harvard wandering mind study

November 24, 2010

A Wandering Mind is an Unhappy One

New research underlines the wisdom of being absorbed in what you do

By Jason Castro

We spend billions of dollars each year looking for happiness, hoping it might be bought, consumed, found, or flown to. Other, more contemplative cultures and traditions assure us that this is a waste of time (not to mention money). ‘Be present’ they urge. Live in the moment, and there you’ll find true contentment.

Sure enough, our most fulfilling experiences are typically those that engage us body and mind, and are unsullied by worry or regret. In these cases, a relationship between focus and happiness is easy to spot. But does this relationship hold in general, even for simple, everyday activities? Is a focused mind a happy mind? Harvard psychologists Matthew Killingsworth and Daniel Gilbert decided to find out.

In a recent study published in Science, Killingsworth and Gilbert discovered that an unnervingly large fraction of our thoughts - almost half - are not related to what we’re doing. Surprisingly, we tended to be elsewhere even for casual and presumably enjoyable activities, like watching TV or having a conversation. While you might hope all this mental wandering is taking us to happier places, the data say otherwise. Just like the wise traditions teach, we’re happiest when thought and action are aligned, even if they’re only aligned to wash dishes.

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The ingredients of simple, everyday happiness are tough to study in the lab, and aren’t easily measured with a standard experimental battery of forced choices, eye-tracking, and questionnaires. Day to day happiness is simply too fleeting. To really study it’s causes, you need to catch people in the act of feeling good or feeling bad in real-world settings.

To do this, the researchers used a somewhat unconventional, but powerful, technique known as experience sampling. The idea behind it is simple. Interrupt people at unpredictable intervals and ask them what they’re doing, and what’s on their minds. If you do this many times a day for many days, you can start to assemble a kind of quantitative existential portrait of someone. Do this for many people, and you can find larger patterns and tendencies in human thought and behavior, allowing you to correlate moments of happiness with particular kinds of thought and action.

To sample our inner lives, the team developed an iPhone app that periodically surveyed people’s thoughts and activities. At random times throughout the day, a participant’s iPhone would chime, and present him with a brief questionnaire that asked how happy he was (on a scale from 1-100), what he was doing, and if he was thinking about what he was doing. If subjects were indeed thinking of something else, they reported whether that something else was pleasant, neutral, or unpleasant. Responses to the questions were standardized, which allowed them to be neatly summarized in a database that tracked the collective moods, actions, and musings of about 5000 total participants (a subset of 2250 people was used in the present study).

In addition to awakening us to just how much our minds wander, the study clearly showed that we’re happiest when thinking about what we’re doing. Although imagining pleasant alternatives was naturally preferable to imagining unpleasant ones, the happiest scenario was to not be imagining at all. A person who is ironing a shirt and thinking about ironing is happier than a person who is ironing and thinking about a sunny getaway.

What about the kinds of activities we do, though? Surely, the hard-partiers and world travelers among us are happier than the quiet ones who stay at home and tuck in early? Not necessarily. According to the data from the Harvard group’s study, the particular way you spend your day doesn’t tell much about how happy you are. Mental presence - the matching of thought to action - is a much better predictor of happiness.

The happy upshot of this study is that it suggests a wonderfully simple prescription for greater happiness: think about what you’re doing. But be warned that like any prescription, following it is very different from just knowing it’s good for you. In addition to the usual difficulties of breaking bad or unhelpful habits, your brain may also be wired to work against your attempts stay present.

Recent fMRI scanning studies show that even when we’re quietly at rest and following instructions to think of nothing in particular, our brains settle into a conspicuous pattern of activity that corresponds to mind-wandering. This signature ‘resting’ activity is coordinated across several widespread brain areas , and is argued by many to be evidence of a brain network that is active by default. Under this view our brains climb out of the default state when we’re bombarded with input, or facing a challenging task, but tend to slide back into it once things quiet down.

Why are our brains so intent on tuning out? One possibility is that they’re calibrated for a target level of arousal. If a task is dull and can basically be done on autopilot, the brain conjures up its own exciting alternatives and sends us off and wandering. This view is somewhat at odds with the Killingsworth and Gilbert’s findings though, since subjects wandered even on ‘engaging’ activities. Another, more speculative possibility is that wandering corresponds to some important mental housekeeping or regulatory process that we’re not conscious of. Perhaps while we check out, disparate bits of memory and experience are stitched together into a coherent narrative – our sense of self.

Of course, it’s also possible that wandering isn’t really ‘for’ anything, but rather just a byproduct of a brain in a world that doesn’t punish the occasional (or even frequent) flight of fancy. Regardless of what prompts our brains to settle into the default mode, its tendency to do so may be the kiss of death for happiness. As the authors of the paper elegantly summarize their work: “a human mind is a wandering mind, and a wandering mind is an unhappy mind.” 

On the plus side, a mind can be trained to wander less. With regular and dedicated meditation practice, you can certainly become much more present, mindful, and content. But you’d better be ready to work. The most dramatic benefits only really accrue for individuals, often monks, who have clocked many thousands of hours practicing the necessary skills (it’s not called the default state for nothing).

The next steps in this work will be fascinating to see, and we can certainly expect to see more results from the large data set collected by Killingsworth and Gilbert. It will be interesting to know, for example, how much people vary in their tendency to wander, and whether differences in wandering are associated with psychiatric ailments. If so, we may be able to tailor therapeutic interventions for people prone to certain cognitive styles that put them at risk for depression, anxiety, or other disorders.

In addition to the translational potential of this work, it will also be exciting to understand the brain networks responsible for wandering, and whether there are trigger events that send the mind into the wandering or focused state. Though wandering may be bad for happiness, it is still fascinating to wonder why we do it.

Are you a scientist? Have you recently read a peer-reviewed paper that you want to write about? Then contact Mind Matters co-editor Gareth Cook, a Pulitzer prize -winning journalist at the Boston Globe, where he edits the Sunday Ideas section. He can be reached at garethideas AT gmail.com

When Mind Wandering is a Strategy, Not a Disadvantage

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Whether we are listening in a meeting or going for a walk, our minds often stray from the present task to other thoughts. People’s minds wander differently across situations, and new research suggests that we can modulate our mind wandering from moment to moment in response to the challenges we expect to encounter in a task.

In a study published in Psychological Science, postdoctoral fellow Paul Seli of Harvard Univeristy and colleagues Jonathan S. A. Carriere, Jeffrey D. Wammes, Evan F. Risko, Daniel L. Schacter, and Daniel Smilek found that people can adjust their rate of mind wandering during an attention-demanding task without decreasing their performance on that task.

Previous research suggests that mind wandering is situation-related and varies based on a task’s difficulty, meaning that easier tasks require fewer executive resources and allow for more mind wandering than difficult, attention-demanding tasks. Although easier tasks may allow an individual to use shared resources for more mind wandering, much of the literature supports the idea that mind wandering is detrimental to a task no matter the difficulty.

Seli wanted to investigate whether mind wandering might be harmless during certain types of attention-demanding tasks:

“To date, the vast majority of tasks used in the literature on mind wandering have been very attentionally demanding,” Seli said . “It occurred to me that many of the tasks we perform in daily life are quite different from these sorts of tasks, since they don’t constantly demand our attention.”

For these tasks, such as riding a bike or reading emails, the researchers were interested to see if people could adjust their mind wandering from moment to moment. This type of flexibility would suggest that in certain contexts, mind wandering does not inhibit performance.

“Since we know that people spend a considerable portion of their lives engaged in mind wandering, such a finding would be very welcomed silver lining,” Seli said.

The authors designed a novel attention task for the study , featuring an analog clock that ticks once per second and makes a full revolution around the clock face every 20 seconds. Participants were instructed to press a button every time the clock hand pointed at 12:00, with correct responses occurring within the 50 msec before or 500 msec after the clock hand reached 12:00. The researchers awarded bonus money for every correct response to increase motivation for good performance. The critical task of pushing the button was completely predictable, and the participants were told that their minds might wander during this task.

The researchers also presented thought probes to measure participants’ rates of mind wandering. Twenty probes appeared during random revolutions, stopping the clock’s rotation — participants reported the extent to which their thoughts were on or off task at that moment. Participants chose from “on task,” “intentionally mindwandering,” or “unintentionally mindwandering,” and the clock would begin to move again.

In the 5 seconds after the task event, participants reported mind wandering about 33% of the time, and in the next 10 seconds their mind wandering increased to about 50% of the time. Although the participants varied in how much they mind wandered, it did not seem to affect performance on the task.

Rates of intentional mind wandering were higher than or the same as rates of unintentional mind wandering, suggesting that participants may have realized they were able to let their minds wander without reducing their performance:

“In other words,” Seli explains, “they strategically copped a daydream when they could afford to do so.”

The results support the theory that mind wandering uses the same executive resources as attention-demanding tasks, and suggests that a control mechanism can determine where to allocate these resources. Mind wandering may even have strategic benefits, allowing us to multitask, problem solve, and think ahead.

The present study opens a door to investigating how people may differ in their ability to modulate mind wandering. Seli and colleagues suggest that age and working memory capacity could potentially affect an individual’s level of control over their wandering mind.

Seli, P., Carriere, J. S. A., Wammes, J. D, Risko, E. F., Schacter, D. L., & Smilek, D. (2018). On the clock: Evidence for rapid and strategic modulation of mind wandering. Psychological Science . doi: 10.1177/0956797618761039

Reuell, P. (19 June, 2018). When wandering minds are just fine. The Harvard Gazette . Retrieved from: https://news.harvard.edu/gazette/story/2018/06/mind-wandering-is-fine-in-some-situations-harvard-based-study-says/

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Mind is a frequent, but not happy, wanderer: People spend nearly half their waking hours thinking about what isn’t going on around them

People spend 46.9 percent of their waking hours thinking about something other than what they're doing, and this mind-wandering typically makes them unhappy. So says a study that used an iPhone web app to gather 250,000 data points on subjects' thoughts, feelings, and actions as they went about their lives.

The research, by psychologists Matthew A. Killingsworth and Daniel T. Gilbert of Harvard University, is described in the journal Science .

"A human mind is a wandering mind, and a wandering mind is an unhappy mind," Killingsworth and Gilbert write. "The ability to think about what is not happening is a cognitive achievement that comes at an emotional cost."

Unlike other animals, humans spend a lot of time thinking about what isn't going on around them: contemplating events that happened in the past, might happen in the future, or may never happen at all. Indeed, mind-wandering appears to be the human brain's default mode of operation.

