Adcock Research

Functional Architecture

How do we preferentially remember what we want to remember? How does your brain selectively encode the memories that are more valuable? Our laboratory uses novel behavioral paradigms in conjunction with functional neuroimaging to understand the functional architecture underlying motivated memory. 

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State and Trait Differences

How are relatively stable predispositions of biological trait or psychological personality trait involved in the dopamine-modulated enhancement in receptivity to learning and memory? On the other hand, how does transitory and labile conative state differences induced by various experimental manipulations with respect to reward context account for the underlying mechanisms of reward-motivated memory formation? A series of projects in our lab is dedicated to exploring these state and trait differences.


In our laboratory we pursue a mechanistic neurobiological understanding of the phenomenon of motivated memory. Specifically, we study the effects of neuromodulatory neurotransmitter systems -- the neurochemistry -- on memory formation in the human brain, with a particular interest in the reward-driven neuromodulation of episodic memory formation that is likely to involve the dopaminergic system. 

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Behavioral Neurostimulation

​Expanding the definition of neurostimulation. Are there ways to directly stimulate neural pathways non-invasively? We are calling it behavioral neurostimulation; that is, using the environment or a mental strategy -- not an implanted device -- to target activity in neural circuits involved in emotion, motivation, and reward processing and thereby enhance learning and memory. 


We often conflate novelty and expectancy violations and use these terms synonymously, even though, in the real-world, these can occur separately or together. This is further perpetuated by the fact that we tend to only study novelty and expectancy violations together in the laboratory setting. In our lab, we are seeking to dissociate novelty and expectancy violations in order to understand the unique neural networks that encode novelty, expectancy violations, and the combination of both.


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