Edited by: Wendy Hasenkamp, Mind and Life Institute, USA
Reviewed by: James M. Broadway, University of California Santa Barbara, USA; Fadel Zeidan, Wake Forest School of Medicine, USA; Matthew L. Dixon, University of British Columbia, Canada
*Correspondence: Micah Allen, Center of Functionally Integrative Neuroscience, Aarhus University Hospital, 44 Nørrebrograde 55, Aarhus, 8000 Denmark e-mail:
This article was submitted to the journal Frontiers in Human Neuroscience.
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Self-generated thoughts unrelated to ongoing activities, also known as “mind-wandering,” make up a substantial portion of our daily lives. Reports of such task-unrelated thoughts (TUTs) predict both poor performance on demanding cognitive tasks and blood-oxygen-level-dependent (BOLD) activity in the default mode network (DMN). However, recent findings suggest that TUTs and the DMN can also facilitate metacognitive abilities and related behaviors. To further understand these relationships, we examined the influence of subjective intensity, ruminative quality, and variability of mind-wandering on response inhibition and monitoring, using the Error Awareness Task (EAT). We expected to replicate links between TUT and reduced inhibition, and explored whether variance in TUT would predict improved error monitoring, reflecting a capacity to balance between internal and external cognition. By analyzing BOLD responses to subjective probes and the EAT, we dissociated contributions of the DMN, executive, and salience networks to task performance. While both response inhibition and online TUT ratings modulated BOLD activity in the medial prefrontal cortex (mPFC) of the DMN, the former recruited a more dorsal area implying functional segregation. We further found that individual differences in mean TUTs strongly predicted EAT stop accuracy, while TUT variability specifically predicted levels of error awareness. Interestingly, we also observed co-activation of salience and default mode regions during error awareness, supporting a link between monitoring and TUTs. Altogether our results suggest that although TUT is detrimental to task performance, fluctuations in attention between self-generated and external task-related thought is a characteristic of individuals with greater metacognitive monitoring capacity. Achieving a balance between internally and externally oriented thought may thus aid individuals in optimizing their task performance.
Our day-to-day lives are rich with thoughts and feelings that emerge without a direct relationship to the here and now. So-called “task-unrelated thoughts” (TUTs) can be quite variable in content. We might think about our dinner plans while waiting for the bus, or rehearse an important speech in the shower. These self-generated experiences are unique insofar as they are not derived directly from an external stimulus; rather they form a train of endogenous thoughts, perceptually decoupled from ongoing sensory information and any task being performed (Smallwood,
Large-scale thought sampling studies investigating the context and intensity of TUTs suggest that self-generated thoughts may comprise a large part of our daily mental activity (Killingsworth and Gilbert,
Investigating self-generated thoughts presents particular methodological difficulties, as their spontaneous nature renders direct experimental manipulation problematic. An established method used in the present investigation is to study the experience of TUTs while people perform an external task; an advantage of this approach is that the experiential reports can be validated by a process of triangulation using behavioral, physiological, and subjective measures recorded during the session (Jack and Roepstorff,
While functional connectivity studies suggest that the DMN may be “anti-correlated” with the salience and control related networks (Fox et al.,
Consistent with a general functional role of self-generated thought, the mPFC is typically activated when thinking about the self and when making judgments about others (Mitchell,
Error monitoring in the context of response inhibition is an extensively researched metacognitive ability, beginning with early work suggesting that correction of errors can occur as early as 200 ms post-error, before conscious awareness of having committed a mistake (Rabbitt,
To measure the experience of TUT we embedded experience sampling probes within the EAT, prompting participants to rate the subjective intensity of TUTs in the preceding interval. Previous investigations have utilized this approach in both behavioral (Mrazek et al.,
In addition to examining overall TUT and stickiness rates, we were also interested in the variability of these experiences. Within-subject variability reflects important dynamical aspects of cognition and experience, reflecting discrete state transitions (Varela et al.,