To track this behavior, Killingsworth developed an iPhone web app that contacted 2,250 volunteers at random intervals to ask how happy they were, what they were currently doing, and whether they were thinking about their current activity or about something else that was pleasant, neutral, or unpleasant.

Subjects could choose from 22 general activities, such as walking, eating, shopping, and watching television. On average, respondents reported that their minds were wandering 46.9 percent of time, and no less than 30 percent of the time during every activity except making love.

"Mind-wandering appears ubiquitous across all activities," says Killingsworth, a doctoral student in psychology at Harvard. "This study shows that our mental lives are pervaded, to a remarkable degree, by the non-present."

Killingsworth and Gilbert, a professor of psychology at Harvard, found that people were happiest when making love, exercising, or engaging in conversation. They were least happy when resting, working, or using a home computer.

"Mind-wandering is an excellent predictor of people's happiness," Killingsworth says. "In fact, how often our minds leave the present and where they tend to go is a better predictor of our happiness than the activities in which we are engaged."

The researchers estimated that only 4.6 percent of a person's happiness in a given moment was attributable to the specific activity he or she was doing, whereas a person's mind-wandering status accounted for about 10.8 percent of his or her happiness.

Time-lag analyses conducted by the researchers suggested that their subjects' mind-wandering was generally the cause, not the consequence, of their unhappiness.

"Many philosophical and religious traditions teach that happiness is to be found by living in the moment, and practitioners are trained to resist mind wandering and to 'be here now,'" Killingsworth and Gilbert note in Science. "These traditions suggest that a wandering mind is an unhappy mind."

This new research, the authors say, suggests that these traditions are right.

Killingsworth and Gilbert's 2,250 subjects in this study ranged in age from 18 to 88, representing a wide range of socioeconomic backgrounds and occupations. Seventy-four percent of study participants were American.

More than 5,000 people are now using the iPhone web app the researchers have developed to study happiness, which can be found at www.trackyourhappiness.org .

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Story Source:

Materials provided by Harvard University . Original written by Steve Bradt, Harvard Staff Writer. Note: Content may be edited for style and length.

Journal Reference :

• Matthew A. Killingsworth, Daniel T. Gilbert. A Wandering Mind Is an Unhappy Mind . Science , 2010; 330 (6006): 932 DOI: 10.1126/science.1192439

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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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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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Belardi, A., Chaieb, L., Rey-Mermet, A. et al. On the relationship between mind wandering and mindfulness. Sci Rep 12 , 7755 (2022). https://doi.org/10.1038/s41598-022-11594-x

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Effects of 5 hz auditory beat stimulation on mind wandering and sustained attention in an online experiment.

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With mindfulness, life’s in the moment.

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Harvard Staff Writer

Those who learn its techniques often say they feel less stress, think clearer

Second of two parts

On a cold winter evening, six women and two men sat in silence in an office near Harvard Square, practicing mindfulness meditation.

Sitting upright, eyes closed, palms resting on their laps, feet flat on the floor, they listened as course instructor Suzanne Westbrook guided them to focus on the present by paying attention to their bodily sensations, thoughts, emotions, and especially their breath.

Suzanne Westbrook, a retired internal-medicine doctor, taught an eight-week program that focused on reducing stress.

Rose Lincoln/Harvard Staff Photographer

“Our mind wanders all the time, either reviewing the past or planning for the future,” said Westbrook, who before retiring last June was an internal-medicine doctor caring for Harvard students. “Mindfulness teaches you the skill of paying attention to the present by noticing when your mind wanders off. Come back to your breath. It’s a place where we can rest and settle our minds.”

The class she taught was part of an eight-week program aimed at reducing stress.

Studies say that eight in 10 Americans experience stress in their daily lives and have a hard time relaxing their bodies and calming their minds, which puts them at high risk of heart disease, stroke, and other illnesses. Of the myriad offerings aimed at fighting stress, from exercise to yoga to meditation, mindfulness meditation has become the hottest commodity in the wellness universe.

Modeled after the Mindfulness-Based Stress Reduction program created in 1979 by Jon Kabat-Zinn to help counter stress, chronic pain, and other ailments, mindfulness courses these days can be found in venues ranging from schools to prisons to sports teams. Even the U.S. Army recently adopted it to “improve military resilience.”

Harvard offers several mindfulness and meditation classes, including a spring break retreat held in March for students through the Center for Wellness and Health Promotion . The Office of Work/Life offers programs to managers and staff, as well as weekly drop-in meditation sessions on campus, online guided meditation resources, and even a meditation phone line, 4-CALM (at 617.384.2256).

“We were tasked to find ways for the community to cope with stress. And at the same time, so much research was coming out on the benefits of mindfulness and meditation,” said Jeanne Mahon, director of the wellness center. “We keep offering mindfulness and meditation because of the feedback. People appreciate to have the chance for self-reflection and learn about new ways to be in relationships with themselves.”

More than 750 students have participated in mindfulness and meditation programs since 2012, said Mahon.

Part of mindfulness’ appeal lies in the fact that it’s secular. Buddhist monks have used mindfulness exercises as forms of meditation for more than 2,600 years, seeing them as one of the paths to enlightenment. But in the Mindfulness-Based Stress Reduction program, mindfulness is stripped of religious undertones.

Mark Dennis (from left), Kelly Romirowsky, and Ayesha Hood practice meditation. Metta McGarvey (not pictured) teaches the practice of mindfulness, a workshop for educators inside the Gutman Conference Center.

Kris Snibbe/Harvard Staff Photographer.

Mindfulness’ popularity has been bolstered by a growing body of research showing that it reduces stress and anxiety, improves attention and memory, and promotes self-regulation and empathy. A few years ago, a study by Sara Lazar , a neuroscientist and assistant professor of psychology at Harvard Medical School (HMS) and assistant researcher in psychiatry at Massachusetts General Hospital, was the first to document that mindfulness meditation can change the brain’s gray matter and brain regions linked with memory, the sense of self, and regulation of emotions. New research by Benjamin Shapero and Gaëlle Desbordes is exploring how mindfulness can help depression .

The pioneer of scientific research on meditation, Herbert Benson , extolled its benefits on the human body — reduced blood pressure, heart rate, and brain activity — as early as 1975. He helped demystify meditation by calling it the “relaxation response.” Benson is director emeritus of the Benson-Henry Institute for Mind Body Medicine at Massachusetts General Hospital and Mind/Body Medicine Distinguished Professor of Medicine at HMS.

In the 1980s, mindfulness had yet to become a buzzword, recalls Paul Fulton, a clinical psychologist who has practiced Zen and insight meditation (vipassana) for more than 40 years. In the mid-1980s, when he was working on his doctoral dissertation on the nature of “self” among Buddhist monks, speaking of mindfulness in a medical context among scientists was “disreputable,” he recalled.

“Gradually because of the research, it became chic, no longer disreputable,” said Fulton, a lecturer in psychology in the Department of Psychiatry at HMS and co-founder of the Institute for Meditation and Psychotherapy . “And now you can’t step a foot out of the house without being barraged by mindfulness.”

Melanie Denham, head coach of Harvard women’s rugby team, recently attended a mindfulness workshop, intrigued by the idea of incorporating the techniques into her players’ training regimen to help them cope with the pressures of “expectation and performance.”

Mindfulness meditation made easy

• Settle in : Find a quiet space. Using a cushion or chair, sit up straight but not stiff; allow your head and shoulders to rest comfortably; place your hands on the tops of your legs with upper arms at your side.
• Now breathe: Close your eyes, take a deep breath, and relax. Feel the fall and rise of your chest and the expansion and contraction of your belly. With each breath notice the coolness as it enters and the warmth as it exits. Don’t control the breath but follow its natural flow.
• Stay focused: Thoughts will try to pull your attention away from the breath. Notice them, but don’t pass judgment. Gently return your focus to your breath. Some people count their breaths as a way to stay focused.
• Take 10: A daily practice will provide the most benefits. It can be 10 minutes per day, however, 20 minutes twice a day is often recommended for maximum benefit.

“In and out of the classroom, these student-athletes are immersed in a highly competitive culture,” said Denham. “This is stressful. This kind of training can develop a more-skillful mind and a sense of focus and well-being that can help them better maintain control and awareness of their thoughts, emotions, and presence in the moment.”

The growing interest in the field is reflected in Harvard’s course catalog. This spring, Lazar is teaching “Cognitive Neuroscience of Meditation,” Ezer Vierba leads an expository freshmen writing course on “Buddhism, Mindfulness, and the Practical Mind,” and Metta McGarvey teaches “Mindfulness for Educators” at the Graduate School of Education .

Due to high demand, McGarvey, who holds a doctorate in human development and psychology, teaches a three-day workshop for educators. It offers tools to enhance their work and their focus through breathing practices and self-compassion exercises.

“A lot of them are working in really tough environments, with all kinds of pressures,” said McGarvey. “The rates of burnout in some of the more challenging environments are very high.”

Ayesha Hood, a police officer from Baltimore who is interested in running a day care center, attended McGarvey’s workshop last fall, and found it helpful. “As a police officer, I live in high stress, and as a public servant, I tend to neglect myself,” she said. “I want to calm myself and be conscious about it.”

Christine O’Shaughnessy, a former investment bank executive who lead workshops at Harvard, said, “All day we’re bombarded with social media, colleagues, work, children, etc. We don’t have time to spend it in quiet reflection. But if you practice it at least once a day, you’ll have a better day.”

To skeptics who still view mindfulness as hippie-dippy poppycock, O’Shaughnessy has four words: “Give it a try.” When she first signed up for a mindfulness workshop in 1999, she said she was skeptical too. But once she realized she was becoming calmer and less stressed, she converted. She eventually quit her job and became a mindfulness instructor. (She recently launched a free meditation app .)

“Doing mindfulness is like a fitness routine for your brain,” she said. “It keeps your brain healthy.”

Metta McGarvey teaches the practice of mindfulness, a workshop for educators inside the Gutman Conference Center.

Kris Snibbe/Harvard Staff Photographer

When science meets mindfulness

Mindfulness over matters

Building calm into the day

Eight weeks to a better brain

Mindfulness practitioners admit the practice can offer challenges. It requires consistency because its effects can be better felt over time, and discipline to train the wandering mind to keep coming back to the present, without judgment. A 2014 study said that many people would rather apply electroshocks to themselves than be alone with their thoughts. Another study showed that most people find it hard to focus on the present and that the mind’s wandering can lead to stress and even suffering.

