The ability to ignore distracting information is a key component of selective attention, and critical to goal-directed behavior. Recent studies suggest that while attention can be flexibly directed at goal-relevant information, suppression of distracting information is not under similar voluntary top-down control (Chelazzi et al., 2019; Noonan et al., 2018), as was previously generally assumed (Jensen and Mazaheri, 2010). Rather, this work shows that distractor suppression strongly depends on distractor-based learning, for example, about its likely location in the visual environment (Failing et al., 2019a; Ferrante et al., 2018; Goschy et al., 2014; Reder et al., 2003; Sauter et al., 2018; Wang and Theeuwes, 2018a) or its non-spatial features, for example its color or shape (Cunningham and Egeth, 2016; Stilwell et al., 2019; Vatterott and Vecera, 2012). Based on these observations, it has been proposed that distractor inhibition may be dependent on expectations derived from past experience about the likelihood of events (Noonan et al., 2018; Moorselaar and Slagter, 2020). This idea is grounded in recent notions of predictive processing (Friston, 2009) in which the brain continuously uses statistical regularities in the environment to predict the outside world with the overall aim of reducing the mismatch between its a priori predictions and the sensory input. In this framework, processing of any expected stimulus, whether relevant or irrelevant, is reduced (as it elicits a smaller prediction error). Distractor expectations should hence reduce subsequent distractor processing and interference. Yet, it remains unclear how learned inhibition, which often occurs implicitly (Ferrante et al., 2018; Wang and Theeuwes, 2018a), is neurally implemented.

Many studies have shown that attention can bias sensory processing of expected task-relevant (or target) information in advance, both on the basis of non-spatial feature (Battistoni et al., 2017) and location information (Giesbrecht et al., 2006; Hopfinger et al., 2000; Kok et al., 2012). Yet, it is currently unclear if expectations about upcoming distracting information can similarly prepare the system in advance by modifying activity in sensory regions representing the expected distractor location or its non-spatial features, or alternatively, that distractor learning only becomes apparent upon integration with incoming sensory information (Moorselaar and Slagter, 2020). Thus far, this question has only been directly addressed in the context of spatial distractor expectations, with mixed findings. One recent EEG study reported increased pre-stimulus alpha-band activity over visual regions representing the likely distractor location, which was interpreted to reflect top-down preparatory inhibition (Wang et al., 2019). In contrast, two other recent EEG studies did not observe any learning-induced changes in preparatory activity of visual regions representing the expected distractor location, as captured by pre-stimulus alpha-band activity (Noonan et al., 2016) or by changes in anticipatory spatial tuning as revealed by inverted encoding modeling based on alpha-band activity (van Moorselaar and Slagter, 2019). This discrepancy in findings may be explained by the fact that in the former study by Wang et al., 2019 distractor learning could only be location-based, as the shape and color of the distractor stimulus varied from trial to trial, while in the latter two studies distractor features were fixed, also permitting build-up of feature-based expectations. This may have led to stronger location-based learning in the Wang et al., 2019 study. Indeed, in this study, it was also found that even targets occurring at the likely distractor location were inhibited, as reflected by slower response times. Behavioral studies have also shown, however, that when targets and distractors are assigned unique and fixed features, the suppression at high-probability distractor locations becomes specific to distractors (Allenmark et al., 2019; Zhang et al., 2019), indicative of feature-based inhibition. Together, these findings suggest that the locus of distractor learning in the processing hierarchy is flexible and depends on the extent to which expectations are location- and/or feature-specific.

It is currently highly controversial whether expected distractor features can also be represented as so called ‘templates for rejection’ (Woodman and Luck, 2007). Although some behavioral findings are in line with this idea (Arita et al., 2012; Carlisle and Nitka, 2018; Park et al., 2007; Woodman and Luck, 2007), to date there is no neural evidence for feature-based distractor templates that set up the system in advance to inhibit distractor processing. However, the few studies that investigated this, cued the distractor on a trial-by-trial basis (de Vries et al., 2019; Reeder et al., 2018), preventing distractor learning and leaving open the possibility that anticipatory distractor feature templates only arise after repeated encounters with the to-be-ignored feature, that is when statistical learning is possible.

In the current study, we aimed to determine if and how location- and feature-based distractor expectations may independently or in interaction bias sensory regions in advance to suppress distractor processing using visual search tasks and EEG. Across visual searches the distractor appeared more frequently on one location (i.e. high-probability distractor location) than on all other locations (i.e. low-probability distractor locations). Critically, the target and distractor either shared the same spatial frequency, or had different spatial frequencies, that could either be predicted in advance or not (see Figure 1). This allowed us to investigate whether preparatory suppression of activity in brain regions representing the high-probability distractor location, as reflected in pre-stimulus alpha-band activity, is dependent on expectations at the feature level. In addition, using multivariate decoding, we could determine if learning about upcoming distractor features (i.e. their spatial frequency) modulated activity of visual regions representing these features in advance, suggestive of a template for rejection.

Figure 1

Download asset Open asset

Although we specifically set out to find anticipatory markers of expectation-based distractor location and feature suppression, it is important to realize that the absence of such effects does not necessarily imply that inhibition is purely reactive. It has recently been proposed that salient distractors can be filtered out pre-attentively (Gaspelin and Luck, 2018b; Sawaki and Luck, 2010). This so-called signal suppression hypothesis is largely based on the observation that in some contexts, salient distractors can be suppressed, as indicated by a Pd, an ERP marker linked to inhibition, before they capture attention, as indicated by the absence of a distractor-evoked N2pc (Burra and Kerzel, 2013; Gaspar and McDonald, 2014; Gaspelin and Luck, 2018a; Jannati et al., 2013; Wang et al., 2019). Intriguingly, we recently found that in a context that enforced learning at the spatial level, as distractors and targets were very similar at the feature level (i.e. same spatial frequency), distractors at expected locations continued to elicit an N2pc, but the subsequent Pd was virtually eliminated. We interpreted this as if there was no more need for post-distractor inhibition once participants had learned that information at a specific location could be safely ignored (van Moorselaar and Slagter, 2019). Here, we therefore also examined whether a similar pattern is observed when spatial expectations can be combined with feature expectations, or whether distractor-feature specific learning does allow for pre-attentive suppression.

In brief, our results demonstrate that expectations about features of upcoming distractors in the environment induce pre-stimulus sensory templates. Crucially, these sensory templates differed as a function of whether the feature was a predictable feature of the target or of the distractor, providing evidence for distinct target and distractor templates. Yet, we find no evidence for a preparatory spatial bias as a function of distractor location predictability, as no modulations of pre-stimulus alpha-band activity were observed. Distractors at expected locations did evoke a smaller Pd, indicative of a reduced need for inhibition, independent of distractor feature expectations. Thus, neural effects of predictions based on regularities in the environment critically depend not only on the task relevance (target, distractor), but also the dimension (spatial, feature) of these regularities.

We conducted two experiments, a behavioral experiment and an EEG experiment. The methods and analyses of both these experiments were preregistered at the Open Science Framework (https://osf.io/7ek45/).

Experiment 1 - Is spatial suppression modulated by feature expectations?

We first conducted a behavioral experiment to examine how distractor expectations at the feature and spatial level may interact in reducing distractor interference. We specifically predicted that suppression at high-probability distractor locations would become specific to distractors (i.e. not affect target processing) when distractors are assigned unique and fixed spatial frequencies, as has been shown previously using shapes and colors (Allenmark et al., 2019; Zhang et al., 2019). To this end, observers (N = 18) performed a visual search task in which the distractor and the target either had the same spatial frequency and only differed in orientation (referred to as Distractor predictable Target predictable [DpTp] identical condition), or differed both in spatial frequency and orientation. In this latter scenario, target and distractor spatial frequencies were either selected at random (referred to as Distractor variable Target variable [DvTv] unique condition) or fixed across a sequence of trials (referred to as DpTp unique condition). This allowed us to test whether learning selectively occurs at the spatial level when search is necessarily feature non-specific, either because target and distractors share the same spatial frequency or because their spatial frequency varies randomly, but not when distractor features can be predicted in advance during feature-specific search (see Figure 1).

