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Extant literature suggests that performance on visual arrays tasks reflects limited-capacity storage of visual information. However, there is also evidence to suggest that visual arrays task performance reflects individual differences in controlled processing. The purpose of this study is to empirically evaluate the degree to which visual arrays ta...
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... primary question of interest is; are individual differences in visual arrays k capacity score explained more by differences in working memory capacity, attention control, or both? To answer this, we conducted a structural equation model with working memory capacity and attention control predicting k capacity scores on the VA-orient-S task (see Figure 2). Attention control, but no working memory capacity, uniquely predicted k capacity scores in the VA-orient-S task. ...
Context 2
... indirect effect through working memory capacity, but not processing speed, was statistically significant. 10 As in Data Set 1, we conducted a post hoc structural equation model estimating the residual correlation between working memory capacity and processing speed, accounting for attention control (see Figure S2). This was done because the mediation model tacitly assumes no residual correlation between working memory capacity and processing speed independent of attention control, which could contribute to lack of model fit (although model fit was excellent). ...
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Context. The proposed article relates to the field of visual information processing in a computer environment, more precisely to the determination the parameters of the interest object in the image, in particular, the contour of the interest object In most cases, the contour of an object is a simply connected sequence of curve arcs. Objective. The...
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... We measured attention control with the antisaccade, selective visual arrays, a Stroop task with an adaptive response deadline, and auditory versions of the flanker, Simon, and Stroop tasks with an adaptive response deadline. See Draheim et al. (2021Draheim et al. ( , 2023, Martin et al. (2021), Tsukahara and Engle (2023), and Burgoyne et al. (2024) for the reliability and validity of the attention control measures. ...
... While it is possible that other manipulations of selective attention might have generated a different result (e.g., Fox et al., 2009), we found this result surprising given other results in both the attention control and inspection time literatures. For example, Martin et al. (2021) found evidence suggesting that including distractors in visual change detection tasks tends to strengthen their relationship with attention control. We included a similar manipulation in our selective inspection time task and found no evidence of a selective attention advantage. ...
Mental speed theories of intelligence suggest that people are smarter because they are faster. We argue that attention control plays an important and fundamental role in mediating the relationship between basic sensory processes and more complex cognitive processes such as fluid intelligence. One of the most successful paradigms for establishing a mental speed theory of intelligence is the inspection time task. In this article, we examine the mental speed and the attention control perspectives on the inspection time task and its relationship with fluid intelligence. Integrating experimental and correlational approaches, we find that attention control statistically explains the inspection time task’s correlation with fluid intelligence and working memory capacity. Attention control and inspection time are correlated beyond their relationship with other measures of processing speed. Further, while we find no evidence that selective attention specifically is related to inspection time performance, both attention control and inspection time predicted declines in accuracy as participants sustained their attention over time; other measures of processing speed did not predict sustained attention performance. Collectively, these results indicate that inspection time is related to the ability to control attention, especially the ability to sustain attention over time.
... Attention control mechanisms are well known to be highly related to working memory ability and have largely overlapping variance in individual ability. A common way to measure VWM is to ask participants to memorize an array of colors over a brief period of time and test them on whether the color of an item of the array changed or remained the same (Martin et al., 2021). The covariance structure of performance on this task suggests that it mostly taps on to working memory abilities, and also shared variance between working memory and attentional control (WMAC), and finally attentional control the least. ...
... We tested our hypothesis between working memory ability and visual LTM in four experiments, with a well-powered sample of participants recruited from the Prolific online platform (n = 1,250 in total), and diverse types of materials to be learned (visual in Experiments 1-3 and 5 and verbal in Experiment 4, Figure 2). Previous research has shown that individual differences in WMAC abilities directly affected performances in VWM task, where participants memorized multiple visual stimuli on screen simultaneously and maintained them over a brief delay period (Martin et al., 2021). Therefore, we measured working memory abilities in our Experiments 1-4 using the change detection task, a highly reliable VWM task that required participants to detect the change in perceptual features between the encoding array and the test probe (Luck & Vogel, 1997;Xu et al., 2018;. ...
