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. Author manuscript; available in PMC: 2020 Aug 1.
Published in final edited form as: Cortex. 2019 Mar 6;117:77–88. doi: 10.1016/j.cortex.2019.02.026

Neural mechanisms of internal distraction suppression in visual attention

Abhijit Rajan 1, Sreenivasan Meyyapan 1, Harrison Walker 1, Immanuel Babu Henry Samuel 1, Zhenhong Hu 1, Mingzhou Ding 1
PMCID: PMC6686907  NIHMSID: NIHMS1525812  PMID: 30933692

Abstract

When performing a demanding cognitive task, internal distraction in the form of task-irrelevant thoughts and mind wandering can shift our attention away from the task, negatively affecting task performance. Behaviorally, individuals with higher executive function indexed by higher working memory capacity (WMC) exhibit less mind wandering during cognitive tasks, but the underlying neural mechanisms are unknown. To address this problem, we recorded functional magnetic resonance imaging (fMRI) data from subjects performing a cued visual attention task, and assessed their WMC in a separate experiment. Applying machine learning and time-series analysis techniques, we showed that (1) higher WMC individuals experienced lower internal distraction through stronger suppression of posterior cingulate cortex (PCC) activity, (2) higher WMC individuals had better neural representations of attended information as evidenced by higher multivoxel decoding accuracy in the dorsal attention network (DAN), (3) the positive relationship between WMC and DAN decoding accuracy was mediated by suppression of PCC activity, and (4) the dorsal anterior cingulate (dACC) was a source of top-down signals that regulate PCC activity as evidenced by the negative association between Granger-causal influence dACC→PCC and PCC activity and higher WMC individuals exhibiting stronger dACC→PCC Granger-causal influence. These results shed light on the neural mechanisms underlying the executive suppression of internal distraction in tasks requiring externally oriented attention and provide an explanation of the individual differences in such suppression.

Keywords: Posterior Cingulate Cortex, Dorsal Anterior Cingulate Cortex, Granger Causality, Multi-voxel Pattern Classification, Working Memory Capacity

INTRODUCTION

When performing a task requiring externally oriented attention, internal distraction in the form of mind wandering and task-irrelevant thoughts can negatively impact task performance 1–4. The decline in task performance is thought to be the direct consequence of deteriorated neural representations of the external environment and task-relevant information 1,2,5–7. Suppression of internal distraction as a means to enhance task performance is a key function of executive attention 8. Like any other cognitive function, there are significant individual differences in the susceptibility to internal distraction, and these differences are shown to be quantifiable by working memory capacity (WMC), an index of executive attention. Higher WMC individuals suffer less from internal distraction by having lower levels of mind wandering 5,9 and intrusive thoughts 10–12. Despite these advances at the behavioral level, the neural basis underlying the suppression of task-irrelevant thoughts and mind wandering remains to be better understood.

What is the neural substrate of internal distraction? The default mode network (DMN), particularly its major component the posterior cingulate cortex (PCC), mediates internal mentation and self-referential processes. Past research has implicated PCC as a source of internal distraction by showing that PCC undergirds such task-irrelevant processes as mind wandering and stimulus independent thoughts 13–17. It has been widely reported that, during externally oriented attention, PCC is deactivated, and its insufficient suppression is associated with attention lapses and poor task performance 15,18–20. Based on these studies, we hypothesized that task-related fluctuations of PCC activity can be used as a quantitative neural measure of internal distraction.

WMC, as a measure of domain general executive attention, is known to play important roles in a wide range of cognition functions, including language comprehension 21, fluid intelligence 22, and episodic memory 23. Recent studies suggest that WMC’s positive relationship with high-level cognitive functions is mediated by the suppression of internal distraction 9. For instance, higher WMC individuals’ ability to perform better in reading comprehension and in sustained attention is shown to be partially mediated by their ability to suppress task-irrelevant thought processes 5,9. It has been further suggested that neurally, higher WMC individuals’ ability to better maintain neural representations of task-relevant information is driven partially by their ability to more effectively suppress mind wandering and task-irrelevant internal thought processes 5. Based on these considerations, we hypothesized that at the neural level, the suppression of PCC activity underlies the positive relationship between WMC and neural representations of task-relevant information.