Behavior

Figure 2A (left plot) shows the mean reaction times (RTs) for the search task as a function of the distractor location for all conditions. These measures were entered into a two-way repeated measures ANOVA (N = 18) with the within-subjects factors Condition (DpTp identical, DvTv unique, DpTp unique) and Distractor Location (high-probability distractor location, low-probability distractor location). Replicating previous findings (e.g. Ferrante et al., 2018; Wang and Theeuwes, 2018a), RTs were faster when the distractor appeared at high relative to low-probability distractor locations (main effect Distractor location; F (1, 17)=97.0, p<0.001, ${\eta }_p^2$ = 0.85). Moreover, there was a main effect of Condition (F (2, 34)=15.8, p<0.001, ${\eta }_p^2$ = 0.48) reflecting faster RTs in the DpTp unique condition, in which spatial frequencies of both the target and distractor could be anticipated, relative to conditions in which those spatial frequencies could not guide search, either because the target and distractor spatial frequency were identical or because they varied from trial to trial. Critically, a significant interaction (F (2, 34)=5.2, p=0.011, ${\eta }_p^2$ = 0.23) showed that the RT benefit at high-probability distractor locations was modulated by the extent to which distractor- and target-specific predictions could be formed at the feature level. Planned comparisons revealed that while distractors were more efficiently ignored on high-probability distractor locations across all conditions (all t’s > 6.7, all p’s < 0.001, all d’s > 1.58), RTs were reduced most by distractor location predictability when distractors and targets could not be dissociated at the feature level in advance. Specifically, in feature specific search (i.e. DpTp unique) spatial benefits were smaller than during feature non-specific searches (DpTp unique vs. DpTp identical, t (17)=3.0, p=0.008, d = 0.71; DpTp unique vs. DvTv unique, t (17)=2.0, p=0.06, d = 0.48; BF₁₀ = 1.3), whereas spatial benefits did not differ between the two feature non-specific search conditions (DpTp identical vs. DvTv unique, t (17)=1.3, p=0.22, d = 0.30; BF₀₁ = 2.1). These findings are indicative of stronger suppression at the high-probability distractor location in the condition in which expectations could necessarily only develop at the spatial level.

Figure 2

Download asset Open asset

To ensure that the observed reductions in distractor interference at high-probability distractor locations do not merely reflect intertrial location priming (Maljkovic and Nakayama, 1994), we repeated the preceding analysis after excluding all trials in which the distractor location repeated. This control analysis mimicked the main findings, although the main effects of Condition and Distractor Location (all F’s > 16.4, all p’s < 0.001, all ${\eta }_p^2$‘s > 0.49) were no longer accompanied by a significant interaction (F (2, 34)=1.9, p=0.17, ${\eta }_p^2=0.10;$ BF₀₁ = 4.9). Importantly, however, across all conditions distractors were more efficiently ignored on high-probability distractor locations (all t’s > 2.6, all p’s < 0.018, all d’s > 0.62) demonstrating that intertrial location priming could not entirely account for the observed reduction in distractor interference at the high-probability distractor location, as also reported in previous studies (e.g. Failing et al., 2019b; Ferrante et al., 2018).

To examine whether the efficiency of target selection was also affected by distractor location and/or feature foreknowledge, we repeated the same analysis but now tuned to the target location (see Figure 2A; right). Consistent with generic suppression at the high-probability distractor location, target processing was also impaired at high-probability distractor locations (main effect Distractor location; F (1, 17)=28.2, p<0.001, ${\eta }_p^2=$ 0.62). However, this impairment did not differ between conditions (Condition by Distractor Location interaction: F (2, 34)=0.09, p=0.92, ${\eta }_p^2=0.005;$ BF₀₁ = 6.7) suggesting that processing of any target was suppressed to the same extent at the high-probability distractor location independent of its similarity to the distractor at the feature level or whether participants had advance knowledge about its spatial frequency.

To summarize, Experiment 1 showed that (1) distractor interference is most reduced when distractors can be predicted at both the spatial and feature level; (1) distractor location learning is more pronounced when distractors can only be predicted on the basis of location, but not feature information; and that (3) any stimulus presented at the expected distractor location is automatically inhibited, including targets. Specifically, target processing at the high-probability distractor location was slowed uniformly across conditions (independent of feature expectations), while the effects of spatial expectations on distractor interference, although present in each condition, were most pronounced in the absence of any feature expectations. These findings suggest that while the level of suppression tied to the high-probability location was fixed, subsequent inhibition of stimuli at the likely distractor location is modulated by expectations about what search items look like at the feature level. It should be noted, however, that the observed interaction between spatial and feature expectations in the distractor-tuned analysis disappeared when we controlled for intertrial spatial priming, an aspect that we will revisit in Experiment 2, which contained many more trials per condition, and hence permitted for the development of more robust spatial and feature expectations.

EEG

Time-frequency analyses: pre-stimulus alpha-band activity does not increase contralateral to the high-probability distractor location

At the behavioral level, we found that spatial distractor expectations generally reduced distractor interference during visual search, albeit most strongly so in the absence of distractor feature expectations, and furthermore, that target search was slowed down when the target occurred on the high-probably distractor location, indicative of generic spatial suppression. Using time-frequency analyses, we first examined if location-based distractor expectations modulated activity of visual regions representing the likely distractor location in advance, as indexed by a pre-stimulus alpha-band lateralization index at posterior electrode sides (POS), where contralateral and ipsilateral were relative to the high-probability distractor location:

${\displaystyle \frac{a_{contralateralPOS}-a_{ipsilateralPOS}}{a_{contralateralPOS}+a_{ipsilateralPOS}}}$

and if so, whether pre-stimulus alpha-band lateralization reduced when learning could also occur at the feature level. As we were specifically interested in lateralized alpha power as a measure of anticipatory spatial suppression, we limited the statistical analyses to the anticipatory time window (i.e. −750 ms – 0 ms). Consequently, the analysis was only sensitive to learned expectations and hence could not be confounded by the actual stimulus configuration, which allowed us to include all trials. The lateralization index, which was analyzed at electrodes PO7/8 and O1/2, has the benefit of not having to select a pre-stimulus baseline window, the same time window where we expected alpha-band modulations. Note, that for the same reason, Wang et al., 2019 developed a Z-transformation procedure, which we (as preregistered) also applied here. This resulted in virtually identical results as obtained with the lateralization index.

As visualized in Figure 3A/B, cluster-based permutation tests across time and frequency and across time for lateralization index averaged within the alpha-band (8–12 Hz) did not show any reliable increase of alpha-power contralateral vs. ipsilateral to the high-probability distractor location in any condition. Nevertheless, Figure 3B also suggests that in the DpTp identical condition - the one condition in which learning could only take place at the location level, but not at the feature level - a contralateral increase in alpha-band power was present numerically, albeit not significantly so. To increase sensitivity to a potentially weak effect, in an exploratory analysis, we entered the $a$ lateralization index averaged over the entire anticipatory time window into a repeated measures ANOVA. Critically, this analysis did not show a main effect of Condition (F (2, 46)=0.4, p=0.68, ${\eta }_p^2=0.016;$ BF₀₁ = 5.70), neither did the $a$ lateralization index differ from zero in the DpTp identical condition (t (23)=1.2, p=0.21, d = 0.27; BF₀₁ = 2.22). Thus, these time-frequency findings suggest that the observed spatial suppression at the high-probability distractor location at the behavioral level was not mediated by a contralateral increase of alpha-band power in anticipation of search display onset, even in a condition in which learning could only occur at the spatial level and across a longer time span than in two previous studies that also did not observe distractor learning-related changes in pre-stimulus alpha-band activity (Noonan et al., 2016; van Moorselaar and Slagter, 2019), but in contrast to another study (Wang et al., 2019).

