Abstract
Theory of Mind is an inferential system central to human–human communication by which people ascribe mental states to self and other, and then use those deductions to make predictions about others’ behaviors. Despite the likelihood that ToM may also be central to interactions with other types of agents exhibiting similar cues, it is not yet fully known whether humans develop ToM for mechanical agents exhibiting properties of intelligence and sociality. A suite of five tests for implicit ToM were performed (white lie test, behavioral intention task, facial affect inference, vocal affect inference, and false-belief test) for three different robots and a human control. Findings suggest that implicit ToM signals are consistent across variably human-like robots and humans, so long as the social cues are similar and interpretable, but there is no association between implicit ToM signals and explicit mind ascription; findings suggest that heuristics and deliberation of mental status of robots may compete with implicit social-cognitive reactions.
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Notes
Interrater reliability metrics, in accounting for chance agreement, are punishing when the phenomenon is infrequent [49]. Because (a) mentalistic explanations for intentionality and (b) emotional state explanations for facial affect each occurred in only ~ 10–20% of the sample, percent agreement is taken here as an acceptable metric for interrater reliability.
A robust test of equality of means could not be conducted here because the low-anthropomorphic robot condition featured no variance—no respondent in that condition detected sarcasm.
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Acknowledgements
The author gratefully acknowledges the contributions of Tiffany A. Dykstra-DeVette and Katie Burgess in creating stimulus materials, of Mia Del Borrello and Tai Nguyen in data coding, and of Nick Bowman’s input on this manuscript. Thanks also to West Virginia University Department of Communication, where a portion of this work was conducted.
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Banks, J. Theory of Mind in Social Robots: Replication of Five Established Human Tests. Int J of Soc Robotics 12, 403–414 (2020). https://doi.org/10.1007/s12369-019-00588-x
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DOI: https://doi.org/10.1007/s12369-019-00588-x