Users can make judgments about web pages in a glance. Outside of a few studies, no known research has examined what semantic information can be extracted from a web page within a single fixation, but the scene perception literature provides a possible framework for understanding how viewers can extract diverse semantic information from scenes in a glance. The purpose of this dissertation was to explore whether semantic information about a web page could be extracted within a single fixation and to explore the effects of size and resolution on extracting this information. Initially, the classification of web pages was explored, which provided web page stimuli for the following two studies. Using a rapid serial visual presentation (RSVP) paradigm, the first study explored whether certain semantic categories of websites (i.e., news, search, shopping, and social networks/blogs) could be detected from stream of web page stimuli that were presented briefly. Natural scenes, which have been shown to be detectable within a single fixation in the literature, served as a baseline for comparison. The second study examined the effects of stimulus size and resolution on observers' ability to detect the presence of a certain website category using similar methods. Results from this research showed that users have conceptual models of websites. These models allowed detection of web pages from a fixation's worth of stimulus exposure when provided with additional time for processing information. For the website categories other than search, detection performance decreased significantly when web elements were no longer discernible due to decreases in size and/or resolution. The implications of this research are that website conceptual models rely heavily on page elements and less on the spatial relationship between these elements.