Figure 3

Download asset Open asset

Decoding: target and distractor-based feature expectations induce pre-stimulus sensory templates

Our behavioral findings showed that distractor feature-specific expectations in the absence of any target expectations modulated spatial expectation-dependent suppression, suggesting that non-spatial information about upcoming distractors reduced distractor interference. To investigate whether feature-specific distractor expectations induced pre-stimulus sensory templates, that is were already evident in the anticipatory EEG signal, we trained a classifier on the response pattern of 64 electrodes using each spatial frequency (i.e. low, medium, high) for each timepoint, separately for targets and distractors (see Materials and methods section for details). ERP studies have demonstrated differences in early visual-evoked responses as a function of stimulus spatial frequency (e.g. Kenemans et al., 1993; Proverbio et al., 1996), an effect that is attributed to macro-scale differences in sensitivity to spatial frequency across visual regions (Kenemans et al., 2000). Therefore, one would expect that multivariate pattern analysis techniques are also sensitive to spatial frequency as measured with EEG. Yet, to our knowledge, no study has so far shown that it is also possible to decode spatial frequency from the pattern of scalp-EEG activity.

Classification performance visualized in Figure 4 shows that the classifier could discriminate not only the target, but also the distractor’s spatial frequency, in all conditions(as it is unclear whether in the DpTp identical condition, in which targets and distractors were of the same spatial frequency, classification was driven by the spatial frequency of the target, of the distractor or both, we did not contrast this condition with the unique feature conditions). Interestingly, both in anticipation and during stimulus processing, target decoding was numerically higher when its spatial frequency was predictable (i.e. DpTp unique condition) compared to when it was not (i.e. DpTv unique condition), whereas the opposite pattern was observed for distractor decoding: the spatial frequency of the distractor could be decoded with higher accuracy when only the spatial frequency of the distractor, but not the target spatial frequency could be predicted in advance (in the DpTv unique compared to the DpTp unique condition). The observation that the anticipatory EEG signal already appeared to contain information about the upcoming distractor is especially interesting as it may support the notion of a negative sensory template or preparatory feature-specific distractor inhibition. Critically, anticipatory target decoding was absent in the DpTv unique condition, the one condition where target expectations could not develop, arguing against the possibility that the observed anticipatory decoding can be explained by an artificial shift of the stimulus-evoked response (van Driel et al., 2019). Also note that anticipatory above chance decoding was not sustained throughout the entire pre-stimulus period, but only emerged close to search display onset, raising the possibility suggesting that sustained anticipatory expectation signals were subtracted out by the baselining procedure (see Materials and methods). Control analyses, however, using either a condition-specific pre-stimulus baseline (collapsed across spatial frequency) or no baseline at all, yielded identical results indicating that the observed feature specificity in the EEG signal only emerged in close temporal proximity to search display onset.

Figure 4

Download asset Open asset

Cluster-based permutation tests across time confirmed this pattern of observations. As we were specifically interested in anticipatory markers of distractor feature expectations, these tests were performed separately for the anticipatory (−750 to 0 ms) and the reactive (0–550 ms) time windows. As visualized by the red and green colored horizontal lines in Figure 4A (p<0.05, cluster-corrected), anticipatory target decoding was specific to the DpTp unique condition, whereas anticipatory distractor decoding was specific to the DpTv unique condition. These condition differences were confirmed by a direct contrast between DpTp and DpTv unique conditions, which yielded anticipatory and reactive differences between conditions, both for target and distractor decoding (p<0.05, cluster-corrected; double-colored lines in Figure 4A). These statistical results reveal that when both the targets and the distractors spatial frequency could be predicted, participants especially relied on target feature-specific information to guide search. However, when they could not know what the target would look like, as it randomly varied across trials, and the target should thus also not be evident in the anticipatory neural code, participants relied more on distractor feature-specific information to bias search, and importantly, already in anticipation of search display onset.

To more directly characterize these effects, we followed up with an exploratory analysis in which average decoding performance in separate time windows (pre-search display: −250 ms to 0 ms; or post-search display: 0 ms to 550 ms) was used as the dependent variable in a repeated measures ANOVA with within-subject factors Stimulus Class (target, distractor) and Condition (DpTp unique, DpTv unique). As visualized in Figure 4B, and consistent with the pattern described thus far, this revealed significant effects of being able to predict the target and distractor spatial frequency in advance, that differed as a function of condition, both before the onset of the search display (in the preparatory period) and after search display onset, as revealed by significant interactions between Stimulus Class and Condition (F (1, 23)=11.3, p=0.003, ${\eta }_p^2=0.33$ and F (1, 23)=59.3, p<0.001, ${\eta }_p^2=0.72$, respectively). These interactions reflected better target decoding in the DpTp unique than in the DpTv unique condition both before and after target presentation (t (23)=2.1, p=0.044, d = 0.44 and t (23)=4.0, p<0.001, d = 0.82, respectively) and vice versa for distractor decoding (t (23)=2.1, p=0.046, d = 0.43 and t (23)=2.5, p=0.02, d = 0.51, respectively). Together, these findings show that we could not only decode the spatial frequency of the target from the pattern of pre-search display brain activity, but also, when the targets spatial frequency was unpredictable, the spatial frequency of the distractor, suggestive of a distractor feature template.

Cross-class decoding: distractor and target expectations induce distinct pre-stimulus sensory templates

The observed anticipatory distractor decoding is in line with the idea that distractor-specific expectations may help to filter out distractors preemptively. Yet, it leaves open the question how they may do so, as decoding by itself is uninformative about the nature of the underlying neural representation (Moorselaar and Slagter, 2020). A hallmark of an advance rejection template is that processing of the coded features is inhibited and the neural representation of a rejection template should thus be distinct from a target-defined attentional template which in contrast facilitates processing of matching stimuli.

To test this prediction, we explored whether the pattern driving anticipatory distractor decoding differed from that driving target decoding. Specifically, we trained the classifier separately on either target or distractor spatial frequencies using the same trials that were used in the preceding analyses. However, we now used the resulting weights to classify the spatial frequency of the target in an independent set of trials, namely target-only trials without a distractor. The logic here is that if targets and distractors are represented via the same neural code, it should not matter which of the two is used in the training stage. Yet, distractor weights should not generalize to targets when they are represented via distinct neural codes.

As visualized in Figure 5A (solid lines) and as one would expect, the target trained model tested on target-only trials replicated the findings of the main analysis: the spatial frequency of the target could be reliably decoded post search display in all conditions, but also in anticipation of the search display selectively in the condition in which the spatial frequency of the target was predictable (DpTp unique; right plot). Cluster-based permutation tests across time confirmed that target-target decoding was not only reliable in both the pre- and post-stimulus window (p<0.05, cluster-corrected; solid green horizontal lines), but also differed from distractor-target decoding (p<0.05, cluster-corrected; black horizontal lines). Indeed, using distractor trained weights, the exact same spatial frequency could no longer be decoded above change in advance when it was a predictable feature of the target, potentially indicative of a distinct distractor template (Figure 5A; dashed lines). That is, the predictable target spatial frequency could only be decoded from the pattern of pre-stimulus EEG activity using target-based classification weights, but not using distractor-based classification weights, even though this same spatial frequency could be reconstructed using distractor-based decoding (Figure 4). What’s more, pre-stimulus target decoding performance of the distractor-trained model did not differ between the conditions in which the target spatial frequency could be predicted in advance vs. could not be predicted in advance, neither when tested with cluster correction nor with paired t-tests within the pre-search display window (t (23)=0.57, p=0.57, d = 0.12; BF₀₁ = 4.03) or the post-search display window (t (23)=0.14, p=0.89, d = 0.028; BF₀₁ = 4.61). Nonetheless, the Stimulus Class (target trained, distractor trained) by Condition interaction was not significant in the pre-search display window (F (1, 23)=0.6, p=0.44, ${\eta }_p^2=0.026;$BF₀₁ = 2.29; Figure 5B). Next to these anticipatory effects, cluster-based permutation tests identified significant below chance decoding during visual search using the distractor trained weights (p<0.05, cluster-corrected; dashed green horizontal lines). Together, these findings suggest that spatial frequency was differently represented in the pattern of scalp EEG activity as a function of whether it was a feature of the target or of the distractor.