... Filtering Change Localization. The filtering change localization task was modified from the filtering change detection task (Luck & Vogel, 1997;Martin et al., 2021;Zhao & Vogel, 2024). In each trial, a word, either RED or BLUE, denoting the color of the items to be attended (the selection instruction), was presented for 200 ms, followed by a 100-ms interval. ...
Individual differences in working memory predict a wide range of cognitive abilities. However, little research has been done on whether working memory continues to predict task performance after repetitive learning. Here, we tested whether working memory ability continued to predict long-term memory (LTM) performance for picture sequences even after participants showed massive learning. In Experiments 1–3, subjects performed a source memory task in which they were presented a sequence of 30 objects shown in one of four quadrants and then were tested on each item’s position. We repeated this procedure for five times in Experiment 1 and 12 times in Experiments 2 and 3. Interestingly, we discovered that individual differences in working memory continually predicted LTM accuracy across all repetitions. In Experiment 4, we replicated the stable working memory demands with word pairs. In Experiment 5, we generalized the stable working memory demands model to attentional control abilities. Together, these results suggest that people, instead of relying less on working memory, optimized their working memory and attentional control throughout learning.
... The main bulk of evidence stems from latent variable approaches, examining the association between tasks supposed to involve scope of attention (such as the running span and visual array tasks mentioned above), tasks supposed to involve control of attention (mainly storage and processing complex WM span tasks such as the listening span or operation span tasks; Daneman & Carpenter, 1980;Turner & Engle, 1989) and tasks measuring another cognitive domain thought to be associated with WM such as fluid intelligence. These studies showed that estimates of scope of attention and control of attention generally load on distinct factors and show specific associations with fluid intelligence measures (e.g., J. D. Martin et al., 2021;Shipstead et al., 2012Shipstead et al., , 2015. Similar results have also been observed in developmental studies. ...
... It should be noted here that the type of attention being probed and its association with WM may further depend on task design. For example, some significant overlap between scope of attention and control of attention has been observed if the scope of attention tasks include a dimension of selectivity and hence also a dimension of top-down control (e.g., instructing participants to focus their attention exclusively on the left side of the stimulus display in a visual array task, J. D. Martin et al., 2021). Regarding complex WM tasks, by virtue of task design, these tasks are likely to involve control of attention ability to a larger extent than scope of attention ability. ...
... In many of these tasks, such as the reading or listening complex span tasks, focalization and retention of target stimuli are continuously interrupted by the processing part of the task (e.g., sentence anomaly judgment), retention of information being possible only via controlled strategies such as blocking nontarget stimuli, as well as actively refreshing and rehearsing stored target information while new information is being processed and memoranda are extracted. While in some of the abovementioned studies, complex WM measures have sometimes been used as a proxy of attentional control, it should also be noted here that J. D. Martin et al. (2021), using more direct measures of attentional control (e.g., antisaccade or flanker tasks), showed that complex WM tasks cannot be reduced to an attentional control construct (see also Wilhelm et al., 2013). It remains to be examined to what extent scope of attention and control of attention naturally intervene in different types of WM tasks, independently of the attentional control requirements that may have been imposed or not. ...
Most models of verbal working memory (WM) consider attention as an important determinant of WM. The detailed nature of attentional processes and the different dimensions of verbal WM they support remains, however, poorly investigated. The present study distinguished between attentional capacity (scope of attention) and attentional control (control of attention) and examined their respective role for two fundamental dimensions of verbal WM: the retention of item versus serial order information and the simple versus complex nature of WM tasks. Three hundred four young and older adult participants performed simple or complex recall or reconstruction tasks involving the retention of item and/or serial order information, as well as attention tasks estimating scope and control of attention abilities. In young participants, scope of attention measures was most robustly associated with all WM tasks; control of attention measures were additionally involved when item and order information had to be maintained in more complex WM tasks. Older adult participants presented a similar pattern of results with, however, a tendency for increased reliance on control of attention already for the simple storage of information, and this most robustly for serial order information. These results reveal the task-dependent and partly age-dependent intervention of scope and control of attention in verbal WM measures, calling for dynamic models of verbal WM and attention.
