Attention Allocation Aid for Visual Search

This paper outlines the development and testing of a novel, feedback-enabled attention allocation aid (AAAD), which uses real-time physiological data to improve human performance in a realistic sequential visual search task. Indeed, by optimizing over search duration, the aid improves efficiency, while preserving decision accuracy, as the operator identifies and classifies targets within simulated aerial imagery. Specifically, using experimental eye-tracking data and measurements about target detectability across the human visual field, we develop functional models of detection accuracy as a function of search time, number of eye movements, scan path, and image clutter. These models are then used by the AAAD in conjunction with real time eye position data to make probabilistic estimations of attained search accuracy and to recommend that the observer either move on to the next image or continue exploring the present image. An experimental evaluation in a scenario motivated from human supervisory control in surveillance missions confirms the benefits of the AAAD.Comment: To be presented at the ACM CHI conference in Denver, Colorado in May 201

Financial Contagion and Attention Allocation

This paper explains financial contagion between two independent stock markets by fluctuations in international investors' attention allocation. I model the process of attention allocation that underlies portfolio investment in international markets using rationally inattentive agents. Investors optimally allocate more attention to a region hit by a financial crisis, to the detriment of other markets. The resulting endogenous increase in uncertainty causes the risk premium on all risky assets to rise. Hence, stock prices around the world collapse and there is a flight to quality. I show that the degree of (non)anticipation of a crisis is crucial for the existence of contagion. Using Financial Times coverage as a proxy for attention allocation, I find strong support for the model's predictions.Financial Crises, Rational Inattention, Portfolio Choice

Rational Attention Allocation Over the Business Cycle

The literature assessing whether mutual fund managers have skill typically regards skill as an immutable attribute of the manager or the fund. Yet, many measures of skill, such as returns, alphas, and measures of stock-picking and market-timing, appear to vary over the business cycle. Because time-varying ability seems far-fetched, these results call into question the existence of skill itself. This paper offers a rational explanation, arguing that skill is a general cognitive ability that can be applied to different tasks, such as picking stocks or market timing. Using tools from the rational inattention literature, we show that the relative value of these tasks varies cyclically. The model generates indirect predictions for the dispersion and returns of fund portfolios that distinguish this explanation from others and which are supported by the data. In turn, these findings offer useful evidence to support the notion of rational attention allocation.

Attention allocation before antisaccades

In the present study, we investigated the distribution of attention before antisaccades. We used a dual task paradigm, in which participants made prosaccades or antisaccades and discriminated the orientation of a visual probe shown at the saccade goal, the visual cue location (antisaccade condition), or a neutral location. Moreover, participants indicated whether they had made a correct antisaccade or an erroneous prosaccade. We observed that, while spatial attention in the prosaccade task was allocated only to the saccade goal, attention in the antisaccade task was allocated both to the cued location and to the antisaccade goal. This suggests parallel attentional selection of the cued and antisaccade locations. We further observed that in error trials-in which participants made an incorrect prosaccade instead of an antisaccade-spatial attention was biased towards the prosaccade goal. These erroneous prosaccades were mostly unnoticed and were often followed by corrective antisaccades with very short latencies (<100 ms). Data from error trials therefore provide further evidence for the parallel programming of the reflexive prosaccade to the cue and the antisaccade to the intended location. Taken together, our results suggest that attention allocation and saccade goal selection in the antisaccade task are mediated by a common competitive process

Attention allocation before antisaccades

In the present study, we investigated the distribution of attention before antisaccades. We used a dual task paradigm, in which participants made prosaccades or antisaccades and discriminated the orientation of a visual probe shown at the saccade goal, the visual cue location (antisaccade condition), or a neutral location. Moreover, participants indicated whether they had made a correct antisaccade or an erroneous prosaccade. We observed that, while spatial attention in the prosaccade task was allocated only to the saccade goal, attention in the antisaccade task was allocated both to the cued location and to the antisaccade goal. This suggests parallel attentional selection of the cued and antisaccade locations. We further observed that in error trials-in which participants made an incorrect prosaccade instead of an antisaccade-spatial attention was biased towards the prosaccade goal. These erroneous prosaccades were mostly unnoticed and were often followed by corrective antisaccades with very short latencies (<100 ms). Data from error trials therefore provide further evidence for the parallel programming of the reflexive prosaccade to the cue and the antisaccade to the intended location. Taken together, our results suggest that attention allocation and saccade goal selection in the antisaccade task are mediated by a common competitive process

Error rate information in attention allocation pilot models

The Northrop urgency decision pilot model was used in a command tracking task to compare the optimized performance of multiaxis attention allocation pilot models whose urgency functions were (1) based on tracking error alone, and (2) based on both tracking error and error rate. A matrix of system dynamics and command inputs was employed, to create both symmetric and asymmetric two axis compensatory tracking tasks. All tasks were single loop on each axis. Analysis showed that a model that allocates control attention through nonlinear urgency functions using only error information could not achieve performance of the full model whose attention shifting algorithm included both error and error rate terms. Subsequent to this analysis, tracking performance predictions for the full model were verified by piloted flight simulation. Complete model and simulation data are presented

Dynamics of Attention in Depth: Evidence from Mutli-Element Tracking

The allocation of attention in depth is examined using a multi-element tracking paradigm. Observers are required to track a predefined subset of from two to eight elements in displays containing up to sixteen identical moving elements. We first show that depth cues, such as binocular disparity and occlusion through T-junctions, improve performance in a multi-element tracking task in the case where element boundaries are allowed to intersect in the depiction of motion in a single fronto-parallel plane. We also show that the allocation of attention across two perceptually distinguishable planar surfaces either fronto-parallel or receding at a slanting angle and defined by coplanar elements, is easier than allocation of attention within a single surface. The same result was not found when attention was required to be deployed across items of two color populations rather than of a single color. Our results suggest that, when surface information does not suffice to distinguish between targets and distractors that are embedded in these surfaces, division of attention across two surfaces aids in tracking moving targets.National Science Foundation (IRI-94-01659); Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

Extinction of likes and dislikes : effects of feature-specific attention allocation

The evaluative conditioning (EC) effect refers to the change in the liking of a neutral stimulus (conditioned stimulus, CS) due to its pairing with another stimulus (unconditioned stimulus, US). We examined whether the extinction rate of the EC effect is moderated by feature-specific attention allocation. In two experiments, CSs were abstract Gabor patches varying along two orthogonal, perceptual dimensions (i.e. spatial frequency and orientation). During the acquisition phase, one of these dimensions was predictive of the valence of the USs. During the extinction phase, CSs were presented alone and participants were asked to categorise the CSs either according to their valence, the perceptual dimension that was task-relevant during the acquisition phase, or a perceptual dimension that was task-irrelevant during the acquisition phase. As predicted, explicit valence measures revealed a linear increase in the extinction rate of the EC effect as participants were encouraged to assign attention to non-evaluative stimulus information during the extinction phase. In Experiment 1, Affect Misattribution Paradigm (AMP) data mimicked this pattern of results, although the effect just missed conventional levels of significance. In Experiment 2, the AMP data revealed an increase of the EC effect if attention was focused on evaluative stimulus information. Potential mechanisms to explain these findings are discussed

A model for dynamic allocation of human attention among multiple tasks

The problem of multi-task attention allocation with special reference to aircraft piloting is discussed with the experimental paradigm used to characterize this situation and the experimental results obtained in the first phase of the research. A qualitative description of an approach to mathematical modeling, and some results obtained with it are also presented to indicate what aspects of the model are most promising. Two appendices are given which (1) discuss the model in relation to graph theory and optimization and (2) specify the optimization algorithm of the model