Figure 5

Download asset Open asset

ERPs: the distractor Pd reduces as a function of spatial expectations

Finally, we examined how distractor feature and locations expectations modulated stimulus processing as indexed by the lateralized N2pc and Pd components, signaling attentional selection (Eimer, 2014; Luck, 2012; Luck and Hillyard, 1994) and suppression (Gaspelin and Luck, 2018a; Hickey et al., 2009), respectively. To isolate distractor- and target-specific components, separate difference waveforms (contralateral – ipsilateral) were computed for lateralized distractors below the horizontal midline with concurrent targets on the vertical midline, or vice versa, using O1/O2, PO3/PO4 and PO7/PO8 as electrodes of interest (see Materials and methods section for details).

As visualized in Figure 6, we observed a clear distractor-evoked N2pc in all conditions, which only seemed to be somewhat reduced in amplitude at the high-probability distractor location in the DpTp unique condition. Likewise, as shown in Figure 7, the target-evoked N2pc also appeared to be virtually unaffected by feature and/or location expectations. Yet, notably, and in line with our previous study (van Moorselaar and Slagter, 2019), the distractor-evoked Pd was virtually eliminated when distractors appeared at high-probability distractor locations, signaling a reduced need for post-distractor inhibition.

Figure 6

Download asset Open asset

Figure 7

Download asset Open asset

To statistically test these effects, voltages within the N2pc window (114–194 ms; see Materials and methods section for details) were submitted to a Stimulus Class (target, distractor) by Condition (DpTp identical, DpTp unique, DpTv unique) by Location (high distractor probability, low distractor probability) by Hemifield (contralateral, ipsilateral) repeated measures ANOVA. This analysis confirmed that the N2pc was reliably evoked across conditions (main effect Hemifield: F (1, 23)=65.5, p<0.001, ${\eta }_p^2=0.74$) and also showed that as could be expected, the N2pc was more pronounced in response to targets than distractors (interaction Stimulus Class by Hemifield: F (1, 23)=37.3, p<0.001, ${\eta }_p^2=0.62$). Critically, in the DpTp unique condition, when participants had foreknowledge about both the targets and distractors features, the distractor N2pc was reduced and the target N2pc increased relative to the other conditions without unique target feature predictions (interaction Stimulus Class by Condition by Hemifield: F (2, 46)=5.3, p=0.009, ${\eta }_p^2=0.19$), indicating that expectations at the target level made attentional selection more efficient. However, there was no evidence whatsoever that location expectations modulated the amplitude of the N2pc, neither for distractors or for targets (all F’s < 1.1, all p’s > 0.33, all ${\eta }_p^2$‘s > 0.048).

The N2pc results suggest that distractors continued to capture attention to the same extent at high as at low-probability distractor locations, albeit generally not as strongly as targets. Yet, at high-probability locations there appeared to be a reduced need to actively counteract this shift of attention as evidenced by a reduction in the amplitude of the distractor-evoked Pd. To statistically test this, average voltage values within the Pd window (243–323 ms; see Materials and methods section for details) elicited by distractors were submitted to a Condition (DpTp identical, DpTp unique, DpTv unique) by Location (high distractor probability, low distractor probability) by Hemifield (contralateral, ipsilateral) repeated measures ANOVA. Critically, this analysis yielded a Location by Hemifield interaction (F (1, 23)=5.1, p=0.033, ${\eta }_p^2=0.18$), reflecting a reduction in Pd amplitude at high- vs. low-probability distractor locations across conditions. Together these results replicate our previous findings that in a scenario where location expectations do not influence the initial selection of distracting information, they can nevertheless reduce the subsequent need to actively suppress the expected distracting information (van Moorselaar and Slagter, 2019). The main effect of Hemifield and all other interaction effects with Location were non-significant (all F’s < 1.9, all p’s > 0.16, all ${\eta }_p^2$‘s > 0.077).

The aim of the present study was to gain more insight into the neural mechanisms underlying learning to ignore distracting information on the basis of spatial and/or non-spatial regularities. While there is evidence that expectations based on regularities in the environment induce pre-stimulus sensory templates (Kok et al., 2017; van Moorselaar and Slagter, 2019), it remains unclear whether the generation of such sensory templates is dependent on the task relevance of the sensory information, and hence is also evident when distractor rather than target information can be predicted in advance. Here we manipulated the predictability of the distractor at both the spatial and feature level, to address this outstanding question. At the behavioral level, responses were faster when the distractor occurred at the high-probability distractor location, but slower when the target occurred at this location, relative to any other location, indicative of generic expectation-dependent spatial suppression. Moreover, when distractors were also predictable at the feature level, location-based distractor suppression became less pronounced, suggesting that the locus of inhibition in the processing hierarchy is flexible and dependent on whether expectations are location- and/or feature-specific. Yet, at the neural level, even when distractors were not predictable at the feature level, we observed no evidence for preparatory spatial inhibition as a function of distractor location learning, as indicated by the absence of a modulation of pre-stimulus lateralized alpha-band activity. In contrast, when distractor features (their spatial frequency) were predictable, they could already be decoded from the pattern of scalp EEG activity before search display onset, potentially indicative of a distractor feature template. Strikingly, spatial frequency was differently represented in the pattern of pre-stimulus scalp EEG activity as a function of whether it was a predictable feature of the target or of the distractor, suggesting distinct target and distractor templates. Spatial distractor expectations did reduce post-distractor inhibition, as indicated by a strongly reduced distractor-evoked Pd. Together these findings demonstrate that location and feature-based expectations in part rely on different neural mechanisms, but also interact, in inhibiting distracting information.

At the spatial level, there is some evidence that pre-stimulus alpha-band activity contributes to expectation-dependent suppression (Wang et al., 2019), but based on other work (Noonan et al., 2016; van Moorselaar and Slagter, 2019), we reasoned that this may be specific to conditions that enforce learning at the spatial level. Counter to this idea, however, here lateralized alpha-band activity was insensitive to distractor location learning, even when the target and distractor shared the same spatial frequency and learning could only be location based. The absence of pre-stimulus alpha-band modulations in any condition despite clear behavioral evidence for generic space-based suppression adds to a growing body of literature challenging the widely embraced view that anticipatory distractor suppression is implemented via a spatially specific increase in alpha-band activity (Moorselaar and Slagter, 2020). Previously observed modulations of pre-stimulus alpha-band activity may instead reflect attending away rather than suppression of distracting information, as also argued previously (Foster and Awh, 2019). Indeed, in our recent study using a similar visual search task, but in which the target or the distractor was repeatedly presented at the same location, across four consecutive trials, the topographic distribution of preparatory alpha power was sensitive to the expected target location, but not to the expected distractor location (van Moorselaar and Slagter, 2019; see also Noonan et al., 2016). Together, these findings suggest that target facilitation and distractor suppression rely on distinct neural mechanisms, at least when space-based. Albeit speculative, distractor location learning may be implemented via an activity silent mechanism or synaptic efficiency (Stokes, 2015). An intuitively appealing idea, one that can also be easily reconciled with proposals that (spatial) expectations exert their influence only after initial processing of sensory input (Alilović et al., 2019; Rao et al., 2012), is that distractor learning changes plasticity in priority maps of space (Ferrante et al., 2018). Alternatively, it is possible that the spatial bias was evident in the neural signal, but our analysis, which focused on lateralized alpha power, was simply not sensitive enough to detect such a bias. Note, however, that in our previous work we also found no evidence for an anticipatory bias using either a multivariate decoding or a forward encoding approach, although that study provided considerably less time for distractor location predictions to develop (van Moorselaar and Slagter, 2019). Nonetheless, in line with a large body of work relating changes in lateralized pre-stimulus alpha-band activity to spatial attention (Foxe and Snyder, 2011; Jensen and Mazaheri, 2010), in that study, the expected target location was represented in the pattern of pre-stimulus alpha-band activity. Future work in humans and animals is thus necessary to determine whether changes in synaptic plasticity indeed underlie distractor location learning, or alternatively whether spatial tuning can be observed in the absence of lateralized alpha modulations with more sensitive methods, such as intracranial EEG.