In summary, the present experiment examined the relationship amongst within- and between-subject variability in TUT intensity and stickiness, response inhibition accuracy, and the awareness of consequent mistakes. Based on prior research, we expected increased TUT to be associated with worse inhibition performance and to engage prefrontal nodes of the DMN. The main focus of the experiment, however, was to ascertain the relationship between TUT and metacognition, which was assessed by measuring self-reported stickiness and error monitoring. One possibility is that better metacognition increases the ability to regulate mind-wandering (Schooler,
42 participants (27 females) were recruited from an online participant pool system in Aarhus, Denmark, from both the local university and community. The average age of participants was 34.8 years (±0.9 SEM, range = 25–47 years), with 17.6 mean years education (±0.5 SEM, range = 10–23 years). All procedures were approved by the local research ethics committee, De Videnskabsetiske Komitéer for Region Midtjylland, in accordance with the declaration of Helsinki. As part of a separate investigation concerning the impact of mindfulness on EAT and visual sensitivity, half of our participants (
Here we were specifically interested in how individual differences in TUT experience and variability would predict EAT performance; inclusion of meditation practitioners in our sample was thus used as a strategy to maximize TUT-related variability within the sample. To ensure that our present findings were not biased by systematic group differences, all analyses were conducted using group status as a nuisance covariate. Specific group contrasts are not examined here; although they will be reported in a follow-up investigation of the impact of mindfulness training on EAT performance and visual sensitivity. Groups were matched for age (mean age meditation = 35.1 years, mean age control = 34.6 years), gender (meditation = 14 males, control = 15 males), and education (controls mean education = 16.5 years; meditation mean education = 18.6 years). In our meditation study we aimed to specifically sample “adept” practitioners; inclusion criteria specified that participants must practice at least 20 min per day at a minimum of 3 times per week over the two years prior to the study, and have attended at least 1 meditation retreat in the previous year (mean hours practiced = 1303.6).
All fMRI scans were acquired over a one-week period following enrollment in the study. Participation in the fMRI scan was incentivized with a 200DKK (approximately $35 USD) reimbursement, and to control motivation all participants were instructed that the top 1/3rd of scores on the scanning task would receive an additional 200DKK (Jensen et al.,
Before scanning, participants were informed that the purpose of the study was to investigate individual differences in their attentional ability. Participants visited the lab twice, once to provide informed consent and complete a psychophysical vision sensitivity test (data not reported here) and again to complete the fMRI scan. Specifically the psychophysics test was the “theory of visual attention task” (TAVT). This measure was included to replicate a previous result that meditation experience improves TAVT performance irrespective of motivation levels (Jensen et al.,
TUTMean | – | 0.269 | 0.784 |
0.241 | −0.561 |
−0.128 |
TUTVariance | – | 0.163 | 0.654 |
0.098 | 0.417 |
|
StickMean | – | 0.401 |
−0.436 |
−0.127 | ||
StickVariance | – | −0.069 | 0.299 | |||
SA | – | 0.332 |
||||
EA | – |
To assess individual differences in response-inhibition and error monitoring, we adapted the delayed-response EAT from Hester et al. (
Participants completed 6 runs of the EAT within the scanner, each consisting of 200 Go and 25 pseudo-randomly intermixed Stop trials, for a total of 1350 trials. In order to maximize unaware errors and mind-wandering, participants were trained to respond during the interstimulus interval, emphasizing accuracy and timing consistency over absolute response speed, increasing the repetitive nature of the Go task as in Shalgi et al. (
Because the occurrence of TUTs is negatively related to task difficulty (Christoff et al.,
Individual stop accuracy and error awareness scores were determined for each participant. Stop accuracy was calculated as the ratio of correctly withheld stop trials over the total number of stop trials (Correct stops/total stops), and error awareness as the ratio of number of reported error trials over total number of error trials (aware errors/unaware errors). Go accuracy was calculated as the ratio of the total number of correct responses (e.g., reaction time > 0 in a Go trial) over the total number of Go trials, excluding trials following errors (which are confounded by the error reporting response). Mean TUT and stickiness scores, as well as within-subject standard deviations were calculated for each participant as measures of the average content and variance of each subjective dimension.
