- Top of page
- Individual differences in distractibility
- The mechanisms of the relation between WMC and cross-modal auditory distraction
- Research directions
This paper reviews the current literature on individual differences in susceptibility to the effects of background sound on visual-verbal task performance. A large body of evidence suggests that individual differences in working memory capacity (WMC) underpin individual differences in susceptibility to auditory distraction in most tasks and contexts. Specifically, high WMC is associated with a more steadfast locus of attention (thus overruling the call for attention that background noise may evoke) and a more constrained auditory-sensory gating (i.e., less processing of the background sound). The relation between WMC and distractibility is a general framework that may also explain distractibility differences between populations that differ along variables that covary with WMC (such as age, developmental disorders, and personality traits). A neurocognitive task-engagement/distraction trade-off (TEDTOFF) model that summarizes current knowledge is outlined and directions for future research are proposed.
The scientific investigation of individual differences and identifying the general principles and underlying mechanisms that explain those differences is not only a valuable but a necessary endeavor in the pursuit of understanding human cognition. This paper reviews recent studies that have used this approach in an attempt to understand how the human mind creates selective attention.
Try to read this paper in a noisy environment. Are you distracted? Individual differences in distractibility vary quite a lot, extending from slight facilitation from a noisy background to severe disruption (Ellermeier & Zimmer, 1997; Sörqvist, 2010b). What is the basis of these individual differences? In a previous review of the current literature, Sörqvist (2010c) attempted to answer this question. The review resulted in three general conclusions. First, individual differences in working memory capacity (WMC) underpin individual differences in susceptibility to auditory distraction across a wide range of tasks and contexts. WMC is typically measured by so-called complex-span tasks that combine mnemonic short-term memory processes with distracter activities (Conway et al., 2005). Complex-span tasks show tremendous predictive power and are basically able to predict individual differences on any task that requires some cognitive control, particularly if there is a need to overcome distraction (Engle, 2002). Based on this finding, it has been argued that WMC actually reflects individual differences in the ability to control attention and avoid distraction (Conway, Cowan, & Bunting, 2001; Kane, Bleckley, Conway, & Engle, 2001). Auditory distraction is indeed no exception. By using complex-span tasks as a measure of WMC and correlating this variable with person-specific measures of distractibility, it has, for instance, been shown that high-WMC individuals are less susceptible to the effects of aircraft noise (Sörqvist, 2010a) and background speech (Beaman, 2004; Sörqvist, Halin, & Hygge, 2010; Sörqvist, Ljungberg, & Ljung, 2010) on memory and comprehension of written materials.
The second conclusion that emerged from a prior review (Sörqvist, 2010c) is that individual differences in WMC seem to be able to explain differences between age groups and other populations. Older adults (Bell, Buchner, & Mund, 2008; Boman, Enmarker, & Hygge, 2005) and children (Elliott, 2002) are typically more susceptible to auditory distraction than young adults. As the capacity of working memory increases throughout adulthood (Gathercole, Pickering, Ambridge, & Wearing, 2004) and then declines at older ages (Gazzaley, Cooney, Rissman, & D'Esposito, 2005), it seems like age differences in distractibility reflect life-span changes in WMC. Likewise, children with attention-deficit/hyperactivity disorder (ADHD; Gumenyuk et al., 2005; Zentall & Shaw, 1980) and children with low intelligence (Johansson, 1983) are more susceptible to auditory distraction than their counterparts. This fits well with the finding that these populations typically demonstrate low WMC (Martinussen, Hayden, Hogg-Johnson, & Tannock, 2005; Shelton, Elliott, Matthew, Hill, & Gouvier, 2010) and with the notion that low WMC makes individuals more distractible.
