Attentional templates guide visual search by biasing attention toward target features and away from distractors, yet how these templates adjust as a function of cue precision and directional deviation remains poorly understood. In two experiments, we manipulated cue precision to assess how imprecise color-based templates guide voluntary attentional selection. With positive cues (indicating the target template), robust benefits were stable at small cue–target deviations (≤36°), but shifted to an optimal tuning pattern at larger deviations (≥48°), where selection favored cues displaced away from distractor features. In contrast, negative cues (indicating the distractor template) yielded little benefit and no systematic modulation by cue precision or direction. These results indicate that target templates flexibly shift from coarse, category-like representations to optimally tuned configurations when target-distractor discriminability is challenged. Together, these findings reveal distinct mechanisms and varying degrees of flexibility underlying target versus distractor templates in voluntary visual search.

The impact of prior knowledge about an upcoming distractor (i.e., a negative cue) on performance in a subsequent search task has yielded mixed results. A central question is whether negatively cued features can be preemptively suppressed (i.e., before distractors capture attention) or whether they initially capture attention followed by reactive distractor handling (i.e., after distractors capture attention). Notably, negative cueing benefits tend to emerge when the search task is difficult, yet most prior electrophysiological studies have employed easy search tasks that may discourage negative cue use. Therefore, direct neural evidence for negative cueing effects under difficult search is rare. To address this gap, we conducted two experiments. Experiment 1 manipulated task difficulty by varying the number of task-relevant items while keeping the overall set size constant. Results revealed that negative cues enhanced search efficiency in difficult search (8 task-relevant items), but not in easy search (4 items), suggesting that negative cueing benefits depended on the number of items requiring attention rather than overall set size. Experiment 2 employed EEG during difficult search to delineate the neural underpinnings of distractor processing. ERP results showed that negatively cued distractors elicited a reliable early distractor PD in participants showing a cueing benefit in RT but a distractor N2pc on slow-response trials in participants showing RT cost. While preparatory alpha activity did not differ by cue type, stronger alpha lateralization was associated with larger cueing benefits at the individual level. Collectively, these findings suggest that negative cue use is a flexible, strategic and active process, with its deployment contingent on both task demands and individual differences in control capacity.

Everyday behavior, such as grocery shopping, involves searching for multiple similar objects ("visual foraging"). Although object search is usually performed in order to interact with the object, only a few studies used real objects. In object interaction, the interaction type and precision requirements likely affect object selection. When high precision is required, actions are performed more carefully (e.g., with lower speed). To investigate action context in interactive real-world multi-target foraging, we asked participants to pick and place LEGO bricks and varied the precision requirements with different placing instructions. Movement analysis revealed that participants preferred nearby objects but also prioritized those beneficial to the task, such as larger objects, when creating a pile. This demonstrates that participants planned reach movements by balancing immediate movement costs with future precision demands. In sum, task and action context, such as placing requirements or the environmental layout, must be considered for understanding visual selection in real-world situations.

Interference from a salient distractor is typically reduced when the appearance of the distractor follows either spatial or feature-based regularities. Although there is a growing body of literature on distractor location learning, the understanding of distractor feature learning remains limited. In the current study, we investigated distractor feature learning by using EEG measures. We assumed that learning benefits distractor handling, and we investigated the role of intertrial priming in distractor feature learning. Furthermore, we examined whether distractor feature learning influences later visual working memory (VWM) performance. Participants performed an adapted variant of the additional singleton task with a distractor that appeared more often in a specific color. The behavioral results provided additional evidence that observers can use distractor feature regularities to reduce distractor interference. At the neural level, we found a reduced PD with high-probability compared with low-probability distractors, suggesting that less suppression is required when the distractor appears in the more likely color. This reduced need for suppression was partly driven by intertrial priming. The PD elicited by repeated high-probability trials decreased over time, indicating that experience with the distractor reduced the need for suppression. In addition, the results showed that distractor feature learning did not affect VWM performance. Overall, our findings demonstrate that distractor feature learning decreases the interference of a salient distractor while also benefitting from intertrial priming processes, thereby improving attentional selection. In addition, it seems that learned distractor feature inhibition is not maintained in VWM when the task context is changed.

