Adaptive, goal-driven behavior demands the selection of relevant objects from the visual environment while irrelevant information is being ignored. This requires the neural activation of task-relevant representations in memory – often referred to as attentional templates – which then bias selection towards matching sensory input through top-down recurrent feedback loops (Duncan and Humphreys, 1989; Desimone and Duncan, 1995; Hamker, 2004; Eimer, 2014; Cohen and Tong, 2015). A fundamental yet unresolved question is whether the brain can enhance multiple task-relevant representations concurrently – a question that has recently generated considerable controversy, with arguments both for (Houtkamp and Roelfsema, 2009; Menneer et al., 2009; Kristjánsson and Campana, 2010; Dombrowe et al., 2011; Olivers et al., 2011; van Moorselaar et al., 2014; Liu and Jigo, 2017; Ort et al., 2017; Ort et al., 2018; van Driel et al., 2019) and against (Beck et al., 2012; Irons et al., 2012; Grubert and Eimer, 2015; Grubert et al., 2016; Beck and Hollingworth, 2017; Kristjánsson and Kristjánsson, 2017) a strong bottleneck.

We provide electrophysiological evidence showing that the real bottleneck is not so much in the number of different templates that can be concurrently active in anticipation of a visual task, but in the number of matching sensory representations in the incoming signal that can subsequently be enhanced by those templates. Crucially, for the selection of multiple targets to be truly simultaneous, two requirements have to be met. First, attentional templates need to be set up in memory for each anticipated target feature (whether in visual working memory [VWM] or through activating long-term memory [LTM] representations). Although it is uncontroversial that multiple representations can be active in memory (whether VWM or LTM; e.g., Cowan, 2001), in order to be able to bias selection, each of these representations also needs to be in a state in which it can eventually engage, through recurrent feedback, with matching sensory signals (which is not the same as merely remembering; see Carlisle et al., 2011; Olivers and Eimer, 2011; Kiyonaga et al., 2012; Chatham and Badre, 2015; van Driel et al., 2017). Second, to simultaneously select multiple targets, the visual system must also be able to concurrently use those templates to strengthen multiple matching representations in the incoming sensory signal. In other words, multiple neural feedback loops must be able to engage concurrently. It is important to point out that template activation and template-guided prioritization are distinct (cf., Huang and Pashler, 2007): It may be that at any moment multiple templates are ready to potentially engage in the prioritization of visual input, but that only one can actually do so following visual stimulation. So far, studies of multiple-target selection have only focused on the brain’s limits in the readiness to engage in selection, and ignored potential limits in the selection process itself.

To resolve this, we recorded electroencephalograms (EEG) from the scalp of healthy human individuals while they were presented with heterogeneous visual search displays, from which they always had to select two target objects (see Figure 1A). Crucially, we varied the number of unique target features (one or two colors) that the observer had to prepare for, and the number of unique features that they would need to select from the search display. This allowed us to disentangle the contribution of multiple template preparation on the one hand, and multiple template engagement on the other. A bottleneck could either emerge when going from one to two unique templates (reflecting a limit in the readiness to engage), from one to two unique targets (reflecting a limit in the engagement itself), or both.

Figure 1

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Figure 1—source data 1

Accuracy data across participant that are represented in Figure 1C.

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Figure 1—source data 2

Response time data across participant that are represented in Figure 1D.

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Traditionally, visual target selection is assessed using the N2pc, an event-related potential (ERP) in the EEG signal that is characterized by increased negativity over posterior electrodes contralateral to the hemifield in which the target is located (Luck and Hillyard, 1994; Eimer, 1996). However, because the N2pc can only distinguish between the left versus right hemifield, it is not able to simultaneously track the selection of multiple targets at different locations in more complex visual search displays. To overcome this limitation, we used multivariate decoding, which has been proven to successfully track the spatiotemporal dynamics of feature-based selection processes at any location in a search display (Fahrenfort et al., 2017). Here this technique allowed us to independently track attentional selection over time for multiple concurrent targets at once, and also to investigate the parallel versus serial nature of these selection processes.