The third conclusion that was reached in a previous review (Sörqvist, 2010c) is that WMC—although apparently a very reliable predictor of individual differences in susceptibility to distraction in general—is unrelated to a specific form of distraction, with the key signature being the so-called changing-state effect (Beaman, 2004; Sörqvist, 2010b). The changing-state effect is the observation that serial recall of a visually presented sequence of items (e.g., digits) is more impaired by a concurrently presented sound stream that changes across time (e.g., “k l m v r q c”) compared with the repetitive presentation of the same sound element (e.g., “c c c c c c c”). Whilst WMC is unrelated to the changing-state effect, individual differences in perceptual abilities—specifically the ability to detect subtle changes in tone streams—appear to underpin the magnitude of this effect (Macken, Phelps, & Jones, 2009). In contrast, high WMC attenuates the deviation effect—the observation that serial recall is more impaired by a sound stream that contains a single deviating element (e.g., “c c c m c c c”) compared with a steady-state sound stream (e.g., “c c c c c c c”). Together, this was regarded as evidence in favor of a duplex-mechanism account of auditory distraction (Hughes, Vachon, & Jones, 2007) wherein sound can impair cognitive performance by two functionally different mechanisms: either by capturing attention (which underpins the deviation effect) or by an involuntary (uncontrollable) analysis of order information that inevitably interferes with the deliberate act of serially rehearing to-be-recalled material (which underpins the changing-state effect). A distinguishing feature of the duplex-mechanism account is, hence, that distraction is a function of the characteristics of the sound as well as a function of the processes that the task entails. For example, whilst both a changing state sound sequence (e.g., “k l m v r q c”) and a deviant sound sequence (e.g., “m m m v m m m”) are more disruptive to serial short-term memory (wherein the task is to recall a sequence of visually presented items in their order of presentation) than a steady-state sequence (e.g., “m m m m m m m”), only a deviant sound sequence disrupts short-term memory tasks that do not entail serial order memory (e.g., when the task is to identify the missing weekday from a set of six weekdays). This is because the deviating sound is disruptive owing to it capturing attention, whereas the perceptual processing of a changing state sequence is only disruptive inasmuch as it comes into conflict with the processes that are required by the task.
Here, the original review is expanded upon in an attempt to test whether the same general patterns still hold against more recent evidence. Furthermore, studies that have tried to identify the mechanisms and the neural basis of the relation between WMC and auditory distraction are discussed. Finally, the current knowledge is integrated into a model that attempts to describe the role of WMC in auditory distraction and we discuss the model's generalizability and future directions.
The mechanisms of the relation between WMC and cross-modal auditory distraction
- Top of page
- Individual differences in distractibility
- The mechanisms of the relation between WMC and cross-modal auditory distraction
- Research directions
Increasing task difficulty reduces the effects of sound on that particular task (e.g., Halin, Marsh, Haga, Holmgren, & Sörqvist, 2013; Kim, Kim, & Chun, 2005), as sound loses its ability to capture attention away from the visual task (Hughes et al., 2013; SanMiguel, Corral, & Escera, 2008) and the neural processing of the background sound is constrained (Regenbogen et al., 2012; Sörqvist, Stenfelt, & Rönnberg, 2012). This happens whether task difficulty is increased as a consequence of higher cognitive load (e.g., a greater memory load; Sörqvist, Stenfelt, & Rönnberg, 2012) or higher perceptual load (e.g., a greater difficulty identifying the task materials; Halin et al., 2013; Hughes et al., 2013).
Variations in WMC appear to have a functionally similar effect on distractibility as variations in task difficulty: Higher WMC is associated with a more steadfast locus of attention and less processing of background sound. Strong support for a more steadfast locus of attention in high-WMC individuals comes from studies demonstrating that high-WMC individuals are less susceptible to the deviation effect (Hughes et al., 2013; Sörqvist, 2010b; Sörqvist et al., 2013; Sörqvist, Stenfelt, & Rönnberg, 2012). Thus, high-WMC individuals are more able to resist the call for attention from deviating background sound. Support for the assumption that high-WMC individuals have a more efficient auditory-sensory gating (i.e., the degree to which the background sound is processed) comes from neurometric studies. When sound reaches the ear, it is transformed into a neural signal at the outer hair cells. Then it passes through the brainstem and the thalamus before it eventually ends up in the auditory cortex. The brainstems' responsiveness to task-irrelevant background sound depends on individual differences in WMC. In a study by Sörqvist, Stenfelt, & Rönnberg (2012), participants were requested to undertake a visual version of the n-back task wherein sequences of letters were presented and the participants' task was to decide whether the presented letter was identical to that presented one, two, or three steps back in the sequence. The participants were also concurrently presented with a task-irrelevant sound they were instructed to ignore. The experiment revealed that the amplitude of the brainstem response (i.e., the number of neurons that respond to the sound) gets smaller in magnitude when the task difficulty increases. Moreover, the difference in magnitude between the one-back condition and the three-back condition covaried with individual differences in WMC. The difference was greater in high-WMC individuals, which suggests that they are more able to constrain processing of task-irrelevant sound. Similar results have been found for the primary auditory cortex: When visual-verbal task load is high the primary auditory cortex is deactivated (Regenbogen et al., 2012). Taken together, high-WMC individuals appear to have a superior ability to modulate the auditory-perceptual filter in comparison with their low capacity counterparts at early (subcortical) and at late (cortical) processing stages.
