Uncovering the Neural Signature of Lapsing Attention: Electrophysiological Signals Predict Errors up to 20 s before They Occur

O’Connell, Redmond G; Dockree, Paul M.; Robertson, Ian H.; Bellgrove, Mark A.; Foxe, John J.; Kelly, Simon P.

doi:10.1523/jneurosci.5967-08.2009

Cited by 264 publications

(299 citation statements)

References 31 publications

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“…In Experiment 2, in contrast, the SART showed strong error speeding, which is consistent with previous literature (Mazaheri et al, 2009;O'Connell et al, 2009a). In a "dull" task such as the SART, subjects get into an automatic response mode, resulting in decreased reaction time, with lapses leading to fast errors (Robertson et al, 1997).…”

Section: Error-speeding and Posterror Slowingsupporting

confidence: 89%

“…First, we showed that subtle manipulations of the Simon task [used to study response conflict (Leuthold, 2011)] resulted in varying degrees of theta and alpha oscillatory activity after errors. Second, we showed that errors in the SART [used to study attention lapses (Robertson et al, 1997)] resulted in comparable theta but stronger alpha error-related dynamics. Together, these findings demonstrate that errors may recruit, in varying degrees, different cooccurring neurocognitive processes.…”

Section: Introductionmentioning

confidence: 78%

“…SART (Mazaheri et al, 2009;O'Connell et al, 2009a). The General Simon task was the same as the one used in Experiment 1.…”

Section: Methodsmentioning

confidence: 99%

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Not All Errors Are Alike: Theta and Alpha EEG Dynamics Relate to Differences in Error-Processing Dynamics

2012

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Performance errors in conflict tasks often result from inappropriate action impulses, and are thought to signal the need for increased control over the motor system. However, errors may also result from lapses in sustained attention, which may require different monitoring and adaptation mechanisms. Distinguishing between the mechanisms of adaptation is important as both error types may occur intermixed. To this end, we measured EEG of healthy human subjects while they performed three variants of the Simon task in which errors were more likely to occur due to attentional lapses, failures of motor control, or both. Behavioral results showed that subjects exhibited less conflict effects and less impulsive errors in sustained attention compared with the other Simon conditions. Time-frequency analyses of EEG data showed that the sustained attention Simon condition, compared with the motor control Simon condition, was characterized by: (1) less error-related MFC theta (4 -8 Hz) power and an absence of error-related MFC-DLPFC theta phase synchronization; (2) stronger error-related suppression of parieto-occipital alpha (8 -12 Hz) power and stronger parieto-occipital-frontal alpha synchronization. A control experiment, using SART (the Sustained Attention to Response Test), confirmed that adaptation after attentional lapses involved posterior alpha power suppression, in addition to inter-regional frontal theta activity. Together, these results suggest that at least two cortical mechanisms exist for performance monitoring, and that different tasks and task-settings can recruit these mechanisms in a different way. Post-error brain dynamics thus consist of heterogeneous activity from multiple neurocognitive processes.

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Section: Error-speeding and Posterror Slowingsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 78%

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Not All Errors Are Alike: Theta and Alpha EEG Dynamics Relate to Differences in Error-Processing Dynamics

2012

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“…Previous work has identified some behavioral and physiological measures that are modulated by mind wandering. These include behavioral measures such as response times (McVay & Kane, 2009), physical posture (Seli et al, 2014), prosody (Drummond & Litman, 2010), reading speed , and physiological measures such as brain activity (Christoff, Gordon, Smallwood, Smith, & Schooler, 2009;Mittner et al, 2014;O'Connell et al, 2009;Smallwood, Beach, Schooler, & Handy, 2008;Weissman, Roberts, Visscher, & Woldorff, 2006), peripheral physiological responses (Blanchard, Bixler, Joyce, & D'Mello, 2014;Pham & Wang, 2015;Smallwood et al, 2004), eye movements (Foulsham, Farley, & Kingstone, 2013;Frank, Nara, Zavagnin, Touron, & Kane, 2015;Reichle, Reineberg, & Schooler, 2010;Uzzaman & Joordens, 2011), eye blinks (Frank et al, 2015;Grandchamp, Braboszcz, & Delorme, 2014;Smilek et al, 2010;Uzzaman & Joordens, 2011), and pupil diameter (Franklin, Broadway, Mrazek, Smallwood, & Schooler, 2013;Smallwood et al, 2011).…”

