Biology subjects

Shohamy, D.

Publications and source records attributed to Shohamy, D..

3 recordsLinked to original sources

bioRxiv2018

Two Sides of the Same Coin: The Hippocampus as a Common Neural Substrate for Model-Based Planning and Spatial Memory

Little is known about the neural mechanisms that allow humans and animals to plan actions using knowledge of task contingencies. Emerging theories hypothesize that it involves the same hippocampal mechanisms that support self-localization and memory for locations. Yet, there is limited direct evidence for the link between model-based planning and the hippocampal place map. We addressed this by investigating model-based planning and place memory in healthy controls and epilepsy patients treated using unilateral anterior temporal lobectomy with hippocampal resection. We found that both functions were impaired in the patient group. Specifically, the planning impairment was related to right hippocampal lesion size, controlling for overall lesion size. Furthermore, planning and place memory covaried with one another, but only in neurologically intact controls, consistent with both functions relying on the same structure in the healthy brain. These findings clarify the scope of hippocampal contributions to behavior and the neural mechanism of model-based planning.

neuroscience↗

bioRxiv2018

Value-based decisions involve sequential sampling from memory

Deciding between two equally appealing options can take considerable time. This observation has puzzled economists and philosophers, because more deliberation only delays the reward. Here we show that this seemingly irrational behavior is explained by the constructive use of memory. Using functional brain imaging in humans, we show that how long it takes to decide between two familiar food items is related to activity in the hippocampus, within specific regions shown to be associated with the retrieval of long-term memories. Moreover, we show that value is partially constructed during deliberation to resolve preference, and this constructive process changes behavior and brain responses. These results render memory as a supplier of evidence in value-based decisions, resolving a central paradox of choice.

animal behavior and cognition↗

bioRxiv2016

Dynamic flexibility in striatal-cortical circuits supports reinforcement learning

Complex learned behaviors must involve the integrated action of distributed brain circuits. While the contributions of individual regions to learning have been extensively investigated, understanding how distributed brain networks orchestrate their activity over the course of learning remains elusive. To address this gap, we used fMRI combined with tools from dynamic network neuroscience to obtain time-resolved descriptions of network coordination during reinforcement learning. We found that learning to associate visual cues with reward involves dynamic changes in network coupling between the striatum and distributed brain regions, including visual, orbitofrontal, and ventromedial prefrontal cortex. Moreover, we found that flexibility in striatal network dynamics correlates with participants learning rate and inverse temperature, two parameters derived from reinforcement learning models. Finally, we found that not all forms of learning relate to this circuit: episodic memory, measured in the same participants at the same time, was related to dynamic connectivity in distinct brain networks. These results suggest that dynamic changes in striatal-centered networks provide a mechanism for information integration during reinforcement learning.\n\nSignificance StatementLearning from the outcomes of actions-referred to as reinforcement learning-is an essential part of life. The roles of individual brain regions in reinforcement learning have been well characterized in terms of the updating of values for actions or sensory stimuli. Missing from this account, however, is a description of the manner in which different brain areas interact during learning to integrate sensory and value information. Here we characterize flexible striatal-cortical network dynamics that relate to reinforcement learning behavior.

neuroscience↗