I must have missed that: Alpha-band oscillations track attention to spoken language

Boudewyn, Megan A.; Carter, Cameron S.

doi:10.1016/j.neuropsychologia.2018.05.024

Cited by 46 publications

(66 citation statements)

References 40 publications

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“…In a seemingly contradictory finding, another study employing thought probes while participants listened to stories discovered that not only did alpha power increase broadly over the scalp during MW (a finding that is consistent with previous work showing a similar change in alpha power associated with attentional shifts away from auditory language processing [51]), but that this change was also predictive of comprehension [52]. Together, these findings suggest that the study design and the attentive task used as a control condition may themselves elicit different subtypes (or perhaps definitions) of MW, and/or influence the neural correlates of MW that are subsequently discovered.…”

Section: Introductionsupporting

confidence: 72%

Individualized pattern recognition for detecting mind wandering from EEG during live lectures

et al. 2019

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Neural correlates of mind wanderingThe ability to detect mind wandering as it occurs is an important step towards improving our understanding of this phenomenon and studying its effects on learning and performance. Current detection methods typically rely on observable behaviour in laboratory settings, which do not capture the underlying neural processes and may not translate well into real-world settings. We address both of these issues by recording electroencephalography (EEG) simultaneously from 15 participants during live lectures on research in orthopedic surgery. We performed traditional group-level analysis and found neural correlates of mind wandering during live lectures that are similar to those found in some laboratory studies, including a decrease in occipitoparietal alpha power and frontal, temporal, and occipital beta power. However, individual-level analysis of these same data revealed that patterns of brain activity associated with mind wandering were more broadly distributed and highly individualized than revealed in the group-level analysis.Mind wandering detectionTo apply these findings to mind wandering detection, we used a data-driven method known as common spatial patterns to discover scalp topologies for each individual that reflects their differences in brain activity when mind wandering versus attending to lectures. This approach avoids reliance on known neural correlates primarily established through group-level statistics. Using this method for individual-level machine learning of mind wandering from EEG, we were able to achieve an average detection accuracy of 80–83%.ConclusionsModelling mind wandering at the individual level may reveal important details about its neural correlates that are not reflected when using traditional observational and statistical methods. Using machine learning techniques for this purpose can provide new insight into the varieties of neural activity involved in mind wandering, while also enabling real-time detection of mind wandering in naturalistic settings.

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Section: Introductionsupporting

confidence: 72%

Individualized pattern recognition for detecting mind wandering from EEG during live lectures

et al. 2019

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“…The results of our study highlight the importance of regarding off-task states as a heterogeneous class of mental states and confirm the feasibility of an EEG-based machine learning classifier able to detect different degrees of intentionality during off-task states. It should be noted that considering we found alpha activity to be idiosyncratic of intentional rather than spontaneous off-task states and that other studies ignoring this distinction observed it to be a marker of mind-wandering (Baldwin et al, 2017;Boudewyn & Carter, 2018;Jin et al, 2019;Macdonald et al, 2011;O'Connell et al, 2009;Zhao et al, 2013), it may be the case that at least some of the instances of mind-wandering measured in previous studies were of the deliberate kind.…”

Section: Discussionsupporting

confidence: 39%

“…One hypothesis is that alpha serves a regulator role between suppression and selection of information via a top down process inhibiting task-irrelevant cortical structures and corresponding sensory input (Foxe & Snyder, 2011;Klimesch, 2012;Mazaheri & Jensen, 2010). In the mind-wandering literature, alpha is a known electrophysiological correlate of off-task states (Baldwin et al, 2017;Boudewyn & Carter, 2018;Martel et al, 2014;O'Connell et al, 2009;Zhao, Wu, & Ou, 2013) and was recently found to be the most prominent oscillatory feature for the successful classification of off-task states (Jin et al, 2019). Given alpha's putative role as supporting internally focused attention (Bazanova & Vernon, 2014;Benedek et al, 2014; when engaged deliberately (Martel et al, 2019) our data provides further evidence that alpha is a general index of "internal attention" and suggests that it is a marker of deliberate off-task states in particular.…”

Section: Discussionmentioning

confidence: 99%

Predicting intentional and unintentional task unrelated thought with EEG

Martel

Arvaneh

Robertson

et al. 2019

Preprint

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Our attention seldom remains on a singular activity, instead veering off into thoughts unrelated to the task at hand. Studies adopting a component process view of off-task thought have begun to identify the underlying mechanisms and associated electrophysiological correlates underlying ongoing thought. In the present study, we developed subject-independent classification algorithms based on electroencephalographic (EEG) markers to discriminate on-task vs off-task as well as intentional vs unintentional off-task thought. To that end, spatio-temporal and spectral features extracted from EEG activity prior to reports of ongoing thought during a test of sustained attention were ranked according to their discriminative power. Using data collected from 26 participants, average classification accuracies of 83.4% and 71.6% were achieved using a regularized linear model for on-task vs off-task and intentional vs unintentional off-task thought, respectively. Our results identified gamma oscillations as the most discriminative feature to distinguish on-task from off-task states, and alpha synchronization as the most prominent feature when off-task states are engaged in deliberately rather than when experienced as arising spontaneously. Our work represents the first successful attempt at reliably discriminating the degree of intentionality experienced during task-unrelated thought and highlights the importance of recognizing the heterogeneous nature of off-task states.

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“…An alternative explanation is that Imagine cues led to focused attention toward the substituting thought and away from the stimulus, leading to changes in alpha power. Alpha power has been shown to increase during periods of introspection and mind wandering (Arnau, Löffler, Rummel et al, 2020;Boudewyn & Carter, 2018;Compton, Gearinger & Wild, 2019), which may seem similar to thought substitution (e.g., Delaney et al, 2010); however, mind wandering reflects offtask thought or a lapse in sustained attention, whereas thought substitution may in fact require focused attention away from the processing of the stimulus. This attention toward the substituting thought may always be engaged during thought substitution, but is not the mechanism most related to successful forgetting of the stimulus.…”

Section: Discussionmentioning

confidence: 95%

Separable neural mechanisms support intentional forgetting and thought substitution

Hubbard

Sahakyan

2020

Preprint

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Psychological and neuroscientific experiments have established that people can intentionally forget information via different strategies: direct suppression and thought substitution. However, few studies have directly compared the effectiveness of these strategies in forgetting specific items, and it remains an open question if the neural mechanisms supporting these strategies differ. Here, we developed a novel item-method directed forgetting paradigm with Remember, Forget, and Imagine cues, and recorded EEG to directly compare these strategies. Behaviorally, Forget and Imagine cues produced similar forgetting compared to Remember cues, but through separable neural processes; Forget cues elicited frontal oscillatory power changes that were predictive of future forgetting, whereas item-cue representational similarity was predictive of later accuracy for Imagine cues. These results suggest that both strategies can lead to intentional forgetting, but directed forgetting may rely on frontally-mediated suppression, while thought substitution may lead to contextual shifting, impairing successful retrieval.

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I must have missed that: Alpha-band oscillations track attention to spoken language

Cited by 46 publications

References 40 publications

Individualized pattern recognition for detecting mind wandering from EEG during live lectures

Individualized pattern recognition for detecting mind wandering from EEG during live lectures

Predicting intentional and unintentional task unrelated thought with EEG

Separable neural mechanisms support intentional forgetting and thought substitution

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