A striking aspect of the present findings was that we did find evidence for distractor-specific feature templates based on the pattern of pre-stimulus brain activity when the spatial frequency of the distractor was predictable. The existence of so-called templates for rejection is a long-standing topic of debate in the domain of working memory research (Arita et al., 2012; Beck and Hollingworth, 2015). While behaviorally, there is some evidence in support of an advance distractor-specific inhibitory template (e.g. Carlisle and Nitka, 2018; Woodman and Luck, 2007), previous fMRI studies did not provide support for the notion of a distinct distractor template (de Vries et al., 2019; Reeder et al., 2018). Counter to these previous studies, which cued the distractor feature on a trial-by-trial basis, hence promoting the use of visual working memory, here the distractor ‘template’ could be formed on the basis of predictions derived from regularities in the search context. That we could decode the predictable distractor feature under these conditions thus suggests that a distinct distractor template may only arise as a function of learning from repeated encounters with the to-be-ignored feature. The development of this template may rely on implicit learning mechanisms that bypass working memory altogether (Moorselaar and Slagter, 2020) and hence should not be taken as evidence for the existence of templates for rejection in working memory. Nevertheless, they do convincingly show that distractor feature expectations, like target feature expectations, induce pre-stimulus sensory templates.

Note, however, that decoding is uninformative about the underlying neural representation (Naselaris and Kay, 2015; Moorselaar and Slagter, 2020). The here observed anticipatory distractor decoding could be driven by selective changes in the activity of neural populations representing the predicted distractor feature, selective changes in the activity of neural populations representing non-distractor or target (i.e. the other two spatial frequencies) features, or both. Indeed, it is well known that the visual system can strategically boost features shifted away from the distractor and/or target to increase discriminability between the target and the likely distractors (Geng and Witkowski, 2019; Navalpakkam and Itti, 2007). To determine whether the neural pattern underlying distractor decoding was distinct from the pattern driving target decoding, we also examined whether a model trained on distractor spatial frequencies generalized to target spatial frequencies. Strikingly, we found that while we could decode the anticipated spatial frequency of the target above chance in target-only trials prior to search display onset when the model was trained on target spatial frequencies, we could not decode the same anticipated spatial frequency of the target when the model was trained on distractor spatial frequencies. What’s more, after search display onset there was reliable below-chance decoding, which arguably arose because after search display onset, attention was shifted to the target spatial frequency during training on the distractor spatial frequency, resulting in negative decoding when the classifier was tested on the target-only trials and the target now possessed the distractor spatial frequency. If distractor and target templates were opposite of one another, however, because observers strategically shifted their attention away from the anticipated distractor spatial frequency towards the remaining potential target spatial frequencies, one would have expected to also observe reliable below chance decoding in the anticipatory period. By contrast, pre-stimulus target decoding performance of the distractor-trained model did not differ between the conditions in which the target spatial frequency could be predicted in advance vs. could not be predicted in advance, nor was there below chance decoding. Although these are exploratory null findings and therefore should be interpreted cautiously, they nevertheless argue against the idea that attention was strategically shifted away toward one (or both) of the remaining (and possible target) spatial frequencies, but rather support the notion of a distinct distractor template. Note also that it is unlikely that the observed anticipatory distractor feature decoding simply reflects inter-trial priming as in this scenario, one would not expect a difference in the neural activity patterns driving distractor decoding versus target decoding, as was observed here. Together, these decoding results indicate that target and distractor feature expectations are implemented in distinct neural codes. Future M/EEG studies that apply forward encoding modeling are necessary to more precisely examine the nature of the changes in feature tuning that underlie feature-based distractor suppression.

At the behavioral level, distractor interference was greatly reduced when distractors occurred at the high-probability distractor location. Yet, this benefit was not associated with active anticipatory spatial suppression as implemented by alpha-band activity, as discussed above, nor did we observe enhanced post-distractor suppression as indicated by the amplitude of the Pd ERP component (Feldmann-Wüstefeld and Vogel, 2019; Hickey et al., 2009). That is, replicating our previous study (van Moorselaar and Slagter, 2019), across conditions the distractor-evoked Pd was strongly reduced when distractors appeared at their expected location, as if the brain had learned that stimuli at those locations could be safely ignored, even though they still evoked a reliable N2pc. While a reduction of the Pd appears consistent with a dampened response to expected sensory input and thus less need for suppression, many studies have actually shown that expected distractors, either at the spatial (Wang et al., 2019) or the feature level (Burra and Kerzel, 2013; Jannati et al., 2013), elicit a Pd instead of an N2pc. Following the idea that the Pd signals a suppressive mechanism, which is stronger in contexts that require strong suppression (Hickey et al., 2009), these findings are typically interpreted to reflect the suppression of salient distractors before they capture attention (Gaspelin and Luck, 2018b). To date, it is unclear why in certain contexts the Pd increases, whereas in others, as in the current study, its amplitudes decreases as a function of expectations (Heuer and Schubö, 2020). Differences in distractor salience or in the presence of non-singleton distractors may explain this discrepancy in findings. A better understanding of the factors that modulate distractor learning and suppression is critical for advancing our understanding of learned distractor suppression and should thus be the focus of future work.

Our ERP findings also corroborate the notion that the N2pc and the Pd reflect different attentional mechanisms, as these components were differentially sensitive to feature and spatial expectations. Although it was initially thought that the N2pc reflects a covert shift of attention to a peripheral stimulus (Eimer, 1996; Woodman and Luck, 1999), current evidence suggests that the N2pc indexes object individuation (Mazza and Caramazza, 2015), the formation of an object representation that is segregated from the background and other items in the display (Kahneman et al., 1992). This idea dovetails with our finding that N2pc amplitude was modulated by expectations at the feature level, but not by spatial expectations. The specific perceptual process reflected by the N2pc, however, continues to be the topic of active debate. In one perspective, which is easily reconciled with the idea of object individuation, the N2pc reflects attentional engagement, which according to Zivony et al., 2018 reflects the deployment of higher level processes that enable identification and binding of stimulus features, and, if necessary, consolidation into working memory. Consistent with this perspective, here we show that when the spatial frequency of both the target and the distractor were predictable, and distractors and targets could thus easily be dissociated, the distractor-evoked N2pc was smaller in amplitude, whereas the target-evoked N2pc increased in amplitude. By contrast, as outlined above, the Pd was only sensitive to spatial expectations in that its amplitude was greatly reduced to distractors at the high-probability distractor location. This demonstrates that the Pd, which is often used to index stimulus-driven distractor inhibition (Gaspelin and Luck, 2018b; Sawaki et al., 2012), is specifically sensitive to learned expectations at the spatial level.

Although there is growing evidence that regularities at both the spatial and the feature level shape attentional orienting, it is unclear to what extent, if at all (Wang and Theeuwes, 2018b), feature-based expectations can shape attentional selection above and beyond location-based suppression (Stilwell et al., 2019). At the behavioral level, we found that distractor suppression at the high-probability distractor location, but not target suppression at the high-probability distractor location, was affected by stimulus feature regularities. This may suggest that while spatial distractor expectations may induce feature-blind ‘proactive’ suppression (which uniformly suppresses both distractor and target processing at the expected distractor location), subsequent inhibition of stimuli at the likely distractor location is dependent on expectations about what a distractor looks like at the feature level. This makes sense from an ecological perspective: one would not want to simply inhibit anything occurring at a particular location in space. While the interaction between spatial and feature expectations in the distractor tuned behavioral analyses was significant in both Experiments 1 and 2, after priming effects were removed, it only remained significant in Experiment 2. This may suggest that the interaction also to some extent reflects an interaction between priming and distractor feature expectations, but could also reflect the fact that Experiment 2 allowed for the development of more robust feature and location expectations. That is, counter to Experiment 2, in Experiment 1, conditions not only switched every block (and consequently also the predictable distractor and/or target features), but participants also only completed six blocks per condition. In Experiment 2, distractor location and feature learning could develop over a much longer time scale (18 blocks), and participants completed 36 blocks per condition. Consequently, Experiment 1 may have been less sensitive to detecting the interaction between feature and spatial expectations above and beyond intertrial priming. The fact that the interaction remains reliable in Experiment 2, however, suggests that these different types of expectations can interact above and beyond intertrial biases.