Despite the rising acceptance of mindfulness, many people still think the practice involves emptying their minds, taking mini-naps, or going into trances. Beginners often fall asleep, feel uncomfortable, struggle with difficult thoughts or emotions, and become bored or distracted. Adepts recommend practicing the process in a group with an instructor.

After the training session led by Westbrook, one participant said she couldn’t stop thinking about what was for dinner during the meditation practice; others nodded in agreement. Westbrook reassured her, saying that mindfulness is not about stopping thoughts or emotions, but instead about noticing them without judgment. Mindfulness builds resilience and awareness to help people learn how to ride life’s ups and downs and live happier and healthier lives, said Westbrook, who, after helping heal the bodies of thousands of patients in 36 years as a doctor, plans to devote her second career to caring for people’s spirits and souls, maybe as a chaplain.

“Mindfulness is not about being positive all the time or a bubblegum sort of happiness — la, la, la,” she said. “It’s about noticing what happens moment to moment, the easy and the difficult, and the painful and the joyful. It’s about building a muscle to be present and awake in your life.”

For more information about the Mindfulness & Meditation program at Harvard University, visit its website . For a list of spring courses for Harvard faculty and staff, visit the Mindfulness at Work Program website .

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Libby Copeland

For you, it could be the drive home on the freeway in stop-and-go traffic, a run without headphones or the time it takes to brush your teeth. It’s the place where you’re completely alone with your thoughts—and it’s terrifying. For me, it’s the shower.

The shower is where I’m barraged with all the “what-ifs,” the imagined catastrophes, the endless to-do list. To avoid them, I’ve tried everything from shower radio and podcasts to taking a bath so I can watch an iPad. I’ve always thought this shower-dread was just my own neurosis. But psychological research is shedding insight into why our minds tend to wander without our consent—and why it can be so unpleasant.

Scientists, being scientists, sometimes refer to the experience of mind-wandering as “stimulus-independent thought.” But by any name, you know it: It’s the experience of arriving at work with no memory of the commute. When you’re engaged in mundane activities that require little attention, your brain drifts off like a balloon escaping a child’s hand—traveling to the future, ruminating on the past, generating to-do lists, regrets and daydreams. 

In the last 15 years, the science of mind wandering has mushroomed as a topic of scholarly study, thanks in part to advances in brain imaging. But for a long time, it was still difficult to see what people’s brains were doing outside the lab. Then, when smartphones came on the scene in the late 2000s, researchers came up with an ingenious approach to understanding just how often the human brain wanders in the wilds of modern life.

As it turns out, our brains are wily, wild things, and what they do when we’re not paying attention has major implications for our happiness. 

In 2010, Matt Killingsworth, then a doctoral student in the lab of happiness researcher Daniel Gilbert at Harvard University, designed an iPhone app that pinged people throughout the day, asking what they were experiencing at that very moment. The app asked questions like these, as paraphrased by Killingsworth:

1. How do you feel, on a scale ranging from very bad to very good?

2. What are you doing (on a list of 22 different activities, including things like eating, working and watching TV)?

3. Are you thinking about something other than what you're currently doing?

Killingsworth and Gilbert tested their app on a few thousand subjects to find that people’s minds tended to wander 47 percent of the time. Looking at 22 common daily activities including working, shopping and exercising, they found that people’s minds wandered the least during sex (10 percent of the time) and the most during grooming activities (65 percent of the time)—including taking a shower. In fact, the shower appears to be especially prone to mind wandering because it requires relatively little thought compared to something like cooking.

Equally intriguing to researchers was the effect of all that mind wandering on people’s moods: Overall, people were less happy when their minds wandered. Neutral and negative thoughts seemed to make them less happy than being in the moment, and pleasant thoughts made them no happier. Even when people were engaged in an activity they said they didn’t like—commuting, for example—they were happier when focused on the commute than when their minds strayed.

What’s more, people’s negative moods appeared to be the result, rather than the cause, of the mind wandering. Recently, I asked Killingsworth why he thought mind wandering made people unhappy. “When our mind wanders, I think it really blunts the enjoyment of what it is that were doing,” he told me.

For most, the shower in and of itself is not an unpleasant experience. But any pleasure we might derive from the tactile experience of the hot water is muted, because our minds are elsewhere. Even when our thoughts meander to pleasant things, like an upcoming vacation, Killingsworth says the imagined pleasure is far less vivid and enjoyable than the real thing.

Plus, in daily life we rarely encounter situations so bad that we really need the mental escape that mind wandering provides. More often, we’re daydreaming away the quotidian details that make up a life. “I’ve failed to find any objective circumstances so bad that when people are in their heads they’re actually feeling better,” Killingsworth told me. “In every case they’re actually surprisingly happier being in that moment , on average.”

When I told Killingsworth I spend my time in the shower imagining catastrophes, he wasn't surprised. More than a quarter of our mental meanderings are to unpleasant topics, he’s found. And the vast majority of our musings are focused on the future, rather than the past. For our ancestors, that ability to imagine and plan for upcoming dangers must have been adaptive, he says. Today, it might help us plan for looming deadlines and sources of workplace conflict.

But taken to an extreme in modern day life, it can be a hell of an impediment. “The reality is, most of the things we’re worrying about are not so dangerous,” he said.

In some cases, mind wandering does serve a purpose. Our minds might “scan the internal or external environment for things coming up we may have to deal with,” says Claire Zedelius , a postdoctoral researcher at the University of California at Santa Barbara who works in the lab of mind wandering expert Jonathan Schooler . Mind wandering may also be linked to certain kinds of creativity , and in particular to a creativity “incubation period” during which our minds are busy coming up with ideas, Schooler’s lab has found. 

It’s unclear how our tendency to drift is affected by the diversions and distractions of our smartphones. As Killingsworth pointed out, all those distractions—podcasts, email, texts and even happiness trackers—may mean we’re effectively mind wandering less. But it may also be that “our capacity to direct our attention for sustained periods gets diminished, so that then when we’re in a situation that’s not completely engaging, maybe we have a greater propensity to start mind wandering.”

I took up mindfulness meditation a few years ago, a practice which has made me much more aware of how I’m complicit in my own distress. For about 15 minutes most days, I sit in a chair and focus on the feeling of my breath, directing myself back to the physical sensation when my mind flits away. This has helped me notice how where I go when I mind wander—away from the moment, toward imagined future catastrophes that can’t be solved.

Cortland Dahl , who studies the neuroscience of mind wandering and has been meditating for 25 years, told me that he was six months into daily meditation practice when he witnessed a change in the way he related to the present moment. “I noticed I just started to enjoy things I didn’t enjoy before,” like standing in line, or sitting in traffic, he says. “My own mind became interesting, and I had something to do—‘Okay, back to the breath.’” Killingsworth’s findings help explain this, said Dahl, a research scientist at University of Wisconsin-Madison’s Center for Healthy Minds.

“We tend to think of suffering as being due to a circumstance or a thing that’s happening—like, we’re physically in pain,” he says. “And I think what this research points to is that oftentimes, it’s not actually due to that circumstance but much more to the way we relate to that.”

Killingsworth is still gathering data through Trackyourhappiness.org , which now has data from more than 100,000 people, and he plans to publish more papers based on his findings. He says the lesson he’s taken from his research so far is that we human beings spend lots of time and effort fixing the wrong problem. “A lot of us spend a lot of time trying to optimize the objective reality of our lives,” he told me. “But we don’t spend a lot of time and effort trying to optimize where our minds go.”

A few months ago, I decided to try mindful showering. If I could observe the mental script and divert myself back to breath during meditation, I figured, perhaps I could divert myself back to the present moment while washing my hair. Each time I do it, there’s a brief moment of dread when I step into the shower without a podcast playing. Then, I start to pay attention. I try to notice one thing each time, whether it’s the goose bumps that rise when the hot water first hits, or the false urgency of the thoughts that still come. They demand I follow them, but they’re almost always riddles that can’t be solved.

The trick is in recognizing the illusion— ah yes, there’s that ridiculous clown car of anxiety coming down the road again. The saving grace, when I can manage to focus, is the present moment. 

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Does mind-wandering make you unhappy, when are we happiest when we stay in the moment, says researcher matt killingsworth ..

What are the major causes of human happiness?

It’s an important question but one that science has yet to fully answer. We’ve learned a lot about the demographics of happiness and how it’s affected by conditions like income, education, gender, and marriage. But the scientific results are surprising: Factors like these don’t seem to have particularly strong effects. Yes, people are generally happier if they make more money rather than less, or are married instead of single, but the differences are quite modest.

Although our goals in life often revolve around these sorts of milestones, my research is driven by the idea that happiness may have more to do with the contents of our moment-to-moment experiences than with the major conditions of our lives. It certainly seems that fleeting aspects of our everyday lives—such as what we’re doing, who we’re with, and what we’re thinking about—have a big influence on our happiness, and yet these are the very factors that have been most difficult for scientists to study.

A few years ago, I came up with a way to study people’s moment-to-moment happiness in daily life on a massive scale, all over the world, something we’d never been able to do before. This took the form of trackyourhappiness.org, which uses iPhones to monitor people’s happiness in real time.

My results suggest that happiness is indeed highly sensitive to the contents of our moment-to-moment experience. And one of the most powerful predictors of happiness is something we often do without even realizing it: mind-wandering.

Be here now

As human beings, we possess a unique and powerful cognitive ability to focus our attention on something other than what is happening in the here and now. A person could be sitting in his office working on his computer, and yet he could be thinking about something else entirely: the vacation he had last month, which sandwich he’s going to buy for lunch, or worrying that he’s going bald.

This ability to focus our attention on something other than the present is really amazing. It allows us to learn and plan and reason in ways that no other species of animal can. And yet it’s not clear what the relationship is between our use of this ability and our happiness.

You’ve probably heard people suggest that you should stay focused on the present. “Be here now,” as Ram Dass advised back in 1971. Maybe, to be happy, we need to stay completely immersed and focused on our experience in the moment. Maybe this is good advice; maybe mind-wandering is a bad thing.

On the other hand, when our minds wander, they’re unconstrained. We can’t change the physical reality in front of us, but we can go anywhere in our minds. Since we know people want to be happy, maybe when our minds wander we tend to go to someplace happier than the reality that we leave behind. It would make a lot of sense. In other words, maybe the pleasures of the mind allow us to increase our happiness by mind-wandering.

