Visual alerts are commonly used in video monitoring and surveillance systems to mark events, presumably making them
more salient to human observers. Surprisingly, the effectiveness of computer-generated alerts in improving human
performance has not been widely studied. To address this gap, we have developed a tool for simulating different alert
parameters in a realistic visual monitoring situation, and have measured human detection performance under conditions
that emulated different set-points in a surveillance algorithm. In the High-Sensitivity condition, the simulated alerts
identified 100% of the events with many false alarms. In the Lower-Sensitivity condition, the simulated alerts correctly
identified 70% of the targets, with fewer false alarms. In the control condition, no simulated alerts were provided. To
explore the effects of learning, subjects performed these tasks in three sessions, on separate days, in a counterbalanced,
within subject design. We explore these results within the context of cognitive models of human attention and learning.
We found that human observers were more likely to respond to events when marked by a visual alert. Learning played a
major role in the two alert conditions. In the first session, observers generated almost twice as many False Alarms as in
the No-Alert condition, as the observers responded pre-attentively to the computer-generated false alarms. However, this
rate dropped equally dramatically in later sessions, as observers learned to discount the false cues. Highest observer
Precision, Hits/(Hits + False Alarms), was achieved in the High Sensitivity condition, but only after training. The
successful evaluation of surveillance systems depends on understanding human attention and performance.