Avoiding distraction is critical for our ability to focus, and recent years have seen an increased interest in attentional suppression mechanisms. We now know that we implicitly learn about statistical regularities of our environment, which facilitates inhibition, but it remains unclear if distractors can also be suppressed voluntarily when advance information about their occurrence becomes available. Reasoning that such top-down suppression is likely an effortful process requiring a certain degree of motivation, we aimed to show that distractor cueing can effectively reduce distraction when the incentive is high. In an additional singleton search task, we maximized the incentive to suppress by presenting cues that validly indicated the distractor's specific location and colour, and by rewarding successful suppression. For correct responses, participants received either a low or high reward, depending on distractor colour. Responses were faster in trials with predictive cues than in trials with cues that did not provide any information. These cueing benefits increased over the course of the experiment. Reward magnitude also affected reaction times, indicating that high-reward singletons were more distracting, but did not interact with cueing condition. This performance pattern was complemented by modulations of the PD, a lateralized event-related potential component reflecting active suppression: Smaller amplitudes, indicating that less suppression was required, were observed for low- versus high-reward distractors and, more importantly, for distractors following predictive versus nonpredictive cues. These findings provide proof-of-principle that salient distractors can be anticipatorily suppressed in a top-down manner and highlight the importance of motivation for this voluntary operation.

Visual foraging tasks, where participants collect items by touching or clicking on them, havebecome popular for investigating visual search. They probe selective attention in multi-targetcontexts through naturalistic goal-directed actions, unlike the button presses used in many otherparadigms. Despite their potential, such tasks had not been used to examine the interplay ofattention and goal-directed actions until now, even though this topic has been extensively studiedwith other paradigms and has significant implications for understanding human visual behavior inthe real world. In this study, we applied the visual foraging paradigm to address this gap. We foundthat attentional prioritization of one part in a two-part compound object is accompanied by a motorbias in the collecting action (stylus tap) toward the prioritized part. This bias combines with motorprecision demands, such as aiming for stable contact points. Our findings show that actionplanning not only modulates the attentional landscape at large but also that attentional asymmetries(e.g., prioritizing one object part) feedback into the motor system, combining with motoric factorsto refine goal-directed actions

Positive cues provide information about target features, facilitating processing of the upcoming input. The role of “negative cues,” which signal distractor features, is less clear. Their use seems to incur a certain mental effort, and consequently, their effectiveness could depend on whether the benefit of reducing search effort outweighs the effort required to use them. Crucially, when search effort is predictable, participants can rely on expected effort in deciding whether to use a negative cue. This subjective effort/benefit evaluation may explain the inconsistent effects sometimes observed with negative cues. However, the influence of expected effort has not yet been examined in this context. Varying effort expectation may provide a way to test this prediction. To this end, we manipulated effort expectation by embedding a small number of easy trials within blocks of mainly difficult trials and vice versa. We assumed that expected effort can boost cue utilization, especially when a difficult search was expected. The impact of positive and negative cues was compared to neutral cues under identical expectations. We found that benefits of positive cues increased when search was more effortful. Negative cueing effects, however, showed no reliable benefits, even when higher effort was expected. Interestingly, search performance was better when the expected effort matched actual task difficulty, though this pattern was more robust in difficult compared to easy trials. This asymmetry reveals an interaction between effort expectation and task difficulty. We discuss the neural underpinnings of how these factors might influence visual search.

