Dataset Information

Eye-tracking technology in identifying visualizers and verbalizers: data on eye-movement differences and detection accuracy.

ABSTRACT: Data in this article revealed the eye movement differences of visualizers and verbalizers in viewing four pictures-in-text by analyzing gaze path and fixation data (fixation duration, fixation counts and the average time on each fixation). After imported the documents into Tobii eye-tracker, authors triggered participants' natural reading habits, recorded their eye movement data, and predicted participants as visualizers or verbalizers based on the Felder and Silverman Learning Style Model (FSLSM). Comparing the predictions with self-report results tested by the Index of Learning Styles (ILS) questionnaire, authors got the accuracy results of using eye-tracking technology in identifying visualizers and verbalizers. The data revealed natural preferences of people with different styles, and it can be used in future studies in the field of adaptive learning systems, individual differences, neuroscience in reading habits, and individualized instruction.

SUBMITTER: Luo Z

PROVIDER: S-EPMC6811880 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Eye-tracking technology in identifying visualizers and verbalizers: data on eye-movement differences and detection accuracy.

Luo Zhanni Z Wang Yu Y

Data in brief 20190829

Data in this article revealed the eye movement differences of visualizers and verbalizers in viewing four pictures-in-text by analyzing gaze path and fixation data (fixation duration, fixation counts and the average time on each fixation). After imported the documents into Tobii eye-tracker, authors triggered participants' natural reading habits, recorded their eye movement data, and predicted participants as visualizers or verbalizers based on the Felder and Silverman Learning Style Model (FSLS ...[more]

PMID: 31667221

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Project description:The quantitative assessment of eye tracking data quality is critical for ensuring accuracy and precision of gaze position measurements. However, researchers often report the eye tracker's optimal manufacturer's specifications rather than empirical data about the accuracy and precision of the eye tracking data being presented. Indeed, a recent report indicates that less than half of eye tracking researchers surveyed take the eye tracker's accuracy into account when determining areas of interest for analysis, an oversight that could impact the validity of reported results and conclusions. Accordingly, we designed a calibration verification protocol to augment independent quality assessment of eye tracking data and examined whether accuracy and precision varied between three age groups of participants. We also examined the degree to which our externally quantified quality assurance metrics aligned with those reported by the manufacturer. We collected data in standard laboratory conditions to demonstrate our method, to illustrate how data quality can vary with participant age, and to give a simple example of the degree to which data quality can differ from manufacturer reported values. In the sample data we collected, accuracy for adults was within the range advertised by the manufacturer, but for school-aged children, accuracy and precision measures were outside this range. Data from toddlers were less accurate and less precise than data from adults. Based on an a priori inclusion criterion, we determined that we could exclude approximately 20% of toddler participants for poor calibration quality quantified using our calibration assessment protocol. We recommend implementing and reporting quality assessment protocols for any eye tracking tasks with participants of any age or developmental ability. We conclude with general observations about our data, recommendations for what factors to consider when establishing data inclusion criteria, and suggestions for stimulus design that can help accommodate variability in calibration. The methods outlined here may be particularly useful for developmental psychologists who use eye tracking as a tool, but who are not experts in eye tracking per se. The calibration verification stimuli and data processing scripts that we developed, along with step-by-step instructions, are freely available for other researchers.

Project description:Importance:Objectively measuring how Mohs defect reconstruction changes casual observer attention has important implications for patients and facial plastic surgeons. Objective:To use eye-tracking technology to objectively measure the ability of Mohs facial defect reconstruction to normalize facial attention. Design, Setting, and Participants:This observational outcomes study was conducted at an academic tertiary referral center from January to June 2016. An eye-tracking system was used to record how 82 casual observers directed attention to photographs of 32 patients with Mohs facial defects of varying sizes and locations before and after reconstruction as well as 16 control faces with no facial defects. Statistical analysis was performed from November 2018 to January 2019. Main Outcomes and Measures:First, the attentional distraction caused by facial defects was quantified in milliseconds of gaze time using eye tracking. Second, the eye-tracking data were analyzed using mixed-effects linear regression to assess the association of facial defect reconstruction with normalized facial attention. Results:The 82 casual observers (63 women and 19 men; mean [SD] age, 34 [12] years) viewed control faces in a similar and consistent fashion, with most attention (65%; 95% CI, 62%-69%) directed at the central triangle, which includes the eyes, nose, and mouth. The eyes were the most visually important feature, capturing a mean of 60% (95% CI, 57%-64%) of fixation time within the central triangle and 39% (95% CI, 36%-43%) of total observer attention. The presence of Mohs defects was associated with statistically significant alterations in this pattern of normal facial attention. The larger the defect and the more centrally a defect was located, the more attentional distraction was observed, as measured by increased attention on the defect and decreased attention on the eyes, ranging from 729 (95% CI, 526-931) milliseconds for small peripheral defects to 3693 (95% CI, 3490-3896) milliseconds for large central defects. Reconstructive surgery was associated with improved gaze deviations for all faces and with normalized attention directed to the eyes for all faces except for those with large central defects. Conclusions and Relevance:Mohs defects are associated with altered facial perception, diverting attention from valuable features such as the eyes. Reconstructive surgery was associated with normalized attentional distraction for many patients with cutaneous Mohs defects. These data are important to patients who want to know how reconstructive surgery could change the way people look at their face. The data also point to the possibility of outcomes prediction based on facial defect size and location before reconstruction. Eye tracking is a valuable research tool for outcomes assessment that lays the foundation for understanding how reconstructive surgery may change perception and normalize facial deformity.

Dataset Information

Eye-tracking technology in identifying visualizers and verbalizers: data on eye-movement differences and detection accuracy.

Publications

Eye-tracking technology in identifying visualizers and verbalizers: data on eye-movement differences and detection accuracy.

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