It may seem surprising at first that none of our EEG measures showed an interaction between spatial and feature expectations, while in the corresponding behavioral data, we found that distractor interference was most reduced when both the location and the spatial frequency of the distractor were predictable in advance. Yet, this may be explained by the fact that these EEG measures are specifically tuned to capturing modulations in either spatial processing (i.e. spatially lateralized measures (alpha asymmetry and Pd)) or feature representation (spatial frequency decoding). As such, they may be less sensitive to revealing neural mechanisms that integrate spatial and feature distractor foreknowledge. Indeed, our EEG data did show robust main effects of spatial and feature expectations.

To summarize, our findings add to a rapidly growing body of work showing that the ability to inhibit distracting information critically relies on expectations grounded in statistical regularities in the environment. They extend this work by showing that feature-based distractor expectations, but not location-based distractor expectations, modulate pre-stimulus representations, and in a distinct manner from expectations about upcoming target features, indicative of a distractor template. These findings have important implications for cognitive (neuroscience) theories of selective attention and predictive processing.

All data are publicly available on OSF (https://osf.io/7ek45/). Analysis scripts can be downloaded via GitHub.

1. 1. van Moorselaar D
   2. Lampers E
   3. Cordesius E
   4. Slagter HA

   (2020) Open Science Framework

   ID 7ek45. Manipulating target distractor similarity.

   https://osf.io/7ek45/

1. 1. Alilović J
   2. Timmermans B
   3. Reteig LC
   4. van Gaal S
   5. Slagter HA

   (2019) No evidence that predictions and attention modulate the first feedforward sweep of cortical information processing

   Cerebral Cortex 29:2261–2278.

   https://doi.org/10.1093/cercor/bhz038

   • PubMed
   • Google Scholar
2. 1. Allenmark F
   2. Zhang B
   3. Liesefeld HR
   4. Shi Z
   5. Müller HJ

   (2019) Probability cueing of singleton-distractor regions in visual search: the locus of spatial distractor suppression is determined by colour swapping

   Visual Cognition 27:576–594.

   https://doi.org/10.1080/13506285.2019.1666953

   • Google Scholar
3. 1. Arita JT
   2. Carlisle NB
   3. Woodman GF

   (2012) Templates for rejection: configuring attention to ignore task-irrelevant features

   Journal of Experimental Psychology: Human Perception and Performance 38:580–584.

   https://doi.org/10.1037/a0027885

   • Google Scholar
4. 1. Battistoni E
   2. Stein T
   3. Peelen MV

   (2017) Preparatory attention in visual cortex

   Annals of the New York Academy of Sciences 1396:92–107.

   https://doi.org/10.1111/nyas.13320

   • PubMed
   • Google Scholar
5. 1. Beck VM
   2. Hollingworth A

   (2015) Evidence for negative feature guidance in visual search is explained by spatial recoding

   Journal of Experimental Psychology: Human Perception and Performance 41:1190–1196.

   https://doi.org/10.1037/xhp0000109

   • Google Scholar
6. 1. Burra N
   2. Kerzel D

   (2013) Attentional capture during visual search is attenuated by target predictability: Evidence from the N2pc, Pd, and topographic segmentation

   Psychophysiology 50:422–430.

   https://doi.org/10.1111/psyp.12019

   • Google Scholar
7. 1. Carlisle NB
   2. Nitka AW

   (2018) Location-based explanations do not account for active attentional suppression

   Visual Cognition 27:1–12.

   https://doi.org/10.1080/13506285.2018.1553222

   • Google Scholar
8. 1. Chelazzi L
   2. Marini F
   3. Pascucci D
   4. Turatto M

   (2019) Getting rid of visual distractors: the why, when, how, and where

   Current Opinion in Psychology 29:135–147.

   https://doi.org/10.1016/j.copsyc.2019.02.004

   • Google Scholar
9. 1. Cunningham CA
   2. Egeth HE

   (2016) Taming the white bear: initial costs and eventual benefits of distractor inhibition

   Psychological Science 27:476–485.

   https://doi.org/10.1177/0956797615626564

   • PubMed
   • Google Scholar
10. 1. de Vries IE
    2. van Driel J
    3. Olivers CN

    (2017) Posterior α EEG dynamics dissociate current from future goals in working Memory-Guided visual search

    The Journal of Neuroscience 37:1591–1603.

    https://doi.org/10.1523/JNEUROSCI.2945-16.2016

    • PubMed
    • Google Scholar
11. 1. de Vries IEJ
    2. Savran E
    3. van Driel J
    4. Olivers CNL

    (2019) Oscillatory mechanisms of preparing for visual distraction

    Journal of Cognitive Neuroscience 31:1873–1894.

    https://doi.org/10.1162/jocn_a_01460

    • PubMed
    • Google Scholar
12. 1. Eimer M

    (1996) The N2pc component as an indicator of attentional selectivity

    Electroencephalography and Clinical Neurophysiology 99:225–234.

    https://doi.org/10.1016/0013-4694(96)95711-9

    • Google Scholar
13. 1. Eimer M

    (2014) The neural basis of attentional control in visual search

    Trends in Cognitive Sciences 18:526–535.

    https://doi.org/10.1016/j.tics.2014.05.005

    • Google Scholar
14. 1. Failing M
    2. Feldmann-Wüstefeld T
    3. Wang B
    4. Olivers C
    5. Theeuwes J

    (2019a) Statistical regularities induce spatial as well as feature-specific suppression

    Journal of Experimental Psychology: Human Perception and Performance 45:1291–1303.

    https://doi.org/10.1037/xhp0000660

    • Google Scholar
15. 1. Failing M
    2. Wang B
    3. Theeuwes J

    (2019b) Spatial suppression due to statistical regularities is driven by distractor suppression not by target activation

    Attention, Perception, & Psychophysics 81:1405–1414.

    https://doi.org/10.3758/s13414-019-01704-9

    • PubMed
    • Google Scholar
16. 1. Feldmann-Wüstefeld T
    2. Vogel EK

    (2019) Neural Evidence for the Contribution of Active Suppression During Working Memory Filtering

    Cerebral Cortex 29:529–543.

    https://doi.org/10.1093/cercor/bhx336

    • Google Scholar
17. 1. Ferrante O
    2. Patacca A
    3. Di Caro V
    4. Della Libera C
    5. Santandrea E
    6. Chelazzi L

    (2018) Altering spatial priority maps via statistical learning of target selection and distractor filtering

    Cortex 102:67–95.

    https://doi.org/10.1016/j.cortex.2017.09.027

    • Google Scholar
18. 1. Foster JJ
    2. Awh E

    (2019) The role of alpha oscillations in spatial attention: limited evidence for a suppression account

    Current Opinion in Psychology 29:34–40.

    https://doi.org/10.1016/j.copsyc.2018.11.001

    • PubMed
    • Google Scholar
19. 1. Foxe JJ
    2. Snyder AC

    (2011) The role of Alpha-Band brain oscillations as a sensory suppression mechanism during selective attention

    Frontiers in Psychology 2:154.ARTN.

    https://doi.org/10.3389/fpsyg.2011.00154

    • Google Scholar
20. 1. Friston K

    (2009) The free-energy principle: a rough guide to the brain?