A neurocognitive task-engagement/distraction trade-off model
Variations in task difficulty and variations in WMC seem to have functionally similar consequences for distractibility. We argue that they both operate on the same psychological constructs: the steadfastness of the locus of attention and the sensory gating of task-irrelevant information. However, an important difference between task difficulty manipulations and individual differences in WMC is that a manipulation of task difficulty influences the current “state” of these constructs (e.g., a high task difficulty leads to a more steadfast locus of attention and a more constrained gating of task-irrelevant information) whilst individual differences in WMC reflect a “trait” characteristic of the participants (Ilkowska & Engle, 2010). Individual differences in WMC set the limit for how steadfast the locus of attention and how constrained sensory gating can be for a specific person.
WMC as a person-specific “trait” must be distinguished from other views of the “working memory” concept in order to understand the role of WMC in distractibility. One definition of working memory is that it is the ability to maintain and manipulate information in immediate memory (D'Esposito, 2007). Working memory, defined this way, plays a role in distractibility (de Fockert, 2013). For instance, loading the contents of working memory (i.e., requesting participants to maintain items in immediate memory) while simultaneously performing another unrelated task (e.g., visual search) makes people more susceptible to distraction (as measured by the cost of presenting distracters in the visual search task to the time it takes to find a target) in comparison with a condition with lower working memory load (Lavie, 2010). Here, we argue that the correlations observed between WMC (as measured using complex-span tasks or similar tasks) and individual differences in distractibility are not reflecting working memory load. The advantage in high-WMC individuals is, hence, not that they more easily can handle difficult tasks (i.e., because they are less cognitively loaded), but rather that they can reach higher states of focal-task engagement (i.e., a more steadfast locus of attention and a more constrained sensory gating of irrelevant materials). For example, it is difficult to explain why WMC predicts habituation rate in the context of the classic oddball paradigm (Sörqvist et al., 2012b)—a task that requires speeded classification of the direction of visually presented arrows, which involves very little load on working memory even for low-WMC individuals—from the perspective that the advantage of high-WMC individuals is in lower working memory load. Likewise, it is difficult to see why the difference in magnitude of the auditory brainstem response to a task-irrelevant background sound, between a one-back version and a three-back version of a visual-verbal n-back task, is larger in high-WMC individuals (Sörqvist, Stenfelt, & Rönnberg, 2012). From a working memory load perspective, the difference in task difficulty (or cognitive load) between the one- and the three-back version should be larger for low-WMC individuals.
On a further note, individual differences in WMC may be associated with distractibility in its relation to selective attention abilities (i.e., the steadfastness of the locus of attention and sensory gating of task-irrelevant information) by overruling the manifestation of distraction, and in its relation to compensation processes after distraction has already been manifested. For example, high-WMC individuals are better able to search for items in secondary memory that have been lost from primary memory due to distraction (Unsworth & Engle, 2007). The relation to postdistraction compensatory abilities may explain why WMC supports listening in noisy environments in which lost information in the signal has to be compensated for by, for example, access to long-term memory representations (i.e., postdictive processes that support listening). Moreover, predictions about the target speech can be helped by a working memory system that can hold hypotheses online (i.e., predictive processes that support listening). In all, the ability to process (and comprehend) a target signal (such as speech) is working memory dependent, particularly in noisy environments (Rönnberg et al., 2013). A key difference between this perspective and the objectives of the present article is the role for WMC in target processing, on the one hand, and the role for WMC in distracter processing on the other. Here, we restrict our discussion of the role for WMC in selective attention abilities and the suppression of the task-irrelevant information.