Since I’m a scientist, I wanted to try to resolve this debate with some data. I collected this data using trackyourhappiness.org.

How does it work? Basically, I send people signals at random times throughout the day, and then I ask them questions about their experience at the instant just before the signal. The idea is that if we can watch how people’s happiness goes up and down over the course of the day, and try to understand how things like what people are doing, who they’re with, what they’re thinking about, and all the other factors that describe our experiences relate to those ups and downs in happiness, we might eventually be able to discover some of the major causes of human happiness.

This essay is based a 2011 TED talk by Matt Killingsworth.

In the results I’m going to describe, I will focus on people’s responses to three questions. The first was a happiness question: How do you feel? on a scale ranging from very bad to very good. Second, an activity question: What are you doing? on a list of 22 different activities including things like eating and working and watching TV. And finally a mind-wandering question: Are you thinking about something other than what you’re currently doing? People could say no (in other words, they are focused only on their current activity) or yes (they are thinking about something else). We also asked if the topic of those thoughts is pleasant, neutral, or unpleasant. Any of those yes responses are what we called mind-wandering.

We’ve been fortunate with this project to collect a lot of data, a lot more data of this kind than has ever been collected before, over 650,000 real-time reports from over 15,000 people. And it’s not just a lot of people, it’s a really diverse group, people from a wide range of ages, from 18 to late 80s, a wide range of incomes, education levels, marital statuses, and so on. They collectively represent every one of 86 occupational categories and hail from over 80 countries.

Wandering toward unhappiness

So what did we find?

First of all, people’s minds wander a lot. Forty-seven percent of the time, people are thinking about something other than what they’re currently doing. Consider that statistic next time you’re sitting in a meeting or driving down the street.

How does that rate depend on what people are doing? When we looked across 22 activities, we found a range—from a high of 65 percent when people are taking a shower or brushing their teeth, to 50 percent when they’re working, to 40 percent when they’re exercising. This went all the way down to sex, when 10 percent of the time people’s minds are wandering. In every activity other than sex, however, people were mind-wandering at least 30 percent of the time, which I think suggests that mind-wandering isn’t just frequent, it’s ubiquitous. It pervades everything that we do.

How does mind-wandering relate to happiness? We found that people are substantially less happy when their minds are wandering than when they’re not, which is unfortunate considering we do it so often. Moreover, the size of this effect is large—how often a person’s mind wanders, and what they think about when it does, is far more predictive of happiness than how much money they make, for example.

Now you might look at this result and say, “Ok, on average people are less happy when they’re mind-wandering, but surely when their minds are straying away from something that wasn’t very enjoyable to begin with, at least then mind-wandering will be beneficial for happiness.”

As it turns out, people are less happy when they’re mind-wandering no matter what they’re doing. For example, people don’t really like commuting to work very much; it’s one of their least enjoyable activities. Yet people are substantially happier when they’re focused only on their commute than when their mind is wandering off to something else. This pattern holds for every single activity we measured, including the least enjoyable. It’s amazing.

But does mind-wandering actually cause unhappiness, or is it the other way around? It could be the case that when people are unhappy, their minds wander. Maybe that’s what’s driving these results.

We’re lucky in this data in that we have many responses from each person, and so we can look and see, does mind-wandering tend to precede unhappiness, or does unhappiness tend to precede mind-wandering? This gives us some insight into the causal direction.

As it turns out, there is a strong relationship between mind-wandering now and being unhappy a short time later, consistent with the idea that mind-wandering is causing people to be unhappy. In contrast, there’s no relationship between being unhappy now and mind-wandering a short time later. Mind-wandering precedes unhappiness but unhappiness does not precede mind-wandering. In other words, mind-wandering seems likely to be a cause, and not merely a consequence, of unhappiness.

How could this be happening? I think a big part of the reason is that when our minds wander, we often think about unpleasant things: our worries, our anxieties, our regrets. These negative thoughts turn out to have a gigantic relationship to (un)happiness. Yet even when people are thinking about something they describe as neutral, they’re still considerably less happy than when they’re not mind-wandering. In fact, even when they’re thinking about something they describe as pleasant, they’re still slightly less happy than when they aren’t mind-wandering at all.

The lesson here isn’t that we should stop mind-wandering entirely—after all, our capacity to revisit the past and imagine the future is immensely useful, and some degree of mind-wandering is probably unavoidable. But these results do suggest that mind-wandering less often could substantially improve the quality of our lives. If we learn to fully engage in the present , we may be able to cope more effectively with the bad moments and draw even more enjoyment from the good ones.

About the Author

Matt Killingsworth

Matt Killingsworth, Ph.D., is a Robert Wood Johnson Health and Society Scholar. He studies the nature and causes of human happiness, and is the creator of www.trackyourhappiness.org which uses smartphones to study happiness in real-time during everyday life. Recent research topics have included the relationship between happiness and the content of everyday experiences, the percentage of everyday experiences that are intrinsically valuable, and the degree of congruence between the causes of momentary happiness and of one's overall satisfaction with life. Matt earned his Ph.D. in psychology at Harvard University.

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• Front Psychol

Pros and cons of a wandering mind: a prospective study

Cristina ottaviani.

1 IRCCS Santa Lucia Foundation, Rome, Italy

2 ENPlab, Department of Psychology, Sapienza University of Rome, Rome, Italy

Alessandro Couyoumdjian

Mind wandering (MW) has recently been associated with both adaptive (e.g., creativity enhancement) and maladaptive (e.g., mood worsening) consequences. This study aimed at investigating whether proneness to MW was prospectively associated with negative health outcomes. At time 0, 21 women, 19 men; mean age = 24.5 (4.9) underwent a 5-min baseline electrocardiogram (ECG), a 20-min laboratory tracking task with thought probes, and personality questionnaires. At time 1 (1 year follow-up), the same participants underwent a 24-h Ecological Momentary Assessment characterized by ambulatory ECG recording and electronic diaries. First, we examined if the likelihood of being a “mind wanderer” was associated with specific personality dispositions. Then, we tested if the occurrence of episodes of MW in the lab would be correlated with frequency of MW in daily life. Finally, multiple regression models were used to test if MW longitudinally acted as a risk factor for health, accounting for the effects of biobehavioral variables. Among dispositional traits, the frequency of MW episodes in daily life was inversely associated with the capacity of being mindful (i.e., aware of the present moment and non-judging). There was a positive correlation between frequency of MW in the lab and in daily life, suggesting that it is a stable disposition of the individual. When differentiated from perseverative cognition (i.e., rumination and worry), MW did not predict the presence of health risk factors 1 year later, however, a higher occurrence of episodes of MW was associated with short-term adverse consequences, such as increased 24-h heart rate (HR) on the same day and difficulty falling asleep the subsequent night. Present findings suggest that MW may be associated with short term “side effects” but argue against a long term dysfunctional view of this cognitive process.

Introduction

Mind wandering (MW) has been defined as the default mode of operation of our brain (Mason et al., 2007 ), and it has been associated with maladaptive consequences for health (reviewed in Mooneyham and Schooler, 2013 ). Despite the pervasiveness of MW (almost 50% of our waking time in Killingsworth and Gilbert, 2010 ), little is known about its functionality. It has been hypothesized that MW plays a vital role in healthy cognition (Baars, 2010 ), and recent studies suggest adaptive functions that are served by MW. For example, MW appears to integrate past and present experiences for the purpose of future planning and simulation (i.e., autobiographical planning; Baird et al., 2011 ; Smallwood et al., 2011a ). Consistent with this hypothesis, the MW experience is often future focused (Smallwood et al., 2009a ; D'Argembeau et al., 2011 ), and oriented toward personal goal resolution (e.g., Baird et al., 2011 ; D'Argembeau et al., 2011 ; Smallwood et al., 2011a ). Inspiration is another function that can be intuitively associated with MW, especially considering the well-known benefits of an incubation interval for creative thoughts. Thus, Baird et al. ( 2012 ) demonstrated that MW facilitates creative problem solving. A growing number of studies (e.g., Baird et al., 2011 ; Levinson et al., 2012 ) indicate that the capacity to mentally escape from the constraints of the present permits the management of personal goals (e.g., Smallwood and Schooler, 2006 ; Baumeister and Masicampo, 2010 ). Consistent with this view, Smallwood et al. ( 2013 ) demonstrated that MW is associated with reduced delay discounting, suggesting that MW allows cognition to be devoted to the consideration of personal objectives that extend beyond the current moment, becoming relevant for making choices that are beneficial over the long term. This seems to be true across cultures, as a recent ecological study of a Chinese population showed that MW helped participants to create and maintain an integrated, meaningful sense of self and to cope with upcoming events (Song and Wang, 2012 ). Among other functions, Gruberger et al. ( 2011 ) hypothesized that MW may serve as a learning and consolidation mechanism by augmenting the associative abilities of the brain, in a similar way to what happens when we sleep.

On the flip side, MW has been paradoxically associated with unconstructive consequences in terms of reduced attention and interference with performance on tasks that require substantial controlled processing (reviewed in Mooneyham and Schooler, 2013 ). A number of studies linked MW to poor performance in sustained attention tasks, such as vigilance tasks (Smallwood et al., 2004a ; Allan Cheyne et al., 2009 ; Mrazek et al., 2012 ) or reading (Smallwood et al., 2008 ; Reichle et al., 2010 ; Smilek et al., 2010 ; Franklin et al., 2011 ; McVay and Kane, 2012 ). During reading, MW leads to slower reading times, longer average fixation duration, and absence of the word frequency effect on gaze duration (Foulsham et al., 2013 ), with a negative influence on the comprehension of difficult texts (Feng et al., 2013 ). Moreover, MW has been associated with worse performance on measures of fluid intelligence and working memory (Mrazek et al., 2012 ). Taken together, these impairments can lead to serious consequences that go from the more obvious scholastic failure (Smallwood et al., 2007a ) to traffic accidents (Galéra et al., 2012 ) and medical malpractice (Smallwood et al., 2011b ). If the latter seems counterintuitive, it has to be considered that MW has been shown to affect even higher processes such as decision making, for example by making choices more likely to be biased by past experiences (Demanet et al., 2013 ).

