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Modulating mind-wandering using non-invasive brain stimulation

In our research, we focus on non-invasive brain stimulation to understand and potentially modulate mind wandering, with a particular emphasis on clinical applications, such as for adhd. committed to open-science principles, our team rigorously follows best methodological and statistical practices, including pre-registered reports, ensuring transparency in our findings. exploring both electrical and magnetic stimulation techniques, our work aims to offer insights into cognitive regulation. by maintaining a balanced approach between innovation and adherence to high research standards, we hope to contribute valuable knowledge that may one day translate into practical benefits for clinical populations dealing with attention-related challenges., publications.

• Nawani, H.,   Mittner, M.   & Csifcsák, G. (2023). Modulation of Mind Wandering Using Transcranial Direct Current Stimulation: A Meta-Analysis Based on Electric Field Modeling.   NeuroImage . , pp. 120051   doi:10.1016/j.neuroimage.2023.120051
• Alexandersen, A., Csifcsák, G., Groot, J. &   Mittner, M.   (2022). The Effect of Transcranial Direct Current Stimulation on the Interplay between Executive Control, Behavioral Variability and Mind Wandering: A Registered Report.   Neuroimage: Reports . 2:3, pp. 100109   doi:10.1016/j.ynirp.2022.100109
• Kam, J.,   Mittner, M.   & Knight, R. (2022). Mind-Wandering: Mechanistic Insights from Lesion, tDCS, and iEEG.   Trends in Cognitive Sciences . 0:0   doi:10.1016/j.tics.2021.12.005
• Boayue, N., Csifcsák, G., Kreis, I., Schmidt, C., Finn, I., Vollsund, A. &   Mittner, M.  (2020). The Interplay between Executive Control, Behavioral Variability and Mind Wandering: Insights from a High-Definition Transcranial Direct-Current Stimulation Study.   European Journal of Neuroscience .   doi:10.1111/ejn.15049
• Boayue, N., Csifcsák, G., Aslaksen, P., Turi, Z., Antal, A., Groot, J., Hawkins, G., Forstmann, B., Opitz, A., Thielscher, A. &  Mittner, M.  (2019). Increasing Propensity to Mind-Wander by Transcranial Direct Current Stimulation? A Registered Report.   The European Journal of Neuroscience .   doi:10.1111/ejn.14347

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Neural mechanisms of the wandering mind

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Mind wandering in reading: An embodied approach

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Article Contents

Introduction, mind wandering, cognitive control, why the mind wanders, explanations, predictions, philosophical implications, acknowledgments.

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Why does the mind wander?

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Joshua Shepherd, Why does the mind wander?, Neuroscience of Consciousness , Volume 2019, Issue 1, 2019, niz014, https://doi.org/10.1093/nc/niz014

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I seek an explanation for the etiology and the function of mind wandering episodes. My proposal—which I call the cognitive control proposal—is that mind wandering is a form of non-conscious guidance due to cognitive control. When the agent’s current goal is deemed insufficiently rewarding, the cognitive control system initiates a search for a new, more rewarding goal. This search is the process of unintentional mind wandering. After developing the proposal, and relating it to the literature on mind wandering and on cognitive control, I discuss explanations the proposal affords, testable predictions the proposal makes, and philosophical implications the proposal has.

Makes a novel and empirically tractable proposal regarding why the mind wanders

Offers novel explanations of data on mind wandering

Offers predictions for future work on mind wandering

Integrates literature on cognitive control with the literature on mind wandering

Discusses implications for a philosophical account of the nature of mind wandering

Minds wander

Some wander more than others, but human ones wander a lot. A much-cited estimate, due to Killingsworth and Gilbert (2010) , has it that the awake human mind spends from a third to half its time wandering. That’s a big range, a rough estimate, and there are good reasons to be suspicious of it (see Seli et al. 2018 ). The actual number will likely depend a bit upon the nature of mind wandering, a bit upon whether we have the right measure to produce such an estimate, and of course a bit on individual variability. Estimates aside, though, introspection reports that the mind wanders surprisingly often. My question here is this.

Why does it happen?

Sub-questions include the following. What drives the mind to wander? Does anything drive it to wander? Is the transition from focused thought to meandering thought random? Is it a failure of control, or is there some dark purpose behind these mental movements?

In the next section, I set the table by discussing a few interesting features of mind wandering, as well as a few recent proposals about its etiology, and its function. It is easy to conflate these two, since if mind wandering has a function its etiology may very well help illuminate it, but the questions are distinct. Here, I am more interested in why mind wandering happens—about its etiology. It turns out, though, that on my proposal mind wandering happens for good functional reasons. I develop this proposal, which I call the cognitive control proposal, in Cognitive control and Why the mind wanders sections. In Explanations section, I discuss some explanations this proposal makes possible. In Predictions section, I discuss some predictions that could confirm or disconfirm the proposal. In Philosophical implications section, I discuss implications for a philosophical account of the nature of mind wandering.

By referring to this phenomenon as mind wandering, a term familiar to the lay person, we hope to elevate the status of this research into mainstream psychological thinking (946).

As Murray et al. (2019) report, since that review, usage of “mind wandering” has risen dramatically. Only the Smallwood and Schooler paper used the term in a title or abstract in 2006. In 2018, the term appeared in 132 titles or abstracts.

Increased attention to the range of phenomena grouped together by “mind wandering” is salutary. But theorists recognize that the range of processes the term groups may contain multiple etiologies and processing signatures. Accordingly, theorists have proposed many sub-types of mind wandering, categorizing episodes of mind wandering in at least three distinct ways.

The first two involve a conception of mind wandering as defined in part by the contents of a mind-wandering episode, where the contents are unrelated to a task an agent was performing, or was supposed to perform. But there are various ways for an agent to engage in task-unrelated thought. Some categorize mind-wandering episodes in terms of a relationship to an agent’s intention: mind wandering might occur intentionally or unintentionally ( Giambra 1995 ; Seli et al. 2016 ). A second way to categorize mind-wandering episodes is in terms of a relationship to external stimuli. One might here distinguish between distraction, when the mind is prompted to wander by external stimuli, and mind wandering, when the mind is prompted to wander by internal processes, independently of any particular stimuli (see Stawarczyk et al. 2013 ). Or one could argue that distraction, especially sustained distraction, is a legitimate mind wandering as well.

A third way to characterize mind wandering is not in terms of its contents, but rather its dynamics. So, e.g., Christoff et al. (2016) characterize mind wandering as a species of spontaneous thought, with distinct dynamics. Mind wandering is distinguished from creative thought, and rumination, and other types of mental episodes, by relation to the presence or absence of various constraints on the episode (e.g., what they call “deliberate” and “automatic” constraints).

From a certain height, it appears that these different characterizations may not be in competition. Perhaps there are many routes to mind wandering. Perhaps some of them overlap. Perhaps different questions can be answered by focusing on certain routes in certain contexts. Reasonably, Seli et al. (2018) have recently argued in favor of mind wandering as a natural kind, with different sub-types grouped together by relations akin to family resemblance: “We propose that the field acknowledge mind-wandering to be a multidimensional and fuzzy construct encompassing a family of experiences with common and unique features” (2018, 482).

Methodological and conceptual clarity will simply require, in empirical manuscripts, something like the following sentence: “Here, we conceptualized mind-wandering as ________, and operationally defined it for our participants as ________.” Critically, this approach allows researchers the freedom to study whatever features of mind-wandering they wish, while providing the required specificity about aspects of the experience being explored. (488)

In the same spirit, I note here the sub-type of mind wandering that concerns me. I am interested in unintentional mind wandering—episodes of mind wandering that are neither initiated nor governed by any reportable intention of the agent. This category may cross-cut any relationship to external stimuli, in the sense that unintentional mind wandering could be externally or internally initiated. And it may demonstrate dynamics that are distinct from other sub-types of mind wandering.

Unintentional mind wandering could in principle happen non-consciously. But the literature on human mind wandering has it pegged as a feature of the conscious mind. That is to say, when the mind wanders, what wanders is the stream of consciousness—processes of conscious mentation. So, one key way to study mind wandering is to ask people whether or how often their mind has wandered. People offer reports about it. They recognize that they have been mind wandering. This is not because of mind wandering’s phenomenological signature. It is rather because people have a sense that they were once up to something, and then, more or less unbeknownst to them, they began to be up to something else. Thomas Metzinger (2013) speaks of this as the self-representational blink: an unnoticed shift from pursuing one task to doing whatever it is we do when the mind wanders. Recognizing that your mind has been wandering is always slightly surprising, because you did not plan for things to go in that way. From your perspective, it seems that they just did .

This is puzzling. But calling a mental episode unintentional need not imply that mind wandering is maladaptive, or that it has no function. Indeed, the very frequency with which it occurs had led many to suggest that it must have some functional role (e.g., Baird et al. 2011 ). It may not, of course. Perhaps, we survive in spite of how mentally addled we all are. But it is at least plausible that there is a function.

Some accounts of mind wandering might be taken to deny this. McVay and Kane (2010a ) and Kane and McVay (2012) , e.g., have argued that mind wandering reflects a failure of executive control. They note that a negative correlation exists between working memory capacity and a tendency to experience task-unrelated thoughts (see also Randall et al. 2014 ). Some such correlation is plausible. When one experiences task-unrelated mentation, something has clearly gone wrong. One has failed to stay on task.

But this also fails to imply that mind wandering has no function. Kane and McVay note that the correlation between working memory capacity and task-unrelated thought is not terribly strong: “WMC accounts for only about 5% of the variability in [task-unrelated thought] TUT rates (and vice versa)” (2012, 352). It is possible that mind wandering is both a failure in one sense and adaptive in another.

[W]e found evidence for the hypothesis that cognitive control abilities are specifically involved in the flexible adjustment of mind-wandering to task demands. As was hypothesized, high-WMC participants showed higher levels of TUT adjustment than did low-WMC participants. Thus, a more flexible coordination of the stream of thought appears to be characteristic of high-WMC individuals: They engage in TUTs when situational demands are low but reduce TUTs in attention-demanding situations. (1313)

This hypothesis is consistent with work that has demonstrated that as cognitive control resources diminish with age, the propensity to mind wander diminishes as well ( Maillet and Schacter 2016 ).

If we are to believe that mind wandering is associated with deployments of cognitive control, we need evidence that when agents mind wander, they engage in thought processes that may be beneficial. Some evidence for this is that when agents mind wander, their thoughts very frequently go to non-occurrent goals and needs, and to mentation about how to satisfy these goals in the future ( Klinger 1999 ; Baird et al. 2011 ).

Indeed, as Irving and Thompson (2019) note, it seems that it is possible to manipulate the content of mind wandering episodes by giving agents specific goals. Morsella et al. (2010) told some participants they would, in the near future, have to answer questions about the states in America. Then they gave the participants a different task. About 70 percent of these participants’ task-unrelated thoughts were about U.S. geography. Similarly, Mac Giolla et al. (2017) gave some participants a real future task, and told different participants to only pretend to have (or to lie about having) the same future task. Those participants with genuine intentions reported much more spontaneous thought about the future task than participants without genuine intentions.

It is also possible to manipulate mind wandering by reminding agents of their goals. Kopp et al. (2015) had participants either construct a list of their plans for the week or list features of a car. Participants then performed a reading task. Participants who had just reviewed a set of their own plans and goals reported much more mind wandering during the reading.

There is thus an apparent tension within mind wandering. When the mind wanders (at least unintentionally), agents are distracted from the current task, and performance suffers. But when the mind wanders, it tends to find non-occurrent goals the agent possesses, generating planning that could be beneficial. What’s more, greater cognitive control is associated with increases in mind wandering, especially when task demands are low.

Recall my original question: why does the mind wander? Two related questions that could help: What causes it to start, and what explains what happens as it wanders?

My proposed answer runs through recent work on cognitive control, and on what kinds of mechanisms drive allocations of cognitive control resources. I discuss this work in the next section.

A remarkable feature of the human cognitive system is its ability to configure itself for the performance of specific tasks through appropriate adjustments in perceptual selection, response biasing and the on-line maintenance of contextual information. The processes behind such adaptability, referred to collectively as cognitive control … ( Botvinick et al. 2001 , 624)

Rouault and Koechlin likewise emphasize processes of regulation towards certain ends: “Cognitive control refers to mental processes that evolve as regulating adaptive behavior beyond basic reinforcement and associative learning processes” (2018, 106).

There is a danger here, analogous to the one just discussed regarding definitions of mind wandering, in including far too many process-types under the same heading. “Cognitive control” includes processes like the construction and maintenance of a task set, the switching from one task set to another, the deployment of attention in various ways, the deployment of inhibition, and the monitoring of an agent’s progress towards goal achievement. To get better at understanding how these processes work together (or don’t), it helps to have a label. But the nature of the system is only loosely delineated.

Given this, there is room for differing emphases. So, e.g., Adele Diamond characterizes cognitive control processes as “a family of top-down mental processes needed when you have to concentrate and pay attention, when going on automatic or relying on instinct or intuition would be ill-advised, insufficient, or impossible” (136). This characterization is useful, but not definitive. For the kind of cognitive control processes, I have in mind here might be considered top-down, but do not activate only when agents need to deploy attention. These processes operate outside of the agent’s awareness, influencing the agent’s thought and action in subtle and difficult to detect ways.

So, e.g., Kurzban et al. (2013) have argued that one subtle way cognitive control mechanisms influence thought and action is by generating an experience of effort related to the performance of some task. They hypothesize that the experience of effort is the result of sub-personal computations that determine the current task’s value, as well as the value of nearby available tasks, and output a determination of the opportunity cost of persisting on the current task. The experience of effort is hypothesized to be a signal to the agent to switch tasks.

Kurzban et al. ’s proposal has received a lot of attention. Few agree with all of the specifics, but most agree with the general perspective that sub-personal monitoring mechanisms are concerned to determine the value of succeeding in the current task, as well as the cost of continuing engagement in the current task, and are concerned to, in some sense, direct the agent or her cognitive control resources in a more fruitful way.

Perhaps the most mature theory characterizing the mechanisms that constitute the allocation of cognitive control is the Expected Value of Control theory (see Shenhav et al. 2013 , 2017 ). The general idea is that the cognitive control system “specifies how much control to exert according to a rational cost-benefit analysis, weighing these effort costs against attendant rewards for achieving one’s goals” ( Lieder et al. 2018 , 2). Lieder et al. add to this idea a sophisticated model of how the cognitive control system might come to learn the value of the various control signals it can deploy, and might rely upon what it learns to guide cognition in adaptive ways.

Lieder et al. characterize the position the cognitive control system is typically in as a Markov decision process, specified over certain parameters, driven by reinforcement learning. Those parameters are the initial state of the system, the set of states the system could be in, the set of possible actions (or moves, or operations) the system could take, the conditional probabilities of transitioning between states, and a reward function. Lieder et al. further characterize the actions the system could take as “control signals that specify which computations the controlled systems should perform” (4).

Given this setup, the main aim is to maximize reward via the specification of control signals. The way the system does this is by way of learning algorithms. The system builds and updates a model that specifies transition probabilities between states given different control signals, and that maps these probabilities onto a reward function. The reward function balances the reward associated with an outcome (a new state), together with the computational costs of specifying the computation required to drive the system towards the outcome. So, what the system is designed to do is to take the action (specify the control signal or the package of control signals) that has the highest expected value, given the probabilities of where the action takes the system, and the costs of taking the action.

The hypothesis here is that “the cognitive control system learns to predict the context-dependent value of alternative control signals” (5), and that these predictions determine which actions the system takes.

In cases in which the context is relatively well-known, Lieder et al. posit that the system will depend upon relationships between features of the internal state of the agent and features of the context, and will perform weighted sum calculations to determine the value of various possible actions. Cases in which the context is not well-known are more difficult. But Lieder et al. propose that in such cases the system may utilize exploration strategies to teach itself the value of various actions in the novel situation. These exploration strategies involve drawing samples of the value of control signals in previously encountered contexts, averaging over them, and again selecting the control signal that provides the highest expected value.

Lieder et al. note that “This model is very general and can be applied to model cognitive control of many different processes” (6). And they offer a proof of concept for it, by demonstrating that their model outperforms alternative models across a range of processing types.

These processing types involve learning what features of a task are predictive of reward. Some of them are quite simple. One task on which their model performed well-involved learning where to allocate attention, based upon variable reward offered for attending to different locations. A second task involved learning the difference between colors that indicate reward, and colors that do not. That the model predicts basic learning of this sort is good, but not too surprising.

The expected value of computation depends not only on the rewards for correct performance but also on the difficulty of the task. In easy situations, such as the congruent trials of the Stroop task, the automatic response can be as accurate, faster, and less costly than the controlled response. In cases like this, the expected value of exerting control is less than the EVOC of exerting no control. By contrast, in more challenging situations, such as incongruent Stroop trials, the controlled process is more accurate and therefore has a positive EVOC as long as accurate performance is sufficiently important. Therefore, on incongruent trials the expected value of control is larger than the EVOC of exerting no control. Our model thus learns to exert control on incongruent trials but not on congruent trials. Our model achieves this by learning to predict the EVOC from features of the stimuli. This predicts that people should learn to exert more control when they encounter a stimulus feature (such as a color or word) that is predictive of incongruence than when they encounter a feature that is predictive of congruence. (19)

Of course, agents are rarely aware that a system (or coordinated collection of mechanisms) within them is busy learning the value of different modes of responding, and guiding the way that they deploy cognitive control resources. We are not here explaining explicit deliberation or planning. But we are getting insight into the processes—sub-personal, if you like—that create the cognitive ocean in which more explicit processes swim. What’s more, we are getting insight into the kinds of learning that drive cognitive control operations that agents have to simply live with. Shifts of attention, pulls to engage in various computational operations, a sense of what operations are valuable in what contexts—these are rarely things we explicitly consider. Rather, we depend upon this background to engage in explicit cognition and intentional action.

With this as background, I can suggest an interesting possibility, leading to a proposal regarding the etiology and function of mind wandering.

The possibility is this. Depending on the cognitive control system’s model of the value of various control signals, in cases containing relatively little expected value the system may select a package of control signals leading to exploration. These would be cases in which the goal is to find a new and better goal. And the method, which remains here unclear—although one could imagine it involving shifts of attention, construction of task sets involving imagination, inhibition of current goals, etc.—might be generally described as disengagement from the present task in order to set out upon a search for a more valuable task.

The cognitive control proposal, then, is this. Mind wandering is caused by the cognitive control system precisely when, and because, the expected value of whatever the agent is doing—usually, exercising control towards the achievement of some occurrent goal—is deemed too low, and this “too low” judgment generates a search for a better goal, or task. Perhaps, e.g., the estimation of expected value dips below a value threshold attached to the package of control signals that generate exploration for another goal, or task. Or perhaps the value is always computed in comparison with available options, such that mind wandering is sometimes initiated even in the face of a rewarding current task.

This is a straightforwardly empirical proposal, and should be assessed in terms of the explanations it affords, and by whether the predictions it makes are confirmed or disconfirmed. Before I discuss explanation and prediction, however, I wish to note two things.

First, it would certainly be useful if the cognitive control system contained such an operation. Humans are sophisticated agents, with multiple needs and goals potentially in play in most waking life situations. Fixation on one goal alone, or working towards the satisfaction of one goal at a time, is not a great strategy for flourishing. For, first, if one gets stuck on a difficult goal, or if it becomes apparent (i.e. apparent at least to some system tasked with calculating such a thing) that the present goal is not as rewarding as once calculated, it is much wiser to disengage and seek a better goal. And, second, in many situations progress towards multiple goals at once is possible. All one needs is the capacity to divide attention somewhat, or the capacity to hold multiple goals in mind—or at least within some accessible place—and one might waste much less time. Notice, further, that the above points may hold even if dividing the mind amongst multiple goals leads to performance decrements. Perfect performance is not always required. So long as mediocre performance allows one to satisfy goals and needs, accepting mediocre performance will be a good strategy.

