Abstract
In early 2020, in-person data collection dramatically slowed or was completely halted across the world as many labs were forced to close due to the COVID-19 pandemic. Developmental researchers who assess looking time (especially those who rely heavily on in-lab eye-tracking or live coding techniques) were forced to re-think their methods of data collection. While a variety of remote or online platforms are available for gathering behavioral data outside of the typical lab setting, few are specifically designed for collecting and processing looking time data in infants and young children. To address these challenges, our lab developed several novel approaches for continuing data collection and coding for a remotely administered audiovisual looking time protocol. First, we detail a comprehensive approach for successfully administering the Multisensory Attention Assessment Protocol (MAAP), developed by our lab to assess multisensory attention skills (MASks; duration of looking, speed of shifting/disengaging, accuracy of audiovisual matching). The MAAP is administered from a distance (remotely) by using Zoom, Gorilla Experiment Builder, an internet connection, and a home computer. This new data collection approach has the advantage that participants can be tested in their homes. We discuss challenges and successes in implementing our approach for remote testing and data collection during an ongoing longitudinal project. Second, we detail an approach for estimating gaze direction and duration collected remotely from webcam recordings using a post processing toolkit (OpenFace) and demonstrate its effectiveness and precision. However, because OpenFace derives gaze estimates without translating them to an external frame of reference (i.e., the participant's screen), we developed a machine learning (ML) approach to overcome this limitation. Thus, third, we trained a ML algorithm [(artificial neural network (ANN)] to classify gaze estimates from OpenFace with respect to areas of interest (AOI) on the participant's screen (i.e., left, right, and center). We then demonstrate reliability between this approach and traditional coding approaches (e.g., coding gaze live). The combination of OpenFace and ML will provide a method to automate the coding of looking time for data collected remotely. Finally, we outline a series of best practices for developmental researchers conducting remote data collection for looking time studies.
Highlights
In early 2020, in-person participant testing and data collection dramatically slowed or was completely halted across the world as some labs were forced to close due to the COVID-19 pandemic
We describe our approach for scoring data from the Multisensory Attention Assessment Protocol (MAAP) that have been collected in the home, using a newly developed platform for estimating gaze behavior from video recordings (OpenFace), as well as our development of a machine learning (ML) model to translate the estimates provided by OpenFace into meaningful looking time data
We demonstrate the effectiveness of this approach to online data collection and the OpenFace post processing method using data collected in our lab from the MAAP (Bahrick et al, 2018a)
Summary
In early 2020, in-person participant testing and data collection dramatically slowed or was completely halted across the world as some labs were forced to close due to the COVID-19 pandemic. Developmental researchers who assess looking time (especially those who rely heavily on in-lab eye-tracking or live observer coding) were forced to re-think their methods of data collection. They could either analyze old data or they could attempt to adapt their data collection techniques to remote testing platforms— e.g., online data collection using an internet-connected computer in the child’s home. In an effort to continue data collection, we adapted many of our “in-lab” protocols and tasks to a format suitable for a remote setting. Because offline coding from videos (e.g., frame-by-frame) is time- and labor-intensive, we wanted to find a solution that might expedite the data coding process
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