... For example, in the antisaccade task (Hallett 1978;Hutchison 2007), subjects must inhibit the prepotent response of looking towards a flickering asterisk, and instead look in the opposite direction to detect a briefly presented letter. In the selective visual arrays task (Luck and Vogel 1997;Martin et al. 2021;Shipstead et al. 2014), subjects are shown a memory array and told to selectively attend to and remember a subset of items (e.g., remember the blue items) while ignoring the remaining items (e.g., ignore the red items). ...
... In the sustained attention to cue task (SACT, Draheim et al. 2021;Burgoyne et al. 2023;Draheim, Tshukara, and Engle 2023;Tsukahara and Engle 2023), subjects must remain focused on a cued spatial location on the computer screen for a variable wait period (2-12 s) to detect a briefly presented letter. These attention control measures have been tested extensively for individual differences research (see Burgoyne et al. 2023;Draheim et al. 2021;Kane et al. 2001;Martin et al. 2021;Redick, Heitz, and Engle 2007;Tsukahara and Engle 2023;Unsworth, Schrock, and Engle 2004; for details on their psychometric properties and evidence for their construct validity). ...
... The measure of performance was the proportion correct (i.e., minimum = 0%, maximum = 100%) and the task took approximately 12 min to administer. (Luck and Vogel 1997;Martin et al. 2021;Shipstead et al. 2014). After a central fixation of 1000 ms, a cue word ("RED" or "BLUE") appeared instructing the participant to attend to either red or blue rectangles. ...
Military selection tests leave room for improvement when predicting work‐relevant outcomes. We tested whether measures of attention control, working memory capacity, and fluid intelligence improved the prediction of training success above and beyond composite scores used by the U.S. Military. For student air traffic controllers, commonality analyses revealed that attention control explained 9.1% ( R = .30) of the unique variance in academic performance, whereas the Armed Forces Qualification Test explained 5.2% ( r = .23) of the unique variance. For student naval aviators, incremental validity estimates were small and nonsignificant. For student naval flight officers, commonality analyses revealed that attention control measures explained 11.8% ( R = .34) of the unique variance in aviation preflight indoctrination training performance and 4.3% ( R = .21) of the unique variance in flight performance. Although these point estimates are based on relatively small samples, they provide preliminary evidence that attention control measures might improve training outcome classification accuracy in real‐world samples of military personnel.
... This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Martin et al., 2021) is used as an indicator of AC. Sustained attention ability is also often measured by just taking the performance score in a task that challenges sustained attention. ...
Attention control has been proposed as an ability construct that explains individual differences in fluid intelligence. Evaluating this hypothesis is complicated by a lack of clarity in the definition of attention control. Here, I propose a definition of attention control, based on experimental research and computational models of what guides attention, and how cognitive processes are controlled. Attention is the selection of mental representations for prioritized processing, and the ability to control attention is the ability to prioritize those representations that are relevant for the person’s current goal, thereby enabling them to think and act in accordance with their intentions. This definition can be used to identify appropriate and less appropriate ways to measure individual differences in attention control. An analysis of various approaches to measurement reveals that the current practice of measuring attention control leaves room for improvement. Aligning our psychometric measurements with a clear, theoretically grounded concept of attention control can lead to more valid measures of that construct.
... Further, simple span tasks, which do not layer a processing component on top of a memory component like complex span tasks, also tend to predict outcomes like scholastic aptitude and general cognitive abilities (Colom et al., 2005(Colom et al., , 2006(Colom et al., , 2008Cowan et al., 2005). At the latent level, PM and WMC tend to share about 30%-60% of their variance (Colom et al., 2005(Colom et al., , 2006(Colom et al., , 2008Martin et al., 2021;Robison & Brewer, 2020;Shipstead et al., 2012Shipstead et al., , 2014Shipstead et al., , 2015Unsworth et al., 2014;. ...
