How to improve data quality in dog eye tracking

Abstract

Pupil–corneal reflection (P–CR) eye tracking has gained a prominent role in studying dog visual cognition, despite methodological challenges that often lead to lower-quality data than when recording from humans. In the current study, we investigated if and how the morphology of dogs might interfere with tracking of P–CR systems, and to what extent such interference, possibly in combination with dog-unique eye-movement characteristics, may undermine data quality and affect eye-movement classification when processed through algorithms. For this aim, we have conducted an eye-tracking experiment with dogs and humans, and investigated incidences of tracking interference, compared how they blinked, and examined how differential quality of dog and human data affected the detection and classification of eye-movement events. Our results show that the morphology of dogs’ face and eye can interfere with tracking methods of the systems, and dogs blink less often but their blinks are longer. Importantly, the lower quality of dog data lead to larger differences in how two different event detection algorithms classified fixations, indicating that the results of key dependent variables are more susceptible to choice of algorithm in dog than human data. Further, two measures of the Nyström & Holmqvist (Behavior Research Methods, 42(4), 188–204, 2010) algorithm showed that dog fixations are less stable and dog data have more trials with extreme levels of noise. Our findings call for analyses better adjusted to the characteristics of dog eye-tracking data, and our recommendations help future dog eye-tracking studies acquire quality data to enable robust comparisons of visual cognition between dogs and humans.