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The human motor system is the point of interaction between mind, body, and environment. It provides the gateway for the expression of our mental activity, be it a reflexive response to a sensory event or an intentional action arising from the interplay of exogenous inputs, our current affective state, and a lifetime of experiences. My research examines the reciprocal interaction between cognition and action. Using psychophysical, computational, neuroimaging, and neuromodulation methods, I examine how fundamental principles identified in studies of cognition apply to motor behavior, and in turn, how what we learn from motor processes can teach us about cognition.

Classical conditioning in motor learning

Certain aspects of motor learning have traditionally been modeled from an engineering perspective as a control system in which errors update internal models of a sensorimotor map. However, a central idea in the cognitive literature is that the brain is an associative machine, as exemplified by research on classical conditioning. We bring the two worlds together by showing how core rules of classical conditioning manifest in motor learning (Avraham et al. 2022, eLife). 


Motor memories

A common conception about motor skills is their consolidation in strong implicit memories (e.g., we never forget how to ride a bike). Using tools that we developed to segregate the contributions of explicit and implicit processes, we discovered that the expression of motor memories when relearning a motor task solely reflects an efficient recall of explicit strategies. Not only does implicit memory fail to enhance relearning, but it is actually attenuated (Avraham et al. 2021, PLoS Biol). In looking at the mechanism underlying this attenuation, we found that the implicit motor system is highly sensitive to interference from the extinction period that separates the learning blocks.


Artificial motor intelligence

Inspired by the artificial intelligence literature, we developed a motor version of the Turing test, asking how people recognize a handshake (Karniel, Avraham et al. 2010, JoVE; Nisky, Avraham & Karniel 2012, Presence), and we used computational models to identify features affecting this decision (Avraham et al. 2012, IEEE T Haptics). Aside from providing the means to define biological movements, this approach allows us to understand the psychological aspects of human motor interaction. 


Delay representation in the motor system

It takes time for information to travel through the nervous system. We examined how the motor system copes with time delays in the processing of sensory feedback from movement. Using computational modeling and psychophysical kinematic data, we found that, contrary to common belief, the motor system does not form an accurate representation of the actual temporal lag. Instead, it uses an approximation of the delay using state information (Avraham G et al. 2017a, JNP; Avraham G et al. 2017b, eNeuro; Avraham C et al. 2018 & 2019, Front Hum Neurosci; Avraham G*, Sulimani* et al. 2019, JNP).


Neural representation of movement outcome

We studied the neural representation of erroneous and successful movements (e.g., missing or scoring basketball shots). Using fMRI, we identified multiple brain networks that are involved in the processing of errors and successes in a motor task.


Cognition and neuromodulation for movement disorders

In collaboration with UCSF, we employ new neuromodulation methods, specifically adaptive deep brain stimulation, to examine interactions between motor and cognitive processes at the neural level. We test how an associative framework can help improve these methods in the treatment of patients with movement and cognitive disorders, such as Parkinson’s disease.

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