... For example, it has been demonstrated that one source of variance in changedetection tasks, which we used to measure PM, is the ability to encode and store some information in the presence of irrelevant or otherwise supracapacity amounts of information (Fukuda et al., 2015;Unsworth & Robison, 2016;Vogel et al., 2005). Further, when changedetection tasks force participants to select a particular subset of items, the tasks correlate more strongly with measures of attention control Martin et al., 2021). Finally, the tendency to mind wander during tasks also negatively correlates with performance (Krimsky et al., 2017;Mrazek et al., 2012;Unsworth & Robison, 2016). ...
Working memory capacity (WMC) has received a great deal of attention in cognitive psychology partly because WMC correlates broadly with other abilities (e.g., reading comprehension, second-language proficiency, fluid intelligence) and thus seems to be a critical aspect of cognitive ability. However, it is still rigorously debated why such correlations occur. Some theories posit a single ability (e.g., attention control, short-term memory capacity, controlled memory search) as the primary reason behind WMC’s predictiveness, whereas others argue that WMC is predictive because it taps into multiple abilities. Here, we tested these single- and multifaceted accounts of WMC with a large-scale (N = 974) individual-differences investigation of WMC and three hypothesized mediators: attention control, primary memory, and secondary memory. We found evidence for a multifaceted account, such that no single ability could fully mediate the relation between WMC and higher order cognition (i.e., reading comprehension and fluid intelligence). Further, such an effect held regardless of whether WMC was measured via complex span or n-back.
... In the sustained-attention-to-cue task, subjects must remain focused on a cued spatial location on the computer screen for a variable wait period (2-12 seconds) in order to detect a briefly presented letter. These attention control measures have been tested extensively for individual differences research (for details on their psychometric properties and evidence for their construct validity, see Burgoyne et al., 2023;Draheim et al., 2021Draheim et al., , 2023Kane et al., 2001;Martin et al., 2021;Redick et al., 2007;Tsukahara & Engle, under review;Unsworth et al., 2004). ...
... For an in-depth analysis of selective visual arrays as a measure of attention control and working memory capacity, please seeMartin et al. (2021). ...
Early work on selective attention used auditory-based tasks, such as dichotic listening, to shed light on capacity limitations and individual differences in these limitations. Today, there is great interest in individual differences in attentional abilities, but the field has shifted towards visual-modality tasks. Furthermore, most conflict-based tests of attention control lack reliability due to low signal-to-noise ratios and the use of difference scores. Critically, it is unclear to what extent attention control generalizes across sensory modalities, and without reliable auditory-based tests, an answer to this question will remain elusive. To this end, we developed three auditory-based tests of attention control that use an adaptive response deadline (DL) to account for speed–accuracy trade-offs: Auditory Simon DL, Auditory Flanker DL, and Auditory Stroop DL. In a large sample (N = 316), we investigated the psychometric properties of the three auditory conflict tasks, tested whether attention control is better modeled as a unitary factor or modality-specific factors, and estimated the extent to which unique variance in modality-specific factors contributed incrementally to the prediction of dichotic listening and multitasking performance. Our analyses indicated that the auditory conflict tasks have strong psychometric properties and demonstrate convergent validity with visual tests of attention control. Auditory and visual attention control factors were highly correlated (r = .81)—even after controlling for perceptual processing speed (r = .75). Modality-specific attention control factors accounted for unique variance in modality-matched criterion measures, but the majority of the explained variance was modality-general. The results suggest an interplay between modality-general attention control and modality-specific processing.
... It is worth noting here that the Selective Visual Arrays task is often thought of as a working memory task. However, there is evidence to suggest that this task is more highly correlated with other attentional control tasks than working memory tasks (such as operation span tasks) and loads highly onto latent variables of attentional control Martin et al., 2021;Shipstead et al., 2015). ...