Second, explicit cognitive control already does contain such an operation. Sometimes a task becomes too effortful, too uncomfortable, or too boring. Sometimes—after one has just awakened from a long nap, e.g.—there’s no obvious task at hand. In such cases performing a search for a high-value goal is a familiar operation that we perform explicitly. In other cases, we do not leave behind the current task, but we rather utilize deliberation, prospection, imagination, and other processes in order to find sub-goals, or means to achieve the goal that is currently structuring behavior. These modes of exploration towards discovery of a high-value goal are explicit. Our question here is whether the cognitive control system implicitly—i.e., in the absence of an explicit or conscious formation of intention to do so—initiates mind wandering as a similar mode of exploration, and for similar reasons. The proposal is that it does.

Here are explanations this proposal affords.

First, this proposal offers an explanation for the initiation of mind wandering episodes. These episodes are initiated without the agent’s explicit consent. But they do frequently occur. One possible explanation is that the agent necessarily loses control in these instances. That characterizes the initiation of a mind wandering episode as random. A better explanation, I submit, is that while the initiation of a mind wandering episode is, in one sense, a failure—a failure of the current goal and task set to persist—it is, in another sense, a smart move. It is smart because it results from a cognitive control system that is more or less constantly attempting to determine the value of selecting packages of control signals, and that will act when discrepancies in value are calculated. Note, incidentally, that this could be extended to cases in which the agent is pursuing no particular goal, or has no current task. The system need not always compare value between goals. It might be useful, e.g., to tag expected levels of reward to particular environments, perhaps by averaging over the kinds of rewards an environment-type provides. If agents associate one type of environment—a party, e.g.,—to a plethora of rewarding experiences, then a signal that this environment is near—one can hear party music, e.g.,—might lead the mind to wander in the direction of the kinds of experiences the rewarding environment provides.

The fact that the initiation of mind wandering episodes is smart helps to additionally explain a second fact, namely, that agents with higher levels of cognitive control mind wander more frequently when the current task is easy or non-rewarding.

This is not to deny that mind wandering episodes may sometimes be initiated by affectively salient stimuli, or other distractors. Nor is it to deny the existence of completely unguided, or otherwise guided, episodes of mind wandering. I am not in a position to deny that, e.g., a case of spreading activation in a semantic network could qualify as unintentional mind wandering. It may very well be—indeed it seems plausible—that only some cases of unintentional mind wandering are controlled in the way I here propose. Note, however, that even if this is right, the cognitive control system may be able to interact with uncontrolled mind wandering processes. In some cases, uncontrolled mind wandering could be commandeered if a valuable goal suggests itself.

Third, this proposal offers an explanation for the fact that mind wandering episodes tend to go to other goals the agent possesses. This is a natural place for a process to go if that process is structured by an aim to find a more rewarding goal than the one from which the agent has just disengaged. For it will be much more cost-effective to find existent goals, perhaps by querying memory, than to explore the environment and to construct entirely new goals (although of course this may happen, especially when the environment easily affords novel and rewarding goals).

Fourth, this proposal might be integrated with extant explanations of aspects of mind wandering. Consider, e.g., the decoupling hypothesis ( Antrobus et al. 1970 ; Smallwood et al. 2003 ; Smallwood and Schooler 2006 )—the idea that once mind wandering is underway, domain-general cognitive processes are engaged to maintain the mind wandering episode, by keeping attention decoupled from perceptual input, and by aiding the “continuity and integrity” of the agent’s train of thought ( Smallwood 2013 , 524). As Smallwood (2013) notes, the decoupling hypothesis does not seek to explain the initiation of mind wandering. The cognitive control proposal is consistent with it. That is, the proposal is consistent with domain-general resources being deployed to assist mind wandering episodes. The main comment I wish to make here is that the decoupling hypothesis becomes more plausible, and data on the deployment of domain-general resources in mind wandering more transparent, if the entire process of mind wandering can be seen as goal-directed, where the goal is set by the cognitive control system.

This proposal is also consistent with work on the recruitment of neural areas during mind wandering. Christoff et al. , e.g. ( Christoff et al. 2009 ; Fox et al. 2015 ), have found that episodes of mind wandering recruited not only core areas of the default mode network—medial PFC, posterior cingulate/precuneus, and posterior temporoparietal cortex—but also dorsal anterior cingulate cortex and dorsolateral prefrontal cortex, “the 2 main regions of the executive network” ( Christoff et al. 2009 , 8722). Christoff et al. plausibly link the involvement of the executive network with task performance decrements. The cognitive control proposal adds the possibility that executive network recruitment is associated with the goal-directed nature of (at least some) unintentional mind wandering.

Consider, further, recent work on the dynamics of mind wandering. In a recent review, Christoff et al. (2016) rightly notice that much research on mind wandering has been content-based, “assessing the contents of thoughts in terms of their relationship to an ongoing task or activity” (722). They seek, instead, to offer a taxonomy of thought-types in terms of their dynamics—of how they operate over time. They propose two dimensions along which the dynamics of thought may be influenced. The first dimension is characterized in terms of the degree to which thought is constrained by mechanisms that are “flexible, deliberate, and implemented through cognitive control” (719). The paradigm here is the intentional generation of a deliberative process, or the intentional maintenance of attention on a task. The second dimension is characterized in terms of the degree to which thought is constrained by mechanisms that are automatic, in that they “operate outside of cognitive control to hold attention on a restricted set of information” (719). There are many ways thought may be automatically distracted—Christoff et al. mention affectively salient stimuli as one example.

Within our framework, mind-wandering can be defined as a special case of spontaneous thought that tends to be more-deliberately constrained than dreaming, but less-deliberately constrained than creative thinking and goal-directed thought. In addition, mind-wandering can be clearly distinguished from rumination and other types of thought that are marked by a high degree of automatic constraints, such as obsessive thought. (719)

Now, this is not an explanation of why the mind wanders. It is, instead, a mapping of mind wandering onto a broader taxonomy of cognitive kinds, with special attention given to other modes of spontaneous thought. This taxonomy is useful for a number of reasons. For example, Christoff et al. map their taxonomy onto areas of the brain. So they say, e.g., that the part of the default network that centers on the medial temporal lobe is likely to be involved in the generation of mind wandering, as well as, via “its involvement in contextual associative processing” (724), the conceptual variability of some episodes of mind wandering. They also link the hippocampus to mind wandering, suggesting that it may contribute to the “imaginative construction” of hypothetical scenarios. Such mapping work from aspects of spontaneous thought onto activity patterns in large-scale brain networks affords fruitful suggestions for future study of the kinds of psychological patterns and activities that characterize mind wandering over time.

But there are possibilities and explanations that this approach does not (yet) address, and that potentially have consequences for the taxonomy of cognitive kinds that they offer.

Creative thinking may be unique among other spontaneous-thought processes because it may involve dynamic shifts between the two ends of the spectrum of constraints. The creative process tends to alternate between the generation of new ideas, which would be highly spontaneous, and the critical evaluation of these ideas, which could be as constrained as goal-directed thought in terms of deliberate constraints and is likely to be associated with a higher degree of automatic constraints than goal-directed thought because creative individuals frequently use their emotional and visceral reactions (colloquially often referred to as “gut” reactions) while evaluating their own creative ideas. (Box 1, 720)

I suggest that mind wandering is similarly complex. If the cognitive control proposal is correct, then in at least some cases mind wandering is initiated by processes of cognitive control, even though the goal driving mind wandering is not set explicitly by the agent. This could be captured by adding layers onto Christoff et al. ’s taxonomy, deepening explanations of the etiology and function of each kind of spontaneous thought. And these deeper explanations at each place could be expected to bear fruit for understanding the dynamics of spontaneous thought. In particular, we might hope to find patterns in the neural dynamics that are predictive of the onset as well as the termination of mind wandering episodes, and that differentiate it from dreaming, creative thought, and perhaps from rumination. If the cognitive control proposal is correct, one task would be to map these patterns onto the expected value calculations the cognitive control system is performing. We would expect the dynamics of mind wandering to reflect the initiation of a search for a more rewarding goal, and to reflect attempts to make progress on this search. But now I’m jumping ahead, to predictions the proposal generates.

The cognitive control proposal makes predictions. Confirmation of these would be good news; disconfirmation would be bad news.

First, given the explanation offered for the initiation of mind wandering episodes, the proposal predicts that increases in reward for satisfying an occurrent goal would correlate with decreases in propensity to mind wander. It is well-confirmed that increasing reward leads to boosts in performance level, and to overcoming any purported “ego-depletion,” even for very boring tasks. Paradigms that have established this result could be used to test for the place of mind wandering in the behavioral data.

Second, the proposal predicts that increases in reward for non-occurrent goals the agent possesses would increase mind wandering. We have already seen that reminding agents of goals they possess, or of goals they will soon need to attempt to satisfy, leads to more mind wandering in the direction of these goals. The prediction here is more specific. If one were to, e.g., notify participants that they were soon to perform a task associated with some level of reward, and then to put participants through a low reward task, the prediction is that tendency to mind wander towards this task would be associated with the discrepancy in reward between the current and upcoming task.

Third, this proposal draws upon a view of the cognitive control system on which the learning of values associated with goals, and the learning of values associated with stimuli features predictive of goals, is crucial. So the proposal, plus plausible assumptions about reinforcement learning processes, predicts that it is possible to train participants to associate stimuli with certain goals, and that registration of such stimuli would generate mind wandering to the degree that the associated goal is rewarding. Very costly goals would produce little mind wandering. Cheap but rewarding goals would produce more.

And it may be possible to extend this result. It depends on what the agent associates with rewarding goals. Above I suggested that the system need not always compare value between explicit goals, and that the value computation might include an association between expected levels of reward and particular environments. If so, simply placing an agent in such environments would manipulate levels of unintentional mind wandering.

It may be useful to distinguish predictions this proposal makes from a related proposal: the current concerns hypothesis. The current concerns hypothesis (for which, see Klinger et al. 1973 ; Smallwood and Schooler 2006 ) has it that mind wandering is caused by a shift in salience—when one’s current goals (or concerns: here I use these terms interchangeably), become more salient than the external environment, one’s mind begins to wander. As Smallwood explains the view, “attention will be most likely to shift to self-generated material when such information offers larger incentive value than does the information in the external environment” (2013, 524). This proposal is distinct from mine in the following ways. First, I propose a specific mechanism, connected with recent modeling work in cognitive control, to explain the onset of mind wandering. Thus far, of course, the proposal can be seen as a specification of the current concerns hypothesis. Second, this mechanism initiates mind wandering not by turning attention to one’s current concerns, but by directed thought to search for a more valuable goal than the present one. So the cognitive control proposal makes predictions the current concerns hypothesis does not. For example, the cognitive control proposal predicts that propensity to mind wander could be increased by devaluing the present goal, independently of the salience of any of one’s current goals. That is, no matter how much one’s current goals or concerns lack salience, once could increase mind wandering by devaluing the occurrent goal. And it predicts that mind wandering will not turn directly to one’s other goals—the mind may wander to the environment, rather than to internal concerns, since this is one way the agent may attempt to find a more rewarding task. So we should, e.g., be able to find episodes of more intense environmental scanning as a part of the mind wandering episode. Indeed, if the environment is expected to contain valuable options, one would predict that this is where attention will go, rather than to any internal space of concerns.

This is not to deny that mind wandering represents a failure in some sense. McVay and Kane (2010b ) have argued that mind wandering represents an executive control failure. What fails is a process of goal maintenance: “we suggest that goal maintenance is often hijacked by task-unrelated thought (TUT), resulting in both the subjective experience of mind wandering and habit-based errors” (324). The possibility I am raising is that failures of goal-maintenance could in another sense be successes of a different process. Indeed, perhaps processes of goal-maintenance are closely related to the value-based process of estimating the expected value of continuing on some task, or of searching for a new task, that I propose underlies unintentional mind wandering.

In sum, the proposal is plausible on its face. If correct, it promises to explain a range of data regarding mind wandering, and to explain the—from the agent’s conscious perspective very puzzling—initiation of mind wandering episodes. The proposal may also contribute to explanations of the dynamics of mind wandering. The predictions this proposal makes are testable, and work in this direction might take steps towards further integrating knowledge of how cognitive control works with knowledge of how mind wandering works.

I wish finally to relate this proposal to two leading philosophical accounts of mind wandering. Both of these accounts aim to capture mind wandering quite generally. I have noted in Mind wandering section that this is not my aim. Here, I want only to discuss implications for these more general accounts of mind wandering, if the cognitive control proposal about unintentional mind wandering is on track.

[T]he ability to control the conscious contents of one’s mind in a goal-directed way, by means of attentional or cognitive agency. This ability can be a form of rational self-control, which is based on reasons, beliefs, and conceptual thought, but it does not have to be. What is crucial is the “veto component”: Being mentally autonomous means that all currently ongoing processes can in principle be suspended or terminated. This does not mean that they actually are terminated, it just means that the ability, the functional potential, is given and that the person has knowledge of this fact. M-autonomy is the capacity for causal self-determination on the mental level. (2013, 4)

I think the brush strokes Metzinger uses are too broad. I doubt we have veto control over every conscious process ongoing at a time. But I do think he locates an interesting phenomenon. In unintentional mind wandering, our knowledge (or awareness) that we might suspend, terminate, or re-direct aspects of the stream of consciousness lapses.

My question is this. Should we think of this lapse as the agent’s loss of control? As Metzinger has it, mind wandering essentially involves a lack of ability, and a lack of control—what he calls veto control. I agree that unintentional mind wandering does involve a loss of one kind of control. But I would underline the fact that there are multiple ways for a system to exercise control. Some of these involve consciousness in crucial ways. Some likely do not ( Shepherd 2015 ). Knowledge that one can exercise control in some way at a moment can be useful. But a system may be well-designed, and exercise control in finding or executing goals, even if the system is not explicitly aware of processes that are performing these functions at a time.

Further, there are multiple ways for a system or an agent to possess an ability. The mind wandering agent may lack the ability to suspend, terminate, or re-direct elements of the stream of consciousness in virtue of her knowledge or awareness that she can do so. But she may retain the ability to suspend, terminate, or re-direct elements of the stream of consciousness in virtue of other features—perhaps in virtue of signals that emanate from the cognitive control processes I have emphasized.

This is not a merely verbal distinction. It is about how we understand the constitution of agency, and the kinds of properties that should be ascribed to mind wandering. If the cognitive control proposal is right, mind wandering emerges as an interesting case in which the seams of agency pull apart somewhat—we fail to notice that a non-conscious mechanism has turned the stream of consciousness in a different direction. But there may be good functional reasons for this operation, and it may contribute to an agent’s overall capacities to control the self in various environments and contexts.

An agent A’s attention is unguided if and only if A is not habitually guided to focus her attention on any information. In particular, she does not satisfy the counter-factual condition for attentional guidance: There is no information i such that, if A’s attention isn’t focused on i, she will notice, feel discomfited by, and thereby be disposed to correct this fact. (567)

I am not sure this is right. Mind wandering episodes are sometimes short. Sometimes they stop, it seems to me, precisely because we feel a sense that we were recently up to something, and we feel a pull to return. The cognitive control proposal might be able to explain this—one good move for the cognitive control system, in case of a failure to find a more rewarding task or goal, would be to return to the previous task.

Irving is aware that when it wanders, the mind frequently circles back to the agent’s goals. Does this not suggest guidance of some sort? Irving explains the tension by distinguishing between guidance and motivation. Motivated behavior only requires that an agent’s beliefs, desires, or goals are causal antecedents of the behavior. Guided behavior, by contrast, is explicated in terms of dynamics: it “involves the online monitoring and regulation of behavior” (563). Irving claims that mind wandering may be motivated, but it is not guided.

This aspect of Irving’s account does not compare favorably with the cognitive control proposal—if, of course, future work confirms the proposal. For Irving’s account offers no explanation of how causation by some belief or desire or goal helps explain how or why the wandering mind frequently turns to the agent’s goals. The cognitive control proposal has it that the wandering mind finds goals because that aim is what initiated and governs the mind wandering episode.

Further, if my proposal is right it is not entirely correct to think of mind wandering as unguided. It is, admittedly, not guided by any explicit intention the agent forms. In one sense of “guided,” then, Irving is right. But on the cognitive control proposal, mind wandering is a cognitive control process, and it does have a purpose. It seems purposeless to us in part because it is an interesting case in which some of the seams of agency pull apart somewhat—we do not notice that a non-conscious mechanism has turned the stream of consciousness in a different direction. And it seems purposeless to us in part because the course of the stream of consciousness during mind wandering is, as the cognitive control system plans it, meandering. It is meandering because the goal is to search, to explore, until a more rewarding task is found.

If these considerations are on track, we should say that mind wandering takes the form of a conscious but non-consciously guided process the aim of which is to find a rewarding goal or task. The connection with the cognitive control system explains the guidance aspect—the functionality of mind wandering—and affords the possibility of integration with work on the dynamics of mind wandering. The non-conscious aspect of the guidance explains the air of mystery surrounding mind wandering, why it seems purposeless, and why it seems to come about randomly.

In this article, I have asked why the mind wanders. I focused on a sub-type of mind wandering—mind wandering that occurs independently of any reportable intention. I proposed that unintentional mind wandering is sometimes initiated and sustained by aspects of cognitive control. Unintentional mind wandering is caused by the cognitive control system precisely when, and because, the expected value of whatever the agent is doing—usually, exercising control towards achievement of some occurrent goal—is deemed too low, and this “too low” judgment generates a search for a better goal, or task.

This proposal generates testable predictions, and suggests open possibilities regarding the kinds of computations that may underlie unintentional mind wandering. My hope is that by connecting research on mind wandering with research on cognitive control resource allocation, fruitful strategies for modeling these computations may be taken from cognitive control research and deployed to help explain the initiation and dynamics of mind wandering episodes.

The cognitive control proposal also points us towards a fuller picture of human agency. In this picture, action control and intelligent thought are stitched together by conscious and non-conscious processes operating in concert. Future empirical work is critical to the confirmation of this picture, and to filling in the many unspecified details. This is so not least because, if the proposal I offer is on track, agents are not introspectively aware of the (good) rationale behind many mind-wandering episodes.

The author acknowledges two sources of support. First, funds from European Research Council Starting Grant 757698, awarded under the Horizon 2020 Programme for Research and Innovation. Second, the Canadian Institute for Advanced Research’s Azrieli Global Scholar programme on Mind, Brain, and Consciousness.

Conflict of interest statement . None declared.

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Why Do Our Minds Wander?

A scientist says mind-wandering or daydreaming help prepare us for the future

Tim Vernimmen, Knowable Magazine

When psychologist Jonathan Smallwood set out to study mind-wandering about 25 years ago, few of his peers thought that was a very good idea. How could one hope to investigate these spontaneous and unpredictable thoughts that crop up when people stop paying attention to their surroundings and the task at hand? Thoughts that couldn’t be linked to any measurable outward behavior?

But Smallwood, now at Queen’s University in Ontario, Canada, forged ahead. He used as his tool a downright tedious computer task that was intended to reproduce the kinds of lapses of attention that cause us to pour milk into someone’s cup when they asked for black coffee. And he started out by asking study participants a few basic questions to gain insight into when and why minds tend to wander, and what subjects they tend to wander toward. After a while, he began to scan participants’ brains as well, to catch a glimpse of what was going on in there during mind-wandering.

Smallwood learned that unhappy minds tend to wander in the past, while happy minds often ponder the future . He also became convinced that wandering among our memories is crucial to help prepare us for what is yet to come. Though some kinds of mind-wandering — such as dwelling on problems that can’t be fixed — may be associated with depression , Smallwood now believes mind-wandering is rarely a waste of time. It is merely our brain trying to get a bit of work done when it is under the impression that there isn’t much else going on.

Smallwood, who coauthored an influential 2015 overview of mind-wandering research in the Annual Review of Psychology, is the first to admit that many questions remain to be answered.

This conversation has been edited for length and clarity.

Is mind-wandering the same thing as daydreaming, or would you say those are different?

