Distinguishing vigilance decrement and low task demands from mind-wandering: A machine learning analysis of EEG

Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data

Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups.

A review of visual sustained attention: neural mechanisms and computational models

Sustained attention is one of the basic abilities of humans to maintain concentration on relevant information while ignoring irrelevant information over extended periods. The purpose of the review is to provide insight into how to integrate neural mechanisms of sustained attention with computational models to facilitate research and application. Although many studies have assessed attention, the evaluation of humans' sustained attention is not sufficiently comprehensive. Hence, this study provides a current review on both neural mechanisms and computational models of visual sustained attention. We first review models, measurements, and neural mechanisms of sustained attention and propose plausible neural pathways for visual sustained attention. Next, we analyze and compare the different computational models of sustained attention that the previous reviews have not systematically summarized. We then provide computational models for automatically detecting vigilance states and evaluation of sustained attention. Finally, we outline possible future trends in the research field of sustained attention.

Vigilance decrement and mind-wandering in sustained attention tasks: Two sides of the same coin?

Background

Decrements in performance and the propensity for increased mind-wandering (i.e., task-unrelated thoughts) across time-on-task are two pervasive phenomena observed when people perform vigilance tasks. In the present study, we asked whether processes that lead to vigilance decrement and processes that foster the propensity for mind-wandering (MW) can be dissociated or whether they share a common mechanism. In one experiment, we introduced two critical manipulations: increasing task demands and applying anodal high-definition transcranial direct current stimulation (HD-tDCS) to the left dorsolateral prefrontal cortex.

Method

Seventy-eight participants were randomly assigned to one of four groups resulting from the factorial combination of task demand (low, high) and stimulation (anodal, sham). Participants completed the sustained attention to response task (SART), which included thought probes on intentional and unintentional MW. In addition, we investigated the crucial role of alpha oscillations in a novel approach. By assessing pre-post resting EEG, we explored whether participants’ variability in baseline alpha power predicted performance in MW and vigilance decrement related to tDCS or task demands, respectively, and whether such variability was a stable characteristic of participants.

Results

Our results showed a double dissociation, such that task demands exclusively affected vigilance decrement, while anodal tDCS exclusively affected the rate of MW. Furthermore, the slope of the vigilance decrement function and MW rate (overall, intentional and unintentional) did not correlate. Critically, resting state alpha-band activity predicted tDCS-related gains in unintentional MW alone, but not in vigilance decrement, and remained stable after participants completed the task.

Conclusion

These results show that when a sustained attention task involving executive vigilance, such as the SART, is designed to elicit both vigilance decrement effects and MW, the processes leading to vigilance decrement should be differentiated from those responsible for MW, a claim that is supported by the double dissociation observed here and the lack of correlation between the measures chosen to assess both phenomena. Furthermore, the results provide the first evidence of how individual differences in alpha power at baseline may be of crucial importance in predicting the effects of tDCS on MW propensity.

The steady state visual evoked potential (SSVEP) tracks "sticky" thinking, but not more general mind-wandering

For a large proportion of our daily lives, spontaneously occurring thoughts tend to disengage our minds from goal-directed thinking. Previous studies showed that EEG features such as the P3 and alpha oscillations can predict mind-wandering to some extent, but only with accuracies of around 60%. A potential candidate for improving prediction accuracy is the Steady-State Visual Evoked Potential (SSVEP), which is used frequently in single-trial contexts such as brain-computer interfaces as a marker of the direction of attention. In this study, we modified the sustained attention to response task (SART) that is usually employed to measure spontaneous thought to incorporate the SSVEP elicited by a 12.5-Hz flicker. We then examined whether the SSVEP could track and allow for the prediction of the stickiness and task-relatedness dimensions of spontaneous thought. Our results show that the SSVEP evoked by flickering words was able to distinguish between more and less sticky thinking but not between whether a participant was on- or off-task. This suggests that the SSVEP is able to track spontaneous thinking when it is strongly disengaged from the task (as in the sticky form of off-task thinking) but not off-task thought in general. Future research should determine the exact dimensions of spontaneous thought to which the SSVEP is most sensitive.

