Samuel David McDougle, PhD

One of the defining characteristics of the human species is our massive repertoire of motor skills, and our astoundingly flexible capacity to learn new ones throughout life. From masters (Serena Williams, Mozart) to amateurs (the rest of us), learning and executing skilled actions requires a mix of high-level knowledge, attention, and repetitive practice.

In this lab we are interested in studying how humans learn and remember skilled actions. Skilled action is complex, and skill acquisition relies on a variety of psychological processes and learning algorithms. Here are some broad questions we are currently scratching our heads over:

• How do learned motor behaviors go from controlled and effortful to automatic?

• How do cognitive computations and abstract thoughts interact with movement control?

• How does the brain represent goals, actions, and the rewards that reinforce our actions? And how is this accomplished computationally?

• How does our sense of space influence how we move within it?

• Which aspects of motor memory are explicit, and which are implicit? Do these systems interact?

• How might different cognitive systems vie for control during the selection of movements?

• Do brain regions conventionally linked to motor behavior also play a role in cognition (e.g., the cerebellum)? What are these roles, and what can they tell us about the relationship between sensorimotor and cognitive processes?

Approaches

We leverage multiple methodological approaches for investigating the psychology and neuroscience of human learning and memory.

Behavior

The careful study of behavior is key to addressing psychological and neuroscientific questions. We use a variety of behavioral paradigms to study different aspects of skill learning, including sensorimotor learning (e.g., force-field learning, visuomotor adaptation, etc.), reinforcement learning (e.g., bandit tasks, probabilistic RL, stimulus-response map learning, etc.), and more traditional psychophysics (e.g., evidence accumulation). Our goal is to use behavioral experiments to test novel computational theories of learning.

Computational Modeling

Computational modeling has many functions for our research, from generating precise behavioral and neural predictions, to simply organizing our thoughts. Our modeling approach relies on various machine learning techniques (e.g., state-space modeling, reinforcement learning, simple neural networks) and cognitive psychological approaches (e.g., drift-diffusion models).

Neuroimaging and Neuropsychology

Functional Magnetic Resonance Imaging has made great progress as a neuroscience technique over the last two decades. We use a combination of model-driven and multivariate fMRI methods to characterize teaching signals and map the neural circuits underlying various forms of learning. We also work with populations with particular neural pathologies, such as spino-cerebellar ataxia, to study how different neural regions (e.g., the cerebellum, basal ganglia, etc.) contribute to different aspects of learning, memory, and motor control. We use neuropsychology both to address basic research questions, and to try and inspire improvements in neurorehabilitation protocols.