I think it’s a similar process used in a different context. When you’re on holiday, and you’ve got lots of free time, you might say you’re daydreaming about what you’d like to do next. But when you’re under pressure to perform, you’d experience the same thoughts as mind-wandering.

I think it is more helpful to talk about the underlying processes: spontaneous thought, or the decoupling of attention from perception, which is what happens when our thoughts separate from our perception of the environment. Both these processes take place during mind-wandering and daydreaming.

It often takes us a while to catch ourselves mind-wandering. How can you catch it to study it in other people?

In the beginning, we gave people experimental tasks that were really boring, so that mind-wandering would happen a lot. We would just ask from time to time, “Are you mind-wandering?” while recording the brain’s activity in an fMRI scanner.

But what I’ve realized, after doing studies like that for a long time, is that if we want to know how thinking works in the real world, where people are doing things like watching TV or going for a run, most of the data we have are never going to tell us very much.

So we are now trying to study these situations . And instead of doing experiments where we just ask, “Are you mind-wandering?” we are now asking people a lot of different questions, like: “Are your thoughts detailed? Are they positive? Are they distracting you?”

How and why did you decide to study mind-wandering?

I started studying mind-wandering at the start of my career, when I was young and naive.

I didn’t really understand at the time why nobody was studying it. Psychology was focused on measurable, outward behavior then. I thought to myself: That’s not what I want to understand about my thoughts. What I want to know is: Why do they come, where do they come from, and why do they persist even if they interfere with attention to the here and now?

Around the same time, brain imaging techniques were developing, and they were telling neuroscientists that something happens in the brain even when it isn’t occupied with a behavioral task. Large regions of the brain, now called the default mode network , did the opposite: If you gave people a task, the activity in these areas went down.

When scientists made this link between brain activity and mind-wandering, it became fashionable. I’ve been very lucky, because I hadn’t anticipated any of that when I started my PhD, at the University of Strathclyde in Glasgow. But I’ve seen it all pan out.

Would you say, then, that mind-wandering is the default mode for our brains?

It turns out to be more complicated than that. Initially, researchers were very sure that the default mode network rarely increased its activity during tasks. But these tasks were all externally focused — they involved doing something in the outside world. When researchers later asked people to do a task that doesn’t require them to interact with their environment — like think about the future — that activated the default mode network as well.

More recently, we have identified much simpler tasks that also activate the default mode network. If you let people watch a series of shapes like triangles or squares on a screen, and every so often you surprise them and ask something — like, “In the last trial, which side was the triangle on?”— regions within the default mode network increase activity when they’re making that decision . That’s a challenging observation if you think the default mode network is just a mind-wandering system.

But what both situations have in common is the person is using information from memory. I now think the default mode network is necessary for any thinking based on information from memory — and that includes mind-wandering.

Would it be possible to demonstrate that this is indeed the case?

In a recent study, instead of asking people whether they were paying attention, we went one step further . People were in a scanner reading short factual sentences on a screen. Occasionally, we’d show them a prompt that said, “Remember,” followed by an item from a list of things from their past that they’d provided earlier. So then, instead of reading, they’d remember the thing we showed them. We could cause them to remember.

What we find is that the brain scans in this experiment look remarkably similar to mind-wandering. That is important: It gives us more control over the pattern of thinking than when it occurs spontaneously, like in naturally occurring mind-wandering. Of course, that is a weakness as well, because it’s not spontaneous. But we’ve already done lots of spontaneous studies.

When we make people remember things from the list, we recapitulate quite a lot of what we saw in spontaneous mind-wandering. This suggests that at least some of the activity we see when minds wander is indeed associated with the retrieval of memories. We now think the decoupling between attention and perception happens because people are remembering.

Have you asked people what their minds are wandering toward?

The past and future seem to really dominate people’s thinking . I think things like mind-wandering are attempts by the brain to make sense of what has happened, so that we can behave better in the future. I think this type of thinking is a really ingrained part of how our species has conquered the world. Almost nothing we’re doing at any moment in time can be pinpointed as only mattering then.

That’s a defining difference. By that, I don’t mean that other animals can’t imagine the future, but that our world is built upon our ability to do so, and to learn from the past to build a better future. I think animals that focused only on the present were outcompeted by others that remembered things from the past and could focus on future goals, for millions of years — until you got humans, a species that’s obsessed with taking things that happened and using them to gain added value for future behavior.

People are also, very often, mind-wandering about social situations . This makes sense, because we have to work with other people to achieve almost all of our goals, and other people are much more unpredictable than the Sun rising in the morning.

Though it is clearly useful, isn’t it also very depressing to keep returning to issues from the past?

It certainly can be. We have found that mind-wandering about the past tends to be associated with negative mood.

Let me give you an example of what I think may be happening. For a scientist like me, coming up with creative solutions to scientific problems through mind-wandering is very rewarding. But you can imagine that if my situation changes and I end up with a set of problems I can’t fix, the habit of going over the past may become difficult to break. My brain will keep activating the problem-solving system, even if it can’t do anything to fix the problem, because now my problems are things like getting divorced and my partner doesn’t want any more to do with me. If such a thing happens and all I’ve got is an imaginative problem-solving system, it’s not going to help me, it’s just going to be upsetting. I just have to let it go.

That’s where I think mindfulness could be useful, because the idea of mindfulness is to bring your attention to the moment. So if I’d be more mindful, I’d be going into problem-solving mode less often.

If you spend long enough practicing being in the moment, maybe that becomes a habit. It’s about being able to control your mind-wandering. Cognitive behavioral therapy for depression, which aims to help people change how they think and behave, is another way to reduce harmful mind-wandering.

Nowadays, it seems that many of the idle moments in which our minds would previously have wandered are now spent scrolling our phones. How do you think that might change how our brain functions?

The interesting thing about social media and mind-wandering, I think, is that they may have similar motivations. Mind-wandering is very social. In our studies , we’re locking people in small booths and making them do these tasks and they keep coming out and saying, “I’m thinking about my friends.” That’s telling us that keeping up with others is very important to people.

Social groups are so important to us as a species that we spend most of our time trying to anticipate what others are going to do, and I think social media is filling part of the gap that mind-wandering is trying to fill. It’s like mainlining social information: You can try to imagine what your friend is doing, or you can just find out online. Though, of course, there is an important difference: When you’re mind-wandering, you’re ordering your own thoughts. Scrolling social media is more passive.

Could there be a way for us to suppress mind-wandering in situations where it might be dangerous?

Mind-wandering can be a benefit and a curse, but I wouldn’t be confident that we know yet when it would be a good idea to stop it. In our studies at the moment, we are trying to map how people think across a range of different types of tasks. We hope this approach will help us identify when mind-wandering is likely to be useful or not — and when we should try to control it and when we shouldn’t.

For example, in our studies, people who are more intelligent don’t mind wander so often when the task is hard but can do it more when tasks are easy . It is possible that they are using the idle time when the external world is not demanding their attention to think about other important matters. This highlights the uncertainty about whether mind wandering is always a bad thing, because this sort of result implies it is likely to be useful under some circumstances.

This map — of how people think in different situations — has become very important in our research. This is the work I’m going to focus on now, probably for the rest of my career.

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13.7 Cosmos & Culture

Why do our minds wander.

Sometimes the mind wanders. Thoughts pop into consciousness. Ideas or images are present when just a moment before they were not. Scientists recently have been turning their attention to making sense of this.

One natural picture of the phenomenon goes something like this. Typically, our thoughts and feelings are shaped by what we are doing, by what there is around us. The world captures our attention and compels our minds this way or that. What explains the fact that you think of a red car when there is a red car in front of you is, well, the red car. And similarly, it is that loud noise that causes you to orient yourself to the commotion that is producing it. In such cases, we might say, the mind is coupled to the world around it and the world, in a way, plays us the way a person might play a piano.

But sometimes, even without going to sleep, we turn away from the world. We turn inward. We are contemplative or detached. We decouple ourselves from the environment and we are set free, as it were, to let our minds play themselves.

This natural picture has gained some support from the discovery of the so-called Default Mode Network. The DMN is a network of neural systems whose activation seems to be suppressed by active engagement with the world around us; DMN, in contrast, is activated (or rather, it tends to return to baseline levels of activity) precisely when we detach ourselves from what's going on around us. The DMN is the brain running in neutral.

One of the leading hypotheses to explain mind-wandering and the emergence of spontaneous thoughts is that this is the result of the operation of the brain's Default Mode Network. (See this for a review of this literature.)

A study published in April in the journal NeuroImage by Melissa Ellamil and her colleagues at the University of British Columbia, working in the laboratory of Kalina Christoff, provides evidence that challenges certain aspects of this DMN account. For one thing, she found, using fMRI, that there are neural systems (e.g., the posterior insula) activated just prior to the occurrence of spontaneous thoughts that are outside of the DMN. But she also noticed that some of the areas in DMN activated — for example the hypocampus — are associated with memory and attention. This is intriguing, as it puts pressure on the idea that mind-wandering is quite so passive, or as much a matter of withdrawing from the world, as some scientists have been inclined to support. Even spontaneous free thoughts arise out of memory and experience, it would seem. We are still very much engaged with the world, coupled to it, even when we are simply letting our minds wander.

But to my mind, the real interest — and the potential controversy — of Ellamil's work, has to do with a methodological innovation she undertook to enable her to investigate the neural signatures of the arising of spontaneous thought. It turns out that it isn't easy to find out when thoughts, feelings, images just pop into mind. Ordinary people, it is widely supposed, are not very good at monitoring their own free and undirected mental processes. So how can a scientist gather information about what's going on in the mind of a subject so as to be able to look further at what neural events and processes are, as they say, recruited by those happenings?

Ellamil's solution — and here she draws on what is called "neurophenomenology," which was first developed by the late Chilean neurobiologist Francisco Varela and his colleague, the philosopher Evan Thompson, who is also a co-author on the present study — is to use highly skilled practitioners of Vipassana mindfulness meditation as subjects. This particular style of meditation cultivates, or so it is claimed, precisely the ability notice the coming and going of thoughts and feelings. The idea, then, is that we can use what the meditators say to determine when thoughts arise, as well as what kinds of thoughts they are; on the basis of this data, we can try to figure out how the brain makes it all happen.

What makes these results tricky, it seems to me, is that we don't actually have any reason to believe that the Vipassana meditators do what they say — that is, reliably tell us what is going on in their minds.

The thought that a thought is arising is just another thought that arises. We can't get outside of thought, so to speak, to watch thought happen. At least not in the way that we can stand back and describe what is going on in front us.

Or can we? To do that, we would need to have some kind of access to what is going on in our internal landscape separately from our inclinations to say this or that, or think and feel this or that. But we have no such independent access.

Does the Vipassana meditator have a more reliable and more accurate awareness of his or her own experience? Are they therefore reliable instruments for letting us in on the contents of their own consciousness minds? Or are they just having their own, maybe distinct, maybe not so distinct, consciousness experiences? How would we decide?

This is an unresolved issue. The confidence of the meditators themselves does nothing to help us resolve it.

The point is not that there's anything wrong with mindfulness practices of this sort. I am quite prepared to think that Vipassana meditation is a beautiful and transformative practice, one entirely deserving of our interest and perhaps also our admiration.

But there is no reason to think that what such meditators do is better track independently existing real events in consciousness — and this is because we have no reason think that this picture of introspective self-awareness is even intelligible.

Alva Noë is a philosopher at the University of California, Berkeley, where he writes and teaches about perception, consciousness and art. He is the author of several books, including his latest, Strange Tools: Art and Human Nature (Farrar Straus and Giroux, 2015). You can keep up with more of what Alva is thinking on Facebook and on Twitter: @alvanoe

• spontaneous thought
• default mode network
• mindfulness

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• Published: 11 May 2022

On the relationship between mind wandering and mindfulness

• Angelo Belardi 1 ,
• Leila Chaieb 2 ,
• Alodie Rey-Mermet 1 ,
• Florian Mormann 2 ,
• Nicolas Rothen 1 ,
• Juergen Fell 2 &
• Thomas P. Reber 1 , 2  

Scientific Reports volume  12 , Article number:  7755 ( 2022 ) Cite this article

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Mind wandering (MW) and mindfulness have both been reported to be vital moderators of psychological wellbeing. Here, we aim to examine how closely associated these phenomena are and evaluate the psychometrics of measures often used to quantify them. We investigated two samples, one consisting of German-speaking unpaid participants (GUP, n \(=\) 313) and one of English-speaking paid participants (EPP, n \(=\) 228) recruited through MTurk.com. In an online experiment, we collected data using the Mindful Attention Awareness Scale (MAAS) and the sustained attention to response task (SART) during which self-reports of MW and meta-awareness of MW were recorded using experience sampling (ES) probes. Internal consistency of the MAAS was high (Cronbachs \(\alpha\) of 0.96 in EPP and 0.88 in GUP). Split-half reliability for SART measures and self-reported MW was overall good with the exception of SART measures focusing on Nogo trials, and those restricted to SART trials preceding ES in a 10 s time window. We found a moderate negative association between trait mindfulness and MW as measured with ES probes in GUP, but not in EPP. Our results suggest that MW and mindfulness are on opposite sides of a spectrum of how attention is focused on the present moment and the task at hand.

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Introduction.

Waking experience can be described as a stream of thoughts, perceptions, and emotions that come in and out of the focus of our conscious awareness. Mind wandering (MW) refers to our thoughts becoming decoupled from an ongoing task and coupled to thoughts and feelings not being subject to the task at hand or our surroundings 1 . In comparison, mindfulness refers to the mental act of intentionally resting the focus of awareness on a particular subject of experience in the present moment without judgment 2 . These constructs appear to emphasise aspects which lie on opposite sides of a spectrum of how intentional, focused, and self-aware one is regarding the thoughts and perceptions that make up one’s conscious experience 3 .

In light of these conceptual considerations, it seems surprising that statistical associations between measures of MW and mindfulness are rather low 3 , 4 , 5 . One possible explanation for this may be the low reliability of the psychometric tools used to measure these constructs. Another possibility could be that meta-awareness of MW 6 , 7 , i.e., awareness of the fact that ones contents of consciousness is decoupling from an ongoing task, moderates the relationship between mindfulness and MW. To investigate these questions, we first assessed the psychometrics of a well-established mindfulness questionnaire and self-report measures of MW and meta-awareness thereof in a large sample from an online study. Then we estimated the associations between measures of MW, mindfulness, and meta-awareness.

Evidence for the importance of both MW and mindfulness for psychological wellbeing has been reported numerous times in the literature. Increased propensity to MW was associated with reduced affect 8 and in its extreme form MW can result in persistent negative and repetitive thoughts leading to rumination. Such rumination is at the heart of neurocognitive models of depression 9 , 10 , 11 . Furthermore, distraction due to MW can potentially cause physical harm e.g. when driving 12 , operating heavy machinery 13 , or when working as a medical professional 14 . Excessive MW may also interfere with career goals by affecting work and educational performance 15 . While a majority of studies focus on negative consequences, MW may also facilitate future planning, goal setting, and aid creative problem solving 16 , 17 , 18 . For example, Medea and colleagues 18 found that self-generated cognition during an episode of MW may allow the development of more concrete personal goals.

In contrast, mindfulness has been associated predominantly with an increased feeling of wellbeing. The concept of mindfulness has its origins in eastern philosophy and is closely linked to processes of awareness and attention. Mindfulness describes a state in which a person willingly chooses the focus of conscious experience and takes constant notice of their contents of consciousness. Practicing to achieve this mindful state has been a central tenet of traditional Buddhist meditation, and has been introduced in western cultures as a secular form of mental practice and flavours in psychotherapy, such as e.g., the mindfulness-based stress reduction (MBSR) program or acceptance and commitment therapy 2 , 19 , 20 .

A widely used task to experimentally elicit MW is the sustained attention to response task (SART) 21 . Participants view, for example, a stream of numbers from 0 to 9 appearing in a random sequence and at a constant rate. The participants’ task is to press a button in response to all non-target digits (Go trials) except for one – the target, where they are required to withhold their button press (i.e., the Nogo trial, such as the number 7). Several dependent variables have been used in the SART, such as the performance of the task (i.e., the error rate on Go trials and Nogo trials), the mean reaction time (RT) in Go trials and the variance of these RTs, as well as scores combining performance and RT (e.g. a skills index, calculated as accuracy/RT) 22 . Variants of the task include querying participants intermittently in defined intervals as to whether their mind was ‘on task’ or ‘off task’ using experience sampling (ES) probes to measure MW. Furthermore, meta-awareness of MW is queried after ES of MW in some studies immediately after ‘off task’ responses 23 , 24 . In addition to the self-reports from ES, low performance 25 , 26 , 27 as well as long and widely dispersed RTs 16 in the SART are considered evidence for low sustained attention and potentially for MW. Several versions of the paradigm combining ES probes and SART have been used in previous research. For example, some studies restricted performance and RT analyses to short time windows immediately preceding appearance of the ES probe 16 , 27 . Other studies varied SART difficulty by either adding auditory noise 28 , by making the number stream predictable 29 , or by increasing the inter-stimulus interval (ISI) 30 . Taken together, there is a variety of ways in which the SART is used to elicit and assess MW.

Tools to measure mindfulness, on the other hand, consist predominantly of self-report questionnaires. One of the most commonly used questionnaires is the Mindful Attention Awareness Scale (MAAS) 31 . Previous assessments of the MAAS found that it has a single factor structure and overall robust reliability (Cronbach’s \(\alpha\) between 0.8 and 0.87) 31 . External validity was evaluated with numerous questionnaires assessing a variety of related constructs such as everyday attention, personality traits and anxiety 31 , 32 . Because of the high importance of this questionnaire in mindfulness research, we explored the possibility of shorter versions of the MAAS, based on only 5 and 3 items, which would be quicker to implement in future research.

Despite the close conceptual relationship between MW and mindfulness, estimates of the strength of their association have been surprisingly low 3 , 4 , 5 . Furthermore, none of these previous studies reported an estimate of reliability for ES of MW, making the interpretation of this association difficult. See Table 1 for a detailed summary of previous findings. Together, there is only weak evidence to suggest that a direct measure of MW such as ES during the SART correlates with MAAS scores. Moreover, when these associations were reported, they were moderate at best.

Additional evidence for the relationship comes from a related line of research that investigates whether mindfulness training impacts direct and indirect measures of MW (for a review, see 33 ). Such intervention studies found that the practice of mindfulness usually improved SART performance 3 , 34 , 76 , 36 , 37 , 38 and reduced the frequency of self-reported MW in some cases 36 , 39 but not in others 34 , 38 . Moreover, one study reported higher MAAS scores after mindfulness training 35 . Similarly to the findings of those correlation studies reported before, in these mindfulness training studies the associations between the direct MW measure and mindfulness is not as strong as one might expect.

One possible explanation of low associations between ES of MW and MAAS scores could be that queryi ng participants for whether they were on or off task alone conflates over two forms of MW that are opposingly linked to mindfulness, namely MW with and without meta-awareness 6 . This hypothesis has been put forward by Smallwood and Schooler 7 , and initial empirical evidence for the importance of considering meta-awareness was gathered by the same authors in an ensuing study 9 . Here, ‘zone outs’ (MW without awareness) were linked to higher inhibition errors in an ongoing task while ‘tune outs’ (MW with awareness) were not. How these ‘zone out’ and ‘tune out’ propensities are linked to trait mindfulness, however, seems unclear in the previous literature. Deng et al. 4 found no significant relationship between either the ‘zone out’ or the ’tune out’ rate with trait mindfulness as measured by the MAAS. A more recent study 5 found both rates to be negatively associated with MAAS scores. Together there is inconsistent evidence on the role of meta-awareness as potential mediator between MW and trait mindfulness. Another possible explanation for low correlations between SART, ES, and MAAS is insufficient reliability of measures derived from these instruments. Reliability is an often overlooked quality metric in cognitive tasks while it is routinely reported for questionnaires 40 . Reliability estimates are important as they determine an upper limit of how large correlations between two measures can be. For all the individual measures for mindfulness and MW discussed above, robust psychometric properties have been reported before, though rarely combined and sometimes in small samples: MAAS 31 , 32 , specific SART measures with and without ES for MW 41 , 35 , 36 , 37 , 45 . Table 1 lists all referenced studies that measured the MAAS and/or the SART with or without ES of MW. The table depicts sample sizes, reliability estimates and estimates of association. Most importantly, this table shows that none of the previous studies employed all three measures (MAAS, SART, and ES of MW) and reported both, reliabilities of all measures as well as correlations between all of them. The present study aims to fill this gap and offers data from two new large samples.