What are the neural structures responsible for suppressing internal distraction? A recent study by Wen et al. 15 suggested that dACC issues top-down signals to regulate PCC activity. Applying Granger causality (GC) analysis to fMRI data, they showed that dACC exerts Granger-causal influence on PCC (dACC→PCC), and the stronger the dACC→PCC GC, the more suppressed the BOLD activity in PCC, and the better the behavioral performance in a visual spatial attention task. Consistent with this, in ADHD, diminished functional connectivity between dACC and DMN regions account for increased attentional lapses and mind wandering in ADHD patients24,25. Based on these considerations, we hypothesized that higher WMC individuals have stronger Granger-causal influence from dACC to PCC region, which leads to more effective suppression of internal distraction, and to improved neural representations of task-relevant information and behavioural performance.

We recorded fMRI data from subjects performing a cued visual attention task. On each trial, an auditory cue was presented, which instructed the subjects to attend to either a spatial location or color of an impending stimulus. The magnitude of cue-evoked BOLD response was estimated on a single-trial basis. Multivoxel pattern classification (MVPC) was performed on the single-trial cue-evoked BOLD responses in the dorsal attention network (DAN) and the decoding accuracy was taken to index the quality of neural representations of task-relevant information. The WMC for each subject was measured separately outside the scanner using an operation span task (OSPAN) 26. A mediation analysis was performed to test whether internal distraction mediates the relationship between WMC and neural representations of task-relevant information. Finally, the causal influence of dACC over PCC region, dACC→PCC, was assessed using Granger causality analysis and correlated with WMC to identify the neural structures responsible for suppressing internal distraction and to explain the individual differences in such suppression.

METHODS

Participants

The experimental protocol was approved by the Institutional Review Board of the University of Florida. Twenty right-handed college students (mean age of 24.65 ±2.87, 5 women) with normal or corrected to normal vision and no history of neurological or psychiatric disorders gave written informed consent and participated in the study.

Paradigm

As illustrated in Figure 1, two peripheral locations, 3.6 degrees lateral to the upper left and upper right of the fixation point, were marked on the screen. Each trial starts with an auditory cue instructing the subject to covertly direct attention either to a spatial location (“left” or “right”) or to a color (“red” or “green”). Following a delay period (cue-to-target period), varied randomly from 3000ms to 6600ms, two colored rectangles (red or green) were presented for a duration of 200ms, with one in each of the two peripheral locations. The subject’s task was to report the orientation of the rectangle (target) appearing in the cued location or having the cued color and ignore the other rectangle (distractor). For color trials, the two rectangles displayed were always of the opposite color; for spatial trials, the two rectangle were either of the same color or of the opposite color. On 8% of the trials, cues were invalid and only one rectangle was displayed, which was either not in the cued location for spatial trials or not having the cued color for color trials, and the participants were required to report the orientation of the rectangle. For instance, for an invalid left cue, there would be only one rectangle displayed on the right visual field, whereas for an invalid red cue, there would be only one green rectangle displayed in either left or right visual field. These invalidly cued trials were included to measure the behavioural benefits of valid attentional cuing 27. An inter-trial interval, varied randomly from 8000ms to 12800ms following the target onset, elapsed before the start of the next trial. Trials were organized into sessions with each session consisting of 25 trials with randomly presented color and spatial trials, lasting approximately seven minutes. Each participant completed 10 to 14 session over two days where the number of spatial trials and the number of color trials were balanced. In addition to spatial and color cues, there was a third type of cue (“none”), which did not specify the information to attend. When the two rectangles were displayed, subjects discriminated the orientation of the rectangle with grey patch in the background.

Figure 1.

Figure 1

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Experimental paradigm. Each trial started with an auditory cue (500ms) instructing the subject to covertly attend to a spatial location (“left” or “right”) or to a color (“red” or “green”). Following a variable cue-to-target interval (3000–6600ms), two colored rectangles were displayed (200ms), one in each of the two peripheral locations. Participants were asked to report the orientation of the rectangle (horizontal or vertical) displayed in the cued location or having the cued color. An inter-trial interval, varied randomly from 8000–12800ms following the target onset, elapsed before the start of the next trial.