Foreknowledge of target features biases attention toward those features even before search begins, indicating proactive activation of the target template. However, whether foreknowledge of distractor features leads to proactive inhibition remains debated, raising questions about whether target and distractor templates operate through similar mechanisms. The current study investigated this by presenting multiple task-irrelevant singleton probes before search display onset, combined with EEG. The probes either matched the color of the target, the distractor or had a neutral color. If a feature-specific template is activated proactively, we expect to see a reliable probe N2pc-modulation: With target template enhancement, probes matching the target color should elicit a more pronounced N2pc than neutral probes, whereas a distractor template inhibition should lead to an opposite pattern. In line with this prediction, results showed that the target-colored probes produced a more pronounced N2pc, with the largest amplitude for the last probe in the sequence, suggesting an increasing template activation from probe 1 to 4. Distractor-colored probes elicited a less pronounced N2pc than neutral probes at the first probe, suggesting an inhibited template activation at the start of the preparation. Both ERP and decoding results time-locked to search display onset showed that foreknowledge of the target color facilitated target selection, reaching its peak after 200 ms. Foreknowledge of the distractor color allowed active distractor inhibition within the first 200 ms and then vanished quickly. Taken together, separate temporal profiles during preparation and search support distinct mechanisms for target selection and distractor rejection in visual search.

Humans must weigh various factors when choosing between competing courses of action. In case of eye movements, for example, a recent study demonstrated that the human oculomotor system trades off the temporal costs of eye movements against their perceptual benefits when choosing between competing visual search targets. Here, we compared such trade-offs between different effectors. Participants were shown search displays with targets and distractors from two stimulus sets. In each trial, they chose which target to search for, and, after finding it, discriminated a target feature. Targets differed in their search costs (how many target-similar distractors were shown) and discrimination difficulty. Participants were rewarded or penalized based on whether the target's feature was discriminated correctly. In addition, participants were given a limited time to complete trials. Critically, they inspected search items either by eye movements only or by manual actions (tapping a stylus on a tablet). Results show that participants traded off search costs and discrimination difficulty of competing targets for both effectors, allowing them to perform close to the predictions of an ideal observer model. However, behavioral analysis and computational modeling revealed that oculomotor search performance was more strongly constrained by decision-noise (what target to choose) and sampling-noise (what information to sample during search) than manual search. We conclude that the trade-off between search costs and discrimination accuracy constitutes a general mechanism to optimize decision-making, regardless of the effector used. However, slow-paced manual actions are more robust against the detrimental influence of noise, compared with fast-paced eye movements.NEW & NOTEWORTHY Humans trade off costs and perceptual benefits of eye movements for decision-making. Is this trade-off effector-specific or does it constitute a general decision-making principle? Here, we investigated this question by contrasting eye movements and manual actions (tapping a stylus on a tablet) in a search task. We found evidence for a cost-benefit trade-off in both effectors, however, eye movements were more strongly compromised by noise at different levels of decision-making.

Attention can be biased by previous learning and experience. We present an algorithmic-level model of this selection history bias in visual attention that predicts quantitatively how stimulus-driven processes, goal-driven control and selection history compete to control attention. In the model, the output of saliency maps as stimulus-driven guidance interacts with a historymap that encodes learning effects and a goal-driven task control to prioritize visual features. The model works on coded features rather than image pixels which is common in many traditional saliency models. We test the model on a reaction time (RT) data from a psychophysical experiment. The model accurately predicts parameters of reaction time distributions from an integrated priority map that is comprised of an optimal, weighted combination of separate maps. Analysis of the weights confirms selection history effects on attention guidance. The model is able to capture individual differences between participants’ RTs and response probabilities per group. Moreover, we demonstrate that a model with a reduced set of maps performs worse, indicating that integrating history, saliency and task information are required for a quantitative description of human attention. Besides, we show that adding intertrial effect to the model (as another lingering bias) improves the model’s predictive performance.

