Goal-directed Attentional Control Research Articles

Establishment of an attentional set via statistical learning.

The ability to overcome attentional capture and attend goal-relevant information is typically viewed as a volitional, effortful process that relies on the maintenance of current task priorities or "attentional sets" in working memory. However, the visual system possesses statistical learning mechanisms that can incidentally encode probabilistic associations between goal-relevant objects and the attributes likely to define them. Thus, it is possible that statistical learning may contribute to the establishment of a given attentional set and modulate the effects of attentional capture. Here we provide evidence for such a mechanism, showing that implicitly learned associations between a search target and its likely color directly influence the ability of a salient color precue to capture attention in a classic attentional capture task. This indicates a novel role for statistical learning in the modulation of attentional capture, and emphasizes the role that this learning may play in goal-directed attentional control more generally.

Neural Mechanisms of Attentional Control Differentiate Trait and State Negative Affect

The present research examined the hypothesis that cognitive processes are modulated differentially by trait and state negative affect (NA). Brain activation associated with trait and state NA was measured by fMRI during an attentional control task, the emotion-word Stroop. Performance on the task was disrupted only by state NA. Trait NA was associated with reduced activity in several regions, including a prefrontal area that has been shown to be involved in top-down, goal-directed attentional control. In contrast, state NA was associated with increased activity in several regions, including a prefrontal region that has been shown to be involved in stimulus-driven aspects of attentional control. Results suggest that NA has a significant impact on cognition, and that state and trait NA disrupt attentional control in distinct ways.

The Influence of Anxiety on Visual Attentional Control in Basketball Free Throw Shooting

The aim of this study was to test the predictions of attentional control theory using the quiet eye period as an objective measure of attentional control. Ten basketball players took free throws in two counterbalanced experimental conditions designed to manipulate the anxiety they experienced. Point of gaze was measured using an ASL Mobile Eye tracker and fixations including the quiet eye were determined using frame-by-frame analysis. The manipulation of anxiety resulted in significant reductions in the duration of the quiet eye period and free throw success rate, thus supporting the predictions of attentional control theory. Anxiety impaired goal-directed attentional control (quiet eye period) at the expense of stimulus-driven control (more fixations of shorter duration to various targets). The findings suggest that attentional control theory may be a useful theoretical framework for examining the relationship between anxiety and performance in visuomotor sport skills.

Content-based control of goal-directed attention during human action perception

During the perception of human actions by robotic assistants, the robotic assistant needs to direct its computational and sensor resources to relevant parts of the human action. In previous work we have introduced HAMMER (Hierarchical Attentive Multiple Models for Execution and Recognition) (Demiris and Khadhouri, 2006), a computational architecture that forms multiple hypotheses with respect to what the demonstrated task is, and multiple predictions with respect to the forthcoming states of the human action. To confirm their predictions, the hypotheses request information from an attentional mechanism, which allocates the robot’s resources as a function of the saliency of the hypotheses. In this paper we augment the attention mechanism with a component that considers the content of the hypotheses’ requests, with respect to the content’s reliability, utility and cost. This content-based attention component further optimises the utilisation of the resources while remaining robust to noise. Such computational mechanisms are important for the development of robotic devices that will rapidly respond to human actions, either for imitation or collaboration purposes.