Modeling Infant Free Play Using Hidden Markov Models

Hoang Le; Justine E. Hoch; Ori Ossmy; Karen E. Adolph; Xiaoli Fern; Alan Fern

doi:10.1109/ICDL49984.2021.9515677

Modeling Infant Free Play Using Hidden Markov Models

Hoang Le, Justine E. Hoch, Ori Ossmy, Karen E. Adolph, Xiaoli Fern, Alan Fern

Psychology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English (US)
Title of host publication	IEEE International Conference on Development and Learning, ICDL 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Volume	2021
ISBN (Electronic)	9781728162423
DOIs	https://doi.org/10.1109/ICDL49984.2021.9515677
State	Published - Aug 23 2021
Event	2021 IEEE International Conference on Development and Learning, ICDL 2021 - Virtual, Beijing, China Duration: Aug 23 2021 → Aug 26 2021

Publication series

Name	IEEE International Conference on Development and Learning, ICDL 2021

Conference

Conference	2021 IEEE International Conference on Development and Learning, ICDL 2021
Country/Territory	China
City	Virtual, Beijing
Period	8/23/21 → 8/26/21

Keywords

Behavior Modeling
Developmental Science

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications

Access to Document

10.1109/ICDL49984.2021.9515677

Cite this

Le, H., Hoch, J. E., Ossmy, O., Adolph, K. E., Fern, X., & Fern, A. (2021). Modeling Infant Free Play Using Hidden Markov Models. In IEEE International Conference on Development and Learning, ICDL 2021 (Vol. 2021). (IEEE International Conference on Development and Learning, ICDL 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDL49984.2021.9515677

Modeling Infant Free Play Using Hidden Markov Models. / Le, Hoang; Hoch, Justine E.; Ossmy, Ori et al.
IEEE International Conference on Development and Learning, ICDL 2021. Vol. 2021 Institute of Electrical and Electronics Engineers Inc., 2021. (IEEE International Conference on Development and Learning, ICDL 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Le, H, Hoch, JE, Ossmy, O, Adolph, KE, Fern, X & Fern, A 2021, Modeling Infant Free Play Using Hidden Markov Models. in IEEE International Conference on Development and Learning, ICDL 2021. vol. 2021, IEEE International Conference on Development and Learning, ICDL 2021, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE International Conference on Development and Learning, ICDL 2021, Virtual, Beijing, China, 8/23/21. https://doi.org/10.1109/ICDL49984.2021.9515677

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title = "Modeling Infant Free Play Using Hidden Markov Models",

abstract = "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.",

keywords = "Behavior Modeling, Developmental Science",

author = "Hoang Le and Hoch, {Justine E.} and Ori Ossmy and Adolph, {Karen E.} and Xiaoli Fern and Alan Fern",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Development and Learning, ICDL 2021 ; Conference date: 23-08-2021 Through 26-08-2021",

year = "2021",

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T1 - Modeling Infant Free Play Using Hidden Markov Models

AU - Le, Hoang

AU - Hoch, Justine E.

AU - Ossmy, Ori

AU - Adolph, Karen E.

AU - Fern, Xiaoli

AU - Fern, Alan

PY - 2021/8/23

Y1 - 2021/8/23

N2 - 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.

AB - 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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KW - Developmental Science

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AN - SCOPUS:85114554300

VL - 2021

T3 - IEEE International Conference on Development and Learning, ICDL 2021

BT - IEEE International Conference on Development and Learning, ICDL 2021

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 23 August 2021 through 26 August 2021

ER -

Modeling Infant Free Play Using Hidden Markov Models

Abstract

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Conference

Keywords

ASJC Scopus subject areas

Access to Document

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