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An Examination of Recording Accuracy and Precision From Eye Tracking Data From Toddlerhood to Adulthood.

Kirsten A Dalrymple | Marie D Manner | Katherine A Harmelink | Elayne P Teska | Jed T Elison
Frontiers in psychology | 2018

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.

Pubmed ID: 29875727

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Associated grants

  • Agency: NIMH NIH HHS, United States
    Id: R01 MH104324

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Python Programming Language (tool)

RRID:SCR_008394

Programming language for all operating systems that lets users work more quickly and integrate their systems more effectively. Often compared to Tcl, Perl, Ruby, Scheme or Java. Some of its key distinguishing features include very clear and readable syntax, strong introspection capabilities, intuitive object orientation, natural expression of procedural code, full modularity, exception-based error handling, high level dynamic data types, extensive standard libraries and third party modules for virtually every task, extensions and modules easily written in C, C (or Java for Python, or .NET languages for IronPython), and embeddable within applications as a scripting interface.

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