TVA-based assessment of attentional capacities-associations with age and indices of brain white matter microstructure.

In this study the primary aims were to characterize the effects of age on basic components of visual attention derived from assessments based on a theory of visual attention (TVA) in 325 healthy volunteers covering the adult lifespan (19-81 years). Furthermore, we aimed to investigate how age-related differences on TVA parameters are associated with white matter (WM) microstructure as indexed by diffusion tensor imaging (DTI). Finally, we explored how TVA parameter estimates were associated with complex, or multicomponent indices of processing speed (Digit-symbol substitution, DSS) and fluid intelligence (gF).

Beyond trial types.

Conventional wisdom on psychological experiments has held that when one or more independent variables are manipulated it is essential that all other conditions are kept constant such that confounding factors can be assumed negligible (Woodworth, 1938). In practice, the latter assumption is often questionable because it is generally difficult to guarantee that all other conditions are constant between any two trials. Therefore, the most common way to check for confounding violations of this assumption is to split the experimental conditions in terms of "trial types" to simulate a reduction of unintended trial-by-trial variation.

Neural correlates of age-related decline and compensation in visual attention capacity.

We identified neural correlates of declined and preserved basic visual attention functions in aging individuals based on Bundesen "Theory of Visual Attention". In an interindividual difference approach, we contrasted electrophysiology of higher- and lower-performing younger and older participants. In both age groups, the same distinct components indexed performance levels of parameters visual processing speed C and visual short-term memory storage capacity K.

Distinct neural markers of TVA-based visual processing speed and short-term storage capacity parameters.

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.

Visual attention capacity parameters covary with hemifield alignment.

The theory of visual attention (TVA; Bundesen, 1990. Psychological Review, 97(4), 523-547), allows one to measure distinct visual attention parameters, such as the temporal threshold for visual perception, visual processing capacity, and visual short-term memory (VSTM) capacity. It has long been assumed that visual processing capacity and VSTM capacity parameters are nearly constant from trial to trial.

Identifying bottom-up and top-down components of attentional weight by experimental analysis and computational modeling.

The attentional weight of a visual object depends on the contrast of the features of the object to its local surroundings (feature contrast) and the relevance of the features to one's goals (feature relevance). We investigated the dependency in partial report experiments with briefly presented stimuli but unspeeded responses. The task was to report the letters from a mixture of letters (targets) and digits (distractors).

Attentional priorities and access to short-term memory: parietal interactions.

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.

Removal of BCG artifacts using a non-Kirchhoffian overcomplete representation.

We present a nonlinear unmixing approach for extracting the ballistocardiogram (BCG) from EEG recorded in an MR scanner during simultaneous acquisition of functional MRI (fMRI). First, an overcomplete basis is identified in the EEG based on a custom multipath EEG electrode cap. Next, the overcomplete basis is used to infer non-Kirchhoffian latent variables that are not consistent with a conservative electric field.

Smooth bilinear classification of EEG.

The goal of this paper is to improve on single-trial classification of electro-encephalography (EEG) using linear methods. The paper proposes to combine the classification of the spatial distribution of activity with the classification of its temporal profile. The work is based on the idea that a current source in the brain has a reproducible temporal profile with a static spatial projection to the electrodes.

Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG.

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).