**Exclusion Criteria** Subject data will be excluded on the following grounds: * Subjects reported being under 18 years of age. * Subjects reported problems with experimental playback, such as stuttering, freezing (pre-determined options) or another issue specified in a free-response. A coder who is blind to the condition assignment will examine these open-ended responses to determine whether or not the subject should be excluded. When the response is ambiguous, their data will be excluded. * Subjects reported needing correction to their vision but not wearing it during the experiment. * Subjects reported counts that err by more than 50% in either direction more than once. * Subjects report any abnormalities with their color vision. * Subjects misidentify the number in Ishihara Plate 9. * Subjects reported having performed a similar task before, wherein they tracked multiple objects and something unexpected appeared. If subjects reported participating in a smiliar task, an independent coder will examine their descriptions of prior experience to determine whether the subject should be excluded. * Subjects failed to answer any question during the course of the experiment. **Measures** <br> After each trial in which no unexpected object appears and after the critical trial, we will collect subjects' counts of how many times the attended objects bounced off the edge of the frame. On the critical trial, we will ask whether an extra object was present during the trial, and then ask them to describe the shape and color from a menu of pre-determined options. Subjects will be counted as having noticed the object only if they affirm having seen something new and correctly identify either its shape, color, or both. <br> We will also collect a set of demographic data for exclusion purposes, as well as characterizing our sample. We will verify that there are no systematic differences in the data based on country of origin, and we will verify that the two conditions (attend white vs. attend nonwhite) have similar error rates to ensure that one version of the task is not more difficult than the other. **Analysis** <br> The two hypotheses predict two different, easily distinguishable data patterns. 1. The individual-items hypothesis predicts no difference in noticing rates for the uniquely-colored object when subjects attend to white versus nonwhite objects. In both conditions, the unique item is different from the ignored set, and so should be noticed at relatively high rates. 2. The categorical hypothesis predicts a negative difference score between the attend-white and attend-nonwhite conditions. In the attend-white condition, the unexpected object will be "nonwhite," just like the ignored set of objects, and so should be suppressed and noticed infrequently. In the attend-nonwhite condition, the unique item is both different from the ignored set and similar to the attended set, and should therefore see a boost in noticing rates. For each test, we will determine the difference in noticing rates for each condition and will compute a 95% confidence interval around that difference. While only the difference score for the uniquely-colored object should differ between the two hypotheses, computing the difference in noticing between white and nonwhite objects between the two conditions serves as a useful indicator that the tasks are equal across conditions. Regardless of which hypothesis is true, the difference between white noticing rates across the conditions should be positive, and the difference between nonwhite noticing rates should be negative.