Prior to analysis participants with extremely low stop accuracy (indicating a failure to correctly perform the task) were identified; one participant with <50% accuracy was excluded from all subsequent EAT-related analyses. As overall performance was generally high, three participants had too few errors (<5) to be included in error related behavioral analyses and were excluded (O'Connell et al.,
Subjective awareness of TUTs was assessed in a similar fashion to Christoff et al. (
By fixing this subjective dimension, we aimed to stress the possibility of dissociation between high intensity TUTs with little impact on participants' metacognitive capacity and high-intensity TUTs that fully absorbed the participants' attention (e.g., “sticky” TUTs). Participants were therefor instructed to rate the “stickiness” of their task-irrelevant thoughts, with sticky thoughts defined as those that “distract (the participant) for a greater period of time, and are more attention catching than other task-irrelevant thoughts; this experience is sometimes described as being ‘lost in thought’.” Stickiness was thus included to explore whether ruminative and absorptive TUT compared to non-ruminative and non-absorptive TUT differentially impact sustained attention and error awareness (Koster et al.,
As the BOLD signal reflects complicated neurovascular coupling, a considerable portion of BOLD variability can be explained by non-neural origins such as respiratory and cardiovascular fluctuation (Glover et al.,
All pulse and respiration time series were visually examined for acquisition artifacts (e.g., clipping, drop-out). Due to technical failure of the respiration belt, respiration time series were severely confounded and discarded from further analysis. While inclusion of both respiratory and pulse regressors has been shown to provide an optimal estimation of serial correlation, inclusion of pulse and motion regressors without respiration has been shown to also outperform standard autoregressive (“AR1”) noise-whitening techniques, particularly at faster repetition times (e.g., TR <4 s) (Lund et al.,
Echo-planar images (EPI) were acquired at the Aarhus University Hospital, using a T2*-weighted, gradient echo sequence on a 3 Tesla (Siemens Trio) scanner, equipped with a 32-channel head coil. EPI images were acquired in an interleaved slice acquisition order (
All fMRI preprocessing and data analyses were performed in SPM8 (version 4667) (Friston et al.,
To compare our results with previous experiments using the EAT, we analyzed accuracy and reaction time values across conditions. Go reaction times for each participant were calculated as the mean of all correct Go trials, excluding responses 2 SD below the participants mean Go RT. For comparison to previous experiments with the EAT, stop accuracy, error awareness, and mean reaction times were calculated for each subcategory of stop, i.e., color and repeat stop accuracy, color and repeat aware/unaware errors, and RT to color and repeat stop errors. Mean reaction times where inspected for values ±2
Our first aim was to replicate previously reported relationships between performance and TUTs. To establish whether or not TUT ratings were utilized as a continuous or discrete measure, we created response histograms across all collected ratings, which showed a clear continuous distribution indicating that participants did not treat the scales as discrete binary measures (Figure