Overall the aim of this study is to assess the psychometric quality of several measures for MW and mindfulness from the SART, MAAS, ES of MW and ES of meta-awareness. In a second step, we want to gain an estimate for the statistical association between these constructs. We combined ES of MW during the SART with an established measure of mindfulness in an online study in two large samples collected in an online experiment and by doing so add psychometric estimates for these measures gained in an online study and assessed together.

We recruited two samples of participants for a German and an English version of the experiment. In our first recruitment phase we targeted German-speaking participants through the participant pool of our institution, made up of students and volunteers from the public. Throughout the study, we refer to this sample as German-speaking unpaid participants (GUP). In a second phase, we recruited and paid participants predominantly through Amazon Mechanical Turk (AMT, mturk.com) for an English version of the experiment. We refer to this sample as English-speaking paid participants (EPP). All participants first answered a questionnaire on demographics and the MAAS, then they performed a 20 min version of the SART during which ES probes of MW and meta-awareness wee obtained (see “ Methods ” section).

Sample differences

We initially planned to report our findings as one sample, since the online experiment was identical except for the language. However, after finding significant differences between our two samples in the SART and ES data, we decided post-hoc to report all findings separately for GUP and EPP (see Table 2 for sample differences between all main measures). Most strikingly, EPP reported significantly less than half as often to be ‘off task’ than the GUP \((\hbox {t}(519.01) = -10.06\) , p < .001, d \(=\) 0.81, \(M_E{}_P{}_P = 0.09\) , \(M_G{}_U{}_P = 0.25\) ). This indicates much lower variance in ES data in the EPP. There were also significant differences on all measures derived from the SART directly (RT, accuracy) albeit in a lower magnitude (see Table 2 ).

Factorial structure and reliability of the MAAS

We first checked the correlation matrices of the individual items on the questionnaire and the total score, separately for each of the two samples. In the GUP sample, item 6 had low item-to-total correlation (r \(=\) 0.05) and correlations below r \(=\) 0.2 with most other items. For that reason, we excluded item 6 from further analyses for the GUP. Thus, our total MAAS score for the EPP contained all 15 initial items, while the score of the GUP contained only 14 items.

We then conducted an exploratory factor analysis (EFA) for the MAAS responses for each of the two samples (factor loadings for one-factor EFAs are presented in Table 3 ). Figure 1 depicts scree plots for the EPP and GUP; these plots suggest that a single latent factor drives responses in the MAAS. Further EFAs also revealed that two-factor models only explain little additional variance (EPP: 3% and GUP: 5%), in comparison to that explained by one-factor models (EPP: 63% and GUP: 36%). However, the Kaiser rule (selecting the factors with an eigenvalue above 1; indicated by the dotted line in the scree plots) is also in accordance with a two-factor solution in our GUP.

The model fit statistics from confirmatory factor analyses (CFA) were estimated using the Comparative Fit Index (CFI), the Tucker Lewis Index (TLI), and the Root mean square error approximation (RMSEA). We compared the values against common standards for an acceptable fit (CFI/TLI > 0.9, RMSEA < 0.06) 52 . For one-factor models, the fits are acceptably high in the EPP (CFI \(=\) 0.954, TLI \(=\) 0.946). The fits were poorer, however, for the GUP (CFI \(=\) 0.858, TLI \(=\) 0.832). The RMSEA, which is an absolute fit statistic, indicates a poor approximate fit for both models, in the EPP (RMSEA \(=\) 0.08) and GUP (RMSEA \(=\) 0.096). However, the use of a fixed threshold for the RMSEA is questionable 53 , 54 . The full fit statistics of these two models and of an alternative two-factor model for the GUP can be found in the supplementary materials at https://osf.io/8kg6z . Together, EFA and CFA are mostly consistent with the notion of one single factor driving responses to the MAAS, even though some fit statistics for the CFA were below the threshold for an acceptable fit.

Reliabilities of the MAAS score (mean of individual items) were overall high. For the full MAAS the standardized Cronbach’s \(\alpha\) was 0.88 in the GUP sample and 0.96 in the EPP. We created and then investigated shorter versions of the questionnaire consisting of the three or five items with the highest loadings in the EFAs. In the EPP these items were 7, 8, 10, 1, 11, and in the GUP items 14, 8, 9, 10, 7, in order of decreasing loading (see also Table 3 ). We refer to these shortened scales as the MAAS-5 and MAAS-3. The Cronbach’s \(\alpha\) s of the scales are given in Table 4 and further descriptives of the scores are available in the supplementary materials (Table S7 ). Correlations between short and full MAAS scores were reasonably high (between r = 0.79 and r = 0.97, see full correlation matrices in the supplementary materials (Figs. S9 and S10 ).

Scree plot for MAAS for EPP and GUP samples. This figure was created using R (v. 4.02) 55 with package ‘ggplot2’ (v. 3.3.5) 56 .

Reliability of MW measures taken from the SART and ES

Estimates of reliability of the measures derived from the SART and ES probes are presented in Table 4 . They are split-half reliabilities derived using a permutation-based approach with 5000 random splits 40 , 57 . For further descriptives of the measures, see Table S7 in the supplementary materials. From the SART, we report these measures: accuracy, the mean (M) and standard deviation (SD) of RTs during all trials and also in only those trials preceding the ES probes within a 10-s time window, a measure used in MW neuroimaging studies 27 . SART values are reported separately for correct Go trials and incorrect Nogo trials. From ES probes, we report the proportion of all ES probes in which participants answered that they were off-task (Attention Off) and the proportion of meta-awareness probes in which participants answered that they were unaware that their attention was off task (Meta-Awareness Off). The sample sizes for the meta-awareness probes were smaller, because they exclude participants who reported that they were always on task. Split-half reliabilities for measures from Go trials in the SART and for ES probes are generally high. Reliabilities for Nogo trials were markedly lower, and were further reduced when restricting the analyses to the 10-s time windows immediately preceding ES probes. It is noteworthy that the sample sizes varied for these different measures due to the structure of the data and restrictions for the split-half calculations: Each participant needed at least four valid data points for the split-half procedure, as each split required two data points to calculate a mean or standard deviation. Furthermore, only 10.6% of all trials were Nogo trials and participants only reacted to 15.2% of Nogo trials, making Nogo trials with participant reaction somewhat scarce.

Estimates of association between the MAAS, SART, and ES

In a next step, we assessed the hypothesized negative association of MW with mindfulness. To this aim, we correlated measures derived from the SART and ES with the MAAS (Fig.  2 ). For the link between the direct measure of MW and mindfulness, we found ES probes (Attention Off) were moderately negatively associated with the MAAS in GUP ( \(\hbox {r} = -.29\) , \(p< 0.001\) ) but not in EPP (r \(=\) 0.04, \(p > 0.1\) ). Between indirect measures of MW and mindfulness, there was no indication for an association between the SART and the MAAS in GUP. In EPP, however, there were small correlations between MAAS total score and SD of RTs in the Go trials during the 10 s window before ES probes ( \(\hbox {r} = -.23\) , \(p < 0.05\) ), between MAAS total score and accuracy in all Nogo trials ( \(\hbox {r} = .13\) , \(p < 0.05\) ), and a medium association between MAAS total score and accuracy of Nogo trials in the 10 s window before ES probes ( \(\hbox {r} = -.43\) , \(p < 0.01\) ). The pattern is mostly consistent with the idea of a negative association of MW and mindfulness. There was no association between meta-awareness probes and MAAS scores in both samples. All pairwise correlations for both samples are available in Tables S1 and S2 in the supplementary materials at https://osf.io/8kg6z .

To check whether these correlations might have been heavily influenced by outliers or non-normally distributed data, we additionally bootstrapped the correlation coefficients and 95% confidence intervals (CIs) for these pairwise correlations (1000 iterations, 100 random participants sampled in each). In addition, we compared the Pearson product-moment correlations to Spearman rank correlations. These analyses showed a similar pattern of results from the Pearson correlations reported above in the GUP, but in the EPP the three reported associations with ES probes were not significant in the Spearman correlations. This further indicates the different answer patterns in self-reported MW between our two samples. The detailed results of these additional versions of the correlations are available in Tables S3 – S6 in the supplementary materials.

Pairwise Pearson correlations for MAAS, SART, and ES measures. Correlation coefficients are reported for whole sample (‘Corr’), and for EPP and GUP samples separately. Individual plots below the diagonal are scatter plots with regression lines for the two variables intersecting at this cell, those on the diagonal show density distribution plots for the two samples. Significance markers: . \(=\) \(p< 0.1\) , * \(p< 0.05\) , ** \(p< 0.01\) , *** \(p< 0.001\) . This figure was created using R (v. 4.02) 55 with packages ‘ggplot2’ (v. 3.3.5) 56 and ‘GGally’ (v. 2.1.2) 58 .

This study entailed between-subject manipulations hypothesized to affect MW that are out of the scope of the current work. Briefly, we investigated whether exposing participants to auditory stimuli (5 Hz monaural or binaural auditory beats, silence, 440 Hz sine tone) could reduce their propensity to MW. Since such a finding has been reported earlier, in particular for participants exhibiting high proportions of MW 24 , we experimentally manipulated the occurrence of MW in three different ways. First, we varied the inter-stimulus-interval (1 vs. 2 s). Second, we implemented the stimuli in the SART predictably or unpredictably. Third, a creative problem-solving task was executed for a second time after the SART, and participants were either informed before the SART about the second execution or they were not informed.

These between-subject manipulations may have affected our estimates of associations between MW and mindfulness. To investigate this possibility, we first calculated ANOVAs with the experiment’s main manipulations (and all pairwise interactions) as predictors and measures from SART and ES as outcome variables. We then added the MAAS score as covariate to these, to create a set of comparable ANCOVAs. To evaluate whether our associations were affected by the experimental manipulations, we then checked two things. First, we compared the effect sizes ( \(\eta ^2\) ) of the total MAAS score in these ANCOVAs with the coefficient of determination ( \(r^2\) ) between the MAAS score and SART and ES measures. Second, we calculated model comparisons between the ANOVAs and ANCOVAs using likelihood-ratio tests (Table 5 ).

The effect sizes were for most combinations very similar in the correlations and the ANCOVAs. In all but one case, adding the MAAS score as covariate did not significantly improve the model fit. Only in the ES MW variable in GUP did adding the MAAS score as covariate significantly improve the model fit. There the estimate of association between ES MW and the MAAS score slightly increased when accounting for experimental manipulations. This result provides confirmatory evidence that MAAS and ES MW are weakly negatively associated in the GUP sample.

We examined the psychometrics of MW, meta-awareness of MW, and trait mindfulness, as well as the associations between these constructs. Overall, we found reasonably good psychometrics of all measures, and evidence that MW and trait mindfulness are indeed moderately negatively correlated. This association was not moderated by meta-awareness of MW. Neither the psychometrics nor moderating effects of meta-awareness can therefore readily explain that associations between MW and mindfulness are of a rather low magnitude.

In keeping with previous studies, we found overall good psychometric properties and evidence mostly consistent with a single-factor structure for the MAAS questionnaire. Our estimates of reliability of the MAAS were slightly higher than those reported in earlier studies, in both the EPP and GUP. For the English MAAS, the original publication reported internal consistencies in the range of [0.8, 0.87] 31 , and a further study reported 0.89 48 , but this value was 0.96 in our EPP. For the German MAAS, a Cronbach’s \(\alpha\) of 0.83 was reported in the initial publication on the psychometric properties of the questionnaire 49 , while the value in our GUP was 0.88. Very high internal consistencies might indicate redundancy in a questionnaire, suggesting some items are superfluous and can be removed, which would lead to a more efficient assessment 59 . Results on the proposed shorter versions of the MAAS (MAAS-5 and MAAS-3) outlined in this study support this notion and may provide researchers with tools to optimize data collection.

One peculiarity we observed in the MAAS data for the GUP was item 6 ( “I forget a person’s name almost as soon as I’ve been told it for the first time.” 31 ), which correlated very poorly with all other items and the total score. Interestingly, the authors of the German MAAS also observed complications with this item but decided to include it to ensure international comparability 49 . Specifically, they found an item-to-total correlation of r \(=\) 0.18 for item 6 while the next-lowest correlation was for item 1 (r \(=\) 0.26) and those for all other items ranged from 0.33 to 0.65 We did not observe, however, such a low item-to-total correlation of item 6 in EPP. Nevertheless, we assume that cultural differences or mere issues related to translation cannot account for low item-to-total correlation for this item, as it was also observed in a study with English-speaking participants from New Zealand 50 . Moreover, item 6 was also one of the most poorly correlated items in the original English article detailing the MAAS 31 . We suggest item 6 may only occasionally be problematic as its meaning is ambiguous, and can be understood in two different ways. First, it could—probably as intended by the authors of the scale—measure attention usually directed to a person introducing themselves, or it can be understood as asking for self-report on one’s long-term memory abilities, which is arguably an altogether different trait than mindfulness.

While reliability is routinely reported for questionnaires such as the MAAS, they are less common for cognitive behavioral measures, e.g. for the MW measures derived from the SART and ES 40 . Still, earlier studies generally reported high reliabilities also for the SART: e.g. between 0.83 and 0.89 for overall accuracy in the SART 42 , 44 , between 0.92 and 0.98 for SDs of RT 44 , 45 , and even as high as 0.94 to 0.98 for the accuracy of Nogo trials 41 (see Table 1 ). Some of these studies, however, used a shorter stimulus-onset asynchrony (SOA) and much smaller sample sizes (13 42 and 12 41 participants). Also, earlier studies reporting SART reliabilities were usually laboratory studies with more controlled environments. These factors might have led to even higher reliabilty estimates for measures derived from Nogo trials. Our study adds further reasonably high reliabilities with alphas ranging from 0.84 to 0.99, on measures derived from the Go trials of the SART. In contrast to previous studies, reliability estimates for measures derived from Nogo trials were markedly lower (between 0.24 and 0.71) in our samples. These were probably low in our study due to only a small fraction of the SART trials that can be used to derive these measures as we increased the SOA from the original version in order to foster MW. Overall reliabilities are further reduced when restricting the analyses to a short time window preceding ES probes. Filtering the usable trials to a specific time window seems predominately appropriate for neuroimaging studies looking to isolate brain activity patterns of MW, which is where this analysis strategy originated 27 . Researchers focusing on Nogo trials and segmenting the data accordingly, should therefore take care to ensure that the number of trials analyzed remains reasonably large, and bear in mind that reliability of measures derived with these strategies is likely limited. Our reliability estimates for the ES MW probes during the SART (0.91 in GUP and 0.89 in EPP) were within the range of what earlier studies reported (e.g., 0.89 43 and 0.93 45 ). Together with the reliability estimates of the MAAS, our study demonstrates that high reliabilities of the MAAS, SART, and ES for MW can also be obtained in an online study setting.

Our results also stress notions of caution related to recruiting participants via crowdsourcing platforms such as—as in our case—Amazon Mechanical Turk (AMT, mturk.com). We noticed that the two samples behaved differently in the SART and ES, in that AMT participants (the EPP) were less likely to respond that their attention had been ‘off task’ but at the same time showed lower accuracy rates and slower, more varied RTs during the SART. This is likely to have also affected the estimate of association between self-reports of MW in ES probes and the MAAS score. A significant correlation was found in GUP, but not in EPP. The absence of a significant correlation could be due to lower variance in the ES probes of EPP versus GUP. We suggest the different patterns of results relating to the ES probes is not simply due to cultural or language differences, but rather due to differences in motivation to participate. Requesters at AMT are allowed to withhold payment if they are not satisfied with the performance of the participant. It thus seems reasonable to assume that some participants recruited through AMT reported being on task even when they were not. Our data underlies arguments made earlier that caution is warranted when recruiting via AMT and similar platforms, especially when using measures that are susceptible to the issues discussed above 60 , 61 . It might help to explicitly ensure participants that they will experience no disadvantages when they report being off task.

Our results support the hypothesis of a negative link between trait mindfulness and MW. Associations, however, were scattered over different measures and differed between our two samples: There was a moderate correlation of the MAAS with the self-report measure of MW (ES probes during the SART) in one of our samples (GUP) and with SART SDs of RTs and SART accuracy in the SART in the other sample (EPP). Low and absent associations between MW and mindfulness cannot be explained by low reliabilities of the measures we used, as reliabilities were generally satisfyingly high, with the exception of measures derived from SART Nogo trials. With that in mind, the associations based on measures with high realiabilities are only two: that between MAAS total score and ES MW in the GUP, and between MAAS total score and SDs of RTs in SART Go trials during the 10 s window before ES probes in the EPP. One potential explanation for finding the clear association between MAAS and ES MW only in the GUP might be a lack of variance in the EPP data as mentioned above. The lack of variance was due to a large proportion of participants who answered that they were rarely or never ‘off task’ during the experiment.

Despite good psychometrics of our measures, the link between trait mindfulness and MW was only moderate. A further explanation for rather low associations could be that meta-awareness of MW moderates the hypothesized associations. Our finding that meta-awareness of MW is not linked to mindfulness goes against such a hypothesis and some empirical evidence 7 , 23 . However, our results are in accordance with more recent papers that also do not find a moderating effect of meta-awareness on the association between MW and mindfulness 4 , 5 . Nayda et al. 5 reported negative associations between both, the propensity to ‘tune out’ (meta-aware MW) with mindfulness, and the propensity to ‘zone out’ (meta-unaware MW). An earlier publication by Deng et al. 4 found insignificant correlations between trait MW and both ‘zone out’ and ‘tune out’ propensities. It seems noteworthy that both correlations of the Deng et al. 4 study are in the same range and direction as in Nayda et al. 5 but do not reach statistical significance likely due to the low sample size (N \(=\) 23). A potential caveat here is that these rates are calculated using the total of MW probes, rather than the total of meta-awareness probes only. These estimates are therefore biased in that the sum of the ‘tune out’ and ‘zone out’ rates is perfectly inverse proportional to the ‘on-task’ rate. In our analyses, we calculated the meta-awareness rate as proportion of the total of meta-awareness probes instead of the total of MW probes. We found no significant correlation between meta-awareness of MW and mindfulness. Thus, further research seems needed to isolate a potentially moderating effect of meta-awareness on the correlation between MW and mindfulness.

A further reason for low associations between MW and mindfulness could result from the difference in the trait versus transient nature of the constructs. Mindfulness is conceived and measured as a general personality trait. However, MW is a much more transient and fluctuating phenomenon during an ongoing and often boring task. Moreover, boredom itself may explain the low associations between MW and mindfulness. In MW research, the SART is often chosen as an ongoing task, because it is boring and therefore is thought to facilitate MW. The notion that boredom is an enabling factor for MW is supported by two findings. First, boredom has been shown to correlate with attentional lapses as measured with the SART 62 . Second, positive correlations between boredom and MW have been recently reported 63 . In contrast, when participants respond to the mindfulness questions of the MAAS, it is unclear to what extent participants consider boring ongoing tasks (e.g., “I rush through activities without being really attentive to them.” see Table 3 for the complete list of items of the MAAS). Therefore, while boredom seems a relevant aspect of MW when measured with the SART, this is not assessed with the MAAS. Together, this emphasizes the necessity of investigating the role of boredom in the relation between MW and mindfulness in future studies.