    Trends in Cognitive Sciences 13:293–301.

    https://doi.org/10.1016/j.tics.2009.04.005

    • Google Scholar
21. 1. Gaspar JM
    2. McDonald JJ

    (2014) Suppression of Salient Objects Prevents Distraction in Visual Search

    Journal of Neuroscience 34:5658–5666.

    https://doi.org/10.1523/JNEUROSCI.4161-13.2014

    • Google Scholar
22. 1. Gaspelin N
    2. Luck SJ

    (2018a) Combined electrophysiological and behavioral evidence for the suppression of salient distractors

    Journal of Cognitive Neuroscience 30:1265–1280.

    https://doi.org/10.1162/jocn_a_01279

    • PubMed
    • Google Scholar
23. 1. Gaspelin N
    2. Luck SJ

    (2018b) The Role of Inhibition in Avoiding Distraction by Salient Stimuli

    Trends in Cognitive Sciences 22:79–92.

    https://doi.org/10.1016/j.tics.2017.11.001

    • Google Scholar
24. 1. Geng JJ
    2. Witkowski P

    (2019) Template-to-distractor distinctiveness regulates visual search efficiency

    Current Opinion in Psychology 29:119–125.

    https://doi.org/10.1016/j.copsyc.2019.01.003

    • Google Scholar
25. 1. Giesbrecht B
    2. Weissman DH
    3. Woldorff MG
    4. Mangun GR

    (2006) Pre-target activity in visual cortex predicts behavioral performance on spatial and feature attention tasks

    Brain Research 1080:63–72.

    https://doi.org/10.1016/j.brainres.2005.09.068

    • Google Scholar
26. 1. Goschy H
    2. Bakos S
    3. Müller HJ
    4. Zehetleitner M

    (2014) Probability cueing of distractor locations: both intertrial facilitation and statistical learning mediate interference reduction

    Frontiers in Psychology 5:1195.

    https://doi.org/10.3389/fpsyg.2014.01195

    • Google Scholar
27. 1. Gramfort A
    2. Luessi M
    3. Larson E
    4. Engemann DA
    5. Strohmeier D
    6. Brodbeck C
    7. Parkkonen L
    8. Hämäläinen MS

    (2014) MNE software for processing MEG and EEG data

    NeuroImage 86:446–460.

    https://doi.org/10.1016/j.neuroimage.2013.10.027

    • Google Scholar
28. 1. Hand DJ
    2. Till RJ

    (2001) A simple generalisation of the area under the ROC curve for multiple class classification problems

    Machine Learning 45:171–186.

    https://doi.org/10.1023/A:1010920819831

    • Google Scholar
29. 1. Heuer A
    2. Schubö A

    (2020) Cueing distraction: electrophysiological evidence for anticipatory active suppression of distractor location

    Psychological Research 84:2111–2121.

    https://doi.org/10.1007/s00426-019-01211-4

    • PubMed
    • Google Scholar
30. 1. Hickey C
    2. Di Lollo
    3. McDonald JJ

    (2009) Electrophysiological Indices of Target and Distractor Processing in Visual Search

    Journal of Cognitive Neuroscience 21:760–775.

    https://doi.org/10.1162/jocn.2009.21039

    • Google Scholar
31. 1. Hopfinger JB
    2. Buonocore MH
    3. Mangun GR

    (2000) The neural mechanisms of top-down attentional control

    Nature Neuroscience 3:284–291.

    https://doi.org/10.1038/72999

    • Google Scholar
32. 1. Jannati A
    2. Gaspar JM
    3. McDonald JJ

    (2013) Tracking target and distractor processing in fixed-feature visual search: evidence from human electrophysiology

    Journal of Experimental Psychology: Human Perception and Performance 39:1713–1730.

    https://doi.org/10.1037/a0032251

    • Google Scholar
33. Software

    1. JASP-TEAM

    (2018) JASP, version 0.8.2

    JASP.

    https://jasp-stats.org/
34. 1. Jensen O
    2. Mazaheri A

    (2010) Shaping functional architecture by oscillatory alpha activity: gating by inhibition

    Frontiers in Human Neuroscience 4:186.

    https://doi.org/10.3389/fnhum.2010.00186

    • PubMed
    • Google Scholar
35. 1. Kahneman D
    2. Treisman A
    3. Gibbs BJ

    (1992) The reviewing of object files: Object-specific integration of information

    Cognitive Psychology 24:175–219.

    https://doi.org/10.1016/0010-0285(92)90007-O

    • Google Scholar
36. 1. Kenemans JL
    2. Kok A
    3. Smulders FTY

    (1993) Event-related potentials to conjunctions of spatial frequency and orientation as a function of stimulus parameters and response requirements

    Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section 88:51–63.

    https://doi.org/10.1016/0168-5597(93)90028-N

    • Google Scholar
37. 1. Kenemans JL
    2. Baas JMP
    3. Mangun GR
    4. Lijffijt M
    5. Verbaten MN

    (2000) On the processing of spatial frequencies as revealed by evoked-potential source modeling

    Clinical Neurophysiology 111:1113–1123.

    https://doi.org/10.1016/S1388-2457(00)00270-4

    • Google Scholar
38. 1. Kok P
    2. Rahnev D
    3. Jehee JF
    4. Lau HC
    5. de Lange FP

    (2012) Attention reverses the effect of prediction in silencing sensory signals

    Cerebral Cortex 22:2197–2206.

    https://doi.org/10.1093/cercor/bhr310

    • PubMed
    • Google Scholar
39. 1. Kok P
    2. Mostert P
    3. de Lange FP

    (2017) Prior expectations induce prestimulus sensory templates

    PNAS 114:10473–10478.

    https://doi.org/10.1073/pnas.1705652114

    • PubMed
    • Google Scholar
40. Book

    1. Luck SJ

    (2012) Electrophysiological correlates of the focusing of attention within complex visual scenes: n2pc and related ERP components

    In: Luck S. J, Kappenman E. S, editors. The Oxford Handbook of Event-Related Potential Components. Oxford University Press. pp. 329–360.

    https://doi.org/10.1093/oxfordhb/9780195374148.013.0161

    • Google Scholar
41. 1. Luck SJ
    2. Hillyard SA

    (1994) Electrophysiological correlates of feature analysis during visual search

    Psychophysiology 31:291–308.

    https://doi.org/10.1111/j.1469-8986.1994.tb02218.x

    • Google Scholar
42. 1. Maljkovic V
    2. Nakayama K

    (1994) Priming of pop-out: I. Role of features

    Memory & Cognition 22:657–672.

    https://doi.org/10.3758/BF03209251

    • Google Scholar
43. 1. Mathôt S
    2. Schreij D
    3. Theeuwes J

    (2012) OpenSesame: An open-source, graphical experiment builder for the social sciences

    Behavior Research Methods 44:314–324.

    https://doi.org/10.3758/s13428-011-0168-7

    • Google Scholar
44. 1. Mazza V
    2. Caramazza A

    (2015) Multiple object individuation and subitizing in enumeration: a view from electrophysiology

    Frontiers in Human Neuroscience 9:162.

    https://doi.org/10.3389/fnhum.2015.00162

    • Google Scholar
45. 1. Moorselaar D
    2. Slagter HA

    (2020) Inhibition in selective attention

    Annals of the New York Academy of Sciences 1464:204–221.

    https://doi.org/10.1111/nyas.14304

    • Google Scholar
46. 1. Naselaris T
    2. Kay KN

    (2015) Resolving ambiguities of MVPA using explicit models of representation

    Trends in Cognitive Sciences 19:551–554.

    https://doi.org/10.1016/j.tics.2015.07.005

    • PubMed
    • Google Scholar
47. 1. Navalpakkam V
    2. Itti L

    (2007) Search goal tunes visual features optimally

    Neuron 53:605–617.

    https://doi.org/10.1016/j.neuron.2007.01.018

    • PubMed
    • Google Scholar
48. 1. Noonan MP
    2. Adamian N
    3. Pike A
    4. Printzlau F
    5. Crittenden BM
    6. Stokes MG

    (2016) Distinct mechanisms for distractor suppression and target facilitation

    The Journal of Neuroscience 36:1797–1807.

    https://doi.org/10.1523/JNEUROSCI.2133-15.2016

    • PubMed
    • Google Scholar
49. 1. Noonan MP
    2. Crittenden BM
    3. Jensen O
    4. Stokes MG

    (2018) Selective inhibition of distracting input

    Behavioural Brain Research 355:36–47.

    https://doi.org/10.1016/j.bbr.2017.10.010

    • Google Scholar
50. 1. Oostenveld R
    2. Fries P
    3. Maris E
    4. Schoffelen JM

    (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

    Computational Intelligence and Neuroscience 2011:1–9.

    https://doi.org/10.1155/2011/156869

    • PubMed
    • Google Scholar
51. 1. Park S
    2. Kim M-S
    3. Chun MM

    (2007) Concurrent working memory load can facilitate selective attention: evidence for specialized load

    Journal of Experimental Psychology: Human Perception and Performance 33:1062–1075.

    https://doi.org/10.1037/0096-1523.33.5.1062

    • Google Scholar
52. 1. Pedregosa F
    2. Varoquaux G
    3. Gramfort A
    4. Michel V
    5. Thirion B
    6. Grisel O
    7. Dubourg V

    (2011)

    Scikit-learn: machine learning in Python

    Journal of Machine Learning Research 12:2830.

