UC Santa Barbara

Visual search is a critical and pervasive part of our everyday lives. However, the ease of which we perform search can mask its remarkable computational complexity. Search targets are often difficult to detect and embedded in statistically complex backgrounds. To optimize search, human observers often exploit known statistical properties of the visual environment which provide information about target location. Regularities in the spatial organization of the visual environment (e.g. predictive cues) have shown to be one such type of statistical property which can be leveraged to increase search efficiency. Here, a series of three studies examines how well human observers can exploit spatially predictive cues during multi-fixation search. Further exploration focuses on highlighting human flexibility in altering search strategy to enhance perceptual performance, as well as delineating situations in which predictive information in the environment may actually hurt search performance.

Predictive spatial cues have been shown to improve perceptual performance for a variety of tasks, including visual search, under conditions of forced fixation. However, the potential benefits of predictive cues during multi-fixation search are poorly understood. In the first study, we present a letter identification search task, done in the presence and absence of an array of spatial cues which framed potential target locations. We show that human observers direct their eye movements towards cued locations to improve their search performance compared to when the cues are absent. We also develop a foveated eye movement model, which takes into account the diminishing acuity of the human visual system in the periphery, for the task. Model predictions reveal substantial performance benefits via predictive cues, the size of which are much larger than what is seen in human data.

In the second study, we investigate whether human observers will utilize peripheral predictive cues which reside in display regions which have no chance of containing the target (which we call remote cues). In doing so, observers must depart from a commonly used "saccadic targeting" strategy, where eyes are directed to likely target locations. When informed of the predictive nature of the remote cues, observers readily adopt an atypical eye movement strategy which favors non-target locations to enhance task performance. A foveated model which ignores the cues reveals that these performance benefits are a likely result of foveating peripheral predictive information. Interestingly, a version of the remote cue task in which observers were not informed of cue contingencies, reveals that while observers can adopt atypical saccadic strategies to improve performance, they do not readily engage in such behavior without explicit information.

In the final study, we explore a situation in which predictive cues actually hinder search performance. In natural viewing environments and everyday search tasks, predictive cues often do not mark the only target locations, but merely likely ones. We return to the letter identification search task and modify the cues so that they are only partially predictive of target location. With this increased uncertainty, the presence of predictive cues actually leads to worse performance when the target is highly visible, compared to when cues are absent. A control task in which the spatial cues were not predictive rules out the possibility of the effect being driven by exogenous attentional capture, and a foveated eye movement model reveals that cues hindering search performance would not be predicted by a rational observer. Implications for real-life and vocational search are discussed.

In all, we see that human observers are willing and able to utilize predictive elements via intelligent eye movement selection of the environment to enhance search performance. In addition, when cue information is made clear, observers are able to readily adopt even extremely atypical eye movement strategies to optimize performance. However, human efficiency in implementing these strategies often falls short of that predicted by a near-optimal observer, even leading to a decrement in performance in extreme cases. Understanding the interaction between human performance and predictive cues, then, is critical to assessing natural visual search and optimizing vocational and life-critical search displays.