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

Homo economicus in visual search

How do reward outcomes affect early visual performance? Previous studies found a suboptimal influence, but they ignored the non-linearity in how subjects perceived the reward outcomes. In contrast, we find that when the non-linearity is accounted for, humans behave optimally and maximize expected reward. Our subjects were asked to detect the presence of a familiar target object in a cluttered scene. They were rewarded according to their performance. We systematically varied the target frequency and the reward/penalty policy for detecting/missing the targets. We find that 1) decreasing the target frequency will decrease the detection rates, in accordance with the literature. 2) Contrary to previous studies, increasing the target detection rewards will compensate for target rarity and restore detection performance. 3) A quantitative model based on reward maximization accurately predicts human detection behavior in all target frequency and reward conditions; thus, reward schemes can be designed to obtain desired detection rates for rare targets. 4) Subjects quickly learn the optimal decision strategy; we propose a neurally plausible model that exhibits the same properties. Potential applications include designing reward schemes to improve detection of life-critical, rare targets (e.g., cancers in medical images)

Receiver Operating Characteristic and Location Analysis of Simulated Near-Infrared Tomography Images

Receiver operating characteristic (ROC) analysis was performed on simulated near-infrared tomography images, using both human observer and contrast-to-noise ratio (CNR) computational assessment, for application in breast cancer imaging. In the analysis, a nonparametric approach was applied for estimating the ROC curves. Human observer detection of objects had superior capability to localize the presence of heterogeneities when the objects were small with high contrast, with a minimum detectable threshold of CNR near 3.0 to 3.3 in the images. Human observers were able to detect heterogeneities in the images below a size limit of 4 mm, yet could not accurately find the location of these objects when they were below 10 mm diameter. For large objects, the lower limit of a detectable contrast limit was near 10% increase relative to the background. The results also indicate that iterations of the nonlinear reconstruction algorithm beyond 4 did not significantly improve the human detection ability, and degraded the overall localization ability for the objects in the image, predominantly by increasing the noise in the background. Interobserver variance performance in detecting objects in these images was low, suggesting that because of the low spatial resolution, detection tasks with NIR tomography is likely consistent between human observers

What visual information is used for stereoscopic depth displacement discrimination?

There are two ways to detect a displacement in stereoscopic depth, namely by monitoring the change in disparity over time (CDOT) or by monitoring the inter-ocular velocity difference (IOVD). Though previous studies have attempted to understand which cue is most significant for the visual system, none have designed stimuli that provide a comparison in terms of relative efficiency between them. Here we used two-frame motion and random dot noise to deliver equivalent strengths of CDOT and IOVD information to the visual system. Using three kinds of random dot stimuli, we were able to isolate CDOT or IOVD or deliver both simultaneously. The proportion of dots delivering CDOT or IOVD signals could be varied, and we defined discrimination threshold as the proportion needed to detect the direction of displacement (towards or away)1. Thresholds were similar for stimuli containing CDOT only, and containing both CDOT and IOVD, but only one participant was able to consistently perceive the displacement for stimuli containing only IOVD. We also investigated the effect of disparity pedestals on discrimination. Performance was best when the displacement crossed the reference plane, but was not significantly different for stimuli containing CDOT only, or containing both CDOT and IOVD. When stimuli are specifically designed to provide equivalent two-frame motion or disparity-change, few participants can reliably detect displacement when IOVD is the only cue. This challenges the notion that IOVD is involved in the discrimination of direction of displacement in two-frame motion displays.PreprintPeer reviewe

DETECTION OF CARIES ADJACENT TO TOOTH COLORED PROXIMAL RESTORATIONS USING STATIONARY INTRAORAL TOMOSYNTHESIS

Objectives: Caries adjacent to restorations (CAR) is the most common reason for replacing restorations. This study compared the ability of stationary intraoral tomosynthesis (s-IOT) and conventional bitewing radiographs in detecting CAR. Methods: Extracted teeth (N=77) with 113 proximal tooth-colored restorations were used. Observers (N=7) utilized a 5-point scale to rate their confidence that CAR was present and stereomicroscopy was used to establish ground truth. Results: S-IOT had a statistically higher (ANOVA p0.05). Conclusion: S-IOT showed higher diagnostic accuracy and sensitivity than conventional bitewing radiographs for detecting caries around proximal composite restorations. While the clinical effect size is small, s-IOT is a promising imaging modality for advancing the detection of CAR.Master of Scienc

INVESTIGATION OF THE VISUAL BOUNDARY FOR IMMEDIATE PERCEPTION OF VERTICAL RATE OF DESCENT

Visual boundary for human perception of vertical rate of descen