Brief report: Lack of processing bias for the objects other people attend to in 3-year-olds with autism.
Hand-coding toddler eye-gaze yields more usable data than automatic tracking and can change the story you see.
01Research in Context
What this study did
Falck-Ytter et al. (2015) watched how toddlers with autism looked at toys an adult was staring at.
They compared two ways to record the looks: a computer eye-tracker and a person who coded each video frame by hand.
All kids were about three years old and had an autism diagnosis.
What they found
Manual coding kept more usable data; the machine lost many trials.
The two methods also gave different answers about where the children looked.
Neither method showed that the toddlers followed the adult’s gaze more than chance.
How this fits with other research
Root et al. (2017) later got the same lesson: a GoPro on the partner’s head caught gaze that a fixed camera missed.
Vernetti et al. (2024) showed that even a quick live eye-tracking chat can flag toddlers with autism, but only if you keep the data.
Lemons et al. (2015) looked for eye-contact fear and also found nothing special—another null that lines up with Terje’s null, just in slightly older kids.
Higgins et al. (2021) reviewed dozens of labs and said the field’s eye-tracking recipes are too mixed; Terje’s paper is one early example of why we need one shared recipe.
Why it matters
Before you buy an expensive eye-tracker for assessment, test if a trained coder with pause-button software gives you more clean trials.
If the machine drops half the data, your graph will lie.
Start every new study with a head-to-head pilot like Terje did—pick the method that keeps the most kids in the final picture.
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Run a five-minute pilot: track one child’s gaze with both your eye-tracking software and a slow-motion video review—count how many looks each method actually records.
02At a glance
03Original abstract
Eye-gaze methods offer numerous advantages for studying cognitive processes in children with autism spectrum disorder (ASD), but data loss may threaten the validity and generalizability of results. Some eye-gaze systems may be more vulnerable to data loss than others, but to our knowledge, this issue has not been empirically investigated. In the current study, we asked whether automatic eye-tracking and manual gaze coding produce different rates of data loss or different results in a group of 51 toddlers with ASD. Data from both systems were gathered (from the same children) simultaneously, during the same experimental sessions. As predicted, manual gaze coding produced significantly less data loss than automatic eye tracking, as indicated by the number of usable trials and the proportion of looks to the images per trial. In addition, automatic eye-tracking and manual gaze coding produced different patterns of results, suggesting that the eye-gaze system used to address a particular research question could alter a study's findings and the scientific conclusions that follow. It is our hope that the information from this and future methodological studies will help researchers to select the eye-gaze measurement system that best fits their research questions and target population, as well as help consumers of autism research to interpret the findings from studies that utilize eye-gaze methods with children with ASD. Autism Res 2020, 13: 271-283. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: The current study found that automatic eye-tracking and manual gaze coding produced different rates of data loss and different overall patterns of results in young children with ASD. These findings show that the choice of eye-gaze system may impact the findings of a study-important information for both researchers and consumers of autism research.
Journal of autism and developmental disorders, 2015 · doi:10.3758/s13428-012-0245-6