Following preprocessing, functional BOLD data were analyzed using an event-related hierarchical general linear modeling approach (Friston et al.,
Go accuracy (% correct) | 80.3 | 0.1 |
No-go accuracy (% correct) | 77.0 | 14.3 |
Repeat No-go accuracy | 81.0 | 18.0 |
Color No-go accuracy | 72.8 | 21.0 |
Error awareness (% of aware errors) | 35.5 | 20.0 |
Repeat error awareness | 26.9 | 20.6 |
Color error awareness |
43.7 | 24.4 |
Aware errors (total) | 12.0 | 7.8 |
Unaware errors (total) | 25.3 | 18.6 |
Go | 1103.32 | 11.49 |
Aware error | 1086.38 | 14.68 |
Unaware error |
1060.31 | 17.31 |
Task unrelated thought (1–7) | 3.0 | 1.1 |
Stickiness (1–7) | 2.5 | 1.0 |
Heartbeats per min | 65.1 | 8.7 |
Estimated EA | 49.1 | 26.9 |
Estimate SA | 76.8 | 14.0 |
Difficulty | 56.0 | 23.3 |
Effort | 92.5 | 9.1 |
Interest | 63.2 | 28.3 |
Our random-effects (RFX) analysis focused on three contrasts: correct stops (vs. baseline), aware vs. unaware errors, and the negative correlation of TUT reports and task-related BOLD activity. All RFX analyses were conducted by passing each participant's corresponding contrast image (stops, aware > unaware, TUT ratings) to a one-sample
Participants correctly withheld on the majority of stop trials (Mean
Regression analysis with TUTVariance, TUTMean, StickinessVariance, and StickinessMean, group status, and error awareness as predictors explained 53.5% of stop accuracy variance [
The resulting regression models explained 44.2% of stop accuracy variance [
Across participants, correct stops elicited significant BOLD activations throughout the canonical motor inhibition network, including bilateral anterior insula, superior parietal lobes, supplementary motor areas, and bilateral putamen (Hester et al.,
L Medial frontal gyrus | 2560 | <0.001 | 10.7 | −6 | −8 | 62 |
L Precentral | <0.001 | 10.68 | −50 | −10 | 54 | |
R Supplementary | <0.001 | 7.44 | 10 | −4 | 62 | |
Motor | ||||||
R Insula | 1057 | <0.001 | 9.34 | 30 | 20 | −6 |
R Anterior insula | <0.001 | 8.06 | 34 | 14 | 8 | |
CSF near Putamen | <0.001 | 7.47 | 4 | 6 | 12 | |
White matter near PCC | 579 | <0.001 | 8.46 | 6 | −26 | 22 |
R Precentral | 708 | <0.001 | 7.6 | 48 | −2 | 42 |
R Precentral (BA 6) | <0.001 | 7.53 | 56 | −2 | 50 | |
R Precentral (BA 9) | 0.001 | 6.65 | 44 | 2 | 28 | |
R Supramarginal | 755 | <0.001 | 7.5 | 60 | −44 | 44 |
R Inferior parietal | <0.001 | 7.49 | 46 | −40 | 46 | |
R Inferior parietal | <0.001 | 7.33 | 56 | −42 | 52 | |
L Pallidum | 1022 | <0.001 | 7.2 | −22 | −6 | 16 |
L Anterior insula | 0.001 | 7.01 | −30 | 16 | 6 | |
L Putamen | 0.001 | 6.62 | −20 | 4 | 2 | |
L Inferior parietal | 324 | 0.001 | 6.83 | −44 | −42 | 44 |
L Inferior parietal | 0.007 | 6.04 | −58 | −46 | 44 | |
L Inferior parietal | 0.021 | 5.64 | −52 | −48 | 54 | |
R Middle temporal | 47 | 0.001 | 6.68 | 46 | −40 | 6 |
L Thalamus | 98 | 0.003 | 6.35 | 0 | −12 | −4 |
R Thalamus | 0.024 | 5.57 | 14 | −12 | 2 | |
L Precentral | 125 | 0.004 | 6.28 | −42 | 0 | 26 |
Left rolandic | 0.015 | 5.77 | −48 | 0 | 18 | |
operculum | ||||||
R Middle cingulate | 28 | 0.007 | 6.02 | 10 | 16 | 38 |
L Inferior frontal | 23 | 0.011 | 5.89 | −48 | 32 | 32 |
R Fusiform | 7 | 0.017 | 5.71 | 38 | −48 | −14 |
L Inferior parietal | 12 | 0.02 | 5.65 | −42 | −46 | 30 |
