Journal
NEURAL NETWORKS
Volume 23, Issue 8-9, Pages 1004-1016Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2010.08.008
Keywords
Social cognition; Early interaction; Intentional communication; Machine learning; Modeling
Funding
- National Science Foundation [0808767]
- National Institutes of Health [R01 MH48680, R01 HD047417, R01 HD057284]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0808653] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0808767] Funding Source: National Science Foundation
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The face-to-face interactions of infants and their parents are a model system in which critical communicative abilities emerge. We apply machine learning methods to explore the predictability of infant and mother behavior during interaction with an eye to understanding the preconditions of infant intentionality. Overall, developmental changes were most evident when the probability of specific behaviors was examined in specific interactive contexts. Mother's smiled predictably in response to infant smiles, for example, and infant smile initiations become more predictable over developmental time. Analysis of face-to-face interaction - a tractable model system - promise to pave the way for the construction of virtual and physical agents who are able to interact and develop. (C) 2010 Elsevier Ltd. All rights reserved.
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