All participants went through a training session during which the eye movements were also monitored. The subjects who showed an accuracy above a minimum criterion (>70%) and who were able to maintain proper eye fixation throughout the experiment were chosen to take part in the actual experiment.

Functional MRI acquisition and preprocessing

Functional MRI images were collected on a 3T Philips Achieva scanner (Philips Medical Systems, the Netherlands) equipped with a 32-channel head coil. The echo-planar imaging (EPI) sequence parameters were: repetition time (TR), 1.98 s; echo time, 30 ms; flip angle, 80°; field of view, 224 mm; slice number, 36; voxel size, 3.5 × 3.5 × 3.5 mm; matrix size, 64 × 64. The slices were oriented parallel to the plane connecting the anterior and posterior commissures. Simultaneous EEG was also recorded but not analysed here. To monitor the quality of EEG recording, image acquisition took place during the initial 1.85s within each EPI volume, leaving an interval of 130 ms towards the end of each TR where there was no image acquisition. A MR compatible Eye Tracker (EyeLink 1000, SR Research) was used throughout the experiment to continuously monitor the eye position of each subject.

Functional MRI data were processed in SPM. Preprocessing steps included slice timing correction, realignment, spatial normalization, and smoothing. Slice timing correction was carried out using sinc interpolation to correct for differences in slice acquisition time within an EPI volume. The images were then spatially realigned to the first image of each session by a 6-parameter rigid body spatial transformation to account for head movement during acquisition. Each subject’s images were then normalized and registered to the Montreal Neurological Institute (MNI) space. All images were spatially smoothed using a Gaussian kernel with 7 mm full width at half maximum.

Definition of regions of interest (ROIs)

The role of the dorsal attention network (DAN) in attention control is well established 28,29. The region of interest (ROI) for DAN was selected according to a previously published atlas which included intraparietal sulcus/superior parietal lobule (IPS/SPL) and frontal eye field (FEF) (Figure 2A) 30. The PCC ROI, shown in Figure 3A, was defined as a 5mm radius sphere centered at the voxel coordinate (-8, -56, 26) based on previous studies of DMN 14,31. The dACC ROI was defined as a 5mm radius sphere centered at the voxel coordinate (6, 12, 48) according to previously reported coordinates of dACC activation in a cued attention task15.

Figure 2.

Figure 2

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Neural representations of attended information in DAN. (A) DAN ROI. (B) Individual and mean decoding accuracy between attend-space vs attend-color in DAN. *: p<0.0001.

Figure 3.

Figure 3

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PCC as the neural substrate of internal distraction. (A) PCC ROI. (B) Comparison of cue-evoked PCC activity for fast and slow RT trials. (C) Correlation between cue-evoked PCC activity and attentional decoding accuracy in DAN. *: p<0.05

Estimation of single-trial BOLD response

We applied the beta series regression method 32 to estimate single-trial BOLD responses to the cue. For all trials with correct responses, the cues were modelled as separate regressors. The two stimulus regressors (invalid and validly cued stimulus) and cues with incorrect response were modelled as in conventional GLM analysis. Multivoxel pattern classification (MVPC) analysis was performed on these single-trial BOLD responses to assess neuronal representations of attended information.

Multivoxel pattern classification: Decoding spatial versus feature attention

MVPC is a technique that explores the difference in spatial patterns of BOLD activation to classify different experimental conditions 33,34. In this study, MVPC was used to test whether the patterns of cue-evoked BOLD activities can be used to discriminate between the two types of attended information, namely, spatial location versus color. The decoding accuracy from this analysis is a measure of the distinctiveness of the neural representations of attended information. Higher attentional decoding accuracy signifies better representation of task-relevant information. Given DAN’s importance in spatial and feature attention control 28,35, we focused on using the single-trial beta values across voxels within the DAN region as features for MVPC analysis. The MVPC analysis was performed using linear support vector machine (SVM) pattern classifier with parameter c=1. The classification accuracy for each subject was calculated using a 10-fold cross validation technique. In this technique, 90% of the labelled data (i.e. spatial and color trials) was randomly chosen and used for training the classifier to generate a predictive model (separating hyperplane) and the remaining 10% of the data was used to test the model by comparing the actual labels against the predicted labels. This process is repeated 10 times and the prediction accuracy from each testing batch was averaged to become the reported decoding accuracy. As indicated earlier, the more distinct the patterns of attention-to-space versus attention-to-color, the higher the decoding accuracy. Whether the decoding accuracy was above chance level was tested using a non-parametric permutation based technique. At the individual subject level, the class labels were shuffled 100 times, and for each shuffled label, the ten-fold cross validation procedure was carried out. Second, at the group level, one accuracy from the 100 shuffled accuracies were chosen randomly for each subject and averaged across subjects. This procedure was repeated 105 times, which resulted in 105 chance-level decoding accuracies at the group level. The group level decoding accuracy obtained from the true labels was compared with the empirical distribution of the group level chance accuracies to determine its statistical significance36.