With regards to the effects on health and wellbeing, Killingsworth and Gilbert ( 2010 ) suggested that MW predicts daily unhappiness, whereas other studies support the opposite pathways with negative mood being the cause of an increased tendency of the mind to wander (Smallwood et al., 2009b ; Smallwood and O'Connor, 2011 ; Stawarczyk et al., 2013 ). Moreover, MW has been associated with the occurrence of psychopathological disorders, such as dysphoria (Smallwood et al., 2007b ; Carriere et al., 2008 ) and attention deficit hyperactivity disorder (Liddle et al., 2011 ). As to psychophysiological reactivity, Smallwood et al. ( 2004a ) found heart rate (HR) accelerations during periods of MW in a sustained attention task. Similarly, Smallwood et al. ( 2004b ) showed a positive correlation between physiological arousal (HR) and frequency of MW episodes during a semantic encoding task, and these findings were replicated in a dysphoric population (Smallwood et al., 2007b ). Smilek et al. ( 2010 ) compared blink rates during probe-caught episodes of MW and on-task periods of reading, demonstrating enhancement of the blink reflex during the first condition. The supposed adverse consequences of MW on health extend to the point that this process has been associated with shorter telomere length, indicating a more rapidly aging body (Epel et al., 2013 ).

To our knowledge, no longitudinal studies investigated the costs of MW to health and wellbeing. This study represents a first attempt to do so by the use of multiple measures of MW in the lab and naturalistically. As measuring MW is intrinsically complicated and has been done mostly in the laboratory, which can limit spontaneity of behavior, our first specific aim was to test if the occurrence of episodes of MW in the lab would predict MW in everyday life 1 year later. We wanted to study if: (a) laboratory assessments of MW have ecological validity and (b) whether the tendency to MW is a stable characteristic of the individual. Second, we examined if the likelihood of being a “mind wanderer” was associated with specific personality dispositions. Third, we tested if the frequency of MW longitudinally acted as a protective or a risk factor for health, accounting for the effects of biobehavioral variables. As high ambulatory HR and its variability (HRV) have been shown to predict total and non-cardiovascular mortality (reviewed in Hansen et al., 2008 and Thayer et al., 2010 ), these two variables were used as indices of health vulnerability. Somatic symptoms at follow up were considered as another measure of health vulnerability, in light of the association between somatization tendencies and repetitive thinking (Verkuil et al., 2010 ) and given that MW has been considered a form of repetitive thinking (reviewed in Watkins, 2008 ). It has to be noted that among various types of repetitive thought, basic research mainly focused on MW, whereas clinical research had a long lasting interest in rumination and worry, highlighting their role in the onset and maintenance of psychopathology (Aldao et al., 2010 ). Most researchers, however, included rumination and worry in their operationalization of MW, making it difficult to disentangle the effect of MW per se . As the aim of this study was to clarify the effects of non-pathological MW, the latter was assessed independently from rumination and worry both at state and trait levels. Indeed, there is evidence that repetitive thinking such as rumination and worry also predict longer sleep latency (e.g., Zoccola et al., 2009 ), but no studies have examined this association in the case of MW. It seems intuitively plausible that MW would have a disturbing effect during the same phase of the sleep process, that is, when falling asleep, therefore this specific sleep difficulty was assessed as our last marker of physical and psychological health (e.g., Taylor et al., 2003 ).

Materials and methods

Participants.

Seventy-three subjects participated in the laboratory session of the study (described in Ottaviani et al., 2013 ) and 45 agreed to be contacted at follow up. Of these 45, one participant did not complete the ambulatory session and 3 were excluded due to excessive artifacts or inconsistent diary entries. The final sample was composed of 40 subjects [21 women, 19 men; mean age = 24.5 (4.9) years], recruited among students at Sapienza University of Rome. All subjects were Caucasian. Exclusionary criteria were: a current or past diagnosis of psychiatric disorders, diagnosis of hypertension or heart disease, use of drugs/medications that might affect cardiovascular function, obesity (body mass index >32 kg/m 2 ), menopause, pregnancy, or childbirth within the last 12 months. Participants were compensated (€ 25) for their time. The protocol was approved by the Bioethical Committee of S. Lucia Foundation, Rome.

The study consisted of two phases: a laboratory session at time 0 and an ambulatory session at time 1. The average time between the two sessions was 13.9 (1.2) months.

At time 0, participants were informed of the following restrictions: no caffeine, alcohol, nicotine, or strenuous exercise for 2 h prior to the laboratory session. After reading and signing the informed consent form and a 5-min physiological baseline recording, participants were engaged in two 5-min recall interviews; after each interview, they performed a 20-min tracking task with thought probe. The rationale for the interviews was to increase the likelihood of episodes of MW and perseverative cognition, as the primary goal of the laboratory session was to study the psychophysiological correlates of these cognitive states. Detailed findings from the laboratory session are besides the scope of this study and have been described elsewhere (Ottaviani et al., 2013 ). The first interview required participants to verbally describe a well-known route (i.e., the itinerary from the building where the experiment took place to Rome central station), while, in the second interview, participants were asked to talk about a negative personal event that occurred in the past or will occur in the future and that would elicit stress/worry when “when thinking about it.” At the end of the tasks, participants completed a series of on line personality questionnaires.

At follow up, appointments were scheduled by e-mail. During their visit to the lab, participants were instructed about the use of the electronic diary and the ambulatory HR device. The belt was attached, and the participants left the laboratory. The next morning, they were asked to return the diary and apparatus to the laboratory, were debriefed, and received monetary compensation.

Tracking task with thought probe (t0)

Only measures that are relevant to the aims of the present study will be reported [see Ottaviani et al. ( 2013 ) for specific details about the task]. The task was developed using Superlab 4 software (Credus Corporation). To increase the likelihood of episodes of MW and make the task automatic, the level of difficulty was very low. Participants were asked to keep the cursor inside a white circle in motion on a black screen and press the left mouse button as fast as possible each time the circle turned red. At different time intervals, probes interrupted the task to inquire about subjects' thoughts. The thought probe method used in this study was adapted from Stawarczyk et al. ( 2011 ). We had a total of 16 thought-probes per subject (8 during each tracking task). For each probe, participants were asked to characterize the ongoing conscious experience they had just prior to the probe, among the following: (a) focused on the task, (b) distracted by external stimuli (noise, etc.), (c) MW, (d) worrying about a future event, (e) ruminating about a past stressful event. The only variable that was analyzed in the present study was the number of episodes of MW during the two 20-min tracking tasks (aggregated).

Psychophysiological assessment (t0)

The electrocardiogram (ECG) was continuously monitored (Monitoring, Adatec s.r.l., Italy) with a standard electrode configuration. The signal was digitized at 1000 Hz. Each epoch was manually checked and corrected for artifacts. HR and the root mean square of successive differences (RMSSD), which primarily reflects vagally mediated HRV, were derived using Kubios HRV Analysis Software (Niskanen et al., 2004 ). HR and HRV relative to the 5-min baseline recorded before the beginning of the first interview were used as predictors in the regression analyses.

Questionnaires (t0)

At time 0, participants completed on line a series of socio-demographic (age and sex), lifestyle (nicotine, alcohol, and caffeine consumption, physical exercise), and personality scales: (a) Stress-Reactive Rumination Scale (SRRS; Robinson and Alloy, 2003 ), (b) Penn State Worry Questionnaire (PSWQ; Meyer et al., 1990 ), (c) State-Trait Anxiety Inventory (STAI-X2; Spielberger et al., 1970 ), (d) Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977 ), (e) Five Facets Mindfulness Questionnaire (FFMQ; Baer et al., 2006 ), (f) somatization subscale of the Symptom Checklist-90 Revised (SCL-90 R; Derogatis, 1977 ), (g) PROMIS Sleep Disturbance-short form (Yu et al., 2011 ). The SSRS requires to indicate how frequently one would engage in a series of activities (e.g., “Think about how the negative event will negatively affect your future”) in response to a stressful event on a scale from 0 (Not focus on this at all) to 100 (Focus on this to a great extent). As only the Negative Inferential Style (NIS) subscale has been previously associated with ruminative tendencies (Robinson and Alloy, 2003 ), data related to this subscale of the SRRS were analyzed in the present study. The PSWQ is a 16-item self-report questionnaire commonly used to measure the tendency to worry in an excessive and uncontrollable way (e.g., “Once I start worrying, I cannot stop”) on a on a 5-point scale ranging from 0 (Not at all) to 4 (Most of the time). The STAI—X2 consists of 20 items with multiple choice answers (never, sometimes, often, and always) directed at investigating relatively stable individual differences in trait anxiety. The CES-D is a 20-item self-report scale that assesses the frequency of occurrence of symptoms of depression during the past week. The FFMQ assesses five facets of a general tendency to be mindful in daily life (observing, describing, acting with awareness, non-reactivity to inner experience, and non-judging of inner experience) on a 5-point Likert scale ranging from 1 (never or very rarely true) to 5 (very often or always true). The SCL-90-R somatization subscale is a 12-item measure of commonly experienced physical symptoms that has been widely used as a standalone index of somatization severity (e.g., Güleç et al., 2013 ). The PROMIS Sleep Disturbance-Short form is an 8-item scale that has been shown to be useful for grading the global severity of insomnia (Yu et al., 2011 ); the present study focused on scores of the item “I had difficulties falling asleep.”

Ambulatory session (t1)

HR was recorded as beat-to-beat intervals using a t6 Suunto Memory Belt (SuuntoVantaa, Finland), sampling at 1000 Hz. The Suunto Memory Belt has been shown to be a reliable device to measure the ECG compared to a 5-lead ECG (Weippert et al., 2010 ). Participants were asked to return the HR recorder after 24-h of wearing or in case of any difficulties. Raw beat-to-beat intervals (IBI) were analyzed according to the Task Force Guidelines ( 1996 ). The 24-h IBI data were decomposed into 5-min blocks. Each epoch was manually checked and corrected for artifacts. The Kubios HRV Analysis Software (Niskanen et al., 2004 ) was used to calculate the HRV time domain parameter (RMSSD). After excluding blocks with more than 5% artifact rate, we calculated the average beats per minutes (HR), and RMSSD (HRV). Twenty-four hour HR and HRV were used in the analyses.

Electronic diary (t1)

Participants were provided with an electronic diary implemented on an Android phone via the SurveyPocket App (Questionpro.com). At random times (about every 30 min), the phone signaled participants that it was time to report the specific ongoing cognitive process (focused on the task, distracted by external stimuli, MW, worrying about a future event, ruminating about a past stressful event) and information on factors that may affect HR, including posture, physical activity, and food, caffeine, nicotine, and alcohol consumption since the last diary report. The other questions in the diary are not relevant for the aims of the present study and will not be described here. Before bedtime, subjects were asked to fill out the Patient Health Questionnaire [PHQ-15 for somatization Kroenke et al. ( 2002 )] and, upon awakening, the PROMIS Sleep Disturbance-Short Form, both implemented on the same Android phone.

