access

Summary

The goal of this chapter is to provide an overview and critique of the major theories in the cognitive neuroscience of intelligence. In taking a broad view of this literature, two related themes emerge. First, as might be expected, theoretical developments have generally followed improvements in the methods available to acquire and analyze neural data. In turn, as a result of these developments, along with those in the psychometric and experimental literatures, cognitive neuroscience theories of intelligence have followed a general trajectory that runs from relatively global statements early on, to increasingly precise models and claims. As such, following Haier (2016), it is perhaps most instructive to divide the development of these models into early and later phases.

Type
Chapter
Information
The Cambridge Handbook of Intelligence and Cognitive Neuroscience , pp. 85 - 101
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 8–20. doi: 10.1016/j.tics.2017.10.001.CrossRefGoogle ScholarPubMed
Barbey, A. K., Colom, R., Solomon, J., Krueger, F., Forbes, C., & Grafman, J. (2012). An integrative architecture for general intelligence and executive function revealed by lesion mapping. Brain, 135(4), 1154–1164.Google Scholar
Barulli, D., & Stern, Y. (2013). Efficiency, capacity, compensation, maintenance, plasticity: Emerging concepts in cognitive reserve. Trends in Cognitive Sciences, 17(10), 502–509. doi: 10.1016/J.TICS.2013.08.012.Google Scholar
Basten, U., Hilger, K., & Fiebach, C. J. (2015). Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51, 10–27. doi: 10.1016/j.intell.2015.04.009.Google Scholar
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.Google Scholar
Clark, A. (2015). Surfing uncertainty: Prediction, action, and the embodied mind. Oxford University Press.Google Scholar
Deary, I. J., Der, G., & Ford, G. (2001). Reaction times and intelligence differences: A population-based cohort study. Intelligence, 29(5), 389–399. doi: 10.1016/S0160–2896(01)00062-9.Google Scholar
Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14(4), 172–179. doi: 10.1016/j.tics.2010.01.004.CrossRefGoogle ScholarPubMed
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, 22–30. doi: 10.1016/j.intell.2013.09.005.Google Scholar
Ertl, J. P., & Schafer, E. W. P. (1969). Brain response correlates of psychometric intelligence. Nature, 223(5204), 421–422. doi: 10.1038/223421a0.Google Scholar
Euler, M. J. (2018). Intelligence and uncertainty: Implications of hierarchical predictive processing for the neuroscience of cognitive ability. Neuroscience & Biobehavioral Reviews, 94, 93–112. doi: 10.1016/j.neubiorev.2018.08.013.Google Scholar
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. doi: 10.1016/j.intell.2017.08.003.Google Scholar
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. doi: 10.1016/j.neuroimage.2015.03.078.Google Scholar
Fiandaca, M. S., Mapstone, M. E., Cheema, A. K., & Federoff, H. J. (2014). The critical need for defining preclinical biomarkers in Alzheimer’s disease. Alzheimer’s & Dementia, 10(3), S196–S212. doi: 10.1016/J.JALZ.2014.04.015.Google Scholar
Friston, K. J. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. doi: 10.1038/nrn2787.Google Scholar
Genç, E., Fraenz, C., Schlüter, C., Friedrich, P., Hossiep, R., Voelkle, M. C., … Jung, R. E. (2018). Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Nature Communications, 9(1), 1905. doi: 10.1038/s41467–018-04268-8.Google Scholar
