Search

Visual popout refers to searches in which one feature distinguishes a target and time to detection is relatively unaffected by number of nontargets. Rapid detection can make it seem like information about a target is obtained immediately and passively. We sought to investigate the acquisition of target-related information in the earliest moments of a search. In Experiment 1 a color-singleton display was masked after a 16.7-900 ms exposure, and the task was to click the position that the target occupied. We translated average response-distance-error into entropy, an information-theoretic measure of uncertainty. We could thus ask how much increasing exposure reduces uncertainty about a target's position. In the first 200ms, uncertainty declined linearly, then plateauing (reflecting perceptual and motor precision). We fit two continuous time models to the responses of each participant, one characterizing a passive strategy of estimating an item's position successively from received signals, the other, a more active strategy of querying whether a signal is present in successively smaller portions of an image. The active model fit significantly better. In Experiment 2, we scrutinized individual differences in rate of information gain. We replicated the results of Experiment 1, accompanied by color change detection to measure working memory capacity. Rate of information gain was significantly correlated with memory capacity (r=0.55). This suggests that rate of information acquisition may underwrite broad cognitive functioning as WM capacity is known to do. Additional experiments related these results to reaction times in detection search and other forms of popout. Overall, the results suggest a new way to characterize visual search at the algorithmic level. We discuss how this algorithmic theory is consistent with psychological theories such as 'guided search,' and how it can be extended to account for inefficient conjunction search and search asymmetries. Meeting abstract presented at VSS 2015. Language: en

Intuitively, extrapolating object trajectories should make visual tracking more accurate. This has proven to be true in many contexts that involve tracking a single item. But surprisingly, when tracking multiple identical items in what is known as "multiple object tracking," observers often appear to ignore direction of motion, relying instead on basic spatial memory. We investigated potential reasons for this behavior through probabilistic models that were endowed with perceptual limitations in the range of typical human observers, including noisy spatial perception. When we compared a model that weights its extrapolations relative to other sources of information about object position, and one that does not extrapolate at all, we found no reliable difference in performance, belying the intuition that extrapolation always benefits tracking. In follow-up experiments we found this to be true for a variety of models that weight observations and predictions in different ways; in some cases we even observed worse performance for models that use extrapolations compared to a model that does not at all. Ultimately, the best performing models either did not extrapolate, or extrapolated very conservatively, relying heavily on observations. These results illustrate the difficulty and attendant hazards of using noisy inputs to extrapolate the trajectories of multiple objects simultaneously in situations with targets and featurally confusable nontargets.