Twenty-four participants performed a visual search task for which they were always required to find two color-defined target characters presented among an array of distractor characters, and determine whether these two targets belonged to the same alphanumeric category (see Figure 1A,B). The task-relevant colors were cued prior to a block of trials. To assess if prioritization of multiple targets is limited in terms of the number of attentional templates that can be simultaneously set up, limited in the number of templates that can be simultaneously engaged in the selection of target features in the display, or both, we independently manipulated 1) how many colors were task-relevant and 2) how many of these target colors actually appeared in the search display. Specifically, in 1TMP–1TGT (one template, one target feature) blocks, only one color was task-relevant, so that both targets had the same color and thus participants knew beforehand which color template to prepare. In 2TMP–1TGT (two templates, one target feature) blocks, two unique colors were cued as task-relevant, but per display only one of these was used to distinguish the two targets present (i.e., both targets had the same color). Because participants could not predict which of the two target colors would be present, they had to keep both templates active, even though only one of them was then required for selecting the actual targets. Finally, in 2TMP–2TGT (two templates, two target features) blocks, again two unique colors were cued as task-relevant, but now both these colors also had to be used to select the two target objects from the search display, since one of the targets carried one color, and the other target carried the other color. Note again that in all conditions, subjects had to select two targets, only the number of target-defining features would vary across conditions. This controlled for other task-related factors such as the number of characters that had to be identified and the alphanumeric comparison that had to be performed on them.

Decoding of target positions based on the raw EEG

Next, to determine whether the behavioral costs indeed reflected deficits in the selection of the different targets, we used EEG to track the strength and dynamics of attentional enhancement of the different target positions. To this end, one target was always placed on the vertical meridian, and the other target always on the horizontal meridian, so that we could train separate linear discriminant classifiers (with electrodes as features) for each of the spatial target dimensions to distinguish left from right targets and top from bottom targets, separately for each condition and time sample (see Materials and methods for details). We reasoned that any inefficiencies associated with setting up multiple unique templates (i.e., 1TMP vs. 2 TMP conditions) and/or with actually using those templates to select multiple unique targets (i.e., 1TGT vs. 2TGT conditions) should result in decoding to suffer in terms of relative delays, strength, or both. Figure 2A shows decoding performance for each of the conditions (1TMP–1TGT, 2TMP–1TGT, and 2TMP–2TGT), separately for the horizontal (left versus right) and vertical meridian (top versus bottom). Figure 2B shows the topographical patterns associated with the forward-transformed classifier weights over time, which are interpretable as neural sources (see Haufe et al., 2014 and Materials and methods). As a general finding, we were able to track attentional selection on both the horizontal and vertical meridian, with comparable decoding performance. Decoding performance was tested against chance for every sample and corrected for multiple comparisons using cluster-based permutation testing (Maris and Oostenveld, 2007; also see Materials and methods). After cluster-based permutation, we observed clear significant clusters in each of the three conditions, with significant decoding emerging at different moments in time. For the left-right distinction, the topographical pattern during the early time window (200–350 ms) resembles that of the N2pc, while for later time windows (350–700 ms) it resembles SPCN or CDA-like patterns (Vogel and Machizawa, 2004; Mazza et al., 2007; Grubert and Eimer, 2013). As shown in Figure 2—figure supplement 1, more traditional event-related analyses indeed revealed N2pc and SPCN components, which likely contributed to the classifiers’ performance. For vertically positioned targets a gradient from frontal to posterior channels spread along the midline, similar to recent results from our labs (Fahrenfort et al., 2017; Grubert et al., 2017). The fact that the decoding approach picks up on information related to attentional selection also on the vertical midline is testament to its power over conventional ERP methods, and allowed us to simultaneously track attentional selection of both targets over time. However, as there were no main or interaction effects involving the meridian in any of the comparisons, we averaged decoding performance across the spatial dimensions.

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Figure 2—source data 1

Time series data of classification AUC scores, separately for each condition and horizontal and vertical targets, as shown in Figure 2A.

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Figure 2—source data 2

Time series data of classification AUC scores, separately for each condition (incl. separately for 21TGT switch and repeat trials), but collapsed across target position, as shown in Figure 2C, D and Figure 2—figure supplement 2.