Electrophysiological markers of mind wandering: A systematic review

The ability to mentally wander away from the external environment is a remarkable feature of the human mind. Although recent years have witnessed a surge of interest in examining mind wandering using EEG, there is no comprehensive review that summarizes and accounts for the variable findings. Accordingly, we conducted a systematic review that synthesizes evidence from EEG studies that examined the electrophysiological measures of mind wandering. Our search yielded 42 studies that met eligibility criteria. The reviewed literature converges on a reduction in the amplitude of canonical ERP components (i.e., P1, N1 and P3) as the most reliable markers of mind wandering. Spectral findings were less robust, but point towards greater activity in lower frequency bands, (i.e., delta, theta, and alpha), as well as a decrease in beta band activity, during mind wandering compared to on-task states. The variability in these findings appears to be modulated by the task context. To integrate these findings, we propose an electrophysiological account of mind wandering that explains how the brain supports this inner experience. Conclusions drawn from this work will inform future endeavours in basic science to map out electrophysiological patterns underlying mind wandering and in translational science using EEG to predict the occurrence of this phenomenon.

The EEG spectral properties of meditation and mind wandering differ between experienced meditators and novices

Previous literature suggests that individuals with meditation training become less distracted during meditation practice. In this study, we assess whether putative differences in the subjective experience of meditation between meditators and non-meditators are reflected in EEG spectral modulations. For this purpose, we recorded electroencephalography (EEG) during rest and two breath focus meditations (with and without experience sampling) in a group of 29 adult participants with more than 3 years of meditation experience and a control group of 29 participants without any meditation experience. Experience sampling in one of the meditation conditions allowed us to disentangle periods of breath focus from mind wandering (i.e. moments of distraction driven by task-irrelevant thoughts) during meditation practice. Overall, meditators reported a greater level of focus and reduced mind wandering during meditation practice than controls. In line with these reports, EEG spectral modulations associated with meditation and mind wandering also differed significantly between meditators and controls. While meditators (but not controls) showed a significant decrease in individual alpha frequency / amplitude and a steeper 1/f slope during meditation relative to rest, controls (but not meditators) showed a relative increase in individual alpha amplitude during mind wandering relative to breath focus periods. Together, our results show that the subjective experience of meditation and mind wandering differs between meditators and novices and that this is reflected in oscillatory and non-oscillatory properties of EEG.

Detection of mind wandering using EEG: Within and across individuals

Mind wandering is often characterized by attention oriented away from an external task towards our internal, self-generated thoughts. This universal phenomenon has been linked to numerous disruptive functional outcomes, including performance errors and negative affect. Despite its prevalence and impact, studies to date have yet to identify robust behavioral signatures, making unobtrusive, yet reliable detection of mind wandering a difficult but important task for future applications. Here we examined whether electrophysiological measures can be used in machine learning models to accurately predict mind wandering states. We recorded scalp EEG from participants as they performed an auditory target detection task and self-reported whether they were on task or mind wandering. We successfully classified attention states both within (person-dependent) and across (person-independent) individuals using event-related potential (ERP) measures. Non-linear and linear machine learning models detected mind wandering above-chance within subjects: support vector machine (AUC = 0.715) and logistic regression (AUC = 0.635). Importantly, these models also generalized across subjects: support vector machine (AUC = 0.613) and logistic regression (AUC = 0.609), suggesting we can reliably predict a given individual's attention state based on ERP patterns observed in the group. This study is the first to demonstrate that machine learning models can generalize to "never-seen-before" individuals using electrophysiological measures, highlighting their potential for real-time prediction of covert attention states.

Distinguishing vigilance decrement and low task demands from mind-wandering: A machine learning analysis of EEG

Mind‐wandering is a ubiquitous mental phenomenon that is defined as self‐generated thought irrelevant to the ongoing task. Mind‐wandering tends to occur when people are in a low‐vigilance state or when they are performing a very easy task. In the current study, we investigated whether mind‐wandering is completely dependent on vigilance and current task demands, or whether it is an independent phenomenon. To this end, we trained support vector machine (SVM) classifiers on EEG data in conditions of low and high vigilance, as well as under conditions of low and high task demands, and subsequently tested those classifiers on participants' self‐reported mind‐wandering. Participants' momentary mental state was measured by means of intermittent thought probes in which they reported on their current mental state. The results showed that neither the vigilance classifier nor the task demands classifier could predict mind‐wandering above‐chance level, while a classifier trained on self‐reports of mind‐wandering was able to do so. This suggests that mind‐wandering is a mental state different from low vigilance or performing tasks with low demands—both which could be discriminated from the EEG above chance. Furthermore, we used dipole fitting to source‐localize the neural correlates of the most import features in each of the three classifiers, indeed finding a few distinct neural structures between the three phenomena. Our study demonstrates the value of machine‐learning classifiers in unveiling patterns in neural data and uncovering the associated neural structures by combining it with an EEG source analysis technique.