The role of familiarity and experience in the implementation of efficient visual search strategies

Imagine searching your office for your keys. You will likely start by scanning surfaces in your office such as your desk, table, and shelves. You may then check pockets, bags, and underneath papers, until you either find the keys or give up. How efficient was this search? How much time did you waste looking in places you already inspected, searching an area for too long, or looking in places that contained no useful information? In this proposal, we define search efficiency as the proportion of eye movements that are directed to locations that can be easily ascertained to provide new information. In the office example, some surfaces will be empty, and some cluttered with books and papers. If your keys were in the middle of an empty surface, you would already know where they were; no new information would be gained by looking directly at these locations. An efficient searcher would instead direct their eyes to the cluttered regions, where central vision is needed. Our recent studies using this metric to define efficiency have found a surprisingly large range of individual strategies, with some people being highly efficient, some random, and some highly inefficient. These differences suggest that rather than asking "is search optimal or random?" we should be asking for whom, and in what circumstances, search is optimal or random. This is the aim of the current proposal.

Much is already known about how visual information guides attention during search. Far less is known about search strategy, which contributes far more variance to performance measures. Our key hypothesis is that individual differences in strategy can be explained, at least in part, by differences in experience with the visual content and configuration, even though (in our experiments at least) these have no bearing on what the optimal eye movements are or the difficulty of implementing an efficient strategy. To assess this hypothesis, we systematically measure the effect on search efficiency of visual content, layout of the search array, individual motivation, learning, and prior expertise.

Understanding strategy is fundamental to building a complete model of visual search. The results have implications for understanding the role of experience in shaping strategy that could have relevance beyond the context of visual search. The results can also be useful in designing environments that promote more efficient search, and developing training programs that can lead to faster and more accurate detection of targets.