Motivated Learning & Memory Laboratory

Director: Daniel G. Dillon, PhD

The Motivated Learning & Memory Lab uses methods from cognitive and affective neuroscience to provide new insight into psychiatric illness. We are focused on understanding how depression affects long-term memory and reinforcement learning. Our ultimate goal is to generate new targets for prevention and treatment interventions.

Reward Dysfunction as a Cause of Memory Deficits in Depression

Perhaps the most obvious fact about memory in depressed adults is that it is biased—memory for negative events is excellent, but memory for positive events is often quite poor. Surprisingly, we do not understand why this occurs—there is no solid, mechanistic account of the positive memory deficit in depression. However, work in non-human animals indicates that positive events often trigger dopamine release in brain reward circuits, and that dopamine release enhances memory formation that is mediated by the hippocampus. Work conducted at CDASR and elsewhere indicates that depression may disrupt dopamine release. Therefore, we have proposed that blunted dopaminergic responses to positive events may cause the positive memory deficit in depression, by depriving the hippocampus of the signal that would otherwise strengthen memory formation and consolidation. We are currently using behavioral methods, functional magnetic resonance imaging (fMRI), and computational modeling to test this hypothesis.

The Role of Frontal and Parietal Circuitry in Memory Retrieval

The project described above aims to explain how depression impairs the way memories are formed and stored, and it is focused on dopamine pathways and the hippocampus—evolutionarily ancient circuits that lie deep in the brain. But depression is also known to affect memory retrieval, and the ability to recall detailed, specific memories depends heavily on frontal and parietal circuits that are newer and greatly expanded in humans relative to non-human animals. Electroencephalography (EEG) allows us to study firing in these circuits with incredible temporal precision (in many studies, we obtain 1,000 data points per second). Currently, we are using EEG to examine fronto-parietal activity as depressed and healthy adults attempt to retrieve positive, negative, and neutral memories. Furthermore, because we know that depression frequently involves exaggerated stress responses, we are studying how acute stress influences EEG signals acquired during memory retrieval. In the future, we hope to expand this work by determining whether non-invasive modulation of these circuits could improve memory retrieval ability in depressed adults.

Computational Modeling of Reinforcement Learning in Depression

Finally, we are applying the drift diffusion model and temporal difference models to data from reinforcement learning tasks performed by depressed and healthy adults. Along with many other researchers, we hope that modeling may provide insight into prediction error signaling in depressed adults; because prediction errors appear to be coded by dopamine neurons, and because depression appears to impair dopamine function, we hypothesize that depression may affect the way prediction errors are computed and transmitted throughout the brain. But we are also expanding our modeling work into new territory. A great deal of research in healthy adults and non-human animals has used the drift diffusion model (or related approaches) to study how organisms accumulate evidence in favor of one course of action versus another, and how they use that evidence to make choices. This approach has not been widely used in depression, but preliminary research in our lab indicates that depression markedly reduces the rate of evidence accumulation in reinforcement learning tasks, and also increases the amount of information that must be gathered for a decision to be made. These findings offer a mechanistic account for problems with decision-making in depression, and they may also help us form a link to the neurophysiological literature on decision-making in non-human animals. In this way, we may be able to place the neuroscientific study of reinforcement learning in depression on more solid ground, which could offer a new perspective on the nature of depression and the course we might take to develop better treatments.

Selected publications:

Dillon, D. G., & Pizzagalli, D. A. (2018). Mechanisms of memory disruption in depression. Trends in Neurosciences, 41, 137-149.

Barrick, E. M., & Dillon, D. G. (2018). An ERP study of multidimensional source retrieval in depression. Biological Psychology, 132, 176-191.

Dillon, D. G.*, Wiecki, T. W.*, Pechtel, P., Webb, C., Goer, F., Murray, L., Trivedi, M., Fava, M., McGrath, P.J., Weissman, M., Parsey, R., Kurian, B., Adams, P., Carmody, T., Weyandt, S., Shores-Wilson, K., Toups, M., McInnis, M., Oquendo, M. A., Cusin, C., Deldin, P., Bruder, G., & Pizzagalli, D. A. (2015). A computational analysis of flanker interference in depression. Psychological Medicine, 45, 2333-2344. [*Joint First Authorship/Contributed equally]