Figure 1 illustrates a neurocognitive task-engagement/distraction trade-off (TEDTOFF) model that summarizes what has been said up until this point about the role for WMC in selective attention. The model defines focal-task engagement as a continuum across which the steadfastness of the locus of attention and sensory gating of task-irrelevant information can vary. Focal-task engagement can be manipulated by changing task difficulty (cognitive load and perceptual load) and the person-specific cap for focal-task engagement is set by individual differences in WMC (e.g., Halin et al., 2013; Hughes et al., 2013; Sörqvist, Stenfelt, & Rönnberg, 2012). As mentioned above, WMC is typically assessed using a complex-span task that combines storage and recall of a set of items with a distracter activity. Accordingly, both storage (e.g., maintaining items in short-term memory) and cognitive control abilities (e.g., suppression of items from previous trials) contribute to the person-specific task score, and the WMC construct is a conglomeration of these abilities (Sörqvist, Ljungberg, & Ljung, 2010). Whilst the storage component of WMC seems to have its neural basis in the parietal areas (e.g., Braver et al., 1997; Rönnberg, Rudner, & Ingvar, 2004), the cognitive control component of WMC has its neural basis in the prefrontal cortex (Cabeza & Nyberg, 2000; D'Esposito et al., 1995; Kane & Engle, 2002). However, they are related. For instance, higher storage load (i.e., a need to maintain more items simultaneously in short-term memory) increases prefrontal cortex activity (Braver et al., 1997). The TEDTOFF model assumes that the cognitive control component of WMC (i.e., the prefrontal cortex) is responsible for individual differences in the ability to shield oneself from distraction as the prefrontal cortex is involved in top-down modulation and preparation of stimulus-selective sensory cortices (e.g., Gazzaley & Nobre, 2011; Hopfinger, Buonocore, & Mangun, 2000; Woldorff et al., 1993; Zanto, Rubens, Thangavel, & Gazzaley, 2011). More specifically, WMC orchestrates networks that (a) lock attention to the focal target material in other cortex areas (e.g., parietal cortex) and (b) influences auditory-sensory gating by modulating subcortical (i.e., the brainstem) and cortical (i.e., the auditory cortex) neural responsiveness to external stimuli.
Figure 1. The neurocognitive task-engagement/distraction trade-off (TEDTOFF) model of working memory capacity and cross-modal auditory distraction. Task difficulty and individual differences in working memory capacity determine the state of focal-task engagement (i.e., the size of the attentional span and the steadfastness of the locus of attention). The filtering of the task-irrelevant information takes place at early (and late) processing stages. A narrower attentional span makes background sound gain less access to later, cortical processing stages.
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Some criticism and methodological considerations
Resource theories have long since been questioned for their circularity (e.g., Navon, 1984). The TEDTOFF model also belongs to the family of resource theories and, hence, it can be argued that the model is ultimately circular and therefore logically flawed. For example, if participants are less distracted by background sound in a specific experimental condition (e.g., when reading a text with a hard-to-read font), wherein it is assumed that the task is difficult, in comparison with another experimental condition (e.g., when reading a text in an easy-to-read font), wherein it is assumed that the task is easy, the results would support the model. Yet, if there is no independent measure of task difficulty that ensures that the difficulty manipulation was successful, the reasoning is circular. Because of this, it is methodologically important to measure the success of the experimental manipulation when conducting experiments to test these ideas. This can be done, for example, through self-reports (e.g., asking the participants to rate the difficulty of the tasks in the various task difficulty conditions) and through pupilometric techniques (because an increase in pupilometric size indicates increased effort; Koelewijn, Zekveld, Festen, Rönnberg, & Kramer, 2012). As the basic idea is that high task difficulty protects against the disruptive effects of background sound on people's ability to carry out that particular task, it is also desirable to obtain independent measures of background sound processing rather than relying entirely on task performance. One way is to measure sound processing by event-related potentials (Sörqvist, Stenfelt, & Rönnberg, 2012).
It could also be mentioned that many influential theories of working memory and selective attention have been questioned for their circularity, such as Baddeley's working memory model (Baddeley & Larsen, 2007; Jones, Hughes, & Macken, 2007), Craik and Lockhart's (1972) levels-of-processing ideas (Baddeley, 1978), and Lavie's (2010) load theory (Benoni & Tsal, 2013). Moreover, circular theories may not be as problematic as once believed (e.g., Brown, 1993; Shogenji, 2000), particularly when Bayesian statistics are used to analyze the results, as it can quantify the support for the null hypothesis (Hahn & Oaksford, 2007). Indeed, in the Bayesian meta-analysis mentioned above, WMC was found to be unrelated to the changing-state effect (Sörqvist et al., 2013), a finding that (at least at first glance) questions the TEDTOFF model. Whether this conclusion holds against more crucial experimental manipulations (outlined below) still remains to be tested.
