Robustness and Precision: How Data Quality May Influence Key Dependent Variables in Infant Eye-Tracker Analyses

In recent years, eye-tracking has become a popular method for drawing conclusions about infant cognition. Relatively little attention has been paid, however, to methodological issues associated with infant eye-tracking. Here, we consider the possibility that systematic differences in the quality of raw eye-tracking data obtained from different populations and individuals might create the impression of differences in gaze behavior, without this actually being the case. First, we show that lower quality eye-tracking data are obtained from populations who are younger and populations who are more fidgety and that data quality declines during the testing session. Second, we assess how these differences in data quality might influence key dependent variables in eye-tracking analyses. We show that lower precision data can appear to suggest a reduced likelihood to look at the eyes in a face relative to the mouth. We also show that less robust tracking may manifest as slower reaction time latencies (e.g., time to first fixation). Finally, we show that less robust data can manifest as shorter first look/visit duration. We argue that data quality should be reported in all analyses of infant eye-tracking data and/or that steps should be taken to control for data quality before performing final analyses.