We want to explain how brains produce behaviour, but because brains and behaviour are so complex, we are still trying to understand how humans perform simple behaviours, such as speeded decisions. These are decisions like whether you just saw a red or green light or just heard a knock at the door. To study speeded decision making, researchers approximate real world stimuli with simpler stimuli, such as small moving dots on a computer screen. Manipulating the stimuli or task demands can have observable effects on the outcomes such as response times and accuracy of participants in decision making tasks. This data can then be analysed by a computational model that can not only predict and explain the behaviour but reveal unobservable processes that are being conducted by our minds.

Examples:

Tillman, G., Van Zandt, T., & Logan, G. D. (2020). Sequential sampling models without random between trial variability: The racing diffusion model of speeded decision making. Psychonomic Bulletin & Review, 27, 911-936.

Evans, N. J., Tillman, G., & Wagenmakers, E. J. (2020). Systematic and random sources of variability in perceptual decision-making. Psychological Review, 127(5), 932-944.

Mental health disorders are associated with a range of impairments in cognitive functioning spanning psychomotor speed, processing speed, attention, memory encoding and memory recall. Cognitive functioning in clinical settings is typically assessed using measures such as accuracy and response times in computer-based experiments. The measures used to assess cognitive functioning can also be used to predict whether the patient will respond well to anti-depressant treatment. These cognitive biomarkers are useful not only because of their predictive power, but because they are easily attained at a low cost. Researchers have been looking for ways to improve the predictive power of models that use cognitive data with some luck using machine learning methods. However, the field is yet to explore the usefulness of computational models of decision making, which this lab is doing.

Examples:

Braund, T. A., Breukelaar, I. A., Griffiths, K., Tillman, G., Palmer, D. M., Bryant, R., ... & Korgaonkar, M. S. (2021). Intrinsic Functional Connectomes Characterize Neuroticism in Major Depressive Disorder and Predict Antidepressant Treatment Outcomes. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.

Braund, T. A., Tillman, G., Palmer, D. M., Gordon, E., Rush, A. J., & Harris, A. W. (2021). Antidepressant side effects and their impact on treatment outcome in people with major depressive disorder: an iSPOT-D report. Translational psychiatry, 11(1), 1-8.