Grace Hallenbeck



I'm a graduate student in the Curtis lab interested in how the brain represents information. First, how is it that neural populations can keep information “online” through moment-to-moment conscious experience? Second, what contributes to some of this information becoming prioritized over other information?

To explore these questions, I’m focused on combining three methods. First, I use fMRI to identify regions of interest in the brain during working memory by using retinotopic principles. Second, I use TMS to disturb these regions so that we can measure how working memory is impaired in such an instance. Third, I use eye-tracking and automated data scoring to obtain such a continuous measure of working memory impairment.

Outside the lab, you can find me in the 6th borough* hanging out with my cats and my dog, probably in my garden.

*New Jersey

[CV | github

 Cartoon depiction of how salience and relevance each impact priority maps

Cartoon depiction of how salience and relevance each impact priority maps

 Modeled disturbance induced by TMS

Modeled disturbance induced by TMS


How does the visual system select items in the scene for further processing and guidance of behavior? A prominent theoretical framework posits that different scene elements are indexed according to their 'priority' in a series of interacting retinotopic maps. I seek to understand how manipulations of different factors, such as the visual salience and behavioral relevance of scene elements, alters neural activity patterns in visual, parietal, and frontal regions thought to support priority maps. By applying computational neuroimaging methods, we can reconstruct high-fidelity spatial priority maps from neural activity patterns, and quantify how those maps change across task demands


By using transcranial magnetic stimulation (TMS), we can perturb purported priority maps and thereby infer their functional relevance to working memory processes. Additionally, we can estimate the strength of the stimulation using novel computational modeling (SimNIBS), ensuring our region of interest will be stimulated maximally.

Automatic data scoring

We measure working memory performance by measuring eye-movements, a natural index of cognitive processing. Using custom-built in-house software (iEye), we automatically score the eye-movement data.