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An individual's visual attentional capacity is characterized by 2 central processing resources, visual perceptual processing speed and visual short-term memory (vSTM) storage capacity. Based on Bundesen's theory of visual attention (TVA), independent estimates of these parameters can be obtained from mathematical modeling of performance in a whole report task. The framework's neural interpretation (NTVA) further suggests distinct brain mechanisms underlying these 2 functions. Using an interindividual difference approach, the present study was designed to establish the respective ERP correlates of both parameters. Participants with higher compared to participants with lower processing speed were found to show significantly reduced visual N1 responses, indicative of higher efficiency in early visual processing. By contrast, for participants with higher relative to lower vSTM storage capacity, contralateral delay activity over visual areas was enhanced while overall nonlateralized delay activity was reduced, indicating that holding (the maximum number of) items in vSTM relies on topographically specific sustained activation within the visual system. Taken together, our findings show that the 2 main aspects of visual attentional capacity are reflected in separable neurophysiological markers, validating a central assumption of NTVA., (© The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

The intraparietal sulcus (IPS) has been implicated in selective attention as well as visual short-term memory (VSTM). To contrast mechanisms of target selection, distracter filtering, and access to VSTM, we combined behavioral testing, computational modeling and functional magnetic resonance imaging. Sixteen healthy subjects participated in a change detection task in which we manipulated both target and distracter set sizes. We directly compared the IPS response as a function of the number of targets and distracters in the display and in VSTM. When distracters were not present, the posterior and middle segments of IPS showed the predicted asymptotic activity increase with an increasing target set size. When distracters were added to a single target, activity also increased as predicted. However, the addition of distracters to multiple targets suppressed both middle and posterior IPS activities, thereby displaying a significant interaction between the two factors. The interaction between target and distracter set size in IPS could not be accounted for by a simple explanation in terms of number of items accessing VSTM. Instead, it led us to a model where items accessing VSTM receive differential weights depending on their behavioral relevance, and secondly, a suppressive effect originates during the selection phase when multiple targets and multiple distracters are simultaneously present. The reverse interaction between target and distracter set size was significant in the right temporoparietal junction (TPJ), where activity was highest for a single target compared to any other condition. Our study reconciles the role of middle IPS in attentional selection and biased competition with its role in VSTM access., (Copyright © 2012 Elsevier Inc. All rights reserved.)

We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model.