One may argue that a further reason for low associations between MW and trait mindfulness could be that the on-task state is more heterogeneous than previously thought. Heterogeneous on-task states were identified by assessing ongoing thought with multidimensional experience sampling (MDES), i.e., extending ES with several questions inquiring about the thoughts’ content and nature 64 . Principal component analysis (PCA) of MDES data revealed several components taxing into the on-task state, which were associated with distinct neural correlates 65 , 59 , 60 , 68 . One component was related to self-focused off-task thoughts while another component indicated detailed task focus. This task-focused component was common in cognitively demanding tasks like tasks measuring working memory, task switching, and gambling. However, it was less observed in low-demand tasks like the SART, where self-focused off-task thoughts prevail 64 . Together, these studies suggest that being more mindful might be linked to how people engage with tasks, perhaps by doing so in a more focused way. The possibility of multiple on-task states may therefore, contribute to the relatively low estimate of the association between mindfulness and ongoing thought.

Finally, low associations between MW and mindfulness could be due to insufficient validity, rather than reliability of the measures we used. While our current study focuses on reliability others have focused on issues related to validity, especially concerning the questionnaires to measure mindfulness 69 . On the one hand, the MAAS in particular has been shown to correlate reasonably well with other questionnaires measuring mindfulness such as the Five Facet Mindfulness Questionnaire (FFMQ) 70 . Further evidence for converging validity with, e.g., positive affect or attention, as well as evidence for discriminant validity, e.g., with anxiety and rumination, has been found in studies reporting correlations with MAAS scores 31 , 32 . On the other hand, questionnaires rely on introspective capabilities and may be subject to bias. A recent study by Isbel et al. 70 questioned especially the discriminant validity of the MAAS and the FFMQ as these measures increased following both a mindfulness training intervention and a control training intervention not aimed at mindfulness. Rather, objective accuracy of breath counting has been found to respond selectively to the mindfulness training intervention 70 . A potential reason why the breath counting task responded selectively to the mindfulness training is that mindfulness training itself often consists of exercises to guide one’s attention specifically on the breath. It is hence a rather near transfer from mindfulness training to an increase in accuracy in breath counting. Nevertheless, more research exploring the practical validity of mindfulness measures is required.

Recent methodological developments in MW research highlight limitations in our findings and offer advice for future research. Contemporary studies of ongoing thought that utilized MDES show that different tasks used in MW research elicit several distinct thought patterns to varying degrees 64 , 67 . Our study is consequently limited by the fact that we only used the SART to investigate the association between individual variation in mindfulness and MW instead of several tasks. The SART also has the limitation that it does not lead to much detailed task focus and tends to stimulate self-focused MW 64 . Due to that, it is unclear whether our findings generalize to other tasks or whether they are specific to the SART and thus to those types of ongoing thoughts more likely to be evoked by the SART like self-focused MW.

Besides the heterogeneity of ongoing thoughts, the relationship between MW and mindfulness is likely modulated by various other factors. A recent study has highlighted MW as a complex phenomenon that warrants a multi-faceted approach that includes a) dispositional traits, like conscientiousness, agreeableness, or mindfulness, b) contextual factors, like motivation or alertness, and c) cognitive abilities, like working memory capacity 71 . If the relationship between MW and mindfulness is embedded within such a multi-faceted approach, the association between these two factors might be diluted by other potential confounding factors that were not accounted for. In this regard, future research will benefit from assessing MW and mindfulness with a broad set of tools including MDES and multiple tasks with variable demands that elicit different patterns of ongoing thoughts.

Participants

A total of 715 participants performed or started our online experiment between October 2019 and January 2021. We excluded participants from the data analysis for these reasons and in this order: Repeated participation (n \(=\) 11), incomplete data due to technical issues (n \(=\) 1), incomplete or delayed participation in the experiment (time in experiment < 23 min or > 120 min [n \(=\) 59]), low number of correct SART trials (proportion of correct Go trials < 2/3 [n \(=\) 51], or proportion of correct Nogo trials < 1/2 [n \(=\) 22]), and outliers who took a long time to answer the ES probes (n \(=\) 30). For this last point we established a cutoff based on the interquartile range (IQR) due to the highly skewed distribution of these values. Cutoff was the 75th percentile plus three times the IQR. We based our data analyses on a total sample of 541, separated into 313 GUP (aged between 16 and 85, M \(=\) 38.78, SD \(=\) 12.95) and 228 EPP (aged between 19 and 68, M \(=\) 34.27, SD \(=\) 11.39). Further demographic characteristics are given in Table 6 .

We recruited participants for two different language versions of the experiment through various routes. The GUP (n \(=\) 313) consists of: (a) 97 participants recruited by the students of two classes in the autumn 2019 and spring 2020 semesters at UniDistance Suisse; (b) 200 students and members of the public interested in participating in experimental research from our institute’s pool of research participants; and (c) 16 participants who followed links in an information email to university employees and on different websites. The EPP (n − 228) contains: (a) 217 participants recruited through AMT, (b) 10 who were PhD students at the Department of Epileptology at the University of Bonn, and (c) 1 who followed a link from an external website.

Those recruited through AMT were paid USD 3.50 when they had completed the whole experiment. Students in the Bachelor’s program in Psychology at the UniDistance Suisse received course credits for their participation. Other participants received no compensation. All participants gave informed consent by reading information provided online and then checking off tick boxes in an online form before they proceeded to the experiment. The study was carried out following all the relevant guidelines and regulations. The study and its compliance with relevant guidelines and regulations was approved by the ethical review committee of the Faculty of Psychology at UniDistance Suisse ( https://distanceuniversity.ch/research/ethics-commission/ ). In particular, all procedures are in accordance with the Declaration of Helsinki.

The data reported here was collected in a study also investigating the effects of experimental manipulations on MW. Participants performed the SART with intermittent ES probes to directly obtain self-reports of episodes of MW. These experimental manipulations are outside the scope of the current work as they focus on potential effects of auditory beat stimulation on MW 24 , 72 and will be reported elsewhere. Briefly, experimental manipulations were performed in a \(4\times 2\times 2\times 2\) between-subjects design and included the independent variables Auditory Beat Stimulation (5 Hz binaural, 5 Hz monaural; 440 Hz pure tone; no sound), SART ISI (1 or 2 s), Predictability of the Number Sequence in the SART (random or ascending), and Expectancy of an ensuing creativity task (expected or unexpected). Dependent variables are RTs and Accuracy during the SART and ES MW probes. It was for the purpose of this study, that we collected data using the MAAS.

Instruments

To assess trait mindfulness we applied the MAAS, a 15-item questionnaire that determines attention to the present in everyday experiences 31 . For the German version of the experiment, we used the validated German translation available from the Leibniz Institute for Psychology Information (ZPID) 73 .

To measure MW indirectly through lapses in sustained attention in a deliberately monotonous task, we used the SART 21 . The SART is a Go/No-go task that uses digits as stimuli which are presented individually on screen with a fixation cross shown between stimuli. Participants are asked to react to all digits (Go trials) except for the number 7 (Nogo trials). We adapted the original SART with the intention to make it more monotonous, in order to elicit more MW. Specifically, we displayed each stimulus longer (2 s instead of 250 ms), had a longer ISI (1 or 2 s instead of 900 ms), and used a fixed font size (instead of randomly varying font sizes) to present our stimuli 21 .

We assessed self-reported MW using ES probes during the SART. In intervals between 25 and 35 s, participants were asked: “Immediately before this question appeared, was your attention focused ON the task or OFF task?” with a dichotomous forced-choice answer. When “OFF task” was selected, a second question appeared: “Were you aware that your attention was OFF task?” with a dichotomous forced choice answer again (yes or no). There was no time limit to answer these probes.

To further increase MW by adding a cognitive distraction during the SART and to assess particpants’ creativity, we implemented a short task for divergent thinking based on the alternative/unusual uses concept originally introduced by Guilford 74 . In this unusual uses task (UUT), participants were given 20 s to find alternative uses for a brick, with the original use described as “building houses”. Participants entered their answer in a large text field and were asked to enter one answer per line.

We implemented the MAAS and SART with ES as an online experiment using the JavaScript-based online experiment builder “lab.js” ( https://lab.js.org 75 . Participants were required to wear headphones during the experiment. We included a headphone test before the SART to ensure participants had correctly placed the headphones and could listen to the stimulation. Runnable files and code for both language versions of the experiment can be found in the supplementary materials at https://osf.io/8kg6z .

The online experiment started with information about the experiment, data processing, and informed consent request. This was followed by a short demographic questionnaire, the MAAS, the headphone test, the UUT, and 20 min of the SART. After the SART, a summary page informed the participants about their performance and a debriefing page gave further background information about the study.

Data processing, analysis and creation of figures and tables were done in R (v 4.0.2) 55 , using the following packages in addition to base R: ‘tidyverse’ 76 for various data wrangling and processing tasks and for data visualizations via ‘ggplot2’ 56 , ‘GGally’ 58 for data visualizations, ‘e1071’ 77 for kurtosis and skewness calculations, ‘lubridate’ 78 for handling of date and time data, ‘lavaan’ 79 for confirmatory factor analyses, ‘stargazer’ 80 to create and export LaTeX tables, ‘splithalf’ 57 for permutation-based split-half calculatio ns.

Data availability

The datasets generated and analysed for the current study are available in the Open Science Framework (OSF) repository, https://osf.io/wg9q5 . Tables, figures, and other supplementary materials specifically for this publication are available in a different repository at OSF, https://osf.io/8kg6z .

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Acknowledgements

The authors would like to thank all students of the following two classes at the UniDistance Suisse, who recruited participants for the experiment: “Methoden III: Experimentelle Übungen” during the fall semester 2019, “Wissenschaftliches Arbeiten” during the spring semester 2020.

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Angelo Belardi, Alodie Rey-Mermet, Nicolas Rothen & Thomas P. Reber

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Using the CRediT contributor roles taxonomy (casrai.org/credit/). A.B.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing—original draft, Writing—review & editing. L.C.: Conceptualization, Resources, Writing—review & editing. A.R-M.: Conceptualization, Writing—review & editing. F.M.: Resources, Writing—review & editing. N.R.: Resources, Writing—review & editing. J.F.: Conceptualization, Writing—review & editing. T.P.R.: Conceptualization, Resources, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Supervision, Writing—original draft, Writing—review & editing.

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How to Tame Your Wandering Mind

Learn to take steps to deal with distraction..

Posted April 24, 2022 | Reviewed by Jessica Schrader

• Understanding Attention
• Find counselling to help with ADHD
• We can tame our mind-wandering.
• Three tips can help you use mind-wandering to your advantage.
• These include making time to mind-wander and controlling your response to it.

Researchers believe that when a task isn’t sufficiently rewarding, our brains search for something more interesting to think about.

You have a big deadline looming, and it’s time to hunker down. But every time you start working, you find that, for some reason, your mind drifts off before you can get any real work done. What gives? What is this cruel trick our brains play on us, and what do we do about it?

Thankfully, by understanding why our mind wanders and taking steps to deal with distraction, we can stay on track. But first, let’s understand the root of the problem.

Why do our minds wander?

Unintentional mind-wandering occurs when our thoughts are not tied to the task at hand. Researchers believe our minds wander when the thing we’re supposed to be doing is not sufficiently rewarding, so our brains look for something more interesting to think about.

We’ve all experienced it from time to time, but it’s important to note that some people struggle with chronic mind-wandering : Though studies estimate ADHD afflicts less than 3% of the global adult population, it can be a serious problem and may require medical intervention.

For the vast majority of people, mind-wandering is something we can tame on our own—that is, if we know what to do about it. In fact, according to Professor Ethan Kross, director of the Emotion & Self Control Laboratory at the University of Michigan and author of Chatter: The Voice in Our Head, Why It Matters, and How to Harness It , mind-wandering is perfectly normal.

“We spend between a third to a half of our waking hours not focused on the present,” he told me in an email. “Some neuroscience research refers to our tendency to mind-wander as our ‘default state.’”

So why do we do it?

“Mind-wandering serves several valuable functions. It helps us simulate and plan for the future and learn from our past, and it facilitates creative problem-solving,” Kross explained. “Mind-wandering often gets a bad rep, but it’s a psychological process that evolved to provide us with a competitive advantage. Imagine not being able to plan for the future or learn from your past mistakes.”

Is mind-wandering bad for you?

“Like any psychological tool, however, mind-wandering can be harmful if used in the wrong context (i.e., when you’re trying to focus on a task) or inappropriately (i.e., when you worry or ruminate too much),” according to Kross. In other words, mind-wandering is a problem when it becomes a distraction. A distraction is any action that pulls you away from what you planned to do.

If, for instance, you intended to work on a big project, such as writing a blog post or finishing a proposal, but instead find yourself doing something else, you’re distracted.

The good news is that we can use mind-wandering to our advantage if we follow a few simple steps:

1. Make time to mind-wander

Mind-wandering isn’t always a distraction. If we plan for it, we can turn mind-wandering into traction. Unlike a distraction , which by definition is a bad thing, a diversion is simply a refocusing of attention and isn’t always harmful.

There’s nothing wrong with deciding to refocus your attention for a while. In fact, we often enjoy all kinds of diversions and pay for the privilege.

A movie or a good book, for instance, diverts our attention away from real life for a while so we can get into the story and escape reality for a bit.

Similarly, if you make time to allow your mind to drift and explore whatever it likes, that’s a healthy diversion, not a distraction.

The first step to mastering mind-wandering is to plan time for it. Use a schedule maker and block off time in your day to let your thoughts flow freely. You’ll likely find that a few minutes spent in contemplation can help you work through unresolved issues and lead to breakthroughs. Scheduling mind-wandering also lets you relax because you know you have time to think about whatever is on your mind instead of believing you need to act on every passing thought.

It’s helpful to know that time to think is on your calendar so you don’t have to interrupt your mind-wandering process or risk getting distracted later.

2. Catch the action

One of the difficulties surrounding mind-wandering is that by the time you notice you’re doing it, you’ve already done it. It’s an unconscious process so you can’t prevent it from happening.

The good news is that while you can’t stop your mind from wandering, you can control what you do when it happens.

Many people never learn that they are not their thoughts. They believe the voice in their head is somehow a special part of them, like their soul speaking out their inner desires and true self. When random thoughts cross their mind, they think those thoughts must be speaking some important truth.

Not true. That voice in your head is not your soul talking, nor do you have to believe everything you think.

When we assign undue importance to the chatter in our heads, we risk listening to half-baked ideas, feeling shame for intrusive thoughts, or acting impulsively against our best interests.

A much healthier way to view mind-wandering is as brain static. Just as the random radio frequencies you tune through don’t reveal the inner desires of your car’s soul, the thoughts you have while mind-wandering don’t mean much—unless, that is, you act upon them.

Though it can throw us off track, mind-wandering generally only lasts a few seconds, maybe minutes. However, when we let mind-wandering turn into other distractions, such as social-media scrolling, television-channel surfing, or news-headline checking, that’s when we risk wasting hours rather than mere minutes.

If you do find yourself mentally drifting off in the middle of a task, the important thing is to not allow that to become an unintended action, and therefore a distraction.

An intrusive thought is not your fault. It can’t be controlled. What matters is how you respond to it—hence the word respon-sibility.

Do you let the thought go and stay on task? Or do you allow yourself to escape what you’re doing by letting it lead you toward an action you’ll later regret?

3. Note and refocus

Can we keep the helpful aspects of mind-wandering while doing away with the bad? For the most part, yes, we can.

According to Kross, “Mind-wandering can easily shift into dysfunctional worry and rumination. When that happens, the options are to refocus on the present or to implement tools that help people mind-wander more effectively.”

One of the best ways to harness the power of mind-wandering while doing an important task is to quickly note the thought you don’t want to lose on a piece of paper. It’s a simple tactic anyone can use but few bother to do. Note that I didn’t recommend an app or sending yourself an email. Tech tools are full of external triggers that can tempt us to just check “one quick thing,” and before we know it, we’re distracted.

Rather, a pen and Post-it note or a notepad are the ideal tools to get ideas out of your head without the temptations that may lead you away from what you planned to do.

Then, you can collect your thoughts and check back on them later during the time you’ve planned in your day to chew on your ideas. If you give your thoughts a little time, you’ll often find that those super important ideas aren’t so important after all.

If you had acted on them at the moment, they would have wasted your time. But by writing them down and revisiting them when you’ve planned to do so, they have time to marinate and may become less relevant.

However, once in a while, an idea you collected will turn out to be a gem. With the time you planned to chew on the thought, you may discover that mind-wandering spurred you to a great insight you can explore later.

By following the three steps above, you’ll be able to master mind-wandering rather than letting it become your master.

Nir Eyal, who has lectured at Stanford's Graduate School of Business and the Hasso Plattner Institute of Design, is the author of Indistractable: How to Control Your Attention and Choose Your Life.

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Longitudinal Associations between Metacognition and Spontaneous and Deliberate Mind Wandering During Early Adolescence

Affiliations.

• 1 Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China.
• 2 Faculty of Psychology, Beijing Normal University, Beijing, China.
• 3 Department of Psychology, School of Sociology, China University of Political Science and Law, Beijing, China. [email protected].
• PMID: 38600263
• DOI: 10.1007/s10964-024-01979-8

Although metacognition plays a pivotal role in theoretical accounts of mind wandering, their longitudinal relationships have not yet been investigated during the important developmental period of early adolescence. This study aimed to examine the developmental trajectories of spontaneous and deliberate mind wandering and the dynamic associations between metacognition and two types of mind wandering in early adolescence. A sample of 4302 Chinese students beginning in Grade 4 (47.4% female; initial M age = 9.84, SD age = 0.47) completed questionnaires on five occasions over 2.5 years. The results showed that deliberate mind wandering, but not spontaneous mind wandering, gradually increased from Grade 4 to Grade 6. Metacognition was negatively related to spontaneous mind wandering but positively related to deliberate mind wandering. These findings provide empirical evidence for theoretical viewpoints from both individual differences and developmental perspectives.

Keywords: Early Adolescence; Longitudinal association; Metacognition; Mind wandering; Trajectory.

© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

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Jeff Bezos Reveals His Unconventional Approach To Productivity: 'I Believe In Wandering'

Jeff Bezos shared his unconventional approach to productivity, which involves allowing his mind to wander during meetings. This strategy, he believes, fosters creativity and problem-solving.

What Happened : Bezos, the founder of Amazon AMZN and Blue Origin , revealed his unique approach to productivity in a recent episode of the “Lex Fridman Podcast.”

Bezos, who is currently the second-richest person globally, according to Forbes, does not adhere to a strict schedule or time blocks for meetings. Instead, he encourages creative thinking by allowing his mind to wander during meetings, which can often extend beyond their planned duration.

"I don't keep to a strict schedule," Bezos said, in an episode first released in December. "My meetings often go longer than I plan for them to, because I believe in [mind] wandering."

“A lot of people feel like wandering is inefficient," he added.

He advocates for “messy meetings” where ideas are freely exchanged, and there is no set end time. Bezos believes that this approach, which some may consider inefficient, can actually enhance productivity, creativity, and happiness.

"When I sit down a meeting, I don't know how long the meeting is going to take if we're trying to solve a problem," Bezos said. "The reality is we may have to wander for a long time … I think there's certainly nothing more fun than sitting at a whiteboard with a group of smart people and spit-balling and coming up with new ideas and objections to those ideas, and then solutions to the objections and going back and forth." He added that "a lot of people feel like wandering is inefficient." 

Bezos’s approach is supported by a 2016 study that found allowing the mind to wander can significantly improve creativity. He suggests that taking breaks from a structured routine to let the mind wander can be an effective way to solve problems that seem unsolvable.

See Also: Elon Musk Agrees Every American Household Will Have A $1,000-Per-Month Home Robot In 7 Years’ Time

Why It Matters : Bezos’s approach to productivity is in line with his innovative thinking and risk-taking, as evidenced by his recent ventures.

In January, it was reported that Bezos’s net worth had increased by $70 billion in 2023, reaching $177 billion. This remarkable growth was attributed to his strategic investments and business decisions.

