What draws in human attention and can we create computational models of it which work the same way? Here we explore this question with several attentional models and applications of them. They are each designed to address a missing fundamental function of attention from the original saliency model designed by Itti and Koch. These include temporal based attention and attention from non-classical feature interactions. Additionally, attention is utilized in an applied setting for the purposes of video tracking. Attention for non-classical feature interactions is handled by a model called CINNIC. It faithfully implements a model of contour integration in visual cortex. It is able to integrate illusory contours of unconnected elements such that the contours "pop-out" as they are supposed to and matches in behavior the performance of human observers. Temporal attention is discussed in the context of an implementation and extensions to a model of surprise. We show that surprise predicts well subject performance on natural image Rapid Serial Vision Presentation (RSVP) and gives us a good idea of how an attention gate works in the human visual cortex. The attention gate derived from surprise also gives us a good idea of how visual information is passed to further processing in later stages of the human brain. It is also discussed how to extend the model of surprise using a Metric of Attention Gating (MAG) as a baseline for model performance. This allows us to find different model components and parameters which better explain the attentional blink in RSVP.