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

Socpus ID

85016768873 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85016768873

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