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If there is a limit on how many templates can be prepared for, we should find reduced and/or delayed classification for the 2TMP–1TGT condition compared to the 1TMP–1TGT condition (Figure 2C). If the limitation is on how many templates can be engaged in selection, the cost should emerge in the comparison of the 2TMP–2TGT and 2TMP–1TGT conditions (Figure 2D). Indeed, we observed reliable differences for both comparisons that directly resembled the behavioral pattern. First, we compared the latencies at which target positions became decodable, thus providing a window on any delays in attentional selection. Because differences in onset of significant clusters cannot be reliably interpreted as reflecting differences in onsets of the underlying neurophysiological processes (Sassenhagen and Draschkow, 2019), we instead used a jackknife-based approach to quantify the latency of the 50% maximum amplitude in the decoding window (Miller et al., 1998; Luck, 2014; Liesefeld, 2018, see Materials and methods). This revealed a reliable onset difference between the 1TMP–1TGT (M = 216 ms) and 2TMP–1TGT (M = 237 ms) conditions (M = 21 ms, t_c(23) = 2.21, p = 0.04; Figure 2C), indicating that attentional selection is delayed as a result of having to prepare for two different target colors compared to having to prepare for only a single target color. Comparing the onsets between the 2TMP–1TGT (M = 237 ms) and 2TMP–2TGT (M = 263 ms) conditions yielded a further delay of 25 ms associated with having to engage in selecting two target colors compared to selecting a single target color (t_c(23) = 2.35, p = 0.03; Figure 2D).

Finally, and also similar to the behavioral responses, the onset of the neurophysiological response in the 2TMP–1TGT condition was delayed by 23 ms when the target color switched from one trial to the next, compared to when it repeated (t_c(23) = 4.34, p<0.001; see Figure 2—figure supplement 2).

Next, we assessed the strength of classification over time by testing AUC values of the relevant conditions against each other using paired t-tests and cluster-based permutation testing to correct for multiple comparisons (see Materials and methods). This procedure revealed an early and short-lasting difference of the number of templates (i.e., between 1TMP–1TGT and 2TMP–1TGT conditions; see Figure 2C), with stronger classification for the single template condition that reflects the onset latency difference reported above. Again, in line with the behavioral results, more substantial cost in decoding performance emerged when the number of target features in the displays increased from one to two (i.e., between the 2TMP–2TGT and 2TMP–1TGT conditions; see Figure 2D). To directly compare the cost of preparing two templates to the cost of engaging them in selection, we also ran a cluster-based permutation test on the difference scores (i.e. [2TMP–1TGT – 2TMP–2TGT] – [1TMP–1TGT – 2TMP–1TGT]). This revealed a window of 250 to 500 ms post stimulus in which the cost of engaging was greater than the cost of preparing selection (first cluster: extent: 266–378 ms, p = 0.001; second cluster: extent: 436–495 ms, p = 0.013, results not shown in Figure). This suggests that generally engaging two templates is more costly than preparing two templates.

Thus, both the onset latency and strength of decoding performance show clear deficits in attentional selection when observers need to select two different targets from a display (i.e., engage two templates in selection) compared to when they have to select two targets based on the same target color (i.e., engage one template in selection). In contrast, having to set up two templates instead of one came with only minor onset latency differences and no overall differences in decoding strength. This clearly points to a deficit when multiple templates need to be engaged simultaneously rather than when multiple templates need to be prepared simultaneously.

Sample-wise correlation of classifier confidence across trials as a measure of inter-target dependency

While the previous section showed a clear impairment when two templates need to be engaged in selection, it leaves unanswered whether selection is hindered by limitations in parallel processing or by a serial bottleneck. That is, engaging two templates during search may prioritize both unique targets in parallel but in a mutually competitive manner (Barrett and Zobay, 2014), or the two templates may only be engaged (and thus the corresponding targets prioritized) sequentially, possibly in continuously alternating fashion (e.g., Ort et al., 2017).

