Title
Of Mental States And Machine Learning: How Social Cues And Signals Can Help Develop Artificial Social Intelligence
Abstract
Using research in social cognition as a foundation, we studied rapid versus reflective mental state attributions and the degree to which machine learning classifiers can be trained to make such judgments. We observed differences in response times between conditions, but did not find significant differences in the accuracy of mental state attributions. We additionally demonstrate how to train machine classifiers to identify mental states. We discuss advantages of using an interdisciplinary approach to understand and improve human-robot interaction and to further the development of social cognition in artificial intelligence.
Publication Date
1-1-2016
Publication Title
Proceedings of the Human Factors and Ergonomics Society
Number of Pages
1361-1365
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1177/1541931213601314
Copyright Status
Unknown
Socpus ID
85016768873 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85016768873
STARS Citation
Best, Andrew; Warta, Samantha F.; Kapalo, Katelynn A.; and Fiore, Stephen M., "Of Mental States And Machine Learning: How Social Cues And Signals Can Help Develop Artificial Social Intelligence" (2016). Scopus Export 2015-2019. 4244.
https://stars.library.ucf.edu/scopus2015/4244