| Title |
An automated behavioral measure of mind wandering during computerized reading
|
|---|---|
| Published in |
Behavior Research Methods, February 2017
|
| DOI | 10.3758/s13428-017-0857-y |
| Pubmed ID | |
| Authors |
Myrthe Faber, Robert Bixler, Sidney K. D’Mello |
| Abstract |
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 (r model = -.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. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 8 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 63% |
| Scientists | 2 | 25% |
| Science communicators (journalists, bloggers, editors) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 150 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 150 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 32 | 21% |
| Student > Master | 13 | 9% |
| Researcher | 12 | 8% |
| Student > Doctoral Student | 12 | 8% |
| Student > Bachelor | 10 | 7% |
| Other | 33 | 22% |
| Unknown | 38 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 43 | 29% |
| Computer Science | 13 | 9% |
| Neuroscience | 13 | 9% |
| Linguistics | 8 | 5% |
| Social Sciences | 8 | 5% |
| Other | 18 | 12% |
| Unknown | 47 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 November 2020.
All research outputs
#3,344,316
of 25,382,440 outputs
Outputs from Behavior Research Methods
#404
of 2,526 outputs
Outputs of similar age
#65,596
of 424,548 outputs
Outputs of similar age from Behavior Research Methods
#2
of 35 outputs
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