Bezos’s financial acumen was further demonstrated in March when he sold $8.5 billion worth of Amazon stock and saved $600 million in taxes by relocating to Florida. This move allowed him to redirect a portion of his wealth into Blue Origin, his space exploration venture, showcasing his commitment to innovation and exploration.

Bezos’ net worth has also been a topic of discussion. Despite a fierce competition with Tesla CEO Elon Musk , Bezos reclaimed the title of the world’s wealthiest individual with a net worth of $200 billion.

Read Next: Elon Musk Reacts To Tucker Carlson Saying There’s ‘Ton Of Evidence’ That Aliens Live Among Us: ‘With 6000 Satellites Orbiting Earth, I Think I Would Know’

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Mind wandering perspective on attention-deficit/hyperactivity disorder

• • Excessive, spontaneous mind wandering is associated with attention deficit hyperactivity disorder (ADHD).
• • Deficient regulation of the default mode network in ADHD might lead to this type of mind wandering.
• • This neural dysregulation might also underpin inattention and deficient cognitive performance.
• • Converging evidence draws parallels between regulatory processes of mind wandering and deficient regulation in ADHD.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder associated with a range of mental health, neurocognitive and functional problems. Although the diagnosis is based on descriptions of behaviour, individuals with ADHD characteristically describe excessive spontaneous mind wandering (MW). MW in individuals with ADHD reflects constant mental activity which lacks topic stability and content consistency. Based on this review of the neural correlates of ADHD and MW, we outline a new perspective on ADHD: the MW hypothesis. We propose that altered deactivation of the default mode network, and dysfunctional interaction with the executive control network, leads to excessive and spontaneous MW, which underpins symptoms and impairments of ADHD. We highlight that processes linked to the normal neural regulation of MW (context regulation, sensory decoupling, salience thresholds) are deficient in ADHD. MW-related measures could serve as markers of the disease process, as MW can be experimentally manipulated, as well as measured using rating scales, and experience sampling during both cognitive tasks and daily life. MW may therefore be a potential endophenotype.

1. Introduction

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder affecting 5–6% of children and 3–4% of adults worldwide ( Fayyad et al., 2007 ; Polanczyk et al., 2007 ). ADHD is characterised by developmentally inappropriate and impairing levels of inattentive, hyperactive and impulsive behaviours. The disorder is often accompanied by emotional lability ( Skirrow et al., 2009 ), cognitive performance deficits ( Banaschewski et al., 2012 ; Kofler et al., 2013 ) and mental health problems including anxiety, mood, personality and substance use disorders ( Fayyad et al., 2007 ). ADHD is further linked to detrimental outcomes including educational and occupational failure, transport accidents with increased mortality ( Kooij et al., 2010 ; Asherson et al., 2016 ) and criminal behaviour ( Lichtenstein and Larsson, 2013 ).

Despite considerable progress in understanding the symptoms and impairments of ADHD and the availability of effective treatments (NICE, 2008), key clinical issues remain to be resolved. ADHD, particularly in adults, remains a disorder that often goes undiagnosed and untreated. One explanation is diagnostic uncertainty due to the high rates of psychiatric comorbidity and overlap of ADHD symptoms with other common mental health disorders ( Asherson et al., 2016 ). The diagnosis also relies on subjective reports of symptoms and behaviours, leading to both under and over reporting of symptoms ( Barkley et al., 2002 ; Du Rietz et al., 2016 ; Faraone and Biederman, 2016 ). Another problem is that current medications provide short-term control of ADHD symptoms, but do not bring about longer-term symptom remission, or adequate control of symptoms in all cases. Further progress in diagnosis, prevention and treatment will likely require a better understanding of the underlying neural and cognitive mechanisms that lead directly to the symptoms and impairments of ADHD, and can be targeted by treatment interventions.

Here we propose a novel approach that focuses on a measurable component of ADHD psychopathology: excessive, spontaneous mind wandering (MW) ( Mowlem et al., 2016 ). We put forth a new hypothesis for ADHD (see Fig. 1 ) in which aberrant regulation within the default mode networks, and between default mode and executive control networks, leads to spontaneous MW, which in turn leads to symptoms and impairments of ADHD, and may also underlie some of the cognitive performance deficits seen in ADHD. This is an alternative to the usual model, which views measures of cognitive function, such as sustained attention and inhibitory control deficits, as intermediate endophenotypes on the pathway from genes to behaviour ( Castellanos and Tannock, 2002 ; Rommelse et al., 2008 ).

Visualisation of the linear relationship between neural activity, mind wandering (MW), inattentive symptoms and attentional lapses in the MW hypothesis. The top, central image represents the three neural networks underlying excessive and spontaneous mind wandering in ADHD. The key hubs of the default mode network are the posterior cingulate cortex, and ventromedial prefrontal cortex, associated with active mind wandering. The central executive network includes dorsolateral prefrontal cortex, and posterior parietal cortex, which are active during cognitively demanding tasks and serve as a marker of task focus. The salience network involves the anterior cingulate cortex, and anterior insula, linked to the regulation of the interaction between the default mode and central executive network. The bottom, left image represents inattentive symptoms in ADHD. The bottom, right image represents the greater variability in the distribution of reaction time scores in ADHD.

We propose this as a promising new avenue for research as MW has been linked to ADHD and ADHD-associated impairments, and unlike ADHD symptoms such as inattention, MW can be measured using a range of direct and indirect measures. These include rating scale state and trait measures, experience sampling in daily life, experience sampling during experimental paradigms, and the neural correlates of MW. Potentially these may provide new clinical and neural biomarkers of ADHD that could provide new insights into the neurobiological basis of ADHD, which can be used for diagnosis and prediction and monitoring of treatment effects, and could lead to novel treatments targeting the regulation of MW in ADHD.

2. ADHD, mind wandering and the default mode network

2.1. what is mind wandering.

Mind wandering (MW) occurs when one’s mind drifts away from the primary task and focuses on internal, task-unrelated thoughts and images. MW is a universal experience that represents up to 50% of daily thinking time ( Smallwood and Schooler, 2015 ). While some forms of MW can be beneficial to individuals (e.g. strategic thinking about a grant proposal while driving a car), other forms can be detrimental (e.g. spontaneous uncontrolled thoughts that interfere with tasks such as listening to a lecture). These two types of MW have been referred to as deliberate and spontaneous, respectively, and are thought to reflect a different balance of regulatory processes on internal self-generated thought ( Christoff et al., 2016 ; Seli et al., 2015 ). Spontaneous MW, detrimental to performance, has been proposed as a mechanism explaining many of the symptoms and impairments of ADHD ( Mowlem et al., 2016 ; Seli et al., 2015 ) believed to reflect dysfunctional connectivity between the brain’s default mode network (DMN) and executive control networks ( Fox et al., 2015 ; Sripada et al., 2014 ).

2.2. Spontaneous mind wandering is associated with ADHD

The first study of MW in ADHD was conducted using an experience sampling technique to measure on-task and off-task thoughts during a simple attention task ( Shaw and Giambra, 1993 ).The frequency of task-unrelated thoughts was found to be increased in college students with a childhood history of ADHD diagnosis, compared to controls. Among the controls, male and female groups that reported high levels of childhood ADHD symptoms also demonstrated more task-unrelated thoughts than controls reporting low levels of childhood ADHD symptoms.

A further study, using the MW Deliberate and Spontaneous scales ( Carriere et al., 2013 ) found that a group who had been diagnosed with ADHD showed more spontaneous than deliberate MW ( Seli et al., 2015 ). They further showed that spontaneous MW (but not deliberate MW) was significantly correlated with ADHD symptom severity. In another study using an adult community sample, a composite index of ADHD symptoms was positively correlated with a composite index of MW derived from experience sampling data of task-unrelated thoughts during a lab session, and daily life ( Franklin et al., 2017 ). Furthermore, ADHD symptoms were related to MW episodes that were detrimental to the task at hand, and a sub-clinical group with high ADHD symptom scores had disruptive MW episodes that impaired daily-life function. ADHD symptomatology was also positively correlated with a lack of awareness of engaging in MW. In this study, lacking awareness of MW mediated between ADHD symptoms and impairment, suggesting that increasing awareness of MW in ADHD might lead to functional improvements ( Franklin et al., 2017 ).

In our own studies, we developed a clinical scale reflecting ADHD patient reports of excessive spontaneous MW ( Mowlem et al., 2016 ). The 12-item Mind Excessively Wandering Scale (MEWS) captures three characteristics of MW in ADHD: thoughts constantly on the go, thoughts flitting from one topic to another, and multiple thoughts at the same time. Exploratory factor analysis found the 12-item MEWS to be unidimensional, with a single factor explaining 70% of the variance ( Mowlem et al., 2016 ). Two independent samples revealed significantly elevated ratings of MW in ADHD, and that MW successfully discriminated between cases and controls to a similar extent as rating scale measures of DSM-IV ADHD symptoms (sensitivity and specificity around 0.90 in both studies). MEWS scores were also correlated strongly and positively with measures of inattention (r = .77), hyperactivity/impulsivity (r = .69) and ADHD-related impairment (r = 0.81). Furthermore, repeated analysis over a 6-month period showed moderate to high covariation of change in MEWS scores with change in inattention (r = .53), hyperactivity/impulsivity (r = .31) and impairment (r = .62). Regarding impairment, MEWS scores were the strongest predictor of functional impairment, followed by inattention and hyperactivity/impulsivity, indicating the clinical relevance of MW as a predictor of impairment in daily life.

Overall, these findings suggest that asking about the subjective experience of MW alone, provides a better prediction of ADHD associated impairment than the traditional ADHD inattention and hyperactive/impulsive symptoms. Moreover, as we will discuss, MW may reflect the primary deficit arising directly from dysregulated neural network activity in ADHD that underpins the symptoms and impairments currently used to define ADHD.

2.3. Mind wandering and the default mode network

The neural basis of MW has generated considerable interest since 2006 ( Callard et al., 2013 ). The DMN has been implicated as a potential source of self-generated thoughts unrelated to external goal-directed tasks. The DMN reflects a network of interacting brain regions (i.e. medial prefrontal cortex, posterior cingulate cortex and medial temporal regions) which show correlated neural activation, most active during the resting state, when the person is awake but in a daydreaming or MW state. The network can be conceptualised as switching off during external goal-directed tasks, and switching on when there are internal self-generated thoughts ( Buckner and Vincent, 2007 ).

Among the first investigations of MW-associated neural activity were two functional magnetic resonance imaging (fMRI) studies using tasks with low cognitive demand ( McKiernan et al., 2006 ), or highly practiced cognitive tasks ( Mason et al., 2007 ), during which episodes of MW were frequent. These studies found a strong correlation between the reduced deactivation of the DMN during on-task conditions and frequency of subjectively-reported MW. The MW-associated neural activity patterns were in stark contrast with those seen during novel or high cognitive demand conditions, when MW was less frequent. McKiernan et al. (2006) conducted three different cognitive tasks under three levels of task difficulty, making a total of nine different task/difficulty conditions, each associated with a different frequency of task-unrelated thoughts. They reported that 81% of the variance in the frequency of task-unrelated thoughts was explained by task-induced deactivation of the DMN, which is remarkably high for an association between a direct measure of brain function using fMRI, and subjective reports of a mental phenomenon. Similarly, Mason et al. (2007) found activation in DMN regions during episodes of MW, including the precuneus.

A limitation of these early studies was that the frequency of task-unrelated thoughts was measured outside the scanning sessions. To provide a more direct investigation of neural activity during periods of MW, Christoff et al. (2009) used in-scanner experience-sampling probes to identify periods of task-related and task-unrelated thoughts during a sustained attention task. The experience-sampling probes asked two questions: “Where was your attention focused just before the probe – on task or off task?”, and “How aware were you of where your attention was focused – aware or unaware?”. The strongest activation was seen in two normally anti-correlated networks (default mode and executive control networks) during MW episodes, and even stronger during “unaware” compared to “aware” MW.

The finding of co-activation of both executive and default networks during periods of task-unrelated thoughts in control subjects was subsequently confirmed by meta-analysis of fMRI studies ( Fox et al., 2015 ). Examining 24 functional neuroimaging studies of spontaneous thought processes, meta-analysis using activation likelihood estimation (ALE) found that both DMN regions (medial prefrontal cortex, posterior cingulate cortex, medial temporal lobe, bilateral inferior parietal lobule), and non-DMN regions (rostrolateral prefrontal cortex, dorsal anterior cingulate cortex, insula, temporopolar cortex, secondary somatosensory cortex, and lingual gyrus) were consistently recruited during periods of spontaneous thoughts. They concluded that in addition to DMN activity, fronto-parietal network (FPN) and other non-DMN regions also played a central role in the neuroscience of MW and other forms of spontaneous thoughts.

However, not all studies have reported co-activation of DMN and executive control regions during periods of MW. Andrews-Hanna et al. (2010) introduced longer delays between stimuli. These delays induced MW which was most strongly correlated with DMN hubs rather than co-activation of both DMN and executive control networks. Likewise, in another study, task-unrelated thoughts were associated with the highest level of DMN (medial prefrontal cortex) activation compared to intermediate levels of activation for external distraction and task-related inferences, and the lowest DMN activity during periods of on-task thoughts ( Stawarczyk et al., 2011a ). Further evidence comes from studies focusing on the role of executive control mechanisms in MW.

2.4. Mind wandering and executive control

Overall the findings discussed above confirm the association of DMN activity with the frequency of task-unrelated thoughts. However, the role of executive control networks may depend on task conditions or type of MW and other forms of spontaneous thought. Typically, executive control and DMN function in an anti-correlated manner. Early work showed increased activation in executive control and decreased activation in DMN regions with increasing attentional demands, and vice versa ( Fox et al., 2005 ). Likewise, attenuated DMN activity during periods of on-task was linked with poor adjustment to attentional demands, or attentional lapses ( Weissman et al., 2006 ). Therefore, task-unrelated thoughts are expected to be associated with anti-correlation (or reduced co-activation) between executive control and DMN regions.

However, as discussed, fMRI research has been inconclusive on the role of executive control on MW, which sparked off a debate. One argument is that MW results from a failure in executive control to prevent automatic task-unrelated thoughts from becoming conscious ( McVay and Kane, 2012 ). For example, the emergence and increased frequency of MW under high demand conditions is associated with deficits in executive control, including working memory capacity and response inhibition ( McVay and Kane, 2012 ). An opposing argument states that executive control is required to maintain personally salient task-unrelated thoughts (i.e. strategic/deliberate MW) during low cognitive demand conditions, and is associated with reduced MW under high cognitive demand conditions to prevent performance decrements ( Smallwood, 2010 ; Smallwood and Schooler, 2006 ). In line with this view, MW increased linearly with time-on-task in low demand vigilance paradigms ( Randall et al., 2014 ; Thomson et al., 2015 ) and is more frequent in practiced compared to novel tasks ( Mason et al., 2007 ).

To investigate the relative role of different neural networks, an activation likelihood estimation (ALE) meta-analysis was conducted on different types of internal thoughts, including experimentally directed episodic future thinking, and spontaneous MW ( Stawarczyk and D’argembeau, 2015 ). The results showed that while these domains of internal thought activated a common set of brain regions within the default network (e.g. medial prefrontal cortex); regions supporting executive control processes (e.g. dorsolateral prefrontal cortex) were also recruited to a lesser extent during undirected MW than during directed episodic future thinking. Thus, different types of internally generated thoughts may recruit varying levels of executive control.

To address the various findings, recent models of MW better reflect the complexity of internally generated thoughts. They clarify that MW is not a unitary construct, but rather an umbrella term that captures different types of MW experiences in the general population. A key conceptual paper proposes a dynamic framework in which MW is understood as a subtype of spontaneous-thought phenomena that also includes creative thought and daydreaming ( Christoff et al., 2016 ). The authors propose a dimension of deliberate constraints related to executive control activity, with unconstrained daydreaming at one end, and constrained goal-directed thought at the other. Creative thought is proposed to reflect a more constrained form of spontaneous thought that is under greater executive control than daydreaming or MW, but less than goal-directed thoughts. They further propose a second dimension of automatic constraints on content of thought, which is weakest for common forms of daydreaming, and strongest for mental phenomenon such as ruminations and obsessions. Christoff et al. (2016) proposed that under their model, ADHD would be reflected by a problem with excessive variability in thought movement, with low deliberate constraints (excessive MW and dream-like thoughts) and low automatic constraints (thoughts that flit from one topic to another).

2.5. ADHD is associated with deficient regulation of DMN activity

Resting state connectivity studies of ADHD in children and adults have examined interactions between the DMN and executive control network, as well as connectivity within the DMN itself. These studies consistently find that anti-correlation between the executive control (fronto-parietal) network and DMN is attenuated, and that resting state connectivity within the DMN itself is reduced ( Posner et al., 2014 ). ADHD is associated with hyperactivation (deficient deactivation) of the DMN in task compared to resting state conditions ( Fassbender et al., 2009 ; Helps et al., 2010 ; Liddle et al., 2011 ; Peterson et al., 2009 ).

The observation of DMN abnormalities in ADHD led to a hypothesis known as the DMN interference model ( Sonuga-Barke and Castellanos, 2007 ). This model proposed that increased very low frequency oscillations (0.01 - 0.1 Hz) and synchronization within the DMN, which usually attenuate during goal-directed tasks, persist and interfere with task-specific neural processes, leading to lapses in attention and performance deficits ( Sonuga-Barke and Castellanos, 2007 ). Reduced DMN deactivation from rest to task, and suppressed DMN activity (up-regulation) at the transition to rest conditions, suggest inadequate neural switching in response to changes in context in ADHD compared to controls ( Liddle et al., 2011 ; Sidlauskaite et al., 2016 ). Consistent with this hypothesis, Liddle et al. (2011) found that failure to deactivate DMN regions during a low-salient (slow, boring, unrewarded) inhibition task requiring sustained attention, was reversed by methylphenidate in children with ADHD. In a further study both improvement of inattentive symptoms and normalisation of very low electroencephalography (EEG) frequencies as well as omission errors followed the use of methylphenidate ( Cooper et al., 2014 ).

Related to the default mode interference hypothesis, it has also been proposed that activation of DMN hubs will depend on motivation, cognitive load, attentional demands and individual state regulation/capacity ( Stawarczyk et al., 2011a ). This is in line with the cognitive-energetic model which proposes that the efficiency of task performance in ADHD is determined by the interplay of basic cognitive processes (e.g. stimulus encoding, memory search, binary decision and motor preparation), and the availability of these processes related to arousal and activation levels and is further modulated by interplay with executive control functions ( Sergeant, 2005 ). Both the DMN interference and cognitive-energetic models point to an inability to adapt neural network activity to changing task demands.

The DMN interference hypothesis also proposed that a certain threshold of DMN activity needs to be reached before DMN interference occurs ( Sonuga-Barke and Castellanos, 2007 ). In relation to our model of MW in ADHD, we propose that this critical threshold of DMN activity is linked to excessive spontaneous MW, which leads to the inattentive symptoms of ADHD and detrimental effects on daily-life function. Cognitive performance deficits associated with ADHD may be a direct result of internal distractibility secondary to excessive MW, or may be secondary to the direct effects of high default mode activity interfering with task-dependent neural functions. We further propose that this critical threshold of DMN activity may not be reached when there is a continuous co-activation of DMN and executive control regions associated with deliberate/strategic forms of MW. For example, individuals without a clinical diagnosis of ADHD may more readily co-activate DMN and executive control regions, and engage in deliberate rather than uncontrolled/excessive forms of spontaneous MW. Previous authors have also proposed that reduced deactivation of the DMN during task performance may explain MW and interference with task positive processes, leading to the symptoms and impairments of ADHD ( Liddle et al., 2011 ; Mowlem et al., 2016 ; Posner et al., 2014 ). However, at the time of writing this has yet to be formally evaluated.

