Right place, right time: Spatiotemporal predictions guide attention in dynamic visual search.

Abstract

Visual search is a fundamental element of human behavior and is predominantly studied in a laboratory setting using static displays. However, real-life search is often an extended process taking place in dynamic environments. We have designed a dynamic-search task in order to incorporate the temporal dimension into visual search. Using this task, we tested how participants learn and utilize spatiotemporal regularities embedded within the environment to guide performance. Participants searched for eight instances of a target that faded in and out of a display containing similarly transient distractors. In each trial, four of the eight targets appeared in a temporally predictable fashion with one target appearing in each of four spatially separated quadrants. The other four targets were spatially and temporally unpredictable. Participants' performance was significantly better for spatiotemporally predictable compared to unpredictable targets (Experiments 1-4). The effects were reliable over different patterns of spatiotemporal predictability (Experiment 2) and primarily reflected long-term learning over trials (Experiments 3, 4), although single-trial priming effects also contributed (Experiment 4). Eye-movement recordings (Experiment 1) revealed that spatiotemporal regularities guide attention proactively and dynamically. Taken together, our results show that regularities across both space and time can guide visual search and this guidance can primarily be attributed to robust long-term representations of these regularities.

What to expect where and when: how statistical learning drives visual selection

“…Similar results were reported in a study measuring eye movements to explore spatial orienting in time [84]. Participants performed a search task in which the presentation of targets was spatiotemporally predictable, such that the target appeared in a specific quadrant at a specific time point in time within a trial.…”

Consequences of predictable temporal structure in multi-task situations

“…Yet, a carefully controlled study using dual-task designs has shown that foreperiod effects survive in the face of a simultaneously performed task ( van Lambalgen and Los, 2008 ). In a similar vein, recent work from our lab has revealed that learned (spatio)temporal regularities regarding external events can guide visual-search performance even when interacting with temporally unpredictable intervening events ( Boettcher, Shalev, Wolfe, & Nobre, 2021 ). Likewise, temporal expectations affect performance even during dynamic streams that require inhibition of temporally competing distractors ( Chauvin, Gillebert, Rohenkohl, Humphreys, & Nobre, 2016 ; Davranche, Nazarian, Vidal, & Coull, 2011 ; Zokaei et al, 2021 ).…”

Temporal Preparation Drives Statistical Learning of Regular Intervals in Complex Temporal Environments

“…As it might be impossible to design tasks ruling out timing to such variable intervals, they are important to consider in any temporal task. For example, such ‘irregular’ timing might similarly play a role in recent work that found visual search to be facilitated for regular versus irregular spatiotemporal patterns (Boettcher et al, 2022). This performance benefit, expressed as average RT and HR split on regularity, was interpreted to arise from learning the spatiotemporal pattern that guides attention in visual search.…”

“…These regularity benefits are commonly interpreted to emerge from the ability to extract regular intervals from the environment (e.g., Baker et al, 2004;Boettcher et al, 2022;Fiser & Aslin, 2001;Salet et al, 2021;Sherman et al, 2020;Zhao et al, 2013). SL's temporal aspects have particularly been studied in temporal variants of the serial reaction time (SRT) tasks (Gobel et al, 2011;Heideman et al, 2018Heideman et al, , 2016Olson & Chun, 2001;O'Reilly et al, 2008;Salidis, 2001;Sanchez et al, 2015;Shin & Ivry, 2002, 2003 In classic SRT tasks (Nissen & Bullemer, 1987), participants make a corresponding keypress to each item in a stimulus sequence (e.g., stimuli A-B-A responded to with index-middle-index finger).…”

“…Instead, to extract regular intervals, a mechanism must monitor them over and beyond other intervening events. To test this in WAM, we presented both regular and irregular sequences simultaneously (see Boettcher et al, 2022, for a similar design in visual search). Three stimulus sequences, one regular and two irregular, were positioned in three locations.…”