Integration of visuospatial information encoded from different viewpoints.

Two experiments investigated whether separate sets of objects viewed in the same environment but from different views were encoded as a single integrated representation or maintained as distinct representations. Participants viewed two circular layouts of objects that were placed around them in a round (Experiment 1) or a square (Experiment 2) room and were later tested on perspective-taking trials requiring retrieval of either one layout (within-layout trials) or both layouts (between-layout trials). Results from Experiment 1 indicated that participants did not integrate the two layouts into a single representation. Imagined perspective taking was more efficient on within- than on between-layout trials. Furthermore, performance for within-layout trials was best from the perspective that each layout was studied. Results from Experiment 2 indicated that the stable environmental reference frame provided by the square room caused many, but not all, participants to integrate all locations within a common representation. Participants who integrated performed equally well for within-layout and between-layout judgments and also represented both layouts using a common reference frame. Overall, these findings highlight the flexibility of organizing information in spatial memory.

Estimating the joint disease outbreak-detection time when an automated biosurveillance system is augmenting traditional clinical case finding.

The goals of automated biosurveillance systems are to detect disease outbreaks early, while exhibiting few false positives. Evaluation measures currently exist to estimate the expected detection time of biosurveillance systems. Researchers also have developed models that estimate clinician detection of cases of outbreak diseases, which is a process known as clinical case finding. However, little research has been done on estimating how well biosurveillance systems augment traditional outbreak detection that is carried out by clinicians. In this paper, we introduce a general approach for doing so for non-endemic disease outbreaks, which are characteristic of bioterrorist induced diseases, such as respiratory anthrax. We first layout the basic framework, which makes minimal assumptions, and then we specialize it in several ways. We illustrate the method using a Bayesian outbreak detection algorithm called PANDA, a model of clinician outbreak detection, and simulated cases of a windborne anthrax release. This analysis derives a bound on how well we would expect PANDA to augment clinician detection of an anthrax outbreak. The results support that such analyses are useful in assessing the extent to which computer-based outbreak detection systems are expected to augment traditional clinician outbreak detection.