Mediation analysis

Mediation analysis was performed to test the hypothesis that the internal distraction (M) represented by BOLD activity in the PCC region mediates the association between working memory capacity (X) and task-relevant representation represented by attentional decoding accuracy in DAN (Y). Mathematically:

M=i1+aX+e1
Y=i2+bM+cX+e2
Y=i3+dX+e3

where i1, i2, i3 are the intercepts, e1, e2, e3 are the residuals, Y is the dependent variable, X is the independent variable, M is the mediator, a is the coefficient relating M and X, b is the coefficient relating M and Y while controlling for X, c is the coefficient relating X and Y while controlling for M, and d is the coefficient relating X and Y. The statistical significance of the mediation effect is evaluated by the Sobel test in which the product of coefficient a and coefficient b, ab, was divided by their standard error and compared with the standard normal distribution 37,38.

Granger causality analysis

Granger causality (GC) is a method to assess the causal interactions between two simultaneously recorded time series 39,40. Its basic idea is that if the variance of the prediction error of first time series is reduced by using the past measurements of the second time series, then the second time series is supposed to have a Granger-causal influence over the first. The roles of the two time series can be reversed to estimate the Granger-causal influence in the opposite direction. In this study, GC analysis was performed at the session level to assess the causal influence of dACC region over PCC region. This analysis included the following steps. First, the pre-processed BOLD time series from all voxels within a ROI was averaged to yield one time series. Second, the first five BOLD time points for each session was removed to eliminate transient effects 41, and the temporal mean was removed in order to meet the zero-mean requirement assumed by the autoregressive (AR) model used to estimated GC 40,42. Third, because Granger causality requires that the time series be approximately stationary, the stationarity of the BOLD time series was tested using the augmented Dickey-Fuller test. All time series passed the augmented Dickey-Fuller test. Fourth, autoregressive (AR) models were estimated from BOLD time series of one complete session, from which GC values were derived. The AR model order was determined to be 11 by minimizing the mean square error between the spectral estimates of the BOLD time series from the AR model and that from the Fourier method 43,44 (see Supplementary Material, Figure S1, for more details). Fifth, the GC values were averaged across sessions within a subject, and correlated with the corresponding behavioral and BOLD measures.

Assessment of working memory capacity

Working memory capacity (WMC) was estimated outside the scanner in a separate experiment where the subjects performed the computerized operation span (OSPAN) task 26. In this task, the subject was shown a series of letters to remember, and after each letter presentation, they were asked to solve a simple mathematical problem. Each trial consisted of a set size of 3 to 7 letters, and each subject completed 15 trials. The OSPAN score, taken as a measure of WMC, was the sum of all correctly recalled letters 26,45,46.

RESULTS

Behavioral analysis

All subjects performed the task according to instructions. The mean reaction time (RT) and accuracy across all trial types were 1011ms ± 183.5ms and 93.66% ± 3.82%, respectively. For spatial trials, RT=1016ms ±178ms and accuracy=93.94% ±3.83%, and for color trials, RT=1006ms ±188.44ms and accuracy=93.39% ±4.5%. There were no significant differences in either RT (p=0.6) or accuracy (p=0.54) between these two types of trials. Valid attentional cueing improved behavioral performance for both spatial trials (Invalid RT> Valid RT, p=0.00013) and color trials (Invalid RT> Valid RT, p<10−5). These results provide evidence that attention was paid to the cued sensory attributes and the level of attentional control elicited by the two types of cues was equated.