- Top of page
- Individual differences in distractibility
- The mechanisms of the relation between WMC and cross-modal auditory distraction
- Research directions
One research direction for future studies is to identify associations between WMC and neural auditory processing stages other than the brainstem (Sörqvist, Stenfelt, & Rönnberg, 2012) and the auditory cortex (Regenbogen et al., 2012; Tsuchida, Katayama, & Murohashi, 2012; Yurgil & Golob, 2013). Sound is processed in the inner ear before it reaches the brainstem and thus WMC could perhaps operate on auditory processing at even earlier stages. In support for this assumption, selectively attending a specific sound signal while deliberately ignoring another, or focusing on a visual aspect of the environment while ignoring the sound, modulates the outer hair cells' responsiveness to the to-be-ignored sound (Bauer & Bayles, 1990; de Boer & Thornton, 2007; Giard, Collet, Bouchet, & Pernier, 1994; Meric & Collet, 1992; but see Michie, LePage, Solowij, Haller, & Terry, 1996). The outer hair cells are less responsive to ignored sound than to attended sound. Hence, one hypothesis that could be tested in future experiments is whether greater focal-task engagement in the visual modality, as a consequence of higher WMC, is associated with lower activity in the outer hair cells.
The specifics of the TEDTOFF model outlined in Figure 1 describe how WMC is involved in the processing of background sound while a visual-verbal task is carried out. One research direction for the future is to delineate how this model may be expanded to explain the role for WMC when the task is auditory in nature. As is well-known, WMC supports listening and speech comprehension in noisy environments (e.g., Conway et al., 2001; Rönnberg et al., 2013; Sörqvist & Rönnberg, 2012) and has been linked to indices of task difficulty, such as arousal level, in these conditions (Koelewijn et al., 2012). Furthermore, high WMC protects against cross-modal (Beaman, 2004) as well as within-modal semantic auditory distraction (Sörqvist & Rönnberg, 2012). Also, WMC appears to modulate neural responses to to-be-ignored sound when the task is to attend to another sound source. Tsuchida et al. (2012) asked participants to either attend to or ignore specific tones in a sound stream. They found that the primary auditory cortex is more responsive to to-be-attended tones in high-WMC individuals and less responsive to to-be-ignored tones in high-WMC individuals, in comparison with their low WMC counterparts. Very similar results have also been reported by Yurgil and Golob (2013). Thus, the role for WMC in cross-modal auditory distraction appears to be quite similar to its role in within-modal auditory distraction (i.e., when both the to-be-attended and the to-be-ignored information is sound). One starting point could be to use a dichotic listening paradigm (Conway et al., 2001) and manipulate the perceptual load in the to-be-attended channel (e.g., by manipulating the signal-to-noise ratio) and measure responsiveness (both neural and behavioral) to information presented in the to-be-ignored ear. Higher perceptual load (i.e., higher task difficulty) should decrease responsiveness to the stimuli in the to-be-ignored channel, and the magnitude of this effect should be related to individual differences in WMC.
Another interesting line of research would be to investigate the domain-generality and the direction of the trade-off between task engagement and distractibility. In the typical cross-modal paradigm, the task is visual and the sound is to be ignored (e.g., Macdonald & Lavie, 2011; Zhang, Chen, Yuan, Zhang, & He, 2006). There are some notable exceptions, however, where the task is auditory and the visual modality is to be ignored. It has, for instance, been shown that the activity in cortex areas that serve visual processing decreases when auditory working memory load is increased (Klemen, Büchel, Bühler, Menz, & Rose, 2010). A more general version of the TEDTOFF model would predict that higher cognitive load in the auditory task (e.g., an auditory version of the n-back task) would protect against the potentially disruptive effects of unexpected information presented in the visual modality.
In this context, it would be useful to discuss differences and similarities between the TEDTOFF model and Lavie's (2010) load theory. According to the load theory, cognitive load (i.e., maintaining items in working memory) increases distractibility, whereas perceptual load (i.e., difficulty identifying the target materials) decreases distractibility. Whilst the load theory and the TEDTOFF model agree on the assumption that perceptual load should decrease distractibility, the models disagree on the assumption that cognitive load makes people more susceptible to distraction, because the TEDTOFF model assumes that higher task difficulty—whether it is manipulated through perceptual load or through cognitive load—protects against distraction. An experiment that would differentiate between the two views is to use a dichotic listening paradigm and manipulate cognitive load by presenting an auditory version of an n-back task in the to-be-attended channel and measure responsiveness to the stimuli in the to-be-ignored channel. The TEDTOFF model predicts that distractibility should become smaller when cognitive load is increased. It should be noted, though, that studies demonstrating that cognitive load can increase distractibility typically involve a dual-task setting wherein cognitive load is manipulated by requesting participants to maintain items in working memory (e.g., one item in the low load condition and four items in the high load condition) while they simultaneously carry out an unrelated task (e.g., visual search) and the cost of cognitive load is measured by the effects of attention capture on the unrelated task (e.g., Lavie, Hirst, de Fockert, & Viding, 2004). The TEDTOFF model assumes that higher cognitive load protects against distraction to the particular task for which cognitive load is high, because the higher cognitive load (i.e., higher task difficulty) increases focal-task engagement (i.e., locks attention to the task materials and attenuates extratask information processing).