R Middle temporal | 9 | 0.023 | 5.6 | 64 | −40 | 12 |
L Thalamus | 6 | 0.025 | 5.56 | −4 | −2 | −2 |
L Calcarine (BA 17) | 5064 | <0.001 | 10.73 | −10 | −96 | 16 |
L Calcarine (BA 17) | <0.001 | 10.51 | −2 | −92 | 10 | |
R Calcarine (BA 17) | <0.001 | 10.03 | 6 | −92 | 8 | |
L Superior medial | 932 | <0.001 | 7.96 | 0 | 52 | 46 |
R Superior medial | <0.001 | 7.77 | 8 | 68 | 20 | |
L Superior medial | <0.001 | 7.48 | −2 | 66 | 18 | |
R Supplementary | 60 | 0.001 | 6.85 | 4 | 18 | 70 |
motor (BA 6) | ||||||
L Inferior frontal | 34 | 0.01 | 5.9 | −54 | 28 | −2 |
L Middle temporal | 6 | 0.021 | 5.64 | −40 | −58 | 20 |
L Pallidum | 497 | <0.001 | 7.68 | −14 | −4 | 2 |
L Caudate | 0.004 | 6.53 | −12 | 10 | −4 | |
L Putamen | 0.009 | 6.2 | −26 | 10 | 6 | |
L Superior frontal | 775 | <0.001 | 7.52 | −24 | −6 | 64 |
L Superior | 0.001 | 7.34 | −16 | −4 | 66 | |
frontal (BA 6) | ||||||
L Superior | 0.001 | 7.18 | −28 | −8 | 50 | |
frontal (WM) | ||||||
R Inferior | 126 | 0.001 | 7.35 | 50 | 4 | 30 |
frontal (BA 44) | ||||||
R Supplementary | 1523 | 0.001 | 7.31 | 12 | 2 | 70 |
motor (BA6) | ||||||
R Middle | 0.001 | 7.25 | 6 | 4 | 38 | |
Cingulate | ||||||
R Superior | 0.001 | 7.03 | 16 | 0 | 62 | |
frontal | ||||||
R Caudate | 615 | 0.001 | 7.3 | 10 | 4 | −2 |
R Caudate (WM) | 0.001 | 7.05 | 16 | 0 | 14 | |
R Putamen | 0.001 | 6.97 | 24 | 16 | 0 | |
L Inferior | 1267 | 0.001 | 7.28 | −40 | −34 | 48 |
parietal (BA 2) | ||||||
L Supramarginal | 0.001 | 7.1 | −48 | −44 | 38 | |
L Inferior parietal | 0.001 | 7.1 | −52 | −34 | 46 | |
R Precentral | 100 | 0.001 | 7.08 | 34 | −6 | 44 |
R Thalamus | 67 | 0.001 | 7.06 | 4 | −20 | −4 |
R Supramarginal | 324 | 0.002 | 6.86 | 54 | −22 | 32 |
R Inferior parietal | 0.005 | 6.45 | 54 | −28 | 42 | |
R Inferior parietal | 0.005 | 6.44 | 52 | −42 | 38 | |
L Thalamus | 20 | 0.002 | 6.76 | −18 | −14 | 16 |
R Middle temporal | 49 | 0.004 | 6.54 | 44 | −72 | 28 |
R Inferior temporal | 57 | 0.008 | 6.25 | 56 | −50 | −6 |
L Middle frontal | 136 | 0.008 | 6.24 | −32 | 46 | 28 |
L Middle frontal | 0.037 | 5.65 | −38 | 34 | 32 | |
R Middle temporal | 13 | 0.012 | 6.09 | 48 | −54 | 20 |
R Parahippocampal | 6 | 0.014 | 6.04 | 18 | 0 | −16 |
L Superior | 20 | 0.014 | 6.03 | −54 | −48 | 22 |
temporal | ||||||
R Middle frontal | 13 | 0.017 | 5.95 | 30 | 34 | 34 |
R Posterior | 7 | 0.018 | 5.94 | 8 | −32 | 36 |
Cingulate (in WM) | ||||||
R Superior frontal | 47 | 0.018 | 5.94 | 26 | 42 | 26 |
R Inferior frontal (BA 44) | 7 | 0.02 | 5.9 | 52 | 6 | 14 |
R Middle temporal | 9 | 0.026 | 5.79 | 54 | −36 | −8 |
L Precentral (BA 44) | 12 | 0.026 | 5.79 | −50 | 8 | 38 |
We found significant correlations between probe-related BOLD signal and TUT intensity reports in clusters located in the mPFC (
L Superior medial | 645 | 0.047 | 0.002 | −12 | 60 | 8 |
R Superior medial | 0.002 | 8 | 64 | 8 | ||
L Anterior cingulate | 0.002 | −8 | 44 | 6 | ||
89 | 0.776 | 0.004 | −4 | 42 | 26 | |
9 | 0.981 | 0.004 | −46 | −72 | 28 | |
9 | 0.981 | 0.006 | −4 | −56 | 22 |
Consistent with previous fMRI work (Mason et al.,
Our results also inform our understanding of the link between TUT and task performance. We replicated the finding that overall levels of TUT can interfere with demanding tasks, potentially reflecting the role of TUT in facilitating perceptual decoupling (Smallwood,
