THE SMITH LABORATORY

Ryan Smith, Ph.D.

Principal Investigator, Laureate Institute for Brain Research
Research Associate Professor, The University of Tulsa
Email: rsmith@laureateinstitute.org 

Curriculum Vitae
Google Scholar Profile


​Research Approach

Dr. Smith’s lab focuses on understanding the neurocomputational mechanisms underlying emotion-cognition interactions and how they relate to brain-body interactions, with a special focus on information-seeking behavior, prospective planning, and interoception. Lab projects also focus on how these mechanisms are affected in depression, anxiety, and substance use disorders. The primary research methods used in the lab are neuroimaging and computational modeling, including a wide range of modeling approaches (e.g., drift-diffusion models, reinforcement learning models, active inference models). A major overarching focus is to characterize information processing differences between mentally healthy and unhealthy individuals with the goal of improving diagnosis and treatment selection within psychiatry and clinical psychology.


​Research Program Highlights

Main Questions
How does the brain decide when information-seeking will be beneficial to achieving long-term goals? How does the brain simulate future outcomes of different possible action plans? How does the brain perceive and regulate internal bodily states? How are these processes influenced by current emotional states? How might these computational processes be affected in psychiatric disorders? Could understanding these mechanisms help us improve treatment approaches?

Approach
We employ several methods aimed at providing multiple levels of description in characterizing psychological and biological processes, including self-reported experience, behavioral and physiological responses, functional neuroimaging, and computational modeling.

​Future Directions
We aim to establish (1) the influence of anxiety on information-seeking and planning, (2) how such processes contribute to the vulnerability to, and the onset and maintenance of, psychiatric symptoms, and (3) how characterizing such processes in individual participants might inform more individualized and targeted treatment selection. We also aim to better understand veridical and false perception of internal bodily states in affective disorders; and whether measurable individual differences in computational mechanisms may improve our understanding of the determinants of subjective well-being.​

Scientific Background

Dr. Smith received his Bachelor’s degree in the science of Psychology from Arizona State University in 2010. He subsequently completed three graduate degrees from the University of Arizona between 2011 and 2015. This included Master’s degrees in Neuroscience and in Philosophy (with a focus on Philosophy of Science and Mind), and a Ph.D. in Psychology (with a Cognitive Neuroscience focus). His graduate work in philosophy focused on the relationship between scientific explanation and understanding and on the relationship between physical and mental processes. This work was done primarily under the supervision of Jenann Ismael, who is a recognized leader in the fields of Philosophy of Cognitive Science and Philosophy of Physics. His graduate work in Neuroscience/Psychology focused on the role of prefrontal cortex in emotion and its pathology in Major Depression, using both structural and functional neuroimaging as well as measurement of heart rate variability. This work was done primarily under the supervision of Dr. Richard Lane, who is both a practicing psychiatrist and a recognized pioneer in studying the brain basis of emotion.
 
Dr. Smith subsequently completed a 3-year postdoctoral fellowship with Dr. W.D. Scott Killgore in the Psychiatry Department within the University of Arizona College of Medicine. During this fellowship he designed and ran a neuroimaging study focused on understanding the ability to hold emotional information within working memory. He also played a primary role in creating an online emotional intelligence training program designed to minimize the development of emotional disorders in military personnel.

Upon completing this postdoctoral fellowship, Dr. Smith accepted an associate investigator position at LIBR, following completion of a four-month visiting fellowship with Dr. Karl Friston at University College London. This fellowship focused on designing computational models of brain function for use in understanding emotions and mechanisms underlying psychopathology. In 2022, Dr. Smith was promoted to Principal Investigator and received an appointment as Research Associate Professor at the University of Tulsa.
 
Currently, Dr. Smith has authored over 100 publications within peer-reviewed journals and scholarly books, based in part on the work described above. Many of these publications have also made theoretical contributions, proposing neurocognitive and computational models of conscious/unconscious emotion, neurovisceral integration, emotional intelligence, and depression. Most recently, his work has demonstrated multiple altered computational mechanisms in depression, anxiety, and substance use disorders associated with learning rates, information-seeking drives, and planning under emotional conflict.