Statistical analyses

Data are expressed as means (SD). To correct for multiple comparisons, only Bonferroni adjusted p -values are presented. Laboratory data processing and data analyses were performed with Systat 11.0 (Systat Software Inc., Richmond, CA).

The effects of biobehavioral (body mass index, age, years of education, physical activity, alcohol, nicotine consumption) and personality factors (scores at the STAI, CES-D, SRRS, PWSQ, and FFMQ) on the dependent variables (24-h HR and HRV, somatization levels, and difficulties falling asleep after 1 year) were analyzed by Pearson correlations. Differences due to sex were analyzed by t test.

To test if laboratory assessments of MW have ecological validity and the tendency to MW is a stable characteristic of the individual, we first ran Pearson correlations between frequency of episodes of MW in the lab and in daily life (1 year later). Second, we examined if the likelihood of being a “mind wanderer” was associated with specific personality dispositions. To do so, Pearson correlations between the frequency of episodes of MW and scores of the dispositional questionnaires (STAI, CES-D, SRRS, PWSQ, and FFMQ) were computed. Third, we tested if the frequency of MW longitudinally acted as a protective or a risk factor for health, accounting for the effects of biobehavioral variables. A series of multiple regression analysis were conducted according to the following model:

where (1) a (Alpha) is the constant or intercept; (2) Y is each examined dependent variable (24-h HR, 24-h HRV, scores of the PHQ-15, and scores of the item “I had difficulty falling asleep” of the PROMIS sleep scale, respectively), and (3) X 1 , X 2 , X 3 , X 4 , and X 5 are the predictors for that specific model (e.g., sex, baseline level of the dependent variable at time 0, occurrence of episodes of MW at time 0, and occurrence of episodes of MW at time 1). Results are reported in terms of both the regression coefficients (B) and the standardized regression coefficients (β), which are obtained by applying the regression models to standardized dependent and independent variables. Statistical significance of the standardized coefficients was tested by F -tests.

To control for the effects of biobehavioral variables without decreasing too much the degrees of freedom for the present sample size, only those that had a significant bivariate correlation with a given dependent variable were entered in the subsequent regression models.

The only significant associations that emerged between socio-demographic variables and our outcome measures were: (1) nicotine consumption and 24-h HR ( r = 0.33; p = 0.04), (2) worry tendencies (PSWQ) and 24-h HRV ( r = −0.42; p = 0.01), (3) ruminative tendencies (NIS) and somatization ( r = 0.33; p = 0.04), and (4) trait anxiety (STAI) and difficulties falling asleep ( r = 0.36; p = 0.02), thus these variables were included as predictors in the corresponding multiple regression models.

Table ​ Table1 1 shows sex differences for the main variables of the study. The only significant difference regarded higher levels of depressive symptoms in women compared to men [ t (38) = 2.1, p = 0.04] and higher baseline HR in men compared to women at baseline [ t (38) = −2.2, p = 0.04], therefore sex was included as a predictor in all the multiple regression models.

Sex differences for the main variables of the study .

As shown in Figure ​ Figure1, 1 , a significant relationship emerged between frequency of episodes of MW in the lab and in daily life after 1 year ( r = 0.41, p = 0.01).

Scatterplot illustrating the relationship between the number of episodes of MW in the lab (MW at t0) and in daily life after 1 year (MW at t1) .

As to personality dispositions, a relationship between scores of the subscales Non-judging and Acting with awareness of the FFMQ were significantly related with the occurrence of MW episodes in daily life ( r = − 0.56, p = 0.001 and r = − 0.42, p = 0.03; scatterplots depicted in Figure ​ Figure2 2 ).

Scatterplots illustrating the relationship between scores of the subscale Acting with Awareness (upper graph) and Non-judging (lower graph) of the FFMQ and the occurrence of episodes of MW in daily life after 1 year .

With regard to the prediction of psychophysiological risk factors for health, Table ​ Table2 2 shows the results of the multiple regressions for 24-h HR and 24-h HRV. Model 1 accounted for 30% of the variance of 24-h HR, with a significant effect of nicotine consumption ( F = 5.86; p = 0.02) and MW at time 1 ( F = 7.26; p = 0.01) as predictors. In Model 2, baseline HRV at time 0 ( F = 31.52; p < 0.0001) and trait worry ( F = 5.05; p = 0.03) were significant predictors of 24-h HRV, accounting for 63% of the variance.

Summary of multiple regression analysis for the prediction of 24-h HR (Model 1) and 24-h HRV (Model 2) at time 1 .

B, unstandardized regression coefficient; SE, standard error of the regression coefficient; β, standardized regression coefficient.

Table ​ Table3 3 shows the multiple regression models for the prediction of self-reported risk factors for health at time 1. Trait rumination and somatization tendencies at time 0 (SCL-90 R) were significant predictors of somatization at time 1 ( F = 4.56; p = 0.04 and F = 30.3; p < 0.0001, respectively). Specifically 54% of the variance of the PHQ-15 was accounted for by Model 3. In Model 4, gender ( F = 4.37; p = 0.04) and MW at time 1 ( F = 4.97; p = 0.03) were significant predictors of difficulties of falling asleep at time 1, accounting for 42% of the variance.

Summary of multiple regression analysis for the prediction of somatization tendencies (Model 3) and difficulties falling asleep (Model 4) at time 1 .

The present study was designed to prospectively examine three characteristics of MW: its stability over time, its relationship with determined personality measures, and its role as a predictor of established risk factors for health.

First, a surprisingly high correlation emerged between the frequency of episodes of MW at time 0 and the same measure at time 1. This result is particularly relevant if we consider that the two assessments took place not only at different times (about 1 year apart) but also in totally different contexts (i.e., in the laboratory and in participant's daily life). The stability of MW across different contexts had already been studied by McVay et al. ( 2009 ) with consistent results: subjects who reported more MW during a laboratory task endorsed more MW experiences during everyday life. Similarly, Unsworth et al. ( 2012 ) measured various cognitive abilities in the laboratory and then recorded everyday attention failures, such as MW or distraction in a diary over the course of a week, supporting evidence for the ecological validity of laboratory measures of attention control. Our study replicated and extended these findings, with the introduction of the longitudinal dimension between the two measures of MW.

With regard to dispositional variables, although the role of MW as a marker for depressive thinking had been previously highlighted, as shown by studies linking this cognitive process to dysphoria (Smallwood et al., 2007b ; Carriere et al., 2008 ) and negative moods (Smallwood et al., 2009b ; Killingsworth and Gilbert, 2010 ; Smallwood and O'Connor, 2011 ; Stawarczyk et al., 2013 ), we failed to replicate an association between the occurrence of MW and depressive symptoms both cross-sectionally and longitudinally. A possible explanation for the inconsistency may derive from the fact that previous studies included ruminative thoughts in their conceptualization of MW. Although MW, rumination, and worry are often included under the same umbrella term of “repetitive thinking,” the only study that directly compared these processes in terms of their affective correlates, suggested that the negative effects of MW on moods vanish when differentiated from perseverative cognition (Ottaviani et al., 2013 ). In agreement with previous results, a negative association between measures of dispositional mindfulness and MW emerged. The reciprocal link between these two apparently opposite constructs has been recently confirmed by Mrazek and colleagues (Mrazek et al., 2012 ), who found an inverse relationship between a dispositional measure of mindfulness (i.e., the Mindful Attention and Awareness Scale) and converging measures of both self-reported and indirect markers of MW. Here, we replicated these results by using a different self-report measure, i.e., the FFMQ that further allowed us to provide insights on which specific facets of mindfulness would be more closely linked to MW tendencies. The non-judging and acting with awareness features emerged as the most relevant, again with surprisingly strong correlations. This constitutes an intriguing result as these are the two facets that play the most important role in mindfulness clinical applications, such as Mindfulness Based Stress Reduction (MBSR) and Mindfulness Based Cognitive Therapy (see Grossman et al., 2004 ; Chiesa and Serretti, 2009 for reviews). Indeed they are the two most effective factors in preventing intrusive thoughts: increased awareness may allow patients to break the ruminative cycle by attending to the present and not to the past, and non-judging may foster acceptance rather than avoidance and its ironic effects on unwanted thoughts (“white bear effect,” Wegner et al., 1987 ).

As to the examined risk factors for health, MW appeared to be associated with short term maladaptive consequences but not with noxious effects 1 year later. In fact, MW at time 0 was not a significant predictor in any of the regression models, while the frequency of episodes of MW during the ecological momentary assessment predicted 24-h HR in the same day and difficulties falling asleep the subsequent night. Results on the association between MW and simultaneous increases in HR had been already demonstrated by Smallwood et al. ( 2004a , b , 2007b ) during a series of laboratory task and were here replicated in a more ecological setting. Surprisingly, no previous studies investigated the relationship between MW and sleep difficulties. However, on the flip side, evidence suggests that increased practice of mindfulness techniques is associated with improved sleep and that MBSR participants experience a decrease in sleep-interfering cognitive processes (reviewed in Winbush et al., 2007 ). Taken together, findings argue for a link between MW and sleep difficulties that needs to be further investigated.

Interestingly, baseline HRV and worry tendencies, assessed by the PSWQ, were significant predictors of 24-h HRV 1 year later and the amount of variance the model accounted for was particularly large (63%). The stability of HRV over time has been extensively demonstrated (e.g., Bertsch et al., 2012 ). As worry was significant in the prediction but MW was not, it seems evident that a distinction needs to be made between future-oriented MW, which has been associated with autobiographical planning (Baird et al., 2011 ; Smallwood et al., 2011a ) and future worrisome thoughts, which have conversely been related to decreased HRV both during the day and the subsequent night (Brosschot et al., 2007 ; Pieper et al., 2010 ).

Finally, trait rumination and somatization tendencies at time 0 significantly predicted somatization at time 1. Again, this results fits with a large amount of data linking perseverative cognition with somatization tendencies (see Verkuil et al., 2010 for a review). Still, it seems that MW refers to a different phenomenon, which is less pathogenic as it is probably not associated with the sustained physiological reactivity that has been shown during rumination (e.g., Ottaviani et al., 2009 , 2011 ; Ottaviani and Shapiro, 2011 ). Although being the first prospective study in the field, the fact that we did not find maladaptive consequences of MW in the long term is not a standalone result. There are studies showing greater life satisfaction and socio-emotional well-being associated with a particular form of MW, that is daydreaming about close family and friends (Mar et al., 2012 ).