Section: Abstract Mind Wandering Reading Eye Gaze Machine Learningmentioning

confidence: 99%

An automated behavioral measure of mind wandering during computerized reading

2017

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Mind wandering is a ubiquitous phenomenon in which attention shifts from task-related to task-unrelated thoughts. The last decade has witnessed an explosion of interest in mind wandering, but research has been stymied by a lack of objective measures, leading to a near-exclusive reliance on self-reports. We addressed this issue by developing an eye-gaze-based, machine-learned model of mind wandering during computerized reading. Data were collected in a study in which 132 participants reported self-caught mind wandering while reading excerpts from a book on a computer screen. A remote Tobii TX300 or T60 eyetracker recorded their gaze during reading. The data were used to train supervised classification models to discriminate between mind wandering and normal reading in a manner that would generalize to new participants. We found that at the point of maximal agreement between the model-based and self-reported mind-wandering means (smallest difference between the group-level means: M model = .310, M self = .319), the participant-level mind-wandering proportional distributions were similar and were significantly correlated (r = .400). The model-based estimates were internally consistent (r = .751) and predicted text comprehension more strongly than did self-reported mind wandering (rmodel = −.374, r self = −.208). Our results also indicate that a robust strategy of probabilistically predicting mind wandering in cases with poor or missing gaze data led to improved performance on all metrics, as compared to simply discarding these data. Our findings demonstrate that an automated objective measure might be available for laboratory studies of mind wandering during reading, providing an appealing alternative or complement to self-reports.Keywords Mind wandering . Reading . Eye gaze . Machine learning It is common for one's attention to shift toward spontaneously generated, task-unrelated thoughts. This phenomenon is called mind wandering. Numerous studies have investigated mind wandering across a range of tasks and have found that it occurs anywhere between 20%-50% of the time (Kane et al.,

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“…Because the frequent nontarget trials require constant responding, not-X CPTs enable investigation of RT patterns that precede and predict errors. For example, faster and more erratic correct responses have been shown to foreshadow errors within seconds (Cheyne, Carriere, & Smilek, 2006;Robertson et al, 1997), with neural markers of attention lapses preceding errors by up to 20 s (O'Connell et al, 2009). In these tasks, the most common errors are failures to inhibit response on target trials-that is, commission errors, which have been associated with reduced attention to task.…”

Section: Studies Of Vigilancementioning

confidence: 99%

Sustaining visual attention in the face of distraction: a novel gradual-onset continuous performance task

Rosenberg

Noonan

DeGutis

et al. 2013

Atten Percept Psychophys

147

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Sustained attention is a fundamental aspect of human cognition and has been widely studied in applied and clinical contexts. Despite a growing understanding of how attention varies throughout task performance, moment-tomoment fluctuations are often difficult to assess. In order to better characterize fluctuations in sustained visual attention, in the present study we employed a novel continuous performance task (CPT), the gradual-onset CPT (gradCPT). In the gradCPT, a central face stimulus gradually transitions between individuals at a constant rate (1,200 ms), and participants are instructed to respond to each male face but not to a rare target female face. In the distractor-present version, the background distractors consist of scene images, and in the distractor-absent condition, of phase-scrambled scene images. The results confirmed that the gradCPT taxes sustained attention, as vigilance decrements were observed over the task's 12-min duration: Participants made more commission errors and showed increasingly variable response latencies (RTs) over time. Participants' attentional states also fluctuated from moment to moment, with periods of higher RT variability being associated with increased likelihood of errors and greater speedaccuracy trade-offs. In addition, task performance was related to self-reported mindfulness and the propensity for attention lapses in everyday life. The gradCPT is a useful tool for studying both low-and high-frequency fluctuations in sustained visual attention and is sensitive to individual differences in attentional ability.

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Uncovering the Neural Signature of Lapsing Attention: Electrophysiological Signals Predict Errors up to 20 s before They Occur

Cited by 264 publications

References 31 publications

Not All Errors Are Alike: Theta and Alpha EEG Dynamics Relate to Differences in Error-Processing Dynamics

Not All Errors Are Alike: Theta and Alpha EEG Dynamics Relate to Differences in Error-Processing Dynamics

An automated behavioral measure of mind wandering during computerized reading

Sustaining visual attention in the face of distraction: a novel gradual-onset continuous performance task

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