Dataset Information

Mind-Wandering Tends to Occur under Low Perceptual Demands during Driving.

ABSTRACT: Fluctuations in attention behind the wheel poses a significant risk for driver safety. During transient periods of inattention, drivers may shift their attention towards internally-directed thoughts or feelings at the expense of staying focused on the road. This study examined whether increasing task difficulty by manipulating involved sensory modalities as the driver detected the lane-departure in a simulated driving task would promote a shift of brain activity between different modes of processing, reflected by brain network dynamics on electroencephalographic sources. Results showed that depriving the driver of salient sensory information imposes a relatively more perceptually-demanding task, leading to a stronger activation in the task-positive network. When the vehicle motion feedback is available, the drivers may rely on vehicle motion to perceive the perturbations, which frees attentional capacity and tends to activate the default mode network. Such brain network dynamics could have major implications for understanding fluctuations in driver attention and designing advance driver assistance systems.

SUBMITTER: Lin CT

PROVIDER: S-EPMC4808905 | biostudies-other | 2016 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Mind-Wandering Tends to Occur under Low Perceptual Demands during Driving.

Lin Chin-Teng CT Chuang Chun-Hsiang CH Kerick Scott S Mullen Tim T Jung Tzyy-Ping TP Ko Li-Wei LW Chen Shi-An SA King Jung-Tai JT McDowell Kaleb K

Scientific reports 20160217

Fluctuations in attention behind the wheel poses a significant risk for driver safety. During transient periods of inattention, drivers may shift their attention towards internally-directed thoughts or feelings at the expense of staying focused on the road. This study examined whether increasing task difficulty by manipulating involved sensory modalities as the driver detected the lane-departure in a simulated driving task would promote a shift of brain activity between different modes of proces ...[more]

PMID: 26882993

Similar Datasets

Project description:A significant body of literature supports the contention that pupil size varies depending on cognitive load, affective state, and level of drowsiness. Here we assessed whether oculometric measures such as gaze position, blink frequency and pupil size were correlated with the occurrence and time course of self-reported mind-wandering episodes. We recorded the pupil size of two subjects engaged in a monotonous breath counting task while keeping their eyes on a fixation cross. This task is conducive to producing mind-wandering episodes. Each subject performed ten 20-min sessions, for total duration of about 4 h. Subjects were instructed to report spontaneous mind-wandering episodes by pressing a button when they lost count of their breath. After each button press, subjects filled in a short questionnaire describing the characteristics of their mind-wandering episode. We observed larger pupil size during the breath-focusing period compared to the mind-wandering period (p < 0.01 for both subjects). Our findings contradict previous research showing a higher baseline pupil size during mind wandering episodes in visual tasks. We discuss possible explanations for this discrepancy. We also analyzed nine other oculometric measures including blink rate, blink duration and gaze position. We built a support vector machine (SVM) classifier and showed that mean pupil size was the most reliable predictor of mind wandering in both subjects. The classification accuracy of mind wandering data segments vs. breath-focusing data segments was 81% for the first subject and 77% for the second subject. Additionally, we analyzed oculometric measures in light of the phenomenological data collected in the questionnaires. We showed that how well subjects remembered their thoughts while mind wandering was positively correlated with pupil size (subject 1, p < 0.001; subject 2, p < 0.05). Feelings of well being were also positively correlated with pupil size (subject 1, p < 0.001; subject 2, p < 0.001). Our results suggest that oculometric data could be used as a neurocognitive marker of mind-wandering episodes.

Project description:Previous research has found that subtle reminders of negative stereotypes about one's group can lead individuals to underperform on stereotype-relevant tests (e.g., women in math, ethnic minorities on intelligence tests). This so called stereotype threat effect can contribute to systematic group differences in performance that can obscure the true abilities of certain social groups and thereby sustain social inequalities. In the present study, we examined processes underlying stereotype threat effects on women's math performance, specifically focusing on the role of suppression of mind wandering (i.e., task-irrelevant thinking) in stereotype threat (ST) and no threat (NT) situations. Based on a process model of stereotype threat effects on performance, we hypothesized that women under stereotype threat spontaneously suppress mind wandering, and that this suppression impairs performance. An alternative regulation strategy that prevents suppression (i.e., reappraising task-irrelevant thoughts as normal) was predicted to prevent stereotype threat effects on performance. We manipulated stereotype threat (ST vs. NT) and cognitive regulation strategy (suppression, reappraisal, or no strategy) and measured women's performance on a math and a concentration task (N = 113). We expected three groups to perform relatively more poorly: Those in ST with either no strategy or suppression and those in NT with a suppression strategy. We tested the performance of these groups against the remaining three groups hypothesized to perform relatively better: those in NT with no strategy or reappraisal and those in ST with reappraisal. The results showed the expected pattern for participants' math performance, but not for concentration achievement. This pattern suggests that ineffective self-regulation by suppressing mind wandering can at least partly explain stereotype threat effects on performance, whereas a reappraisal strategy can prevent this impairment. We discuss implications for the understanding of processes underlying stereotype threat effects and the benefits of reappraising subjective experience under threat.

Project description: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.

Dataset Information

Mind-Wandering Tends to Occur under Low Perceptual Demands during Driving.

Publications

Mind-Wandering Tends to Occur under Low Perceptual Demands during Driving.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets