Search

People tend to employ suboptimal attention control strategies during visual search. Here we question why people are suboptimal, specifically investigating how knowledge of the optimal strategies and the time available to apply such strategies affect strategy use. We used the Adaptive Choice Visual Search (ACVS), a task designed to assess attentional control optimality. We used explicit strategy instructions to manipulate explicit strategy knowledge, and we used display previews to manipulate time to apply the strategies. In the first two experiments, the strategy instructions increased optimality. However, the preview manipulation did not significantly boost optimality for participants who did not receive strategy instruction. Finally, in Experiments 3A and 3B, we jointly manipulated preview and instruction with a larger sample size. Preview and instruction both produced significant main effects; furthermore, they interacted significantly, such that the beneficial effect of instructions emerged with greater preview time. Taken together, these results have important implications for understanding the strategic use of attentional control. Individuals with explicit knowledge of the optimal strategy are more likely to exploit relevant information in their visual environment, but only to the extent that they have the time to do so., (© 2024. The Author(s).)

Individuals vary substantially in the degree to which they optimize their performance in attentional tasks. How do such individual markers of attentional strategy relate across different tasks? Previous research has failed to observe significant correlations in strategy optimization between distinct visual search tasks (Clarke et al., 2022); suggesting that strategy optimization is not unitary, or determined by a single trait variable. Here we test whether strategy optimization shows some degree of generality, specifically across tasks with similar attentional components. We employed the Adaptive Choice Visual Search (ACVS; Irons & Leber, 2018a), a visual search paradigm designed to directly measure attentional control strategy. In 2 studies, we had participants complete the ACVS and a modified, but similar, task with 1 altered attentional component (specifically, the requirement to use feature-based attention and enumeration, respectively). We found positive correlations in strategy optimization between tasks that do versus do not involve feature-based attention ( r = .38, p = .0068) and across tasks that do versus do not require enumeration ( r = .33, p = .018). These results provide novel evidence for generality of strategy optimization, although the strength of the correlations was weaker than the within-task test-retest reliability of strategy measurements. Thus, while some generality exists, strategy optimization appears to be quite heterogeneous. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

There are many strategies we can use to control attention when approaching a visual search task, but some are more effective than others. How do we choose the most optimal strategy? We have proposed that individuals must appraise the stimulus environment, taking in relevant statistical information about task-relevant features. In the present experiments, we examined whether interfering with the appraisal process via a secondary task decreases participants' use of the optimal strategy. We used a modified version of the Adaptive Choice Visual Search paradigm whereby individuals can freely search for either of two targets on every trial. Each search display was preceded by a colored environmental preview, offering participants time to appraise the display and determine which target would be more optimal to search for. On some blocks, participants also completed a secondary task - a central line-length judgment - either before or during this colored preview. We found that participants were significantly less likely to search optimally when the line task occurred during the colored preview than when it occurred beforehand or was absent. Insofar as the secondary task disrupts an individual's ability to engage in appraisal, these results support the need for such an appraisal mechanism in the optimal choice of attentional control settings.