Neural representations of attended information in DAN

Cue-related neural activity in DAN represents attended information 35,47. Supporting this notion, MVPC analysis applied to the voxelwise single-trial cue-evoked BOLD response in DAN (Figure 2A) revealed that the mean accuracy of decoding between attending spatial location versus attending color was significantly higher than chance level (58%, p<0.0001, with kappa statistic=0.16) (Figure 2B), suggesting that multivoxel patterns in DAN are different for different types of attended information.

Impact of PCC activity on behavior and on neural representations of attended information

The impact of neural activity in PCC (Figure 3A) on behavior and task-relevant neural representations was investigated. Behaviorally, for each subject, the trial-wise RT were sorted in an ascending manner, and divided into a fast RT group (first 1/3 of trials) and a slow RT group (last 1/3 of trials). The beta values in PCC was averaged for trials in each RT group, and found to be significantly more deactivated for fast RT trials than slow RT trials (p=0.04), as shown in Figure 3B, suggesting that insufficient suppression of PCC BOLD activity adversely impacted task performance. A further analysis dividing trials into three RT groups (fast, middle and slow) was also conducted. The relationship between RT and PCC deactivation was quantified by the slope of the linear regression fit to the three data points for each subject. Consistent with the two RT-group result, it was found that this slope was significantly greater than zero (p=0.04), suggesting that the less deactivated the PCC the slower the RT (see Supplementary Material Figure S6). These findings support the interpretation that in tasks that require externally focussed attention, activity in PCC was task-irrelevant, and can be considered as a source of internal distraction 15,18–20,48.

Neurophysiologically, PCC activity also had negative impact on the representation of attended information in DAN, as shown in Figure 3C where a negative correlation between cue-evoked beta in PCC and decoding accuracy in DAN was seen (r=-0.65, p=0.003), suggesting that less deactivation of PCC leads to poorer representations of task-relevant information in DAN.

Working memory capacity and suppression of internal distraction

Individual differences in vulnerability to internal distraction were investigated by relating WMC and task-relevant representations in DAN. A significant positive correlation was observed between WMC and decoding accuracy in DAN, as shown in Figure 4A (r=0.49; p=0.033), meaning that higher WMC individuals had more distinct neural representations of attended information in DAN. In addition, plotting WMC against cue-evoked BOLD response in PCC yielded a significant negative correlation (r=-0.61, p=0.005) (Figure 4B), indicating that higher WMC is associated with more deactivation of PCC (more suppression of internal distraction).

Figure 4.

Figure 4

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Relation between WMC, task-relevant information representation and internal distractor suppression. (A) Relation between WMC and attentional decoding accuracy in DAN. (B) Relation between WMC and cue-evoked PCC activity. (C) Mediation analysis showing that higher WMC enables better representation of attended information by more effective suppression of PCC activity.

The relation between WMC, internal distraction and task-relevant neural representations in DAN was further assessed using a mediation analysis, where internal distraction was treated as the mediator variable, WMC as the independent variable and attentional decoding accuracy in DAN as the dependent variable. As shown in Figure 4C, the internal distraction mediates the relationship between WMC and attentional decoding accuracy (p=0.04), suggesting that the executive control over the formation and maintenance of task-relevant information is enabled by the suppression of internal distraction.

