Poster D35, Wednesday, August 21, 2019, 5:15 – 7:00 pm, Restaurant Hall
How does eye-tracking in the MRI scanner compare to the lab? Evidence from a linguistic prediction task
Jennifer Mack1, Colleen Ward1, Sofia Stratford1;1University of Massachusetts-Amherst
Introduction. Over the past two decades, eye-tracking (ET) has become one of the primary tools used to examine online language processes such as prediction. More recently, ET has been combined with functional magnetic resonance imaging (fMRI) to investigate the neurocognitive mechanisms of these processes. However, little is known about how ET measurements obtained in typical laboratory settings compare to those obtained in the scanner, given cross-environment differences such as lighting, noise, participant position, and ET camera mount. These factors might affect data quality, sensorimotor processes underlying oculomotor control, and/or higher-level cognitive and linguistic processes (e.g., the likelihood of prediction). To examine these questions, in the present study, we compared ET patterns in a linguistic prediction task performed in the lab and in the scanner. Methods. 50 neurotypical young, right-handed, English-speaking adults performed a linguistic prediction task. 25 participants did so in typical lab conditions (desktop-mounted ET system, brightly lit room, seated position, minimal background noise) and 25 in the scanner under typical conditions (long-range ET mount, brightly lit scanner bore, supine position, background scanner noise). Apart from these factors, the ET system and data collection procedures were matched across environments. In the task, two object pictures appeared and disappeared, one at a time. One contained two objects and the other a single object (e.g., two apples, one shirt). Then, the participant heard a predictive cue (“Here is one …”/“Here are two …”) and, following a brief interval, the picture that matched the cue in number (the target picture) re-appeared in its original position. Simultaneously, the participant heard a word, and indicated whether it matched the target picture. To test for differences in ET data quality and sensorimotor processes, we examined ET measures extracted from participants’ eye movements following the appearance of the first picture at the beginning of each trial. To examine linguistic prediction, we extracted ET measures time-locked to the onset of the predictive cue, specifically the likelihood of an eye movement to the target picture’s anticipated position prior to its re-appearance. Results. With respect to the appearance of the first picture, saccadic latencies were approximately 40 ms longer in the scanner than in the lab, and there was a significantly higher level of noise in the data (i.e., an increase in the rate of data loss and blinks). With respect to prediction, a higher rate of predictive eye movements was found in the scanner environment vs. the lab. Conclusion. The study demonstrates that ET measures vary between typical laboratory and scanner environments, in part due to differences in data quality and sensorimotor processes. Higher-level cognitive processes such as linguistic prediction are also affected. Specifically, we observed a higher likelihood of prediction in ET measures collected in the scanner vs. the lab, which was evident despite the higher level of noise in the scanner-based data. Although further research is needed to identify the cause of this increase in prediction, one possibility is that reduced intelligibility of auditory stimuli due to scanner noise contributes to compensatory predictive processing.
Themes: Methods, Meaning: Combinatorial Semantics
Method: Eye Tracking