Gläscher, J., Rudrauf, D., Colom, R., Paul, L. K., Tranel, D., Damasio, H., & Adolphs, R. (2010). Distributed neural system for general intelligence revealed by lesion mapping. Proceedings of the National Academy of Sciences of the United States of America, 107(10), 4705–4709. doi: 10.1073/pnas.0910397107.Google Scholar
Haier, R. J. (2016). The neuroscience of intelligence. Cambridge University Press.Google Scholar
Haier, R. J., Colom, R., Schroeder, D. H., Condon, C. A., Tang, C., Eaves, E., & Head, K. (2009). Gray matter and intelligence factors: Is there a neuro-g? Intelligence, 37(2), 136–144. doi: 10.1016/j.intell.2008.10.011.Google Scholar
Haier, R. J., Siegel, B. V. Jr, 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. doi: 10.1016/0160-2896(88)90016-5.Google Scholar
Hearne, L. J., Mattingley, J. B., Cocchi, L., Neisser, U., Melnick, M. D., Harrison, B. R., … He, Y. (2016). Functional brain networks related to individual differences in human intelligence at rest. Scientific Reports, 6, 32328. doi: 10.1038/srep32328.Google Scholar
Hendrickson, D. E., & Hendrickson, A. E. (1980). The biological basis of individual differences in intelligence. Personality and Individual Differences, 1(1), 3–33.CrossRefGoogle Scholar
Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017). Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence. Intelligence, 60, 10–25. doi: 10.1016/J.INTELL.2016.11.001.Google Scholar
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. doi: 10.1017/S0140525X07001185.Google Scholar
Kapanci, T., Merks, S., Rammsayer, T. H., & Troche, S. J. (2019). On the relationship between P3 latency and mental ability as a function of increasing demands in a selective attention task. Brain Sciences, 9(2), 28. doi: 10.3390/brainsci9020028.Google Scholar
Kievit, R. A., Davis, S. W., Griffiths, J., Correia, M. M., Cam-CAN, , & Henson, R. N. (2016). A watershed model of individual differences in fluid intelligence. Neuropsychologia, 91, 186–198. doi: 10.1016/J.NEUROPSYCHOLOGIA.2016.08.008.CrossRefGoogle ScholarPubMed
Kovacs, K., & Conway, A. R. A. (2016). Process overlap theory: A unified account of the general factor of intelligence. Psychological Inquiry, 27(3), 151–177. doi: 10.1080/1047840X.2016.1153946.Google Scholar
Kretzschmar, A., Spengler, M., Schubert, A.-L., Steinmayr, R., Ziegler, M., Kretzschmar, A., … Ziegler, M. (2018). The relation of personality and intelligence – What can the Brunswik symmetry principle tell us? Journal of Intelligence, 6(3), 30. doi: 10.3390/jintelligence6030030.Google Scholar
Kruschwitz, J. D., Waller, L., Daedelow, L. S., Walter, H., & Veer, I. M. (2018). General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set. NeuroImage, 171, 323–331. doi: 10.1016/J.NEUROIMAGE.2018.01.018.Google Scholar
Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C., & Jiang, T. (2009). Brain anatomical network and intelligence. PLoS Computational Biology, 5(5), e1000395. doi: 10.1371/journal.pcbi.1000395.Google Scholar
Mackintosh, N. J. (2011). IQ and human intelligence (2nd ed.). Oxford University Press.Google Scholar
Martínez, K., Madsen, S. K., Joshi, A. A., Joshi, S. H., Román, F. J., Villalon-Reina, J., … Colom, R. (2015). Reproducibility of brain-cognition relationships using three cortical surface-based protocols: An exhaustive analysis based on cortical thickness. Human Brain Mapping, 36(8), 3227–3245. doi: 10.1002/hbm.22843.CrossRefGoogle ScholarPubMed
McKinney, T. L., & Euler, M. J. (2019). Neural anticipatory mechanisms predict faster reaction times and higher fluid intelligence. Psychophysiology, 56(10), e13426. doi: 10.1111/psyp.13426.Google Scholar