To investigate these competing hypotheses, we assessed performance for each target dimension (horizontal and vertical) separately . A serial model predicts that attention to a target on one dimension should go at the expense of attention to the target on the other dimension, and thus decoding performance for the vertical and horizontal axes to correlate negatively. In case of parallel, independent selection, there should be no systematic relationship between classification confidence for one dimension and classification confidence for the other dimension, as selection of one target is impervious to the selection of the other target. A positive correlation would arise from a common mechanism driving selection of two different targets. Note that these possibilities are difficult to assess at the group level as individuals may have different serial strategies. For example, one observer may prefer to first select targets from the horizontal axis, while another may prefer the vertical axis first, such that any existing correlation (if present) might cancel out. Hence, we first plotted average performance over time separately for each individual and separately for the horizontal and vertical axis. Then, to reveal whether consistent temporal dependencies existed for any given participant, we correlated classification performance over time in the 150 ms to 700 ms post stimulus window. Although this revealed incidental positive and negative correlations for individual participants, there was no systematically positive or negative relationship (average correlation Spearman’s ρ = 0.11; min-max range: −0.37–0.63; see Figure 3—figure supplement 1).

However, even individual participants themselves may not behave consistently across trials, and, while selection is still serial, whether participants first prioritize horizontal or vertical targets may also vary from trial to trial. Therefore, selection needs to be assessed at the trial level. To this end, for every participant, trial and time point, we extracted the classifier confidence scores separately for the horizontal and vertical dimension (see Ritchie and Carlson, 2016; Grootswagers et al., 2018 and Materials and methods), and correlated the two dimensions across trials using Spearman’s ρ. Classifier confidence, expressed as the distance from the decision boundary, reflects the certainty of a classifier in predicting the class membership of a certain instance. In the present design, classifiers predicted based on the specific EEG activity pattern across electrodes on a given trial whether one target appeared on the left or right and whether the other target appeared at the top or bottom position. The confidence scores indicate how certain the classifiers were that a target appeared at a particular position. We reasoned that if prioritization is limited to a single target at a time, a classifier cannot simultaneously have high confidence about both targets, and thus confidence should correlate negatively, that is if the horizontal target position can be predicted with high confidence, then the confidence for the vertical target position should be reduced and vice versa. The correlations between the confidence scores on the two spatial dimensions are plotted in Figure 3A. As can be seen, there was again no systematic relationship between decoding the locations of the two targets, in any of the conditions. Apart from a short-lasting positive correlation around the 500 ms time point in the 2TMP–2TG condition, which is likely to be spurious, correlations for all time points were close to zero, which implies that classifying horizontal and vertical target positions is independent from each other.

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However, given that this is a null result, we sought to make sure that our approach is in principle sensitive to existing correlations. To this end, we simulated a data set with the same overall characteristics as the recorded data, but with either positive, negative, or no correlations injected, under various signal to noise ratios (see Materials and methods). The results of this simulation are summarized in Figure 3—figure supplement 2 and demonstrate that with sufficiently high decoding AUC values (>approx. 0.55–0.60), correlations (whether positive or negative) between the horizontal and vertical position classifiers can, in principle, be reliably detected. However, because group classification performance in our dataset did not exceed 0.59 (in the 1TMP–1TGT condition), we instead assessed for each individual observer the correlation between target dimensions for those time points at which classification performance reached its maximum. As Figure 3B shows, even for individuals with relatively high classification scores, there was no evidence for a correlation between the classification confidence between the two target dimensions. The absence of such a correlation in our data is thus most consistent with a limited parallel independent selection model, rather than a serial model or a parallel model operating under a common mechanism.

Nevertheless, there is the possibility that there was actually a relationship between horizontal and vertical classification, but across time, trials, or both it was too short and inconsistent such that the present approach might not have been sensitive enough to detect it. For example, a negative correlation might exist for a short time window of which the timing shifted across trials, causing the resulting average correlation to be reduced beyond detectability. We emphasize that the conclusion that our data are mostly consistent with a limited parallel model is thus based on a null result and therefore has to be interpreted with care.