3. A comparative analysis of ADHD and mind wandering

So far, we have outlined studies that link ADHD to MW, MW to DMN activity, and DMN activity to ADHD. These studies raise the possibility that deficient regulation of DMN activity leads to excessive spontaneous MW in individuals with ADHD, which might underpin the inattentive symptoms of ADHD and deficits in cognitive task performance. Further support for this hypothesis comes from several observations that draw parallels between processes that underlie the regulation of MW in neurotypical controls, and processes found to be deficient in ADHD. These parallels include: (1) context regulation of MW in controls, and deficient context regulation of neural activity in ADHD; (2) perceptual decoupling of somatosensory processing during MW, and in ADHD; (3) sensitivity of MW and MW-associated neural processes to task salience and rewards; (4) impairments in cognitive task performance, and function in daily life. Below we outline these areas in more detail.

3.1. Context regulation of mind wandering

The context regulation hypothesis states that an adequate capacity to self-regulate mind wandering within a context will reduce a potential negative impact on the primary task performance ( Smallwood and Andrews-Hanna, 2013 ). Context regulation of MW is characterized by adaptation of neural processes and frequency of MW to changing task demands. In community samples, MW frequency is higher during low perceptual and low cognitive demand conditions ( Filler and Giambra, 1973 ; Giambra and Grodsky, 1989 ) and lower under more cognitively demanding conditions, such as tasks with high working memory demands ( Antrobus et al., 1966 ). As discussed above Mason et al. (2007) manipulated the frequency of MW by varying task cognitive-demands, and correlated MW frequency with DMN task induced deactivations. Similar findings have been replicated by others ( Forster and Lavie, 2009 ; Levinson et al., 2012 ; McVay and Kane, 2012 ; Metcalfe and Xu, 2016 ; Ruby et al., 2013 ; Rummel and Boywitt, 2014 ; Smallwood et al., 2007 ; Xu and Metcalfe, 2016 ). Related to these findings, shifts from task-related to task-unrelated thoughts are observed during low cognitive demand tasks ( Stawarczyk et al., 2011b ; Thomson et al., 2014 ).

Regarding the potential role of executive control and working memory capacity, individuals with both low and high working memory capacity report greater levels of MW under low cognitive demand conditions ( Kane et al., 2007 ). However, under high demand conditions, task-unrelated thoughts are greater in individuals with low working memory capacity, compared to those with higher working memory capacity ( Kam and Handy, 2014 ; Kane et al., 2007 ; Unsworth and Robison, 2016 ). This effect is consistent with the role of executive control in the appropriate regulation of MW during cognitively demanding tasks.

Furthermore, higher working memory capacity predicted greater frequency of task-unrelated thoughts in a low demand breath-awareness task ( Levinson et al., 2012 ) but fewer errors (regarded as a behavioural index of less frequent MW) in a high demand 3-back working memory task ( Rummel and Boywitt, 2014 ). Similarly, the level of linguistic expertise (reflecting executive control and working memory capacity) determined the frequency of MW since individuals with high levels of linguistic ability showed more frequent task-unrelated thoughts for easy items, compared to individuals with low to medium linguistic ability ( Xu and Metcalfe, 2016 ). Thus, both working memory capacity and linguistic capacity had a moderating effect on the level of MW under low and high demand conditions.

In summary, these findings indicate that higher working memory/executive control capacity moderate frequency of MW according to task demands. In general, greater working memory capacity is predictive of more MW during low demand conditions, whereas it is predictive of less MW during high demand conditions.

3.2. Context regulation in ADHD

Context regulation of MW in ADHD has yet to be investigated. However, there is consistent evidence for deficient neural adaptation to task demands in ADHD compared to controls, with deficient upregulation of executive control regions, accompanied by deficient deactivation of the DMN. Thus, the neural networks that show deficient context regulation in ADHD are the same networks associated with context regulation of MW in neurotypical controls.

For example, deficient context regulation was seen in ADHD compared to controls using electroencephalography EEG recordings during the Flanker Task. This task contrasts a low cognitive demand (no conflict) with a high cognitive demand (conflict) condition. In contrast to the ADHD group, controls showed a significantly greater increase in frontocentral theta amplitude and decreased phase variability (which correlated with less reaction time variability, RTV) in response to the higher cognitive demands of the conflict condition ( McLoughlin et al., 2009 , 2014 ). Low phase variability over trials is thought to reflect an adaptive mechanism to maintain stable neural processing of a stimulus ( Makeig et al., 2004 ; Papenberg et al., 2013 ). Therefore, the relationship between increased theta phase variability and RTV in ADHD points to a reduced ability to maintain efficient neural adaptation and cognitive performance over trials with increasing cognitive demands.

Similarly, there was greater RTV in ADHD compared to controls, under high demand (very fast or very slow event rates), compared to low demand (medium event rates) conditions ( Metin et al., 2016 ) reflecting deficient behavioural adaptation of task performance to changing task demands. Deficient context regulation of neural (EEG) activity in ADHD is also seen in the transition from rest to task conditions ( Rommel et al., 2016 ; Skirrow et al., 2015 ); a finding that was reversed in response to methylphenidate ( Skirrow et al., 2015 ).

fMRI studies also demonstrate deficient context regulation in ADHD. Compared to controls, ADHD is associated with under-activation of the FPN (left dorsolateral prefrontal cortex) during on-task conditions ( Castellanos and Proal, 2012 ; Cortese et al., 2012 ) and reduced deactivation of the DMN (medial prefrontal cortex) when transitioning from rest to task ( Valera et al., 2010 ). Attenuated deactivation and hyperactivation within the DMN in ADHD compared to controls was also observed with time-on-task during a sustained attention task, in response to high working memory load, and with longer inter-stimulus delays ( Christakou et al., 2013 ; Liddle et al., 2011 ; Metin et al., 2015 ; Paloyelis et al., 2007 ; van Rooij et al., 2015 ).

Taken together, the evidence from cognitive-EEG and fMRI studies points to a reduced ability to modulate task positive and negative neural processes in ADHD. Since the networks involved are the same as those linked to context regulation in neurotypical controls, deficient context regulation in individuals with ADHD may explain the high frequency of task-unrelated thoughts. This hypothesis has yet to be formally tested.

3.3. Perceptual decoupling in mind wandering

A key characteristic of MW is the association with attenuated somatosensory processing, referred to as perceptual decoupling. This means that during periods of MW there is a reduced somatosensory response to sensory stimuli ( Schooler et al., 2011 ; Smallwood et al., 2013 ). One hypothesis is that perceptual decoupling explains the co-activation of the FPN and DMN, during low demand conditions; reflecting active executive control over attention to disengage from perceptual input, so as to enable mental processing of personal goals ( Smallwood et al., 2012 ). In line with this, MW has been linked to anti-correlation and lack of synchronisation between sensory cortices and DMN ( Christoff, 2012 ; Kirschner et al., 2012 ) and a positive correlation between sensory cortices and FPN hubs during on-task conditions. A novel concept suggests that the depth of perceptual decoupling might be able to distinguish between spontaneous and deliberate MW ( Seli et al., 2015 ).

EEG research has consistently reported that event-related potential (ERP) components (P1), markers of early visual information processing within 100 ms, are attenuated during periods of task-unrelated compared to task-related thoughts ( Baird et al., 2014 ; Broadway et al., 2015 ; Kam and Handy, 2014 ). Phase-locking factor analyses reflect phase synchrony of a particular frequency at a particular time across multiple trials of an event ( Tallon-Baudry et al., 1996 ). Episodes of MW were further linked to a lower phase-locking factor in theta within 50–150 ms following a visual stimulus reflecting a neural state of perceptual decoupling during MW ( Baird et al., 2014 ).

Cortical source activity analyses during a visual Sustained Attention to Response Task (SART) also confirm the attenuation of early visual information processing, during periods of MW ( Kirschner et al., 2012 ). In this study, there was also deficient intra-regional (occipital cortex) and inter-regional (visual cortex and right medial temporal lobe) connectivity when attention was focused on internal thoughts ( Kirschner et al., 2012 ). In contrast, during periods when the participants were focused on the visual task, there was greater inter-regional connectivity between the visual cortex and task-positive regions including the anterior/posterior cingulate, orbitofrontal cortex and posterior parietal gyrus. Similarly, MW was linked to both an increase in occipital and parieto-central theta and fronto-central delta power and a decrease in occipital alpha and frontal lateral beta power ( Braboszcz and Delorme, 2011 ).

Collectively, these findings suggest a switch from active cognitive processing to MW, which is facilitated by a state of perceptual decoupling, or detachment of perception from attention. Consequently, a reduced P1 amplitude is regarded as a marker of perceptual decoupling during episodes of MW.

3.4. Perceptual decoupling in ADHD

Somatosensory responses are far less studied in ADHD, and the association between sensory decoupling and ADHD is not well established. Furthermore, there have been no studies that directly investigate the relationship of perceptual decoupling to periods of MW in ADHD. Yet, the few studies that have focused on early sensory processing in ADHD find deficits that are similar to those seen during periods of MW in neurotypical controls.

Initial reports found decreased slow frequency fluctuations within the left sensorimotor cortex ( Yu-Feng et al., 2007 ) and suppression of visual ERP amplitudes during cognitive-performance tasks in children with ADHD compared to controls ( Steger et al., 2000 ). Using magnetoencephalography (MEG), adults with ADHD showed reduced event-related desynchronization in the alpha band, and synchronisation in the beta bands, in primary and secondary somatosensory cortices in response to median nerve stimulation ( Dockstader et al., 2008 ). A similar attenuated cortical sensory response was found in children with ADHD, which improved following successful treatment with methylphenidate ( Lee et al., 2005 ).

Using ERP, a larger P1 (100 ms post-stimulus) amplitude has been seen in children with ADHD compared to controls; a finding that was interpreted as a compensatory mechanism in the absence of performance differences ( Kóbor et al., 2015 ; Shahaf et al., 2012 ). In contrast, when children with ADHD made more omission errors than controls, P1 amplitude was significantly reduced ( Nazari et al., 2010 ). At the time of writing, preliminary findings from our group support this result. Using the SART, we found a reduced P1 amplitude in 33 adults with ADHD compared to 30 controls (p < 0.02), which was associated with trait measures of inattention, and MW measured using the MEWS as a state measure of excessive MW in ADHD (Bozhilova et al., unpublished data). We further found that in the ADHD cases there was a reduced P1 amplitude prior to errors compared to correct responses (p < 0.001). Under the assumption that MW will be higher prior to error than non-error responses, these findings suggest that somatosensory processing deficits could be linked to excessive MW in ADHD. This hypothesis has yet to be formally tested.

3.5. The effects of salience and reward on mind wandering

Several studies find that the frequency of MW is related to task salience or incentives designed to increase motivation. This is not surprising, since almost everyone finds it easier to remain focused on tasks that are inherently interesting, compared to mundane or boring tasks. A high degree of task-related motivation and interest were both associated with lower frequency of MW during the SART ( Seli et al., 2015 , 2016 ) as well as better information retention on a film comprehension task ( Kopp et al., 2016 ).

A related phenomenon is the response to unexpected infrequent stimuli, which tend to be of high salience in auditory oddball paradigms. Infrequent and deviant auditory stimuli were associated with an increased mismatch negativity amplitude, which is a marker of attention allocation to these stimuli, during episodes of task focus compared to a decreased amplitude during MW ( Braboszcz and Delorme, 2011 ). Therefore, MW is linked to poorer attentional engagement with task-salient stimuli. An automatic, momentary coupling of attention and perception for visually salient stimuli (coloured no-go targets) during MW, also occurred in a variation of the Go/No-Go task ( Smallwood, 2013 ) again suggesting switch away from MW.

In another study, monetary incentives increased the number of reports of self-caught MW compared to conditions that were unrewarded ( Zedelius et al., 2015 ). The incentive for accuracy of MW self-report was associated with less probe-caught MW in the absence of greater overall MW ( Zedelius et al., 2015 ). This last finding indicates that reward is likely to increase awareness of MW. Overall, these findings suggest that the effects of task salience and reward have the potential to reduce MW and MW-associated neural activity.

3.6. The effects of salience and reward on ADHD

There are numerous examples of enhanced sensitivity to the effects of task salience and rewards in children and adults with ADHD. Here we discuss some of the most pertinent studies related to MW-associated task performance and neural activity.

In a key fMRI investigation, a sample of children with ADHD and controls completed a Go/No-go task under low- and high-incentive conditions ( Liddle et al., 2011 ). In the low-incentive condition (with low rewards linked to task performance), there was attenuated DMN deactivation in the ADHD group compared to controls. When higher rewards were introduced to increase the salience of the task, DMN deactivation normalised to the same level as controls. Methylphenidate was also found to have the same effect on DMN deactivation in the ADHD group, as increasing the salience of the task through rewards. The authors concluded that both methylphenidate and enhanced salience normalised DMN deactivation and suggested that this had an impact on inattention.

In a series of publications from Kuntsi and colleagues, fast-rewarded (high incentive) conditions compared to slow-unrewarded (low incentive) conditions on tasks requiring sustained attention reduced or abolished ADHD case-control differences for arousal measures using skin conductance ( James et al., 2016 ), RTV ( Andreou et al., 2007 ; Cheung et al., 2017 ; Kuntsi et al., 2009 ; Tye et al., 2016 ) and omission errors ( Uebel et al., 2010 ), but not commission errors ( Kuntsi et al., 2009 ).

Based on the findings that enhancing task saliency alters neural responses, task performance and frequency of MW, we hypothesise that adequate rewards will modulate the frequency of MW in both neurotypical and ADHD populations. In particular, a decrease in MW will lead to better early attentional orienting in ADHD, or successful detection of visual information early on at presentation. Despite similar effects on DMN activity of task saliency (reward) and stimulants in ADHD ( Liddle et al., 2011 ), different mechanisms could be involved. For instance, rewards may moderate the degree of early visual information detection via interactions between the DMN and visual cortex. In contrast, stimulants might lead to changes in MW by altering interactions between large-scale networks: for example, FPN and DMN; DMN and ventral attention network; and DMN and the salience network. Providing both reward and stimulants reduce the frequency of MW and/or facilitate a more controlled form of MW, a combination of both might further enhance treatment effects. Further research is needed to investigate the effects of reward and stimulants on MW in ADHD and mechanisms involved in both ADHD and controls.

3.7. Cognitive performance and daily life impairments associated with mind wandering

MW has an adverse impact on both cognitive task performance and daily life in control populations. Measured performance deficits associated with MW include greater stimuli-response error rates ( Forster and Lavie, 2009 ; Stawarczyk et al., 2011b ), slower reaction times ( Smallwood et al., 2013 ; Stawarczyk et al., 2011b ) reduced accuracy of response ( Kam and Handy, 2014 ; McVay and Kane, 2009 ; Rummel and Boywitt, 2014 ; Smallwood et al., 2008 ; Thomson et al., 2014 ; Unsworth and Robison, 2016 ; Xu and Metcalfe, 2016 ) and greater mean error percentage ( Thomson et al., 2015 ).

Similarly, attenuation in the amplitude of ERPs reflecting late cognitive analysis of stimuli (e.g. attentional resource allocation, response execution/preparation and attentional orienting) has been observed during periods of MW. Examples include reduced N400, mismatch negativity ( Braboszcz and Delorme, 2011 ) and P3 amplitudes ( Barron et al., 2011 ; Riby et al., 2008 ; Smallwood et al., 2007 ; Villena-González et al., 2016 ) as well as greater central negativity (N2) ( Riby et al., 2008 ). Further, frequent MW and poorer effort were related to poorer accuracy ( Brown and Harkins, 2016 ; Seli et al., 2016 ).

MW has also been associated with several daily life impairments, including deficits in reading comprehension ( Mooneyham and Schooler, 2013 ), negative mood ( Smallwood et al., 2009a , b ; Smallwood and O’Connor, 2011 ), poorer learning ( Metcalfe and Xu, 2016 ; Xu and Metcalfe, 2016 ), higher number of car accidents ( Galéra et al., 2012 ) less cautious driving ( Yanko and Spalek, 2013 ), poorer academic performance, life-satisfaction, self-esteem and greater stress ( Mrazek et al., 2013 ).

3.8. Cognitive performance and daily life impairments associated with ADHD

Similar impairments in cognitive task performance and daily life associated with MW are also seen in ADHD. Examples include attentional orienting, performance monitoring, response preparation and inhibitory processes; manifesting in increased error rates, reaction time variability and attenuation in Cue-P3 and No-Go N2 and P3 amplitudes ( Albrecht et al., 2014 ; Cheung et al., 2017 ; McLoughlin et al., 2009 , 2010 ; Michelini et al., 2016a ; Tye et al., 2014 ; Uebel et al., 2010 ).

ADHD is also associated with impairments in daily life including poorer psychosocial, educational and global function ( Asherson et al., 2012 ; Pitts et al., 2015 ). Individuals with ADHD compared to controls are more prone to car accidents ( Vaa, 2014 ), academic underachievement ( Fischer et al., 1990 ; Weyandt and DuPaul, 2008 ), reading comprehension difficulties ( Ghelani et al., 2004 ; Miller et al., 2013 ; Stern and Shalev, 2013 ) and low self-esteem ( Shaw-Zirt et al., 2005 ).

In summary, the findings on impairment show that both ADHD and MW in non-ADHD controls appear to be linked to similar performance deficits. This is consistent with our MW hypothesis for ADHD since we propose that episodes of MW in controls and excessive spontaneous MW in individuals with ADHD lead to similar functional and cognitive impairments. Potentially, excessive spontaneous MW in ADHD can be expected to lead to typical impairments experienced by people with ADHD in their daily lives. Examples could include problems following a conversation, reading and watching a film; maintaining a coherent train of thought for problem solving and holding thoughts in mind; difficulties falling asleep due to constant mental restlessness associated with excessive MW; and feeling exhausted by the mental effort required to sustain focus on daily activities. Further research is required to investigate the extent to which impairment seen in individuals with ADHD can be explained by excessive, spontaneous MW.

3.9. Identifying causal processes

As discussed extensively in this review, deficient deactivation of the DMN during cognitive task performance has been consistently reported in ADHD. However, ADHD is associated with a wide range of cognitive and neural deficits, anyone of which could be contributing to the generation of ADHD symptoms, and be potential targets for treatment. Indeed, the range of deficits associated with ADHD has led many to argue that the neuropathological basis for ADHD is highly heterogeneous ( Coghill et al., 2005 ; Faraone, 2015 ).

Key additional processes highlighted in the literature include: deficits in the dorsal attention network of dorsolateral prefrontal cortex, basal ganglia and parietal regions during tasks of selective and sustained attention ( Hart et al., 2013 ); inhibitory control deficits supported by fMRI studies of reduced activation in key regions of inhibitory control ( Plichta and Scheres, 2014 ). Furthermore stimulants, the first line treatment for ADHD, has been shown to have effects on several of these processes ( Rubia et al., 2014 , 2009 ; Smith et al., 2013 ).

Despite the findings linking various neural functions to ADHD, it remains unclear which of these play a direct causal role in the generation of the inattentive and hyperactive/impulsive symptoms currently used to define ADHD. This is a critical point, because numerous non-causal associations are expected to arise through the process of pleiotropy, in which shared genetic (and environmental) risks can lead to multiple different phenotypic outcomes at the level of brain structure, function, cognitive performance and behaviour. Yet, only a subset of the associated neural deficits may reflect underlying causal processes in ADHD ( Kendler and Neale, 2010 ). There are several approaches that can be used to address this problem ( Kendler and Neale, 2010 ), including conducting mediation analyses with experimental trial data, or longitudinal outcome data, or taking advantage of genetic data to test for causal associations using Mendelian randomisation ( Sheehan et al., 2011 ).

3.10. Remission and persistence of ADHD

In relation to ADHD, important new insights have come from longitudinal outcome studies that investigate the cognitive and neural correlates of remission and persistence in ADHD (Franke, 2015). A central question in causal models of ADHD has been the separation of executive control measures (e.g. inhibition and working memory) from preparation-vigilance measures. Earlier work showed that measures reflecting these processes are both associated with ADHD in children, but are genetically largely uncorrelated, reflecting distinct aetiological pathways ( Kuntsi et al., 2010 , Kuntsi et al., 2014 ).

