How can humans adaptively use temporal regularities within their environment to help guide attention?

Real-world environments are not entirely random but imbued with regularities. We can use these regularities to form predictions about future relevant events and proactively guide our behaviour. Research using static visual-search tasks, where participants search for targets amongst other, distracting items, has shown that we can learn regularities within busy visual scenes. Further, we can use these regularities to predict where targets will appear, or what they will look like, and accordingly guide our attention towards items in these locations, or with these visual features, to find targets more efficiently. However, unlike the visual-search arrays in these tasks, real-world scenes are dynamic. Thus, it is imperative to consider the effects of time, and temporal regularities, on visual search. Effects of temporal regularities on search have only recently begun to be investigated. Initial studies have done so using a dynamic visual-search task where certain targets predictably appeared in the same location and at the same time during trials. Other targets, and distractors, appeared at unpredictable locations and times. Here, participants identified spatiotemporally predictable targets significantly more often, and significantly faster, than spatiotemporally unpredictable targets. These findings suggest that participants learnt temporal regularities, here combined with spatial regularities, and used this knowledge to guide their attention towards certain locations at times when targets were expected to appear there. This initial evidence suggests that humans can use regularity-based temporal predictions as a helpful source of attentional guidance during visual search. However, we know little about the circumstances under which this can occur, or the nature of this time-dependent guidance. This project aims to provide insight into these questions. For example, I will investigate whether we can use temporal regularities to guide search when they operate independently of spatial location, and in combination with non-spatial features like colour. Further, I am interested in how participants' motor responses during search tasks may scaffold their learning and use of temporal regularities. I will investigate these questions, and others, using dynamic visual-search tasks in which targets and distractors appear at different times during trials, sometimes predictably. Depending on the question at hand, elements of these tasks, such as the predictable properties of targets (e.g., location and/or colour and/or time), or the motor responses the tasks require, will vary.
I will also develop new methods to acquire and analyse time series data capturing continuous behavioural and brain measures while people perform the dynamic visual-search tasks. These measures will be more informative for understanding how exactly regularity-based temporal predictions may shape attentional guidance over time, compared to typical accuracy or response-time measures. For example, I will measure the likelihood of participants fixating, with their eyes, a particular distractor over time. I will assess how this likelihood changes in the moments before a target sharing features with that distractor predictably appears. An increase in fixation likelihood of the distractor here would reflect an increase in attentional prioritisation of target features. Here, it will be particularly interesting to see how soon before a temporally predictable target appears this increase may begin. Overall, this project aims to expand and deepen our understanding of how humans can learn and use temporal regularities in their environment to form predictions and adaptively guide their visual attention over time. This project will contribute to our understanding of how we can perform tasks efficiently and effectively in the busy and dynamic real world. Further, it will contribute to wider research investigating the previously neglected topic of timing in selective visual attention.