A hidden Markov model for analyzing eye-tracking of moving objects : Case study in a sustained attention paradigm.
Behav Res Methods
; 52(3): 1225-1243, 2020 06.
Article
em En
| MEDLINE
| ID: mdl-31898297
ABSTRACT
Eye-tracking provides an opportunity to generate and analyze high-density data relevant to understanding cognition. However, while events in the real world are often dynamic, eye-tracking paradigms are typically limited to assessing gaze toward static objects. In this study, we propose a generative framework, based on a hidden Markov model (HMM), for using eye-tracking data to analyze behavior in the context of multiple moving objects of interest. We apply this framework to analyze data from a recent visual object tracking task paradigm, TrackIt, for studying selective sustained attention in children. Within this paradigm, we present two validation experiments to show that the HMM provides a viable approach to studying eye-tracking data with moving stimuli, and to illustrate the benefits of the HMM approach over some more naive possible approaches. The first experiment utilizes a novel 'supervised' variant of TrackIt, while the second compares directly with judgments made by human coders using data from the original TrackIt task. Our results suggest that the HMM-based method provides a robust analysis of eye-tracking data with moving stimuli, both for adults and for children as young as 3.5-6 years old.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Desempenho Psicomotor
/
Atenção
Tipo de estudo:
Health_economic_evaluation
Limite:
Child
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Child, preschool
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Humans
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article