Role of dACC in suppression of internal distraction

Previous studies have suggested that dACC plays an important role in regulating PCC activity during externally focussed attention 15,24. Here, the relationship between dACC and PCC was examined. Cue-evoked BOLD responses in dACC and PCC were estimated from each subject, and a moderate inverse relationship between the two was observed (r=-0.41, p=0.07). In other words, subjects with higher cue-evoked BOLD response in dACC tended to exhibit lower cue-evoked BOLD response in PCC. The difference between dACC activation and PCC deactivation, referred as the dynamic range 49, was examined next. Plotting the cue evoked beta values of dACC and PCC across trials for high and low WMC individuals (based on median split) across trials as shown in Figure 5A, one observes that high WMC individuals clearly had higher dynamic range than low WMC individuals, and the higher dynamic range benefited from both increased dACC activation (beta=2.13 for high WMC group versus beta=1.33 for low WMC group) and PCC deactivation (beta=-0.61 for high WMC group versus beta=0.07 for low WMC group). Across subjects, as shown in Figure 5B, the dynamic range (averaged across trials) was positively correlated with WMC (r=0.60; p=0.006). Comparing Figure 5B with Figure 4B, however, revealed that the magnitude of the two correlation coefficients was very similar (r=0.60 versus r=0.61), suggesting that the dynamic range does not offer additional insight in terms of explaining the variance of WMC beyond PCC deactivation. In addition, as shown in Figure 5C, Granger causality analysis replicated our previous findings 15, showing that higher dACC→PCC was associated with lower cue-evoked BOLD activity in PCC region (r=-0.56, p=0.01). This implies that stronger Granger-causal influence from dACC to PCC is associated with lower PCC activity and thereby lower internal distraction. Furthermore, as shown in Figure 5D, subjects with higher WMC had stronger dACC→PCC and thus more effective suppression of PCC activity (r=0.52; p=0.01). A schematic illustration of the GC results was shown in Figure 5E. The Granger-causal influence of PCC on dACC region, PCC→dACC, was also evaluated. PCC→dACC showed no relation with the cue evoked PCC activity and WMC (see Supplementary Material, Figure S2, for more details). Finally, in addition to dACC, the Granger causal influence from two other major higher order executive structures, DLPFC and anterior insula 50,51, on PCC region was evaluated. Both DLPFC→PCC and Insula→PCC showed no correlation with cue evoked PCC activity or WMC (see Supplementary Material, Figure S4, for more details).

Figure 5.

Figure 5

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Executive suppression of internal distraction. (A) Comparing the cue evoked beta values in dACC and PCC region across trials for high and low WMC individuals. (B) Relation between neural dynamic range (dACC-PCC activity) and WMC. (C) Relation between dACC→PCC and cue-evoked PCC activity. (D) Relation between dACC→PCC and WMC. Here the arrow (→) is understood in the sense of Granger causality (GC). (E) A Schematic illustration of the GC results.

DISCUSSION

Internal distraction in the form of task-irrelevant thoughts and mind wandering negatively impacts task performance. Suppression of internal distraction is an essential function of executive attention. Past work has linked WMC, a measure of executive attention, with the individual differences in the suppression of internal distraction 5,9. We investigated the underlying neural mechanisms in a visual attention task and reported the following results. First, the PCC region of DMN is a neural substrate of internal distraction, and insufficient suppression of PCC activity during externally focussed attention is associated with poorer performance and degraded representations of attended information in DAN. Second, higher WMC is associated with lower BOLD activity in the PCC region and better representations of attended information in DAN. Third, higher WMC enables better representations of task-relevant information in DAN by more effectively suppressing PCC activity. Fourth, stronger Granger-causal influence from dACC to PCC is associated with more deactivation in PCC (i.e., more suppression of internal distraction). Fifth, subjects with higher WMC have stronger Granger-causal influence from dACC to PCC.

PCC as neural substrate of internal distraction

DMN is comprised of several spatially non-overlapping regions and thought to undergird internal mentation and self-referential processes. In tasks requiring externally focussed attention but not autobiographical memory retrieval or theory of mind processes 52,53, such internally generated processes are distracting and need to be suppressed, resulting in the commonly observed deactivation of DMN in task fMRI. PCC and medial prefrontal cortex (mPFC) are core DMN regions. Here, we have chosen to mainly focus on PCC, which has been consistently found to be involved in internal mentation process 13–17. The mPFC, on the other hand, is a highly complex and heterogeneous region, which in addition to internal mentation has also been implicated in externally oriented processes such as watchfulness over the external environment 54. This function of mPFC would be incompatible with the notion of internal distraction in the context of this study.