Lab Members

Annie Chuning
Research Assistant
Samuel Taylor
Research Specialist

Selected Publications

Smith, R., Schwartenbeck, P., Stewart, J. L., Kuplicki, R., Ekhtiari, H., Paulus, M. P., & Tulsa 1000 Investigators. (2020). Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma. Drug and alcohol dependence, 215, 108208. ​
Smith, R., Friston, K. J., & Whyte, C. J. (2022). A step-by-step tutorial on active inference and its application to empirical data. Journal of Mathematical Psychology, 107, 102632. ​
Smith, R., Khalsa, S. S., & Paulus, M. P. (2021). An Active inference approach to dissecting reasons for nonadherence to antidepressants. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(9), 919-934.​
Smith, R., & Lane, R. D. (2015). The neural basis of one's own conscious and unconscious emotional states. Neurosci Biobehav Rev, 57, 1-29. doi:10.1016/j.neubiorev.2015.08.003 ​
Smith, R., & Lane, R. D. (2016). Unconscious emotion: A cognitive neuroscientific perspective. Neurosci Biobehav Rev, 69, 216-238. doi:10.1016/j.neubiorev.2016.08.013 ​
Smith, R., Mayeli, A., Taylor, S., Al Zoubi, O., Naegele, J., & Khalsa, S. S. (2021). Gut inference: A computational modelling approach. Biological psychology, 164, 108152. ​
Smith, R., Thayer, J. F., Khalsa, S. S., & Lane, R. D. (2017). The hierarchical basis of neurovisceral integration. Neuroscience & biobehavioral reviews, 75, 274-296. ​
Smith, R., Alkozei, A., Bao, J., Smith, C., Lane, R. D., & Killgore, W. D. S. (2017). Resting state functional connectivity correlates of emotional awareness. 
​Neuroimage, 159, 99-106. doi:10.1016/j.neuroimage.2017.07.044 
Smith, R., Alkozei, A., Killgore, W. D. S., & Lane, R. D. (2018). Nested positive feedback loops in the maintenance of major depression: An integration and extension of previous models. Brain Behav Immun, 67, 374-397. doi:10.1016/j.bbi.2017.09.011 
Smith, R., Lane, R. D., Parr, T., & Friston, K. J. (2019). Neurocomputational mechanisms underlying emotional awareness: insights afforded by deep active inference and their potential clinical relevance. Neurosci Biobehav Rev. doi:10.1016/j.neubiorev.2019.09.002
Smith, R., Kuplicki, R., Feinstein, J., Forthman, K. L., Stewart, J. L., Paulus, M. P., ... & Khalsa, S. S. (2020). A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disorders. PLoS computational biology, 16(12), e1008484. ​
Smith, R., Killgore, W. D. S., & Lane, R. D. (2018). The structure of emotional experience and its relation to trait emotional awareness: A theoretical review. Emotion, 18(5), 670-692. doi:10.1037/emo0000376
Smith, R., Sanova, A., Alkozei, A., Lane, R. D., & Killgore, W. D. S. (2018). Higher levels of trait emotional awareness are associated with more efficient global information integration throughout the brain: a graph-theoretic analysis of resting state functional connectivity. Soc Cogn Affect Neurosci, 13(7), 665-675. doi:10.1093/scan/nsy047 
Smith, R., Braden, B. B., Chen, K., Ponce, F. A., Lane, R. D., & Baxter, L. C. (2015). The neural basis of attaining conscious awareness of sad mood. Brain Imaging Behav, 9(3), 574-587. doi:10.1007/s11682-014-9318-8
Smith, R., Weihs, K. L., Alkozei, A., Killgore, W. D. S., & Lane, R. D. (2019). An Embodied Neurocomputational Framework for Organically Integrating Biopsychosocial Processes: An Application to the Role of Social Support in Health and Disease. Psychosom Med, 81(2), 125-145. doi:10.1097/PSY.0000000000000661
Smith, R., Kirlic, N., Stewart, J. L., Touthang, J., Kuplicki, R., Khalsa, S. S., ... & Aupperle, R. L. (2021). Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach. Journal of Psychiatry and Neuroscience, 46(1), E74-E87. ​

Research Collaborators

Karl Friston
University College London
Maxwell Ramstead
Verses Inc.
Karen Weihs
​University of Arizona
Julia Sheffield 
Vanderbilt University 
Harald Gundel 
University of Ulm 
Richard Lane
​University of Arizona
Michael Mouttoussis
University College London
Giles Story  
University College London ​
Al Powers 
Yale University 
Sarah Garfinkel 
University College London 
W.D. Scott Killgore
​University of Arizona
Horst Dieter Steklis
​University of Arizona
Edda Bilek
​University College London
​Justin Strickland 
Johns Hopkins ​University
​John J. B. Allen 
University of Arizona