This study has several limitations. First, the sample size was relatively small and may not have been adequate in some of the comparisons. Second, MW was treated as a dichotomy and measured using self-reports, whereas it has recently been demonstrated that it is possible to be mindless at different degrees (Schad et al., 2012 ). Also, we examined 24-h HR and HRV without synchronizing the occurrence of episodes of MW with psychophysiological recordings. We did so, as we were more interested on established risk factors for health and not on the physiological correlates of MW. Finally, our sample was composed of healthy students and 1 year may not be enough to see the long term maladaptive consequences of MW in terms of effects on health risk factors.

Limitations notwithstanding, our preliminary findings extend the results of previous studies by showing MW to be a relatively stable characteristic of the individual, inversely related to specific mindfulness facets such as acting with awareness and non-judging and to have short term negative effects on health and wellbeing (24-h HR and difficulties falling asleep). However, the data failed to show any long term pathogenic effects of MW. Results emphasize the need of prospective studies to clarify under which circumstances the so common process of MW takes the form of pathological rumination or worry, with clear implications for both prevention and therapy.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This work was supported by the Italian Ministry of Health Young Researcher Grant (GR-2010-2312442). The authors would like to thank Prof. Antonino Raffone for providing us with the Italian version of the FFMQ, Prof. David Shapiro for critical reading of the manuscript, and Barbara Medea for her assistance in data collection.

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• Promoting Engagement

If learning is the goal, student engagement may not be sufficient, but in most cases—whether they’re in the classroom or studying on their own—it is necessary. When considering how to promote the greatest likelihood of engagement, a number of internal and external factors come into play. Students need to be actively attentive, for example—and often to maintain that attention over an extended period of time. Internal factors such as alertness (How much sleep did my students get?) and distraction (What sorts of family matters are on my students’ minds? Why does it sound like there's a jackhammer in the room above us right now?) are, as every teacher knows, often completely outside of our control. That being said, whether you're talking about classroom settings—where teachers can more directly regulate many, if not all, aspects of the learning environment—or dorm rooms and libraries—where students must do their own regulating—cognitive science on how attention works offers a range of practical applications for improving student engagement.

In its most basic form, attention refers to the process of noticing and taking in information. As such, it is the first step in the learning process (whether that be academic or otherwise). There are two forms of attention:

• Passive attention . Passive attention is the involuntary intake of external, sensory stimuli. In the learning environment, these stimuli include things such as other students talking, cars driving by, bright flashes of light from a cell phone, and a multitude of other events that are picked up by the senses. This process of information intake is unfiltered, which means that any external events may attract a person’s attention, and distract them from the task at hand (Thorne & Thomas, 2009). 
• Alertness . For students to effectively take in and retain information, they first need to be mentally alert. The reticular activating system (RAS) is one part of the brain responsible for levels of alertness. The RAS operates autonomously and is responsible for triggering mental alertness and activity in individuals when they wake up, and reducing or removing this alertness when individuals need to sleep. Students who are mentally alert are better equipped to engage in the learning experience (Thorne & Thomas, 2009), but as students get tired, their ability to maintain alertness decreases.

• Distraction (avoidance) . An integral part of the selection process is being able to filter out the less salient stimuli that can act as distractions. These distractions can be external (sights and sounds) or internal (thoughts and physical and emotional states). For example, if a student is struggling with mental health issues, these issues and associated emotions may distract them from paying attention to their teacher (Thorne & Thomas, 2009). When internal thoughts act as distractions, this is known as mind-wandering. Mind-wandering is covered in further detail below. However, at this stage it is worth noting two concepts associated with mind-wandering and distraction. The first is executive function. This refers to the cognitive processes (frontal lobes) that control planning, attention, memory, and multitasking. Any impairments of the executive function can result in inattention (distraction) and impulsivity (Banich, 2009). The second concept is that of ego depletion. Ego depletion is the idea that there only exists a certain amount of mental alertness or activity from which willpower or self-control draws. Once this is depleted, it becomes harder for individuals to remain focused on or stay committed to completing a task. While the nature and mechanism of ego depletion is unknown and an area of active research in psychology, findings persist that mental fatigue caused by long periods of focus can result in individuals getting easily distracted (Baumeister et al., 1998).
• Duration . To remain focused on a given task, students need to be mentally alert and maintain alertness or energy throughout the duration of a task. This step of the active attention process can therefore be mentally demanding, as the harder the task is to be completed, the more effort and mental alertness is required to complete it. Often, the tasks a student finds the hardest (and the most cognitively-demanding) to complete are either tasks they do not like or the ones they perceive to be difficult or complex (Thorne & Thomas, 2009).
• Preview . During the fifth step of active attention, an individual identifies the courses of action available to them (given the circumstances or information) and previews or reflects on their consequences to determine the best course of action. In the learning environment, previewing supports a student’s successful completion of a multiple-choice quiz because it requires analysis of each answer option before selecting the correct one. Previewing helps regulate cognitive, verbal, emotional, and physical behavior (Thorne & Thomas, 2009).
• Self-monitoring . Self-monitoring is the process through which individuals monitor and regulate their behaviors by assessing the situation, and modifying their behaviors if necessary. Once the situation is over, or the task is complete, an individual can then complete an additional assessment to ensure their behaviors have led to the appropriate and desired outcome. As it relates to active attention, self-monitoring helps individuals regulate how much attention to dedicate to a task, the speed at which the task can be completed, and the time needed to successfully complete the task. Students can apply this process of self-monitoring by deciding to spend more time focusing on and practicing concepts they find harder to comprehend, for example (Thorne & Thomas, 2009).

Although active attention is largely the responsibility of the student, there are ways for teachers to encourage or better facilitate this process. As a teacher, your focus should be on ensuring that the learning environment is as conducive to active attention as possible. You can do this by:

• Removing auditory distractions (for example, asking students to switch their phones off or keep them muted);
• Promoting a “classroom” culture of respect (for example, encouraging turn-taking during discussions, discouraging interruptions, and ensuring quietness during exams or tests); and
• Removing visual distractions (for example, only putting up posters or notices relevant to the subject of study).

While this list is not exhaustive, it acts as a basis from which teachers can consider how best to promote active attention within their specific learning contexts.

Mind-Wandering

Though we touched briefly on the concept of mind-wandering above, in the context of attention, it is worth taking the time to analyze this concept in greater detail to determine the potential positive and negative effects with which it is associated. For clarity, it is beneficial to reiterate that mind-wandering refers to moments of inattention in which an individual’s attention shifts from the task at hand to internal thoughts. These thoughts may center around aspects of an individual’s past, present, or future (McDonald, 2016).

Traditionally, literature on mind-wandering has been positioned as negative; however, recent research on the impact of mind-wandering shows that this phenomenon may be beneficial to individuals. This is, in part, due to what researchers have discovered about the differences between intentional and unintentional mind-wandering.

• Often, when individuals talk about mind-wandering, they are referring to unintentional mind-wandering in which distraction from the task at hand is accidental and detrimental. In such instances, mind-wandering is undesired (hence unintentional) because of the importance of the task at hand (for example, writing a test) or the potential danger that it may cause (for example, getting distracted while driving, or operating heavy machinery). Studies show that this form of mind-wandering can occur following interruption, distraction, or when the given task is cognitively-demanding (McDonald, 2016). These findings support the “unintentional” nature of this form of mind-wandering. Individuals often do not willingly forget the task at hand. Rather, their attention is involuntarily shifted by internal thought processes.
• On the other hand, intentional mind-wandering has been shown to occur when the task at hand is not cognitively-demanding, or is considered relatively easy. Research has shown that it is this form of mind-wandering that may be beneficial to individuals. This is evident in a recent research study conducted by Seli, Risko, and Smilek (2016), in which they highlight that intentional and unintentional mind-wandering occurs for different reasons and can have different consequences. 

To learn more about the causes of intentional mind-wandering, Seli, Risko, and Smilek (2016) conducted experiments in which participants completed a sustained-attention task. This task required participants to press a button when they saw the numbers 1, 2, 4, 5, 6, 7, 8, and 9 (the targets), and not to press the button when they saw the number 3 (non-target). Participants were split into two groups: one group completed an easy version of the task (the numbers occurred in the correct sequence) and the other completed a harder version (the numbers occurred out of sequence) (Medea et al., 2016). 

At various intervals of their task completion, individuals were then asked to state if they were focusing on the current task or if their minds had wandered. If mind-wandering had occurred, participants were asked to specify if it was unintentional or intentional. Through this experiment, Seli and his colleagues found that participants who performed the easy version of the task experienced more intentional mind-wandering compared to participants who completed the difficult version of the task. Thus, showing that intentional mind-wandering occurs when the context allows a measure of inattention (Seli, Risko & Smilek, 2016).

Through their research on how individuals develop and refine personal goals, Medea et al. (2016) found that mind-wandering facilitated the refining of these goals. To arrive at this conclusion, the researchers conducted a study in which they asked participants to spend 15 minutes writing about their 3 most important life goals. They were then asked to complete a simple cognitive task (matching shapes). At various stages of their task completion, they were asked questions to determine how much attention they were paying to the task. Once the task was complete, they were asked to spend another 15 minutes writing about their 3 most important life goals (Seli, Risko & Smilek, 2016).

Medea et al. (2016) found that the participants who reported mind-wandering during the cognitive task wrote more concrete and specific descriptions of their goals the second time around, compared to their pre-cognitive-task reflection. This study showed that future-oriented thoughts that occur during mind-wandering help individuals clarify personal goals (Medea et al., 2016). 

That said, in the context of the learning environment, it is still unclear how mind-wandering may be beneficial to a student’s performance or learning journey. It is therefore important to encourage students to refrain from mind-wandering and remain engaged and invested in their learning. In fact, Seli, Risko, and Smilek were interested in developing ways for students to actively minimize the amount of unintentional and intentional mind-wandering within the learning environment (Seli, Risko & Smilek, 2016).

