A two-way street: aligned progress in theories of cognition and neuroimaging

James Wallace

In the 30-year history of neuroimaging research, a reciprocal relationship between techniques – most prominently, functional Magnetic Resonance Imaging (fMRI) – and cognitive theories has matured. These techniques test predictions on real-time neural brain activity.

Mather et al. (2013) identified four unique functions of fMRI, in particular: (i) to localise cognitive functions to specific brain regions, (ii) ‘operationalise’ data as markers of specific mental processes, (iii) identify what information is represented in brain regions, and (iv) identify single and multiple mechanisms in observed cognitive tasks. All of which assumes the validity of the mapped psychological constructs under manipulation onto localised neural activity.

Consider how neuroimaging has informed cognition theory. An fMRI study by Wheeler, et al. (2000) implicated the same regions of the visual and auditory cortex in the initial perception and in remembering (cited in Wixted and Mickes, 2013). In the opposite direction, cognition theories have informed fMRI.

In Henson, et al’s. (1999) reproduction of the “Remember/Know” memory paradigm, fMRI data revealed elevated left hemisphere activity for ‘remembering’ rather than ‘knowing’ processes. These findings favoured the dual-process over single-process model, but the larger significance was to demonstrate how the interpretation of identical activity markedly differs based on the adopted cognitive model (Wixted and Mickes, 2013).

Another study by White and Poldrack (2009) utilised multivoxel pattern analysis on the same paradigm. It showed a graded combination of remembering and knowing. Thus, there is a two-way influence: valid interpretation of neural activity relies on the appropriate application cognitive models; and neuroimaging data can inform and constrain model testing (White and Poldrack, 2013).

Neuroimaging techniques have advanced cognition theories which otherwise are unobservable. For example, fMRI data can track memory reactivation which enables covert observation (Levy and Wagner, 2013), opening the door to questions behavioural studies cannot easily answer (e.g., the role of environmental cues in triggering depression/anxiety).

Elsewhere, Park et al. (2010) discovered advanced age is associated with decreased processing speed in ventral visual cortex regions involved in face and scene processing. This study was informed by an existing cognitive theory (i.e. dedifferentiation theory), validated by neuroimaging research, which researchers claimed advanced cognitive ageing theories (Park and McDonough, 2013).

However, neuroimaging has methodological difficulties, such as the possibility that representation observed in one region could be a result of an earlier stage of processing from another.

Uttal (2001) argued most high-level cognitive functions are inadequately defined which undercuts the validity of localisation approach, and to do so is fraught with logical difficulties (e.g., individual variation) and indirect observation limitations (cited in Landreth and Richardson, 2007, Hubbard, 2003).

However, Mather et al. (2013) write these obstacles are best overcome through a blended behavioural and neuroimaging method adoption. Moreover, the arguments against localisation method appear dated in the context of stated cognitive theory advancements (e.g., in memory, ageing, etc.).

Overall, imaging techniques and theories of cognition have bidirectionally influenced one another over the last three decades. While methodological complexities persist, these can usually be overcome through a blended adoption of behavioural and neuroscience models.

References

Coltheart, M. (2013). How Can Functional Neuroimaging Inform Cognitive Theories? Perspectives on Psychological Science, 8(1), 98–103. Doi:10.1177/1745691612469208

Henson, R. (2005). What Can Functional Neuroimaging Tell the Experimental Psychologist. The Quarterly Journal of Experimental Psychology, 58(2), 193-233. Doi:10.1080/02724980443000502

Hubbard, E.M. (2003) A discussion and review of Uttal (2001), The New Phrenology. Cognitive Science Online, 1, 22-33. Retrieved from: cogsci-online.ucsd.edu

Landreth, A., & Richardson, R., C. (2004) Localization and the new phrenology: a review essay on William Uttal’s The new phrenology. Philosophical Psychology, 17:1, 107-123, Doi:10.1080/0951508042000202417

Levy, B., & Wagner, A. (2013). Measuring Memory Reactivation With Functional MRI Implications for Psychological Theory. Perspectives on Psychological Science, 8, 72-78. Doi:10.1177/1745691612469031.

Mather, M., Cacioppo, J. T., & Kanwisher, N. (2013). How fMRI Can Inform Cognitive Theories. Perspectives on Psychological Science, 8(1), 108–113. Doi:10.1177/1745691612469037

Park, J., Carp, J., Hebrank, A., Park, D. C., & Polk, T. A. (2010). Neural specificity predicts fluid processing ability in older adults. Journal of Neuroscience, 30, 9253-9259. Doi:10.1 523/JNEURO-SCI.0853-10.2010

Park, D. C., & McDonough, I. M. (2013). The dynamic aging mind: Revelations from functional neuroimaging research. Perspectives on Psychological Science, 8, 62–67.

Price, C. J. (2018). The evolution of cognitive models: From neuropsychology to neuroimaging and back. Cortex, 107, 37-49. Doi:10.1016/j.cortex.2017.12.020.

Reuter-Lorenz, P. A. (2013). Aging and Cognitive Neuroimaging: A Fertile Union. Perspectives on Psychological Science, 8(1), 68–71. Doi:10.1177/1745691612469023

White, C.N., & Poldrack, R.A. (2013). Using fMRI to Constrain Theories of Cognition. Perspectives on Psychological Science, 8(1), 79-83. Doi:10.1177/1745691612469029

Wixted, J. T., & Mickes, L. (2013). On the relationship between fMRI and theories of cognition: The arrow points in both directions. Perspectives on Psychological Science, 8, 104–107, Doi:10.1177/1745691612469022

Share this post