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New evidence for learning-based accounts of gaze following: Testing a robotic prediction

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New evidence for learning-based accounts of gaze following: Testing a robotic prediction. / Silverstein, P.; Westermann, G.; Parise, E. et al.
2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE, 2019. p. 302-306.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Silverstein, P, Westermann, G, Parise, E & Twomey, K 2019, New evidence for learning-based accounts of gaze following: Testing a robotic prediction. in 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE, pp. 302-306. https://doi.org/10.1109/DEVLRN.2019.8850716

APA

Silverstein, P., Westermann, G., Parise, E., & Twomey, K. (2019). New evidence for learning-based accounts of gaze following: Testing a robotic prediction. In 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (pp. 302-306). IEEE. https://doi.org/10.1109/DEVLRN.2019.8850716

Vancouver

Silverstein P, Westermann G, Parise E, Twomey K. New evidence for learning-based accounts of gaze following: Testing a robotic prediction. In 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE. 2019. p. 302-306 Epub 2019 Aug 19. doi: 10.1109/DEVLRN.2019.8850716

Author

Silverstein, P. ; Westermann, G. ; Parise, E. et al. / New evidence for learning-based accounts of gaze following : Testing a robotic prediction. 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE, 2019. pp. 302-306

Bibtex

@inproceedings{df52c55102854a5ba9bb1a04d074a3b7,
title = "New evidence for learning-based accounts of gaze following: Testing a robotic prediction",
abstract = "Gaze following is an early-emerging skill in infancy argued to be fundamental to joint attention and later language. However, how gaze following emerges has been a topic of great debate. The most widely-accepted developmental theories suggest that infants are able to gaze follow only by understanding shared attention. Another group of theories suggests that infants may learn to follow gaze based on low-level social reinforcement. Nagai et al. [Advanced Robotics, 20, 10 (2006)] successfully taught a robot to gaze follow purely through social reinforcement, and found that the robot learned to follow gaze in the horizontal plane before it learned to follow gaze in the vertical plane. In the current study, we tested whether 12-month-old infants were also better at gaze following in the horizontal than the vertical plane. This prediction does not follow from the predominant developmental theories, which have no reason to assume differences between infants' ability to follow gaze in the two planes. We found that infants had higher accuracy when following gaze in the horizontal than the vertical plane (p =.01). These results confirm a core prediction of the robot model, suggesting that children may also learn to gaze follow through reinforcement learning. This study was pre-registered, and all data, code, and materials are openly available on the Open Science Framework (https://osf.io/fqp8z/).",
keywords = "cognitive development, developmental robotics, gaze following, reinforcement learning, Forecasting, Group theory, Machine learning, Robotics, Robots, Cognitive development, Developmental robotics, Joint attention, Open science, Robot model, Social reinforcement, Vertical plane, Reinforcement learning",
author = "P. Silverstein and G. Westermann and E. Parise and K. Twomey",
note = "{\textcopyright}2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2019",
month = sep,
day = "30",
doi = "10.1109/DEVLRN.2019.8850716",
language = "English",
isbn = "9781538681282",
pages = "302--306",
booktitle = "2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - New evidence for learning-based accounts of gaze following

T2 - Testing a robotic prediction

AU - Silverstein, P.

AU - Westermann, G.

AU - Parise, E.

AU - Twomey, K.

N1 - ©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2019/9/30

Y1 - 2019/9/30

N2 - Gaze following is an early-emerging skill in infancy argued to be fundamental to joint attention and later language. However, how gaze following emerges has been a topic of great debate. The most widely-accepted developmental theories suggest that infants are able to gaze follow only by understanding shared attention. Another group of theories suggests that infants may learn to follow gaze based on low-level social reinforcement. Nagai et al. [Advanced Robotics, 20, 10 (2006)] successfully taught a robot to gaze follow purely through social reinforcement, and found that the robot learned to follow gaze in the horizontal plane before it learned to follow gaze in the vertical plane. In the current study, we tested whether 12-month-old infants were also better at gaze following in the horizontal than the vertical plane. This prediction does not follow from the predominant developmental theories, which have no reason to assume differences between infants' ability to follow gaze in the two planes. We found that infants had higher accuracy when following gaze in the horizontal than the vertical plane (p =.01). These results confirm a core prediction of the robot model, suggesting that children may also learn to gaze follow through reinforcement learning. This study was pre-registered, and all data, code, and materials are openly available on the Open Science Framework (https://osf.io/fqp8z/).

AB - Gaze following is an early-emerging skill in infancy argued to be fundamental to joint attention and later language. However, how gaze following emerges has been a topic of great debate. The most widely-accepted developmental theories suggest that infants are able to gaze follow only by understanding shared attention. Another group of theories suggests that infants may learn to follow gaze based on low-level social reinforcement. Nagai et al. [Advanced Robotics, 20, 10 (2006)] successfully taught a robot to gaze follow purely through social reinforcement, and found that the robot learned to follow gaze in the horizontal plane before it learned to follow gaze in the vertical plane. In the current study, we tested whether 12-month-old infants were also better at gaze following in the horizontal than the vertical plane. This prediction does not follow from the predominant developmental theories, which have no reason to assume differences between infants' ability to follow gaze in the two planes. We found that infants had higher accuracy when following gaze in the horizontal than the vertical plane (p =.01). These results confirm a core prediction of the robot model, suggesting that children may also learn to gaze follow through reinforcement learning. This study was pre-registered, and all data, code, and materials are openly available on the Open Science Framework (https://osf.io/fqp8z/).

KW - cognitive development

KW - developmental robotics

KW - gaze following

KW - reinforcement learning

KW - Forecasting

KW - Group theory

KW - Machine learning

KW - Robotics

KW - Robots

KW - Cognitive development

KW - Developmental robotics

KW - Joint attention

KW - Open science

KW - Robot model

KW - Social reinforcement

KW - Vertical plane

KW - Reinforcement learning

U2 - 10.1109/DEVLRN.2019.8850716

DO - 10.1109/DEVLRN.2019.8850716

M3 - Conference contribution/Paper

SN - 9781538681282

SN - 9781538681299

SP - 302

EP - 306

BT - 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

PB - IEEE

ER -