Selection of task-relevant information from complex visual environments is limited, and a central question in attention research has been whether observers can simultaneously prepare for and select multiple different target objects. The current results provide evidence that these limitations do not so much reside at the level of template preparation (i.e., the number of target representations set up prior to the task), but at the extent to which templates can then be concurrently engaged in selecting matching information from the sensory input. By systematically varying not only the number of different target features observers had to prepare for, but also the number of different target features they would encounter in the displays, we were able to, for the first time, dissociate limitations in template preparation from limitations in template engagement. Specifically, we observed relatively small but reliable costs on both behavioral and EEG classification performance when two templates needed to be activated instead of one, suggesting a reliable but relatively minor bottleneck at this stage of processing. In contrast, substantial costs emerged on both behavioral and EEG performance measures when two templates had to be prepared, and both of these templates (rather than just one) had to be engaged in driving the selection of two different targets.

We propose a model which extends existing frameworks that assume a crucial role for top-down biased competition (Duncan and Humphreys, 1989; Desimone and Duncan, 1995; Hamker, 2004; Bundesen et al., 2005). According to these frameworks, the activation of target templates in memory involves the pre-activation or biasing of associated sensory features. The presence of such features in the input will then trigger a long-range recurrent feedback loop, leading the enhancement of the target representation in VWM (including its location), and thus making it available for other cognitive processes such as response selection (processes which are themselves limited, cf. Dehaene et al., 1998; Lamme, 2003; Baars, 2005). Our data indicates that while multiple top-down feedback connections may be prepared at once, there is a limitation in how these feedback loops are engaged by matching input.

Figure 4 illustrates how we believe the existing framework should be extended. Specifically, we propose that multiple templates may hold each other in a mutually competitive relationship in memory, most likely through laterally suppressive connections (Manohar et al., 2019). Figure 4A depicts the situation when just one of the target features is then encountered in the sensory input. The corresponding feedback loop is triggered, leading to an enhanced representation of that target. If only one target feature is present, the corresponding template will automatically win the competition. Although two templates can be maintained in parallel, the mutual competition between them is slightly disadvantageous. This will lead to the initial delay in target selection that we observed in the data when two templates instead of just one were activated. Moreover, the selective enhancement of one representation over another may carry over to the next trial, thus resulting in the target switch costs that we also observed both in behavior and EEG performance measures.

Figure 4

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The crucial situation occurs when the visual input contains multiple target features and thus multiple feedback loops are being triggered, as is shown in Figure 4B. Because of the mutually suppressive relationship, strengthening one feedback loop will automatically go at the expense of the other. Although both loops are triggered in parallel, the mutually aversive relationship results in slower and weaker accumulation of evidence for either of the targets, consistent with what we observed in the data. In theory, the system may resolve such competition in two ways. The first is to keep selection of both targets running in parallel, and accept the slower evidence accumulation. The second option is to impose a serial strategy in which selection is first biased in favor of one target, and then switched to the other (or alternate between the two). Our data provides no evidence for the serial model. First, the average group data nor the average individual subject data showed any systematic pattern of switching between the two target positions (i.e., differences in classification performance for left-right versus top-bottom). Second, also a trial-based correlation analysis of classifier confidence scores showed the absence of a negative correlation between the target positions. Our findings are therefore most consistent with a limited-capacity parallel model, in which observers maintain two templates active during search, but with mutually aversive consequences. However, we point out that our data do not exclude the possibility of seriality. First, while there may have been little seriality in selecting the targets from the displays on the basis of color, there may have been a serial component in accessing their alphanumeric identity — a component to which our classifier was not sensitive. Moreover, there is still a distinct possibility that imposing seriality is a valid strategy that observers may deploy to resolve competition between different target features, but that such choices depend on tasks, context, or instructions (Cave et al., 2018; Stroud et al., 2019). For example, we previously observed evidence for serial switching in a different paradigm when observers had to select only one of two targets present, and were instructed to switch at least a few times during a block (Ort et al., 2019; van Driel et al., 2019). The current results indicate that the process can occur in parallel, not that it must.

