The balanced mind: the variability of task-unrelated thoughts predicts error monitoring

Abstract

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.

Keywords: error monitoring; metacognition; mind-wandering; neurophenomenology; response inhibition; thought-sampling; variability.

Figure 1

The EAT task with interleaved thought probes. Adapted with permission from Hester et al. (2012). Participants respond by pressing the left button (L) during Go trials and withhold from responding (−) to repeated or font-color matching words. Following commission errors, participants are trained to forgo the normal Go response to instead press the right button “R” indicating error awareness for that trial. Pseudo-randomly intermixed “thought probes” prompted participants to rate the intensity of TUTs and their “stickiness” in the pre-probe interval. See Methods for detailed overview of task timing and instructions.

Figure 2

Regression plots of TUT_mean TUT_Variance vs. error awareness (EA) and stop accuracy (SA). On the left, a histogram of all TUT responses recorded in the session showing continuous distribution of responses from 1 to 7 (scale response value on x-axis). Plotted relationships show a clear relationship between individual differences in TUT_Mean and stop accuracy, such that higher levels of TUT predict worse inhibition performance (bottom right). In contrast high levels of TUT_Variance predict increased error monitoring (top left). Scatterplots depict relationships between TUT_Mean, within-subject standard deviation of TUT (TUT_Variance), or composite mean measures (TUT_Mean + Stickiness_Mean) and composite within-subject variance measures (TUT_Variance + Stickiness_Variance). Prior to analysis TUT scores were reversed so that higher numbers reflect increased TUT, and all variables were transformed into Z-scores. Dependent variables are residualized for group status and opposing variables (e.g., SA for EA, TUT_Mean for TUT_Variance, TUT composite for TUT variance composite, respectively). Data points represent individual participant scores. p-values show significance for each predictor variable from multiple regression model (see Results for more details).

Figure 3

Central executive and salience network BOLD responses to correct stop trials vs. baseline. Significant activations throughout the motor control and salience networks, including premotor/supplementary motor area (top left and right), anterior insula (bottom left), putamen, (bottom left) and middle frontal gyrus (top right) BOLD activations to correct stop trials, shown in yellow. Voxel-wise statistical parametric maps (pFWE < 0.05, k threshold > 5 contiguous voxels) superimposed on SPM canonical anatomical image, average of 305 T1-weighted images. Top left shown at MNI Z = 62, top right at X = 47, bottom left at Z = −3, bottom right at Y = −8. See Table 3 for a complete list of foci.

Figure 4

DMN deactivations during stop trials (blue, top) and correlation with TUT reports (red, top), and mask used for ROI analysis (red, bottom). Note that while both task-unrelated thoughts and response inhibition engage the DMN, they recruit spatially unique regions of the mPFC. To facilitate comparison of spatial topography for EAT and TUT-related DMN activity, both activation maps are overlaid on a single MNI structural brain. In blue, significant deactivations during EAT stop trials (pFWE peak < 0.05, k threshold = 5 contiguous voxels). In red, increased self-reported TUT predicts greater mPFC BOLD activation, pFWE cluster < 0.05, region-of-interest analysis with DMN mask volume (bottom image shown in red), k threshold = 685 contiguous voxels. DMN mask generated using automated meta-analysis for term “mPFC” on neurosynth.org, z-score threshold > 4 (see Methods for further details). Statistical parametric maps superimposed on SPM canonical anatomical image, average of 305 T1-weighted images. Top image shown at MNI X = −5, bottom at X = 0.

Figure 5

Salience and default mode network BOLD responses to Aware > Unaware Errors. During conscious error monitoring we observed significant activations throughout the salience and control networks, including mid-cingulate (top right), middle frontal, dorsolateral prefrontal, bilateral caudate (bottom right), right insula (bottom right), superior, and inferior parietal cortex (bottom left) activations. Note that in addition to common salience and error related regions, we observed bilateral inferior parietal responses to this contrast, i.e., co-activation of salience and DMN. Statistical parametric map (pFWE < 0.05, k threshold > 5 contiguous voxels) superimposed on SPM canonical anatomical image, average of 305 T1-weighted images. Top left “glass brain” displays activation extent in three dimensional space. Top right shown at MNI X coordinate = 1, bottom left at X = −43. Bottom right shown at Z = 1. See Table 5 for a complete list of foci.