Hyper-binding – the erroneous encoding of target and distractor information into associative pairs in memory – has been described as a unique age effect caused by declines in attentional control. Previous work has found that, on average, young adults do not hyper-bind. However, if hyper-binding is caused by reduced attentional control, then young adults with poor attention regulation should also show evidence of hyper-binding. We tested this question with an individual differences approach, using a battery of attentional control tasks and relating this to individual differences in hyper-binding. Participants (N = 121) completed an implicit associative memory test measuring memory for both target-distractor (i.e., hyper-binding) and target-target pairs, followed by a series of tasks measuring attentional control. Our results show that on average, young adults do not hyper-bind, but as predicted, those with poor attentional control show a larger hyper-binding effect than those with good attentional control. Exploratory analyses also suggest that individual differences in attentional control relate to susceptibility to interference at retrieval. These results support the hypothesis that hyper-binding in older adults is due to age-related declines in attentional control, and demonstrate that hyper-binding may be an issue for any individual with poor attentional control, regardless of age.
... Regarding students' characteristics, this study measured WMC through the Letter-Number Sequencing test, which is based on auditory input, but this could also be Backgrounds in instructional videos 12 measured through other instruments, such as a visual arrays task (Martin et al., 2021) which is based on visual input. Also, unlike Merkt et al. (2020), we did not measure other individual differences such as the participants' interest in the topic, or their motivation, even though these can impact learning (e.g., Krapp, 1999). ...
The increasing use of instructional videos in educational settings has emphasized the need for a deeper understanding of their design requirements. This study investigates the impact of virtual backgrounds in educational videos on students' visual information processing and learning outcomes. Participants aged 14-17 (N=47) were randomly assigned to one of three conditions: a video with a neutral, authentic, or off-topic background. Their prior knowledge and working memory capacity (WMC) were measured before watching the video, and eye tracking data was collected during the viewing. Learn-ing outcomes and student experiences were assessed after viewing. The eye tracking data revealed that a neutral background was the least distracting, allowing students to pay better attention to relevant parts of the video. Students found the off-topic background most distracting, but the negative effect on learning outcomes was not statistically significant. In contrast to expectations, no positive effect was observed for the authentic background. Furthermore, WMC had a significant impact on visual information processing and learning outcomes. These findings suggest that educators should consider using neu-tral backgrounds in educational videos, particularly for learners with lower WMC. Consequently, this research underscores the significance of careful design considerations in the creation of instructional videos.
... As a result, while selective visual arrays tasks clearly involve a primary memory (i.e., short-term memory storage) component, individual differences in performance on them are more so driven by attentional factors than memory ones. In support of this, we have found throughout multiple datasets that selective visual arrays tasks: (a) correlate more strongly with other attention measures (namely antisaccade) than working memory capacity tasks, even though these attention control tasks have minimal storage demands, and (b) load more strongly onto factors with other attention tasks than factors with working memory tasks in both exploratory and confirmatory factor analyses (see Draheim et al., 2021;Martin et al., 2021). And while visual arrays has good face validity as a measure of working memory capacity, closer examination of the demands of the task reveal that performance in selective versions of the task are largely driven by attentional factors. ...
There is an increasing consensus among researchers that traditional attention tasks do not validly index the attentional mechanisms that they are often used to assess. We recently tested and validated several existing, modified, and new tasks and found that accuracy-based and adaptive tasks were more reliable and valid measures of attention control than traditional ones, which typically rely on speeded responding and/or contrast comparisons in the form of difference scores (Draheim et al. Journal of Experimental Psychology: General, 150(2), 242-275, 2021). With these improved measures, we found that attention control fully mediated the working memory capacity-fluid intelligence relationship, a novel finding that we argued has significant theoretical implications. The present study was both a follow-up and extension to this "toolbox approach" to measuring attention control. Here, we tested updated versions of several attention control tasks in a new dataset (N = 301) and found, with one exception, that these tasks remain strong indicators of attention control. The present study also replicated two important findings: (1) that attention control accounted for nearly all the variance in the relationship between working memory capacity and fluid intelligence, and (2) that the strong association found between attention control and other cognitive measures is not because the attention control tasks place strong demands on processing speed. These findings show that attention control can be measured as a reliable and valid individual differences construct, and that attention control shares substantial variance with other executive functions.
