The data collected as part of this grant represents the search strategies, biases and habits of a large number of participants searching under a range of different conditions. Eight of the journal articles published to date under this grant involved the collection of new data, which is publicly available in eight separate repositories associated with each respective publication (together with preprints, materials, pre-registrations, and supplementary materials). Unless stated otherwise, the primary task was a variation of the “split-half line segment” (SHLS) search task as described in the grant proposal. Rather than create potential confusion for meta-analysis by duplicating the data in more than one location, we link to the eight public repositories in which they are contained under "related resources". The eight repositories are: 1. https://osf.io/efg8n/: two experiments, 60 participants total. RT, accuracy and fixation data, preregistrations, preprint, r scripts, supplementary materials. We manipulate time pressure and monetary rewards and observe no effect on strategy. We also model the timecourse of strategy improvements over time, within trial and across trials. 2. https://osf.io/9edx6/: five main experiments, 104 participants total, RT, accuracy and fixation data. Across experiments we manipulate the search target and distractors and measure effects on strategy. Also contains supplementary material and links to preregistrations, six pilot experiments and one follow-up control experiment. 3. https://osf.io/5aq4c/: one experiment with 306 participants and one pilot with 30 participants. Participants performed SHLS alongside a battery of other tasks and measures. This was a registered report and the repository contains the stage 1 submission as well as the post-data stage 2 version of the paper. 4. https://osf.io/unx6v/: two experiments with 46 participants total. Tests the hypothesis that making the search target self-relevant will make search more efficient (it does not). Contains pre-registration, data, supplementary material and a preprint version of the paper. 5. https://github.com/Riadsala/single_double_feature_search: Contains stage 1 (preregistered) version of the paper, pilot (N=4) and full experimental data (N=40). Participant search for a target with one or two features (not a SLHS experiment, see paper). 6. https://osf.io/64r7m/ This repository contains supplementary materials and data underpinning a paper demonstrating the broad utility of asymptotic regression as a tool for modelling time course data. The “short-long” experiment data was collected as part of this grant (N=50) and examines the role of experience with short deadlines on the full timecourse of trials, expressed as three parameters (initial performance, rate of change, asymptote). 7. https://osf.io/y6qbv/ Contains data from a group of participants (N=64) performing three different search tasks that have large individual performance differences (SHLS, a foraging task, and a colour-search task). Also contains experiment code, analysis scripts, and supplementary materials. 8. https://osf.io/8qgju/ Contains data from two experiments (N=50 total) where the spatial layout of the SHLS experiment is manipulated. Also contains pre-registrations and supplementary materials. For more details information on the data and methods, see the repositories and associated publications.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.

The majority is laboratory-based experimental psychology data. Anonymous participants' eye movements and visual search task performance were measured and stored together with condition information. The raw data files are either .txt or .csv. One project also includes questionnaire data.