Although deactivated, the residual activity within PCC, acting as a neural correlate of internal distraction, can adversely impact task-relevant processing and behaviour 15,18–20. In line with this idea, we found that higher BOLD activity in PCC, indexing stronger internal distraction, is associated with poorer behavioural performance in the visual attention task. We further demonstrated that, at the neural level, the distinctiveness of neural representations of attended information in DAN (attention to color versus attention to space), measured by MVPC decoding accuracy, shows negative correlation with PCC activity. In other words, individuals with less suppressed or deactivated PCC activity exhibit poorer representations of task-relevant information in DAN. This is in line with the idea that task-irrelevant thought processing and mind wandering can decouple our attention system from the external world to the internal milieu, resulting in deteriorated representations of the task-relevant environmental information, which in turn leads to degraded task performance 1,2,48. Past studies have shown that mind wandering can affect task-relevant neural processing by causing a reduction in the ERP amplitude in response to target stimulus55. Our results extend these findings to show that internal distractions also negatively affect neural representations during the preparatory attention period between the onset of the cue and the onset of the target.

WMC and suppression of internal distraction

WMC, as an index of domain general executive attention, is not simply a measure of the capacity for storing information; instead, it is a measure of the capacity to maintain task-relevant information in the face of distraction 56. Past work on WMC and distraction has mainly considered distractions in the form of task-irrelevant external (environmental) stimuli 57–62. An ERP study showed that high capacity individuals have stronger distractor suppression as evidenced by larger amplitude and shorter latency of the ERP component Pd 62. An fMRI study has identified the basal ganglia and prefrontal cortex as the neural substrate controlling external distractor suppression in high WMC individuals 63. Recently, there is also evidence linking WMC to the suppression of internal distraction. For example, in the experience-sampling task, where a subject is frequently interrupted to report whether his/her own thoughts are on-task or off-task while performing an attention-demanding task, a consistent finding is that higher WMC individuals report less mind-wandering and off-task thoughts 5,9. Further, higher WMC individuals are also better able to suppress intrusive thoughts in a thought suppression experiment 11,12. Our results extend the current behavioral findings to the neural domain, by showing that PCC activity is a neural correlate of internal distraction, and higher WMC individuals are able to more effectively deactivate PCC activity, thereby more effectively suppressing internal distraction. These neural findings not only corroborate the prior behavioral studies but also overcome the potential limitations inherent to subjective behavioral measures such as self-reports.

It is worth noting that previous behavioral studies have shown that WMC’s relation to mind wandering depends on task difficulty 64. In low-difficulty tasks, higher WMC individuals were associated with more mind wandering, whereas in high-difficulty tasks, the relationship is reversed. Since our task requires sustained attention towards the cued information over a long period of time and is associated with relatively long reaction time (950–1000ms), it should be categorized as a high-difficulty task. As such, the results reported here are seen to be in agreement with the previous report 64; specifically, WMC and PCC activity were shown to be negatively correlated, suggesting that higher WMC individuals have less mind wandering.

WMC and neural representations of task-relevant information

Variation in WMC across subjects represents individual differences in the ability to control attention and resist distraction 8. This view is supported by studies showing that higher WMC individuals perform better in a range of attention control tasks, which has minimum memory requirements 57,58,65. It is suggested that higher WMC individuals employ better voluntary attentional control, which allows them to more effectively maintain task-relevant information and commit fewer goal neglect errors. Our result provides neural evidence for these ideas by showing that higher WMC individuals are better able to represent task-relevant information by having more distinct neural representations of attended environmental information (i.e., higher MVPC decoding).

In this work, the MVPC decoding accuracy in DAN is taken as an index of the quality of neural representations of task-relevant information. However, it is possible that the decoding accuracy could be driven by the DAN’s bias towards a particular condition (space or color). We ruled out this possibility by comparing BOLD activities between spatial and color trials and found them to be not different, suggesting that DAN contributes equally to the control of spatial and feature attention. Further, we also tested if the decoding accuracy was influenced by the quality of the neural signals. Using head motion as an index of signal quality, we found that that the DAN decoding accuracy had no correlation with head motion across subjects, suggesting that decoding accuracy is not driven by factors such as head motion and is an accurate indicator of how well attended information is represented in DAN. Although MVPC decoding accuracy in DAN can be considered low (~58%), it is comparable to other fMRI studies using MVPA 66–68. Further, to be conservative, we have used a very strict threshold of p<0.0001 when testing the statistical significance of the decoding accuracy. Several subjects had decoding accuracy at chance level (Fig 2B), but even these decoding accuracies were meaningful because they were associated with higher levels of internal distraction indexed by higher PCC activity and poorer executive attention indexed by smaller WMC.