Regarding developmental change, in a 6-year longitudinal outcome study of children with DSM-IV ADHD, adolescents and young adults who no longer met ADHD criterial (ADHD remitters), but not those who still had ADHD (ADHD persisters), showed a similar profile to controls without ADHD on cognitive and neural measures. These measures indicated response preparation, attention and vigilance processes (RTV, omission errors, delta activity, errors in low-conflict conditions and contingent negative variation) and were significantly correlated to the severity of inattention at outcome. In contrast, executive control measures of working memory, and inhibitory processes did not distinguish in persisters and remitters, and did not correlate with ADHD symptoms at outcome; including commission errors, digit span backwards, and ERP activity of inhibitory control (No-go P3) and conflict monitoring (N2) ( Cheung et al., 2016 ; Michelini et al., 2016b ).

Similarly, in another follow-up study there was no an association between ADHD remission and improvements in executive functioning ( Biederman et al., 2009 ), interference control ( Pazvantoğlu et al., 2012 ), and response inhibition ( McAuley et al., 2014 ). Working memory impairments (e.g. reduced caudate activation) in young adults diagnosed with ADHD in adolescence compared to controls have also been observed regardless of whether they still met an ADHD diagnosis ( Roman-Urrestarazu et al., 2016 ).

A recent study reported increased resting-state fMRI connectivity in ADHD remitters compared to controls in the executive control network, with intermediate connectivity profiles in persisters ( Francx et al., 2015 ). Two other connectivity studies ( Michelini et al., 2017 ; Mattfeld et al., 2014 ) also indicated that connectivity within the executive control network or during executive tasks may not distinguish between ADHD persisters and remitters. Both persisters and remitters showed an increased EEG connectivity during executive control ( Michelini et al., 2017 ) and reduced negative functional correlation between medial (DMN hub) and dorsolateral prefrontal cortex (FPN hub) during resting state fMRI ( Mattfeld et al., 2014 ).

However, in a resting study fMRI, only ADHD persisters showed reduced positive functional correlation between DMN hubs (posterior cingulate and medial prefrontal cortices) ( Mattfeld et al., 2014 ). Reduced posterior cingulate cortex and medial prefrontal cortex connectivity (i.e. DMN intra-connectivity) may therefore be a neurobiological marker of persistence of ADHD ( Uchida et al., 2015 ).

From these findings we conclude that, potentially, DMN activity and preparation-vigilance processes (associated with MW) may have direct aetiological significance in ADHD since they track the symptoms of ADHD during child to adult development. In contrast, deficits in executive control functions appear to dissociate from the clinical course of ADHD symptoms during development and are therefore less likely to play a direct causal role in the ongoing generation of symptoms. Since, the neural processes tracking the clinical disorder are closely aligned to those thought to underpin MW, we propose that targeting the regulation of MW or MW-associated neural dysfunctions could potentially lead to reductions in ADHD symptoms and remission of the disorder.

4. Developmental perspective on mind wandering in ADHD

4.1. mind wandering and typical development of functional networks.

Finally, we provide a developmental perspective on the neural processes linked to MW, since the frequency and impact of MW may vary throughout development. “Small-world” properties represent quantifiable metrics of topographic properties of large-scale/global brain organisation ( Achard et al., 2006 ). These properties are present by the age of 7, and resemble functional brain organisation in young adults ( Supekar et al., 2009 ). The two major hubs of the DMN (PCC and mPFC; also neural correlates of MW), are visible very early on, with DMN formation completed by 2 years of age ( Gao et al., 2009 ). However, formation of the DMN is thought to precede functional specialisation for self-referential cognition and mentalising such as mind wandering ( Gao et al., 2009 ).

An increase in long-range connections, as well as a decrease in short-range connections (segregation) and strengthening of within-network interactions (integration), are thought to govern functional brain development and functional specialisation ( Di Martino et al., 2014 ; Kelly et al., 2008 ; Power et al., 2010 ; Rubia, 2013 ; Rubia et al., 2009 ). The childhood to adolescent years (ages 7–15) are a particularly sensitive period for such functional brain network development ( Mak et al., 2017 ), which is the same age that ADHD symptoms and impairments often emerge ( Asherson et al., 2016 ). We also propose MW is likely to become more frequent during daily life, or even emerge. During this period greater integration develops between DMN and somatosensory regions, contrasting their complete segregation in adulthood. Additionally, early adolescence is marked by a similar degree of DMN deactivation contrasting less task-related deactivations in somatosensory regions compared to adults ( Thomason et al., 2008 ). This deactivation pattern was linked to better task performance in childhood ( Thomason et al., 2008 ), suggesting that the recruitment of somatosensory areas might be a compensatory strategy during high cognitive demand task conditions in children. In summary, while children exhibit a DMN activity similar to adults, the system does still undergo important changes between middle childhood and young adulthood. Therefore, frequency and type of MW may vary with age as function of DMN development.

Within-network integration of DMN hubs ( Fair et al., 2008 ) and segregation from other networks is considered to be a good marker of brain maturation ( Dosenbach et al., 2010 ; Rubia et al., 2009 ), and better than executive control networks whose functional development is less affected by age ( Sato et al., 2014 ). However, executive control networks also show a pattern of strengthening of intra-network and weakening of inter-network connections from childhood to middle adulthood ( Fair et al., 2007 ). These developmental changes are reflected in functional improvement, or greater adaptive control and greater working memory capacity with age ( Fair et al., 2007 ). Similarly, a pattern of increasing anti-correlation between large-scale networks (DMN and FPN) and task-related DMN deactivations is likely to support improved regulation of both executive control and MW ( Fair et al., 2008 ). Potentially, a maturational delay in the functional specialisation of these networks could result in a disrupted regulation of executive control and MW.

4.2. Mind wandering and “Maturational lag hypothesis” of brain development in ADHD

Related to the findings on typical functional brain development, El-Sayed et al. (2003) proposed a maturational lag hypothesis. The hypothesis suggests that a persistent maturational lag in functional brain development might become a sustained functional abnormality leading to symptoms and impairments of ADHD. We further propose that abnormalities in resting-state functional connectivity resulting from co-activation of functionally related brain regions ( Power et al., 2010 ) may lead to the self-generation of excessive, spontaneous and context-independent thoughts, typical of MW, which are externalised as inattentive behaviours over the lifespan. Consistent with this view, recent work in ADHD has shown a maturational lag in major large-scale brain networks, especially within-network integration (DMN and FPN) and interactions between default mode, frontoparietal, ventral attention and salience networks ( Sripada et al., 2014 ). A recent review also summarised findings for decreased synchrony/connectivity between the two major DMN hubs in ADHD ( Castellanos and Aoki, 2016 ). The lagged maturation was associated with DMN interference, poor performance (e.g. greater reaction time variability) during cognitively demanding tasks, and was proportionate to the severity of inattention ( Sripada et al., 2014 ).

With regard to the development of MW in ADHD, to date there has been only one published study, which compared the frequency of MW in children and adults with ADHD ( Van den Driessche et al., 2017 ). Using an experience sampling method, similar frequencies of “mind blanking” (MW without awareness of the content) were seen in 6-12-year-old children and young adults with ADHD. While the authors found no case-control performance differences on the SART, medication-naïve children and adults reported twice as much mind blanking, but fewer episodes of task focus and MW with awareness than controls. We propose that individuals with ADHD in this study tended to report mind blanking rather than MW due to the lack of a coherent reportable content. When comparing a group of children with ADHD treated with methylphenidate with drug naïve children and controls, there was reduced frequency of mind blanking to the level of controls; although the treated group still had a greater frequency of MW with awareness of content than controls ( Van den Driessche et al., 2017 ). Medication was therefore proposed to allow access to awareness of MW. The authors ( Van den Driessche et al., 2017 ) further hypothesised that this effect could be due to restoration of executive resources. These findings suggest similar abnormalities in the frequency of MW-related measures in ADHD during both childhood and young to middle adulthood.

Overall, the developmental studies suggest that understanding the cortical maturation of key networks leading to aware/unaware and spontaneous/deliberate forms of MW, may be important to understanding the onset, course and development of ADHD.

5. Discussion

This review sets out to draw parallels between MW and ADHD, and guide new insights into ADHD psychopathology. The theoretical conceptualisations outlined are designed to set the scene for further hypothesis testing. The basis for this work is the observation that a spontaneous and poorly controlled type of MW is strongly associated with ADHD, and ADHD-related impairments. Currently, there are only a small number of studies that measure MW in ADHD, and none that simultaneously measure ADHD, MW and neural functions associated with both ADHD and MW. The relationship of MW to ADHD symptoms such as inattention is therefore not yet well understood. We therefore set out to summarise what is known about the links between ADHD, MW and their neural correlates.

At the level of neural networks, there are strong parallels in the association of both ADHD and MW with DMN activity during task conditions. Reduced deactivation of DMN activity during tasks requiring sustained attention is associated with the frequency of MW in controls, and is also associated with ADHD compared to controls. Since ADHD is also linked to a specific pattern of spontaneous and poorly controlled MW, a simple hypothesis can be proposed that the normal neural processes that regulate MW are disturbed in ADHD, leading to excessive levels of spontaneous and uncontrolled MW. Furthermore, cognitive and functional impairments that are associated with periods of MW in controls are comparable to the range of impairments associated with ADHD, suggesting that excessive spontaneous MW may underpin functional deficits in ADHD.

In support of our hypothesis we draw on evidence that the usual processes regulating MW are disrupted in ADHD. First, we see that the usual context regulation of DMN and executive control network activity to increasing task demands is associated with MW, and deficient in ADHD. Furthermore, deficient regulation of these processes appears to be reversed by stimulant medications used to treat ADHD. Second, we see that the processes associated with both MW and ADHD are sensitive to the salience of task conditions. In ADHD increasing the salience of task conditions may “normalise” performance and associated neural processes. Finally, we see that both MW and ADHD may be associated with sensory decoupling such as the reduced early P1 response of the occipital cortex to a visual stimulus. We therefore propose that by understanding the processes of normal regulation of MW in the general population, we can identify with more precision aberrant processes in ADHD.

One question that arises from this model is whether measures of MW can be considered distinct from the inattentive symptoms of ADHD. For example, measures of MW may merely reflect alternative measures of the same underlying construct. Currently, there is almost no evaluation of the incremental information provided by measures of MW, although we did report that a rating scale measure of MW was an independent predictor of impairment in ADHD after controlling for ADHD inattention and hyperactivity/impulsivity symptoms ( Mowlem et al., 2016 ). Further research is therefore required to addresses this question. However, since many of the findings related to ADHD also apply to measures of MW, it is a reasonable hypothesis that one reflects the other.

If there is a very close relationship of MW with ADHD-inattention, this would open new avenues for research. MW has the advantage that it can be measured using simple rating scale trait measures, but also experience sampling data in daily life (considered to be more objective reflections of the mental state); and experience sampling during experimental paradigms such as sustained attention tasks, which have been used to investigate the neural correlates of MW in control samples. Measures of MW may also be less dependent than the inattentive symptoms of ADHD on behavioural adaptation, which is influenced by learnt coping strategies, so perhaps a better reflection of the underlying neural condition.

The model we propose views the inattentive symptoms of ADHD as outcomes of MW (i.e. internal distractibility), and MW in ADHD may be a direct outcome of altered neural regulation of internal thought processes. Cognitive performance deficits seen in ADHD may also be a direct outcome of MW. We propose that dysregulated neural functions (DMN overactivity during task conditions for example), are reflected in excessive spontaneous MW, and that such internal distractibility explains behavioural symptoms such as losing track during conversations, avoiding tasks that require sustained attention, not completing tasks, and misplacing things.

Further research is required to investigate heterogeneity the range of internally generated thoughts, and understand the regulatory processes involved. A key question is to fully understand the differences in studies which find that episodes of MW are associated with both reduced or absent ( Kucyi et al., 2016 ; Stawarczyk and D’argembeau, 2015) and present ( Christoff, 2012 ; Spreng et al., 2010 ) co-recruitment of DMN and executive control network regions. In our view, this likely reflects that MW is a multidimensional construct and different types of MW recruit the executive control network differentially.

Spontaneous, unaware (‘zone-off’) MW was associated with decreased functional connectivity ( Golchert et al., 2017 ) and a negative functional correlation ( Gruberger et al., 2011 ) between the DMN and FPN. This pattern contrasts with increased functional connectivity between these two networks during deliberate MW ( Christoff et al., 2016 ). Potentially, both spontaneous and deliberate MW might involve the co-activation of both networks initially. This co-activation allows both deliberate MW and task performance under low demand to be maintained, as well as the controlled and adequate shift to task focus under high demand. Alternatively, this co-activation might also reflect an inability to suppress the DMN activity during on-task conditions. Therefore, constant engagement in spontaneous MW might represent a neural state of overly active DMN hubs during on-task, and hypoconnectivity within the entire DMN at rest. The uncontrolled and spontaneous occurrences of MW will then result in poor context regulation.

In our review, we identified three existing fMRI accounts proposing specific dynamics of MW. The dynamic framework ( Mittner et al., 2016 ) proposes a greater activation of the dorsal attentional network during on-task that transitions to increasing connectivity/activity within the DMN during an off-focused state (selecting between returning to the task or entering a state of MW). Finally, if task-unrelated thoughts appear more salient, an individual enters a state of MW marked by reduced functional connectivity between large-scale networks ( Mittner et al., 2016 ). The other two fMRI accounts speculate on initial recruitment of the right medial temporal lobe, or limbic regions ( Golchert et al., 2017 ) and later FPN ( Fox et al., 2015 ) during MW. However, the dynamics of MW remain poorly understood ( Smallwood et al., 2011 ).

Future research could focus on the use of EEG, which can capture fast and covert cognitive processes such as MW. The increased use of state-of-the-art EEG approaches that provide improved source localisation of EEG signals, combined with EEG’s millisecond temporal resolution, has already demonstrated their better potential to explain the dynamics of MW compared to more traditional scalp-level EEG analyses ( Braboszcz and Delorme, 2011 ; Kirschner et al., 2012 ).

6. Specific predictions from the mind wandering hypothesis for ADHD

The MW hypothesis proposes that altered interaction between the four large scale networks (DMN, executive control network, salience network and visual network), and that deficient DMN deactivation during task activities will lead to excessive spontaneous MW, lacking in coherence and topic stability, which in turn will lead to ADHD symptomatology.

To investigate this hypothesis, future work could apply experimental designs that have been successfully applied to manipulate and measure MW and measures the neural correlates of MW in controls samples, to ADHD case-control studies, or by investigating correlations to dimensional measures of ADHD symptoms. Examples include sustained attention and inhibitory control tasks, varying cognitive load by using manipulations such as altering the stimulus presentation rate ( Christakou et al., 2013 ) or introducing a working memory component ( Baird et al., 2014 ). This would enable investigation of context regulation and sensory decoupling in relation to ADHD. A further manipulation would be to the comparison of high and low reward conditions, to investigate the effects of salience ( Liddle et al., 2011 ).

Inferring causal directions may be difficult because of the close temporal timing of neural, cognitive performance and MW events if they covary strongly with each other. One approach to infer causal relationships is to use treatment interventions such as stimulant mediation, to bring about change in the various outcome measures ( Kendler and Neale, 2010 ). Then the causal relationships between these can be modelled. Mindfulness training is another intervention that is thought to act on the regulatory processes involve in MW and ADHD ( Mrazek et al., 2014 ). Recent studies suggest effects of mindfulness training that are potentially comparable to those seen for ADHD medications ( Hepark et al., 2015 ).

To guide future work, we outline specific predictions derived from this hypothesis.

6.1. Perceptual decoupling

We propose that individuals with ADHD will experience greater perceptual decoupling driven by the proposed ADHD-specific MW, compared to controls during long-lasting (>30 min) sustained attention paradigms. A potential neural correlate is absent or reduced inter-regional synchronisation or positive functional correlation between DMN and visual networks. Using EEG, reduced mean P1 and N1 amplitudes will reflect inefficient early perceptual processes in ADHD.

6.2. Context regulation

We propose that individuals with ADHD will be unable to adapt to increasing cognitive (attentional, control) demands both at the neural level, and behaviourally; and that deficient context regulation of neural activity in ADHD will be related to the frequency of MW. Regarding neural correlates of these processes, we propose that a reduced positive functional connectivity between DMN and salience network will reflect the deficit in switching from rest to task, task to rest and from a low to high demand condition. Another neural correlate will be an absent change in frontal midline theta power from rest to task, hypoactivation in the DMN from task to rest and hypoconnectivity or reduced positive functional connectivity within the DMN (posterior cingulate cortex and medial prefrontal cortex) during rest.

6.3. Behavioural correlates of DMN and FPN dysregulation

We propose that variable reaction times will stem from MW and serve as a behavioural correlate of reduced negative functional correlation (or even disconnection) between default mode and executive control network and hyperactivation within the DMN during cognitive paradigms.

The neural inefficiency in the large-scale networks will manifest in increased phase inconsistency/variability of parietal or frontal theta in response to task-related stimuli, which will coincide with excessively frequent episodes of MW.

Changes in the content and context of MW is likely to reflect differential recruitment of DMN regions (Stawarczyk and D’argembeau, 2015). For instance, depressive thoughts might result in greater connectivity between the core DMN and salience network, and a weaker connectivity between these two networks with the FPN and the medial temporal lobe (DMN hub). Excessive MW was correlated with the highest degree of DMN activation, suggesting that overactivity of the DMN might reflect the frequency rate of MW ( Kucyi et al., 2016 ). Therefore, MW in ADHD will be characterised by hyper-connectivity within both right medial temporal lobe and right medial prefrontal cortex (DMN hubs) during on-task.

7. Conclusion

Converging evidence indicates that both MW ( Kucyi et al., 2016 ) and ADHD ( Sidlauskaite et al., 2016 ) are linked to DMN regulation and regulation of the interaction between DMN and FPN. In particular, ADHD is associated with both within DMN and between DMN-FPN dysregulation. These neural effects are present in ADHD independent of age, clinical characteristics and type of task ( Cortese et al., 2012 ; Plichta and Scheres, 2014 ), supporting the MW hypothesis. A dysfunctional and later absent interaction between the four major networks (DMN, executive control network, salience network and visual network) is proposed to underlie different aspects of cognitive and behavioural impairment associated with MW in ADHD.

Future studies should focus on understanding whether completely different neural processes underlie MW in ADHD compared to controls, or there is simply neural attenuation in ADHD. Understanding context regulation requires the use of conditions varying in cognitive demand. Another necessary study is to measure effects of reward, or whether reward ‘normalsies' MW in ADHD compared to controls. Importantly, validation across different MW measures and conceptualization of different types of MW will enable the investigation of neural activity underlying specific types of MW.

Conflicts of interest

Professor Jonna Kuntsi has given talks at educational events sponsored by Medice: all funds are received by King’s College London and used for studies of ADHD. Kings College London research support account for Professor Philip Asherson received honoraria for consultancy to Shire, Flynn-Pharma, Eli-Lilly, Novartis, Lundbeck and Medice; educational/research awards from Shire, Lilly, Novartis, Flynn Pharma, Vifor Pharma, GW Pharma and QbTech; speaker at sponsored events for Shire, Lilly, Novartis, Medice and Flynn Pharma.

Acknowledgements

Philip Asherson’s research is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, and an NIHR Senior Investigator award (NF-SI-0616-10040).

Natali Bozhilova’s research is supported by a studentship awarded by the Medical Research Council, as part of a doctoral training programme (DTP).

Dr Kai Syng Tan is the author of the figure to this paper. We would like to thank her for visualising the MW hypothesis through her brilliant artwork. http://kaisyngtan.com/ .

Professor Jonna Kuntsi has given talks at educational events sponsored by Medice; all funds are received by King’s College London and used for studies of ADHD.

This paper represents independent research part funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

We would like to thank Jonny Smallwood, Katya Rubia, Florence Mowlem, Bartosz Helfer, Celine Ryckaert and Talar Moukhtarian whose research and hard work has inspired our theoretical views.