Our account has a resemblance to the Boolean Map Theory of Visual Attention (Huang and Pashler, 2007), which proposes a division of attentional selection into two components: (1) The feature-to-location routine, in which task-relevant features are being located in the visual field (referred to by Huang and Pashler as selection, but here analogous to what we call preparation for selection) and (2) the location-to-feature routine in which individuals extract response-relevant features at a target location (access, analogous to what we here call engagement in selection). However, in contrast to the model that we propose, in which only what Huang and Pashler refer to as the access aspect of search is severely limited, the Boolean Map theory poses a capacity limitation of one on both selection and access.

We believe the distinction between template preparation and template engagement in selection has great potential for resolving the current debate on whether observers can look for more than a single target at the same time (Menneer et al., 2009; Beck et al., 2012; Irons et al., 2012; Grubert and Eimer, 2015; Beck and Hollingworth, 2017; Ort et al., 2017; Ort et al., 2018). Studies central to this debate have largely focused on how many templates can be prepared in anticipation for a search, rather than how many of these templates can then be concurrently engaged in selection without costs. From our data, the answer to the question then appears to be yes, observers may look for multiple targets simultaneously at little cost, but it is selecting those targets that runs into real limitations.

Although we found the costs of going from one to two templates to be relatively small, this leaves open the question whether costs will increase more strongly with more templates being added. As there is more opportunity for memory representations to interfere with each other when multiple memory representations need to be maintained, this would be expected. Such interference will depend on the similarity of the to-be-remembered templates, as well as the assumed capacity. Although the capacity of VWM is thought to be around three to four items (at least for the standard colored shapes used in experiments like ours), and VWM is assumed to be central to top-down driven search, there is ample evidence that visual search needs not solely rely on VWM. In fact, given that in our experiment the target template remained the same for a block of trials, observers may have at least partly relied on trained templates in long term memory here (Carlisle et al., 2011; Gunseli et al., 2014). Moreover, work by Wolfe (2012) has shown that observers can successfully search for any one of tens of different target objects if given the opportunity to first commit these objects to long term memory. In our study, effects of capacity limits and any interference arising from it may be have been stronger when the targets would have been cued from trial to trial, rather than from block to block. Conversely, the fact that we find limitations even with repeated targets is testament to the mechanistic bottleneck in the engagement of selection that we propose. One reason may be that even LTM representations would need to become activated for effective task-or context-driven guidance (since only one set of trained colors is relevant in a particular block). Our data suggests that the limit may well be in this goal-driven aspect of search – that is, the deployment of a representation for perceptual bias rather than its storage per se. In line with this, Grubert et al. (2016) reported evidence that attentional selection per se, as measured by the N2pc (which at least partly underlies the signal also used here), is not affected by whether targets are stored in long term memory or working memory. Future research will need to shed further light which memory systems support search templates.

Finally, the question of memory capacity or interference is also important when considering that current limitations were found when both target features were drawn from the same dimension (color). There is evidence that different dimensions may to some extent independently store (e.g., Wang et al., 2017), or guide attention towards (Wolfe, 1994; Jenkins et al., 2017), target features. Our methods may therefore prove useful in assessing the exact limitations of selecting targets defined along different dimensions.

To sum up, we propose that models of visual selection need to consider the difference between preparing for selection and engaging in selection of multiple visual targets. We demonstrate that whereas the first process comes at little cost, the true bottleneck of multiple-target selection is in engaging multiple template representations.

All data and material will be made freely accessible at https://osf.io/3bn64.

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Author details

1. Eduard Ort

   1. Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
   2. Institute for Brain and Behavior Amsterdam, Amsterdam, Netherlands

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   eduardxort@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5546-3561

2. Johannes Jacobus Fahrenfort

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

   Contribution

   Conceptualization, Software, Formal analysis, Supervision, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9025-3436

3. Tuomas ten Cate

   Experimental Psychology, Utrecht University, Utrecht, Netherlands

   Contribution

   Formal analysis, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

4. Martin Eimer

   Department of Psychological Sciences, Birkbeck College, University of London, London, United Kingdom

   Contribution

   Conceptualization, Writing—review and editing

   Competing interests

   No competing interests declared

5. Christian NL Olivers

   1. Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
   2. Institute for Brain and Behavior Amsterdam, Amsterdam, Netherlands

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7470-5378

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