Images of natural scenes are quickly and accurately categorized by human observers, even under conditions of limited attention (Li et al., 2002). Recent work has shown that images rated as highly representative of their category ("good" exemplars) are categorized more accurately by human observers compared to less representative images ("bad" exemplars; Torralbo et al. 2009). Here, we investigated the role of attention on the perception of good and bad natural scene category exemplars. In order to study scene perception under conditions of limited attention, we used statistical pattern recognition algorithms to "decode" scene category from fMRI data when observers' attention was directed toward scenes or a separate task at fixation. This technique has previously been used to show that scene category information is present in scene-selective cortical areas (Walther et al., 2009), and that "good" category exemplars can be decoded more accurately than "bad" ones (Torralbo et al, 2009). On separate runs, participants alternated between searching for predefined color-shape conjunction targets within a stream of small colored crosses superimposed at fixation on a stream natural scenes and detecting repetitions in the stream of scene images themselves. Because the displays were identical in both conditions, this allowed us to measure the influence of observers' attentional focus on scene category decoding accuracy. Our results again showed that decoding accuracy was higher for "good" than "bad" category exemplars. Additionally, we found that scene category decoding accuracy was higher when the task required participants to direct their attention to the scenes, though it remained above chance during the fixation task. However, when attention was directed away from scenes, the difference between "good" and "bad" exemplars was absent. This result indicates that the advantage for good natural scene category exemplars is influenced by the locus of visual attention.