In general, behavioral variability reflects the sensitivity of cognition to fluctuating task demands, and can produce positive or negative outcomes depending on behavioral context (Lutz et al.,
Regardless of the specific relationship, our data extends the role of metacognition in enhancing the flexibility of conscious thought (Flavell,
Although we advance an interpretation of TUT variance as relating to metacognition, it must be noted that there is emerging evidence for a functional dissociation of reflective metacognition and online error monitoring processes (Fleming et al.,
Our results also have implications for understanding the component process view of the DMN (Andrews-Hanna et al.,
In the present design we attempted to control participant motivation through financial reward; it is possible that our motivation manipulation interacted with self-reports, although we specifically instructed participants to report their honest experience. Indeed, we observed activations in reward-related areas including the caudate nucleus and putamen in our error awareness and stop contrasts (Schultz,
We also attempted to apply a subjective distinction between the intensity or subjective frequency of TUTs and their “stickiness,” to generate self-reports capturing unique aspects of phenomenological experience, effectively “front-loading” phenomenological intuition into our experimental design (Gallagher,
In conclusion, our findings confirm previous work suggesting that TUTs interfere with task performance under demanding task conditions. In addition, we found novel evidence that variability in mind-wandering experience related to greater metacognitive ability, suggesting a role for online monitoring in ensuring flexibility in the manner that attention is deployed on both external and self-generated sources of information. We observed activations of both default mode and salience networks during error monitoring, a finding in line with the observation that particular aspects of mind-wandering are related to self-monitoring. We also found that ventral regions of the mPFC increased activity as TUT increased, while more dorsal regions were deactivated when individuals engaged cognitive control, a finding broadly in line with a component process view of the DMN. Given these results we recommend that a time series analysis of subjective variability with continuous self-report measures, or investigation of second-order confidence in TUT ratings, may reveal further granularity in the experience of mind-wandering and related contributions to behavioral performance. Such approaches may prove important in determining the extent to which individuals regulate the balance of conscious thought so as to maximize the benefits of self-generated thought, while simultaneously limiting its costs.
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.
The authors thank Rob Hester for helpful comments on implementing the EAT. We also thank Francesca Fardo, Hauke Hillebrandt, Torben Lund, and Martin Dietz for comments on the draft and assistance with the respiration analysis. This work was supported by a Francisco Varela Award from the Mind and Life Institute and by a grant from the MINDlab Investment Capital for University Research fund. Antoine Lutz was supported by an International Re-integration Grant (IRG), FP7-PEOPLE-2009-RG.