MRI lab leads 
The aim of this project is to understand better the causes of cognitive decline with age through the study of fundamental processes of learning. This project will integrate functional and diffusion-tensor MR-imaging data to investigate age-related change to brain structural and functional connectivity by testing young-adult and aged (>65) adults in an observational-learning paradigm. 
The experiment
Participants will make simple responses to a series of visual stimuli while images sensitive to brain function are acquired. Brain images weighted for diffusion that reveal tissue microstructure will also be acquired after the participant has finished the task. 
We expect that an index of learning derived from behavioural responses will increase across the experiment and that this will be accompanied by a posterior shift in task-related brain activity as frontal regions become less involved in response mediation. We expect that this behavioural evidence for learning and its neural correlates will be strongest in the younger participants. As age-related effects on brain microstructure and brain function are marked by considerable individual variability (Cabeza et al., 2018), we expect that learning and its related neural signature will be greatest in the aged participants in whom white-matter tracts are relatively preserved. These analyses will integrate data on brain structure, brain function, and behavioural performance to delineate further the factors mediating age-related degeneration in the fundamental process of observational learning in an undirected context.
Support and future directions
This project is supported by the University of Plymouth Faculty of Health 2020–2021 Research Pump-Priming scheme. The results of this project will support an application for funding from a UK Research Council. 
Cabeza, R., Albert, M., Belleville, S., Craik, F. I., Duarte, A., Grady, C. L., ... & Rajah, M. N. (2018). Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing. Nature Reviews Neuroscience, 19(11), 701-710.
Karuza, E. A., Emberson, L. L., Roser, M. E., Cole, D., Aslin, R. N., & Fiser, J. (2017). Neural signatures of spatial statistical learning: Characterizing the extraction of structure from complex visual scenes. Journal of cognitive neuroscience, 29(12), 1963-1976.