Evaluation of a decision support system using Bayesian network modeling in an applied Multi-INT surveillance environment.

Sensemaking and decision-making are fundamental components of applied Intelligence, Surveillance, and Reconnaissance (ISR). Analysts acquire information from multiple sources over a period of hours, days, or even over the scale of months or years, that must be interpreted and integrated to predict future adversarial events. Sensemaking is essential for developing an appropriate mental model that will lead to accurate predictions sooner.

Evaluation of Simulated Recognition Aids for Human Sensemaking in Applied Surveillance Scenarios.

Objective: We determined whether the human capability for sensemaking, or for identifying essential elements of information (EEIs), could be enhanced by a simulated recognition aid that directed attention to people and vehicles in scenes or by a simulated recognition aid that directed attention to EEIs.

Background: For intelligence analysts, sensemaking is challenging because it frequently involves making inferences about uncertain data. One way to enhance sensemaking may involve collaboration from a machine recognition aid such as Project Maven, an established algorithm that directs analysts' attention to people and vehicles in scenes.

Computational Modeling of the Effects of Sleep Deprivation on the Vigilance Decrement.

Objective: We developed a computational model of the effects of sleep deprivation on the vigilance decrement by employing the methods of system dynamics modeling.

Background: Situations that require sustained attention for a prolonged duration can cause a decline in cognitive performance, the so-called . One factor that should influence the vigilance decrement is fatigue in the form of sleep deprivation.

Effects of Stereoscopic Depth on Vigilance Performance and Cerebral Hemodynamics.

Objective: We tested the possibility that monitoring a display wherein critical signals for detection were defined by a stereoscopic three-dimensional (3-D) image might be more resistant to the vigilance decrement, and to temporal declines in cerebral blood flow velocity (CBFV), than monitoring a display featuring a customary two-dimensional (2-D) image.

Background: Hancock has asserted that vigilance studies typically employ stimuli for detection that do not exemplify those that occur in the natural world. As a result, human performance is suboptimal.

Training intuitive decision making in a simulated real-world environment.

Objective: We investigated whether naturalistic, intuitive (pattern recognition-based) decision making can be developed via implicit statistical learning in a simulated real-world environment.

Background: To our knowledge, no definitive studies have actually shown that implicit learning plays a causal role in the development of intuitive decision making when the latter is defined as pattern recognition of real-world, or simulated real-world, environmental situations.

Method: The simulated environment was presented dynamically so as to induce a sense of simulated locomotion through the scene and over sequences of objects on the ground.