Another research direction is to test the generality of the TEDTOFF model to various forms of cross-modal auditory distraction. Hughes et al. (2013) have shown that the deviation effect is abolished when the to-be-recalled items are masked by visual noise. Warning the participants prior to the trial has the same effect: they abolish the deviation effect. The authors' interpretation of these findings is that visual noise (i.e., perceptual load) and warnings increase focal-task engagement, which results in a more steadfast locus of attention and, consequently, a smaller susceptibility to the deviation effect. In contrast, the changing-state effect resisted the focal-task engagement manipulations. This is in line with the assumption that the changing-state effect is not a consequence of sound capturing attention but rather a consequence of automatic interference as an inevitable byproduct of perceptual organization processes (Hughes et al., 2007; Macken et al., 2009). However, these results challenge the TEDTOFF model, as the model predicts that higher task difficulty modulates auditory-sensory gating. A more constrained processing of the background sound should influence how much (order) information is abstracted from the sound and thus attenuate the magnitude of the changing-state effect. One possibility is that the state of focal-task engagement, induced by masking visual noise or warning manipulations, was too lenient to abolish the changing-state effect. If combined, however, visual noise and warnings may, together, make the participant reach a high enough state of focal-task engagement to overrule the changing-state effect, particularly in high-WMC individuals (as the task-engagement ceiling is higher in these individuals).
The TEDTOFF model may also have implications for research fields other than distraction. For instance, higher task difficulty should abolish (or at least attenuate) incidental learning from information presented in the background environment, as the background information is filtered/suppressed. For example, people have better (incidental) memory of irrelevant words spoken in the background that are related to the current processing intentions (Marsh, Cook, Meeks, Clark-Foos, & Hicks, 2007). Memory of the task-irrelevant words should be abolished with higher task difficulty, particularly in high-WMC individuals. Similarly, when undertaking a visual task in the presence of background sound, people form expectations of future sound events as a result of incidental auditory sequence learning (e.g., Nöstl, Marsh, & Sörqvist, 2012; Parmentier, Elsley, Andrés, & Barceló, 2011). The TEDTOFF model predicts that learning should be abolished when engagement in the visual task increases and suggests that high-WMC individuals may be learning less from the background sound.
Finally, the TEDTOFF model delineates several applied lines of research. One is clinical research. It may, for instance, be used as a framework for understanding distractibility in persons with attentional engagement deficits as in schizophrenia (Reilly, Harris, Khine, Keshavan, & Sweeney, 2007) and ADHD (Gumenyuk et al., 2005) and the model points toward techniques for how to help these individuals overcome distraction. A second area of applied research concerns human factors. Mind-wandering is a major source of accidents (e.g., He, Becic, Lee, & McCarley, 2011), but can be prevented through perceptual load (Forster & Lavie, 2009) and top-down executive control (Kane et al., 2007). The TEDTOFF model provides a framework for understanding how task difficulty manipulations can be used to prevent mind wandering and, in extension, potential accidents. A third field of applied research is the consequences of environmental noise. Noise is a pervasive source of stress and performance decrements (A. Smith, 2012; Szalma & Hancock, 2011) as it impairs many office- and school-related abilities such as writing (Sörqvist, Nöstl, & Halin, 2012a), reading comprehension (Ljung, Sörqvist, & Hygge, 2009; Sörqvist, Halin, & Hygge, 2010), and long-term memory (Hygge, Evans, & Bullinger, 2002; Sörqvist, 2010a). A generally accepted view is that high cognitive load is bad for learning and performance (Sweller, 1994). However, disfluency can sometimes facilitate learning (Diemand-Yauman, Oppenheimer, & Vaughan, 2011) and cognitive load can have other positive effects such as facilitating physiological restoration processes (Bosch et al., 2012). In line with the positive effects of cognitive load, the TEDTOFF model predicts that high cognitive load should facilitate learning, particularly in noisy environments, as higher task difficulty attenuates distraction (Halin et al., 2013). In general, the TEDTOFF model provides a framework for investigating individual differences in desirable difficulties in noisy and potentially distracting environments.