A Functional Model for Studying Common Trends Across Trial Time in Eye Tracking Experiments

Mingfei Dong, Donatello Telesca, Catherine Sugar, Frederick Shic, Adam Naples, Scott P. Johnson, Beibin Li, Adham Atyabi, Minhang Xie, Sara J. Webb, Shafali Jeste, Susan Faja, April R. Levin, Geraldine Dawson, James C. McPartland and Damla Şentürk ()
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Mingfei Dong: University of California, Los Angeles
Donatello Telesca: University of California, Los Angeles
Catherine Sugar: University of California, Los Angeles
Frederick Shic: Seattle Children’s Research Institute
Adam Naples: Yale University
Scott P. Johnson: University of California
Beibin Li: Seattle Children’s Research Institute
Adham Atyabi: Seattle Children’s Research Institute
Minhang Xie: Seattle Children’s Research Institute
Sara J. Webb: Seattle Children’s Research Institute
Shafali Jeste: University of South California
Susan Faja: Harvard Medical School
April R. Levin: Boston Children’s Hospital and Harvard Medical School
Geraldine Dawson: Duke University
James C. McPartland: Yale University
Damla Şentürk: University of California, Los Angeles

Statistics in Biosciences, 2023, vol. 15, issue 1, No 10, 287 pages

Abstract: Abstract Eye tracking (ET) experiments commonly record the continuous trajectory of a subject’s gaze on a two-dimensional screen throughout repeated presentations of stimuli (referred to as trials). Even though the continuous path of gaze is recorded during each trial, commonly derived outcomes for analysis collapse the data into simple summaries, such as looking times in regions of interest, latency to looking at stimuli, number of stimuli viewed, number of fixations, or fixation length. In order to retain information in trial time, we utilize functional data analysis (FDA) for the first time in literature in the analysis of ET data. More specifically, novel functional outcomes for ET data, referred to as viewing profiles, are introduced that capture the common gazing trends across trial time which are lost in traditional data summaries. Mean and variation of the proposed functional outcomes across subjects are then modeled using functional principal component analysis. Applications to data from a visual exploration paradigm conducted by the Autism Biomarkers Consortium for Clinical Trials showcase the novel insights gained from the proposed FDA approach, including significant group differences between children diagnosed with autism and their typically developing peers in their consistency of looking at faces early on in trial time.

Keywords: Eye tracking; Autism spectrum disorder; Functional data analysis; Functional principal component analysis; Multilevel functional principal component analysis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12561-022-09354-6

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