Which properties of a natural scene affect visual search? We consider the alternative hypotheses that low-level statistics, higher-level statistics, semantics, or layout affect search difficulty in natural scenes. Across three experiments (n n = 20 each), we used four different backgrounds that preserve distinct scene properties: (a) natural scenes (all experiments); (b) 1/f f noise (pink noise, which preserves only low-level statistics and was used in Experiments 1 and 2); (c) textures that preserve low-level and higher-level statistics but not semantics or layout (Experiments 2 and 3); and (d) inverted (upside-down) scenes that preserve statistics and semantics but not layout (Experiment 2). We included "split scenes" that contained different backgrounds left and right of the midline (Experiment 1, natural/noise; Experiment 3, natural/texture). Participants searched for a Gabor patch that occurred at one of six locations (all experiments). Reaction times were faster for targets on noise and slower on inverted images, compared to natural scenes and textures. The N2pc component of the event-related potential, a marker of attentional selection, had a shorter latency and a higher amplitude for targets in noise than for all other backgrounds. The background contralateral to the target had an effect similar to that on the target side: noise led to faster reactions and shorter N2pc latencies than natural scenes, although we observed no difference in N2pc amplitude. There were no interactions between the target side and the non-target side. Together, this shows that-at least when searching simple targets without own semantic content-natural scenes are more effective distractors than noise and that this results from higher-order statistics rather than from semantics or layout.

According to attentional theories of associative learning, organisms tend to prioritize items with a higher predictive value over those with a lower predictive value. We investigated whether experiencing a semantic categorization task with naturalistic object images influences attentional selection in a subsequent visual search task. Participants first categorized either between tool and vehicle or between fruit and vegetable. In the subsequent search task, they searched for a new target object and ignored a distractor that was either from the category they had to distinguish in the former learning task or from a nonrelevant category. We assumed that the extent these distractors interfered with selecting the target depended on their former response predictiveness. Search times were analyzed by using a hierarchical learning curve model.The results showed that objects from previously response predictive categories impaired performance to a greater degree than objects from nonpredicitive categories, regardless of particular object categories. The findings suggest that categorization learning from both basic and superordinate level categories can impact attentional control settings similarly, with fruit and vegetable more likely being basic level categories and tool and vehicle being superordinate level categories.

Everyday behavior, like grocery shopping, often includes search for multiple objects of one kind. Such “visual foraging” tasks have recently received more attention in vision science. However, although object search is usually performed for object interaction, only few studies have been conducted with real objects. When interacting with real-world objects, factors such as the type of interaction and its precision requirements are likely to affect object selection. When high precision is required, actions are performed more carefully (e.g., with lower speed and a longer deceleration phase). In addition, action contexts—such as the type or endpoint of movements—affect adaptive movement planning and action parameters, including grasp point selection and movement speed. To investigate such influences in interactive real-world visual foraging, we asked participants to pick and place LEGO bricks defined by color and varied the precision requirements with different placing instructions. Movement analysis revealed that participants preferred nearby objects but also prioritized those beneficial to the task, such as larger objects when instructed to create a pile. This demonstrates that participants planned reach movements by balancing immediate movement costs with future precision demands. Our results emphasize that task and action context, such as placing requirements or the environmental layout, need to be considered for understanding visual selection in real-world situations.

In everyday tasks, the choices we make incorporate complex trade-offs between conflicting factors that affect how we will achieve our goals. Previous experimental research has used dual-target visual search to determine how people flexibly adjust their behaviour and make choices that optimise their decisions. In this experiment, we leveraged a visual search task that incorporates complex trade-offs, and electroencephalography (EEG), to understand how neural mechanisms of selective attention contribute to choice behaviour in these tasks. On each trial, participants could choose to respond to the gap location on either of two possible targets. Each target was colour coded such that colour indicated which of the two had the easier gap discrimination. Orthogonally, we manipulated the set size of coloured distractors to modulate how efficiently each target could be found. As a result, optimised task performance required participants to trade-off conflicts between the ease of finding a target given the current set size, and the ease of making its associated gap discrimination. Our results confirm that participants are able to flexibly adjust their behaviour, and trade-off these two factors to maintain their response speed and accuracy. Additionally, the N2pc and SPCN components elicited by search displays could reliably predict the choice that participants would ultimately make on a given trial. These results suggest that initial attentional processes may help to determine the choice participants make, highlighting the central role that attention may play in optimising performance on complex tasks.