Showing papers by "Susan M. Courtney published in 2022"
TL;DR: Working memory is considered, not as a process for static maintenance in a particular set of brain regions, but rather as a dynamic process unfolding to serve future needs, reflected in the structural and functional state of multiple brain areas and the trajectory of that state over time.
Abstract: ABSTRACT I propose working memory be considered, not as a process for static maintenance in a particular set of brain regions, but rather as a dynamic process unfolding to serve future needs. Brain regions such as the hippocampus, or sensory and motor regions, may be necessarily recruited during this process, depending on task demands. Information stored in working memory is thus a distributed representation reflected in the structural and functional state of multiple brain areas and the trajectory of that state over time. Recent research is discussed in support of this view.
2 citations
TL;DR: This study develops the first participant-specific feedforward neural network models of reaction time from neural data during a VDAC WM task and finds that right frontal gamma-band activity and fronto-posterior functional connectivity in the alpha, beta, and gamma bands explain individual differences.
Abstract: Value-driven attention capture (VDAC) occurs when previously rewarded stimuli capture attention and impair goal-directed behavior. In a working memory (WM) task with VDAC-related distractors, we observe behavioral variability both within and across individuals. Individuals differ in their ability to maintain relevant information and ignore distractions. These cognitive components shift over time with changes in motivation and attention, making it difficult to identify underlying neural mechanisms of individual differences. In this study, we develop the first participant-specific feedforward neural network models of reaction time from neural data during a VDAC WM task. We used short epochs of electroencephalography (EEG) data from 16 participants to develop the feedforward neural network (NN) models of RT aimed at understanding both WM and VDAC. Using general linear models (GLM), we identified 20 EEG features to predict RT across participants (r=0.53±0.08). The linear model was compared to the NN model, which improved the predicted trial-by-trial RT for all participants (r=0.87±0.04). We found that right frontal gamma-band activity and fronto-posterior functional connectivity in the alpha, beta, and gamma bands explain individual differences. Our study shows that NN models can link neural activity to highly variable behavior and can identify potential new targets for neuromodulation interventions.
25 Jan 2022
TL;DR: The interaction of spatial and temporal structures across perceptual modalities impacts representation in working memory as discussed by the authors , and shared temporal structure between visual targets and a stream of sounds promotes representation of the spatial structure of those sounds.
Abstract: The interaction of spatial and temporal structures across perceptual modalities impacts representation in working memory. Shared temporal structure between a stream of visual targets and a stream of sounds promotes representation of the spatial structure of those sounds. This integration of perceptual information occurs whether helpful or harmful, differentially impacting performance.