Neubauer, A. C., & Fink, A. (2009a). Intelligence and neural efficiency: Measures of brain activation versus measures of functional connectivity in the brain. Intelligence, 37(2), 223–229. doi: 10.1016/j.intell.2008.10.008.Google Scholar
Neubauer, A. C., & Fink, A. (2009b). Intelligence and neural efficiency. Neuroscience & Biobehavioral Reviews, 33(7), 1004–1023. doi: 10.1016/j.neubiorev.2009.04.001.Google Scholar
Nussbaumer, D., Grabner, R. H., & Stern, E. (2015). Neural efficiency in working memory tasks: The impact of task demand. Intelligence, 50, 196–208. doi: 10.1016/j.intell.2015.04.004.CrossRefGoogle Scholar
Owen, A. M., & Duncan, J. (2000). Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neurosciences, 23(10), 475–483. doi: 10.1007/s11631–017-0212-0.Google Scholar
Pineda-Pardo, J. A., Martínez, K., Román, F. J., & Colom, R. (2016). Structural efficiency within a parieto-frontal network and cognitive differences. Intelligence, 54, 105–116. doi: 10.1016/J.INTELL.2015.12.002.Google Scholar
Poldrack, R. A. (2015). Is “efficiency” a useful concept in cognitive neuroscience? Developmental Cognitive Neuroscience, 11, 12–17.Google Scholar
Román, F. J., Abad, F. J., Escorial, S., Burgaleta, M., Martínez, K., Álvarez-Linera, J., … Colom, R. (2014). Reversed hierarchy in the brain for general and specific cognitive abilities: A morphometric analysis. Human Brain Mapping, 35(8), 3805–3818. doi: 10.1002/hbm.22438.Google Scholar
Ryman, S. G., Yeo, R. A., Witkiewitz, K., Vakhtin, A. A., van den Heuvel, M., de Reus, M., … Jung, R. E. (2016). Fronto-Parietal gray matter and white matter efficiency differentially predict intelligence in males and females. Human Brain Mapping, 37(11), 4006–4016. doi: 10.1002/hbm.23291.Google Scholar
Santarnecchi, E., Emmendorfer, A., & Pascual-Leone, A. (2017). Dissecting the parieto-frontal correlates of fluid intelligence: A comprehensive ALE meta-analysis study. Intelligence, 63, 9–28. doi: 10.1016/J.INTELL.2017.04.008.Google Scholar
Santarnecchi, E., Emmendorfer, A., Tadayon, S., Rossi, S., Rossi, A., & Pascual-Leone, A. (2017). Network connectivity correlates of variability in fluid intelligence performance. Intelligence, 65, 35–47. doi: 10.1016/J.INTELL.2017.10.002.CrossRefGoogle Scholar
Schubert, A.-L., Hagemann, D., & Frischkorn, G. T. (2017). Is general intelligence little more than the speed of higher-order processing? Journal of Experimental Psychology: General, 146(10), 1498–1512. doi: 10.1037/xge0000325.Google Scholar
Schultz, D. H., & Cole, M. W. (2016). Higher intelligence is associated with less task-related brain network reconfiguration. The Journal of Neuroscience, 36(33), 8551–8561. doi: 10.1523/jneurosci.0358-16.2016.Google Scholar
Sheppard, L. D., & Vernon, P. A. (2008). Intelligence and speed of information-processing: A review of 50 years of research. Personality and Individual Differences, 44(3), 535–551. doi: 10.1016/j.paid.2007.09.015.Google Scholar
Troche, S. J., Merks, S., Houlihan, M. E., & Rammsayer, T. H. (2017). On the relation between mental ability and speed of information processing in the Hick task: An analysis of behavioral and electrophysiological speed measures. Personality and Individual Differences, 118, 11–16. doi: 10.1016/J.PAID.2017.02.027.Google Scholar
Vakhtin, A. A., Ryman, S. G., Flores, R. A., & Jung, R. E. (2014). Functional brain networks contributing to the Parieto-Frontal Integration Theory of Intelligence. Neuroimage, 103(0), 349–354. doi: 10.1016/j.neuroimage.2014.09.055.Google Scholar
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience, 29(23), 7619–7624. doi: 10.1523/JNEUROSCI.1443-09.2009.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×