Illusion of Competence

The final factor that is associated with attention is the illusion of competence phenomenon. This phenomenon refers to when students believe they have greater knowledge of and competency in a subject area or skill, based on their repeated reading of a textbook or piece of learning material. When studying this content, students falsely believe that they are retaining and comprehending more information than they really are. However, upon being tested, students realize the extent to which they have failed to retain this information effectively (Karpicke, Butler & Roediger, 2009).

In their paper on metacognitive learning strategies, Karpicke, Butler, and Roediger (2009) explain that students struggle to retain information using this study method due to their failure to actively test their recall. By simply rereading information, students do not practice memory retrieval, instead believing that this repetition will ingrain the information in their memories. Through their research, Karpicke, Butler, and Roediger (2009) have found that this belief stems from some students being unaware of the benefits of frequent testing on recall (known as the testing effect).

To arrive at this conclusion, Karpicke, Butler, and Roediger (2009) carried out research in which they asked undergraduate students to complete a two-question survey about their studying habits. The first was an open-ended question about the students’ chosen study methods. The second question asked students to choose how they would study a chapter from a textbook, in preparation for an exam. They were given three options:

• Repeatedly read the chapter.
• Practice their recall of the chapter’s contents (with or without rereading the chapter).
• Study using an alternative method. (Karpicke, Butler & Roediger, 2009:474)

Out of the 177 students surveyed, 10% indicated that they would test themselves to gauge how much knowledge they retained, and only 8% said they would practice recall as it would improve their competence ahead of the exam. These results show that most of the students in this study were unaware of the cognitive benefits associated with self-testing (Karpicke, Butler & Roediger, 2009:746). For a teacher, the results of studies like this are useful in helping students develop study methods that retain their attention (shorter study periods interspersed with frequent testing) and support improved recall.

For more information...

Banich, M.T. 2009. Executive function: The search for an integrated account. Current Directions in Psychological Science. 18(2):89-94. 

Barnett, S.M., & Ceci, S.J. 2002. When and where do we apply what we learn? A taxonomy for far transfer. Psychological Bulletin. 128(4):612.

Baumeister, R.F., Bratslavsky, E., Muraven, M. & Tice, D.M. 1998. Ego depletion: Is the active self a limited resource? Journal of Personality and Social Psychology. 74(5):1252.

Karpicke, J.D., Butler, A.C., & Roediger III, H.L. 2009. Metacognitive strategies in student learning: Do students practise retrieval when they study on their own? Memory. 17(4):471-479.

MacLeod, C.M. 1991. Half a century of research on the Stroop effect: an integrative review. Psychological Bulletin. 109(2):163.

Medea, B., Karapanagiotidis, T., Konishi, M., Ottaviani, C., Margulies, D.M., Bernasconi, A., Bernasconi, N. & Bernhardt, B. et al. 2016. How do we decide what to do? Resting-state connectivity patterns and components of self-generated thought linked to the development of more concrete personal goals.

Pashler, H., McDaniel, M., Rohrer, D. & Bjork, R. 2008. Learning styles: Concepts and evidence. Psychological Science in the Public Interest. 9(3):105-119.

Seli, P., Risko, E.F. & Smilek, D. 2016. On the necessity of distinguishing between unintentional and intentional mind wandering. Psychological Science. 27(5):685-691. DOI: 10.1177/0956797616634068

Simons, D.J., Franconeri, S.L., & Reimer, R.L. 2000. Change blindness in the absence of a visual disruption. Perception. 29:1143-1154.

Thorne, G. & Thomas, A. 2009. What Is attention?

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A Harvard Medical School professor with ADHD shares how he retrained his brain for deep work and reached peak productivity

• Dr. Jeffrey Karp grew up with undiagnosed ADHD, struggling to focus and answer questions in class.
• Using two tactics to retrain his brain, Karp gained confidence and pursued a career in academia. 
• The MIT and Harvard professor shares the benefits of working in a flow state in his new book .

As a professor at Harvard Medical School and MIT, I am very lucky; I get to learn from and collaborate with some of the most innovative minds in the world of medicine, science, and technology. But I was not "supposed" to be here. No one would have predicted this for me.

Growing up with undiagnosed ADHD

When I was a kid in elementary school in rural Canada, I had the attention span of a fruit fly, and I struggled to keep up. Reading, writing, classroom discussion, and teachers' instruction — I couldn't make sense of any of it.

It wasn't just that I was distractible and my brain didn't process things in a conventional way; my mind felt completely open to just existing in the world, in a constant mind meld with the universe. It took a ton of effort for me to narrow my focus so stuff could enter, stick, and stay.

And I was an anxious kid. I couldn't relax and just be myself, feel okay as "the quirky kid" because I felt like something worse than that: an alien, a human anomaly. I realized early on that there were many things I was "supposed" to do, but none of them came naturally or seemed logical.

More troubling still was that much of it didn't feel like the right thing to do; it felt actively wrong. When a teacher asked me a question, whether on a test or in class, I typically found the question confusing and often unanswerable. The "right" answer seemed like just one of many possibilities. So, most of my school years were an exercise in trying to figure out, interpret, and fit others' expectations.

I was a puzzle for my teachers, a misfit in the conventional academic sense, and a total outcast socially. Today, with society's much greater understanding of ADHD, part of my eventual diagnosis, there are evidence-based approaches for building self-regulation skills designed for kids (and adults). But at that time and in that place, the only option was to wing it.

Sea slugs were essential in helping me retrain my brain

Over the years, I slowly gained motivation and became more persistent. I didn't know it at the time but my evolution as a learner mirrored the two fundamental concepts of how neurons change and grow — how they learn — that the neuroscientist Eric Kandel would someday identify as the basis that sea slugs and humans have in common for learning and memory: habituation and sensitization in response to repeated exposure to stimuli.

Habituation means that we become less reactive to stimuli, as you might to traffic noise outside your window. Sensitization means that our reaction is stronger, as happens when, for instance, a sound or a smell or even the thought of something becomes a trigger.

Living my own experiment, I learned to make use of both.

I discovered some basic ways to work with my brain to habituate to some stimuli (ordinary things that distracted me) and sensitize (sharpen my attention) to others to be able to reel in my wandering mind and redirect the synaptic messaging with intention. At one point, in the room where I studied there was a pinball machine next to me and a TV behind me. I learned to ignore both and used playing the pinball machine as a reward for finishing my homework.

Over time I became hyperaware of how to intentionally hijack processes in my brain this way to be less reactive or more sharply focused as needed.

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The result: I was able to focus on what seemed most purposeful, then follow through and maximize impact as opportunities opened up. I tinkered and fine-tuned until I learned how to use these powerful tools to tap into the heightened state of awareness and deep engagement that I call "lit."

What is 'lit' focus

I call it "lit" for two reasons. First, "lit" aptly describes how the flash of inspiration feels—as if a bright light flipped on in the dark. Or a spark has set your thinking ablaze. When you've had an epiphany, been awestruck, or simply been super excited, you've felt that spark. Second, "lit" is how these moments appear to the scientists who study them. Inside the brain (and in the gut as well), engaged states activate neurons. In the brain, this triggers an increase in cerebral blood flow that neuroscientists can see when they use functional magnetic resonance imaging (fMRI).

On a monitor, this oxygenated blood lights up an otherwise gray image of the brain with yellow-orange hot spots of activity. Emerging science shows that this neural activation is associated not only with particular cognitive activity or emotions such as fear and anger but also with love, awe, happiness, fun, and "peak states," or flow.

In "lit" mode, we engage at the highest level of our abilities. We not only develop the mental muscles to stay focused, but we also build the confidence and the dexterity to riff off of new information on the fly.

We're more likely to use our critical thinking skills, which can keep us from blindly accepting what we're told, or told to believe, especially when our intuition says otherwise. We find it easier to connect with people, are more alive to the possibilities all around us, and are better able to capitalize upon them. In a stream of ever-replenishing energy, we're constantly learning, growing, creating, and iterating. We're building our capacity while doing our best work.

As I honed strategies that enabled me to activate my brain this way at will, I identified a dozen that were simple to use and never failed to open my thinking in just the way that was needed, whatever that was.

Whether it was to direct my attention or disrupt it, sharpen my focus or broaden it, do something stimulating or quiet my mind, these Life Ignition Tools (LIT) worked for me, and then for others as I shared them.

Practicing habits that let me access deep work has been integral to my success

Once I learned how to work with my neuroatypical, voraciously curious, but chaotic brain, I discovered infinite opportunity to question, create, and innovate as a bioengineer and entrepreneur on a global scale and help others do the same. These LIT tools took me from being a confused and frustrated kid, sidelined in a special ed classroom in rural Canada, to becoming a bioengineer and medical innovator elected a fellow of the National Academy of Inventors, the Royal Society of Chemistry, the American Institute for Medical and Biological Engineering's College of Fellows, the Biomedical Engineering Society, and the Canadian Academy of Engineering.

As a professor, I've trained more than 200 people, many of whom are now professors at institutions around the world and innovators in industry; published 130 peer-reviewed papers with more than 30,000 citations; and obtained more than a hundred issued or pending national and international patents. The tools also helped me cofound 12 companies with products on the market or in development.

And finally, they've been instrumental in creating a productive, supportive, and dynamic high-energy environment in my lab, which recently morphed from Karp Lab to the Center for Accelerated Medical Innovation.

Having specific tools helped a struggling kid like me

LIT worked for this kid who appeared to show no promise and the young man who remained frustrated and discouraged for many years. Though I still struggle every day in various ways, I'm grateful to be able to say that these LIT tools enabled me to meet and far exceed those dismal early expectations.

If we want breakthroughs in science and medicine, if we want successful, disruptive innovations on all fronts to support healthier communities, and if we want to cut through the noise and focus on what is most important, we must learn how to use all of the tools in nature's playbook, our evolutionary arsenal. We must shake up our thinking — not just now and then but on a daily basis.

In practice, LIT tools make it possible for us to take anything we're hardwired for — including undesirable or unhelpful behaviors and habits — and with intention, channel the energy in them to create a positive outcome. It's easier than you might think because the more you do it, the greater the rewards, the momentum, and your impact for good.

You're never too old to charge your brain this way, and most definitely no one is ever too young. In fact, LIT tools can be lifesavers for kids, as they were for me.

Adapted from LIT: Use Nature's Playbook to Energize Your Brain, Spark Ideas, and Ignite Action by Jeff Karp, PhD, published by William Morrow. Copyright © 2024 by Jeffrey Michael Karp. Reprinted courtesy of HarperCollinsPublishers.

Watch: Microsoft CEO unravels ChatGPT, ethical AI, and going bust

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