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Modeling Infant Free Play Using Hidden Markov Models. 2021

Hoang Le, and Justine E Hoch, and Ori Ossmy, and Karen E Adolph, and Xiaoli Fern, and Alan Fern
School of EECS, Oregon State University.

Infants' free-play behavior is highly variable. However, in developmental science, traditional analysis tools for modeling and understanding variable behavior are limited. Here, we used Hidden Markov Models (HMMs) to capture behavioral states that govern infants' toy selection during 20 minutes of free play in a new environment. We demonstrate that applying HMMs to infant data can identify hidden behavioral states and thereby reveal the underlying structure of infant toy selection and how toy selection changes in real time during spontaneous free play. More broadly, we propose that hidden-state models provide a fruitful avenue for understanding individual differences in spontaneous infant behavior.

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