Enhancement of task-relevant information by suppression of internal distraction

Traditionally, attentional control has been viewed as serving two distinct cognitive functions, enhancing task-relevant information and suppressing task-irrelevant distraction. Our results show that these two functions are intrinsically related, namely, enhanced representations of task-relevant information were achieved by the suppression of task-irrelevant internal distraction. Lower WMC individuals were less able to suppress task-irrelevant thought processes and mind wandering, which in turn leads to poorer representation of the task-relevant information. These results highlight the importance of studying the effects of distractions not only in the form of external distractors, but also in the form of self-generated thoughts and mind wandering, on attention control, for which the neural mechanism is less understood. A study by McVay and Kane 2009 have shown that in a sustained attention go/no-go task, lower WMC individuals are more susceptible to responding to rare no-go trials which is considered to be a goal-neglect error due to poorer representation of goal-relevant information, and this relation between WMC and goal-neglect errors is mediated by the rate of task-irrelevant thought processes. Our mediation analysis provides a neural explanation for this phenomenon. In particular, we showed that low WMC individuals have less distinct goal-relevant neural representations in DAN, because of ineffective suppression of PCC activity. This suggests that effective suppression of internal distraction leads to better attentional control which in turn leads to improved behavioral performance.

Role of dACC in suppression of internal distraction

What neural structures underlie the suppression of internal distraction? Past studies have considered this question and suggested that the dACC region plays an important role in regulating task-irrelevant thoughts and mind wandering generated in PCC 15,24,25. Dysfunctional connectivity between dACC and PCC is the basis for attentional lapses and mind wandering observed in ADHD individuals 24. Further, a study by Wen et al. 15 showed that higher Granger-causal influence from dACC to PCC, dACC→PCC, is associated with lower BOLD activity in PCC and better behavioral performance. Our results reported here replicated this finding. In addition, we showed that higher WMC individuals have stronger dACC→PCC. This suggests that the Granger-causal influence of dACC over PCC could be a plausible neural mechanism through which higher WMC individuals are better able to suppress internal distraction. It is important to note that the GC between dACC and PCC region was computed at a block level, measuring the overall Granger-causal influence of dACC region over PCC across trials. This is in line with the idea that dACC, being part of the task control network, allows sustained maintenance of the task set across trials 69, which includes the suppression of irrelevant thought processes through the deactivation of PCC 15. Further, dACC’s role in internal distraction suppression can be reconciled with its primary cognitive function of conflict processing 70,71. Specifically, because task-relevant information and task-irrelevant information form a conflicting situation, dACC, as the higher-order region tasked to resolve conflicts, suppresses the task-irrelevant information by issuing top-down signals to deactivate PCC. Such a role for dACC has been suggested in the context of external distractor suppression in a selective attention task 72. There is further evidence in support of this idea from prior research showing that dACC is engaged in resolving internal higher-level conflict situations like cognitive dissonance 73,74.

Summary

In summary, we note that internal distraction is inevitable when performing cognitive tasks that require externally oriented attention, and in research on visual selective attention, internal distraction has received less research interest than external distraction. By identifying PCC activity as a neural correlate of internal distraction, we showed that like external distraction, internal distraction can negatively impact behavioral performance by disrupting the neural representations of task-relevant information. Quantifying the individual susceptibility to internal distraction by WMC, we further showed that higher WMC individuals have better neural representations of task-relevant information via the more effective suppression of internal distraction, and the dACC→PCC Granger-causal influence is likely the neural mechanism that underlies the top-down suppression of internal distraction.

Supplementary Material

1

Acknowledgement

This work was supported by NSF Grant BCS-1439188 and NIH Grant MH117991. The authors declare no competing financial interests.

Biography

Abhijit Rajan: Methodology, Conceptualization, Writing-Original Draft, Formal Analysis. Sreenivasan Meyyappan: Methodology, Investigation. Harrison Walker: Investigation. Immanuel Babu Henry Samuel: Software, Investigation. Zhenhong Hu: Writing: Review & Editing. Mingzhou Ding: Conceptualization, Supervision, Writing-Review & Editing

Footnotes

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