    • Google Scholar
53. 1. Peirce JW

    (2008) Generating stimuli for neuroscience using PsychoPy

    Frontiers in Neuroinformatics 2:10.

    https://doi.org/10.3389/neuro.11.010.2008

    • PubMed
    • Google Scholar
54. 1. Perrin F
    2. Pernier J
    3. Bertrand O
    4. Echallier JF

    (1989) Spherical splines for scalp potential and current density mapping

    Electroencephalography and Clinical Neurophysiology 72:184–187.

    https://doi.org/10.1016/0013-4694(89)90180-6

    • Google Scholar
55. 1. Proverbio AM
    2. Zani A
    3. Avella C

    (1996) Differential activation of multiple current sources of foveal VEPs as a function of spatial frequency

    Brain Topography 9:59–68.

    https://doi.org/10.1007/BF01191643

    • Google Scholar
56. 1. Rao V
    2. DeAngelis GC
    3. Snyder LH

    (2012) Neural correlates of prior expectations of motion in the lateral intraparietal and middle temporal Areas

    Journal of Neuroscience 32:10063–10074.

    https://doi.org/10.1523/JNEUROSCI.5948-11.2012

    • PubMed
    • Google Scholar
57. 1. Reder LM
    2. Weber K
    3. Shang J
    4. Vanyukov PM

    (2003) The adaptive character of the attentional system: statistical sensitivity in a target localization task

    Journal of Experimental Psychology: Human Perception and Performance 29:631–649.

    https://doi.org/10.1037/0096-1523.29.3.631

    • Google Scholar
58. 1. Reeder RR
    2. Olivers CNL
    3. Hanke M
    4. Pollmann S

    (2018) No evidence for enhanced distractor template representation in early visual cortex

    Cortex 108:279–282.

    https://doi.org/10.1016/j.cortex.2018.08.005

    • Google Scholar
59. 1. Sauter M
    2. Liesefeld HR
    3. Zehetleitner M
    4. Müller HJ

    (2018) Region-based shielding of visual search from salient distractors: target detection is impaired with same- but not different-dimension distractors

    Attention, Perception, & Psychophysics 80:622–642.

    https://doi.org/10.3758/s13414-017-1477-4

    • PubMed
    • Google Scholar
60. 1. Sawaki R
    2. Geng JJ
    3. Luck SJ

    (2012) A common neural mechanism for preventing and terminating the allocation of attention

    Journal of Neuroscience 32:10725–10736.

    https://doi.org/10.1523/JNEUROSCI.1864-12.2012

    • PubMed
    • Google Scholar
61. 1. Sawaki R
    2. Luck SJ

    (2010) Capture versus suppression of attention by salient singletons: electrophysiological evidence for an automatic attend-to-me signal

    Attention, Perception, & Psychophysics 72:1455–1470.

    https://doi.org/10.3758/APP.72.6.1455

    • PubMed
    • Google Scholar
62. 1. Stilwell BT
    2. Bahle B
    3. Vecera SP

    (2019) Feature-based statistical regularities of distractors modulate attentional capture

    Journal of Experimental Psychology: Human Perception and Performance 45:419–433.

    https://doi.org/10.1037/xhp0000613

    • Google Scholar
63. 1. Stokes MG

    (2015) 'Activity-silent' working memory in prefrontal cortex: a dynamic coding framework

    Trends in Cognitive Sciences 19:394–405.

    https://doi.org/10.1016/j.tics.2015.05.004

    • PubMed
    • Google Scholar
64. Preprint

    1. van Driel J
    2. Olivers CN
    3. Fahrenfort JJ

    (2019) High-pass filtering artifacts in multivariate classification of neural time series data

    bioRxiv.

    https://doi.org/10.1101/530220

    • Google Scholar
65. Software

    1. van Moorselaar D

    (2020) Analysis scripts for EEG, eye tracking and behaviour, version 342ed4f

    GitHub.

    https://github.com/dvanmoorselaar/DvM
66. 1. van Moorselaar D
    2. Slagter HA

    (2019) Learning what is irrelevant or relevant: expectations facilitate distractor inhibition and target facilitation through distinct neural mechanisms

    The Journal of Neuroscience 39:6953–6967.

    https://doi.org/10.1523/JNEUROSCI.0593-19.2019

    • PubMed
    • Google Scholar
67. 1. Vatterott DB
    2. Vecera SP

    (2012) Experience-dependent attentional tuning of distractor rejection

    Psychonomic Bulletin & Review 19:871–878.

    https://doi.org/10.3758/s13423-012-0280-4

    • PubMed
    • Google Scholar
68. 1. Wang B
    2. van Driel J
    3. Ort E
    4. Theeuwes J

    (2019) Anticipatory distractor suppression elicited by statistical regularities in visual search

    Journal of Cognitive Neuroscience 31:1535–1548.

    https://doi.org/10.1162/jocn_a_01433

    • PubMed
    • Google Scholar
69. 1. Wang B
    2. Theeuwes J

    (2018a) Statistical regularities modulate attentional capture

    Journal of Experimental Psychology: Human Perception and Performance 44:13–17.

    https://doi.org/10.1037/xhp0000472

    • Google Scholar
70. 1. Wang B
    2. Theeuwes J

    (2018b) Statistical regularities modulate attentional capture independent of search strategy

    Attention, Perception, & Psychophysics 80:1763–1774.

    https://doi.org/10.3758/s13414-018-1562-3

    • Google Scholar
71. 1. Woodman GF
    2. Luck SJ

    (1999) Electrophysiological measurement of rapid shifts of attention during visual search

    Nature 400:867–869.

    https://doi.org/10.1038/23698

    • PubMed
    • Google Scholar
72. 1. Woodman GF
    2. Luck SJ

    (2007) Do the contents of visual working memory automatically influence attentional selection during visual search?

    Journal of Experimental Psychology: Human Perception and Performance 33:363–377.

    https://doi.org/10.1037/0096-1523.33.2.363

    • Google Scholar
73. 1. Zhang B
    2. Allenmark F
    3. Liesefeld HR
    4. Shi Z
    5. Müller HJ

    (2019) Probability cueing of singleton-distractor locations in visual search: priority-map- versus dimension-based inhibition?

    Journal of Experimental Psychology: Human Perception and Performance 45:1146–1163.

    https://doi.org/10.1037/xhp0000652

    • Google Scholar
74. 1. Zivony A
    2. Allon AS
    3. Luria R
    4. Lamy D

    (2018) Dissociating between the N2pc and attentional shifting: an attentional blink study

    Neuropsychologia 121:153–163.

    https://doi.org/10.1016/j.neuropsychologia.2018.11.003

    • PubMed
    • Google Scholar

Author details

1. Dirk van Moorselaar

   1. Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
   2. Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
   3. Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
   4. Institute of Brain and Behaviour Amsterdam, Amsterdam, Netherlands

   Contribution

   Conceptualization, Software, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   dirkvanmoorselaar@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0491-1317

2. Eline Lampers

   Department of Psychology, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Conceptualization, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Elisa Cordesius

   Department of Psychology, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Conceptualization, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

4. Heleen A Slagter

   1. Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
   2. Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
   3. Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
   4. Institute of Brain and Behaviour Amsterdam, Amsterdam, Netherlands

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4180-1483

• 1,803

  views

• 248

  downloads

• 51

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.