Strategy is a crucial determinant for how attention is controlled. In recent years, researchers have deployed a growing variety of manipulations and dependent measures in service of understanding strategy. This work has revealed a striking degree of diversity and suboptimality in the use of attention, and it prompts the realization that more research on strategy is needed in order to fully understand and explain how attention works. Here, we highlight several approaches to investigate strategy, in what can be considered a 'methodological toolbox' for researchers. These methods can be customized and combined flexibly in what we hope will be a continued expansion of inquiry into this important domain., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Goal-directed attentional control can substanially aid visual search, but only if it is recruited in an effective manner. Previously we found that strategies chosen to control attention vary considerably across individuals, and we proposed that effort avoidance may lead some individuals to choose suboptimal strategies. Here we present a more thorough analysis of individual differences in attentional control strategies. We used the adaptive choice visual search, which provides a method to quantify an individual's attentional control strategy in a dynamically changing, unconstrained environment. We found that individual's strategy choices are highly reliable across sessions, suggesting that attentional control strategies are stable and trait-like. In Experiment 2, we explored the extent to which strategy use was related to subjective evaluations of effort and performance. Results showed that the extent to which individuals found the optimal strategy to be effortful and effective predicted their likelihood of making optimal choices on a subsequent choice block. These results provide the first evidence for a relationship between effort and strategic attentional control, and they highlight the important and often neglected role of strategy in understanding attentional control. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Task preparation involves multiple component processes, including a general evaluative process that signals the need for adjustments in control, and the engagement of task-specific control settings. Here we examined the dynamics of these different mechanisms in preparing the attentional control system for visual search. We explored preparatory activity using pupil dilation, a well-established measure of task demands and effortful processing. In an initial exploratory experiment, participants were cued at the start of each trial to search for either a salient color singleton target (an easy search task) or a low-salience shape singleton target (a difficult search task). Pupil dilation was measured during the preparation period from cue onset to search display onset. Mean dilation was larger in preparation for the difficult shape target than the easy color target. In two additional experiments, we sought to vary effects of evaluative processing and task-specific preparation separately. Experiment 2 showed that when the color and shape search tasks were matched for difficulty, the shape target no longer evoked larger dilations, and the pattern of results was in fact reversed. In Experiment 3, we manipulated difficulty within a single feature dimension, and found that the difficult search task evoked larger dilations. These results suggest that pupil dilation reflects expectations of difficulty in preparation for a search task, consistent with the activity of an evaluative mechanism. We did not find consistent evidence for relationship between pupil dilation and search performance (accuracy and response timing), suggesting that pupil dilation during search preparation may not be strongly linked to ongoing task-specific preparation.

Goal-directed attentional control supports efficient visual search by prioritizing relevant stimuli in the environment. Previous research has shown that goal-directed control can be configured in many ways, and often multiple control settings can be used to achieve the same goal. However, little is known about how control settings are selected. We explored the extent to which the configuration of goal-directed control is driven by performance maximization (optimally configuring settings to maximize speed and accuracy) and effort minimization (selecting the least effortful settings). We used a new paradigm, adaptive choice visual search, which allows participants to choose one of two available targets (a red or a blue square) on each trial. Distractor colors vary predictively across trials, such that the optimal target switches back and forth throughout the experiment. Results (N = 43) show that participants chose the optimal target most often, updating to the new target when the environment changed, supporting performance maximization. However, individuals were sluggish to update to the optimal color, consistent with effort minimization. Additionally, we found a surprisingly high rate of nonoptimal choices and switching between targets, which could not be explained by either factor. Analysis of participants' self-reported search strategy revealed substantial individual differences in the control strategies used. In sum, the adaptive choice visual search enables a fresh approach to studying goal-directed control. The results contribute new evidence that control is partly determined by both performance maximization and effort minimization, as well as at least one additional factor, which we speculate to include novelty seeking.

Previous investigations of the ability to maintain separate attentional control settings for different spatial locations have relied principally on a go/no-go spatial-cueing paradigm. The results have suggested that control of attention is accomplished only late in processing. However, the go/no-go task does not provide strong incentives to withhold attention from irrelevant color-location conjunctions. We used a modified version of the task in which failing to adopt multiple control settings would be detrimental to performance. Two RSVP streams of colored letters appeared to the left and right of fixation. Participants searched for targets that were a conjunction of color and location, so that the target color for one stream acted as a distractor when presented in the opposite stream. Distractors that did not match the target conjunctions nevertheless captured attention and interfered with performance. This was the case even when the target conjunctions were previewed early in the trial prior to the target (Exp. 2). However, distractor interference was reduced when the upcoming distractor was previewed early on in the trial (Exp. 3). Attentional selection of targets by color-location conjunctions may be effective if facilitative attentional sets are accompanied by the top-down inhibition of irrelevant items.

Although models of visual search have often assumed that attention can only be set for a single feature or property at a time, recent studies have suggested that it may be possible to maintain more than one attentional control setting. The aim of the present study was to investigate whether spatial attention could be guided by multiple attentional control settings for color. In a standard spatial cueing task, participants searched for either of two colored targets accompanied by an irrelevantly colored distractor. Across five experiments, results consistently showed that nonpredictive cues matching either target color produced a significant spatial cueing effect, while irrelevantly colored cues did not. This was the case even when the target colors could not be linearly separated from irrelevantly cue colors in color space, suggesting that participants were not simply adopting one general color set that included both target colors. The results could not be explained by intertrial priming by previous targets, nor could they be explained by a single inhibitory set for the distractor color. Overall, the results are most consistent with the maintenance of multiple attentional control settings.