Abstract
Previous research showed people’s explicit (vs. implicit) competence impressions were more sensitive to a robot’s single inconsistent (“oddball”) behavior. We report nine pre-registered studies (N = 3,735 online participants) testing the scope and underlying causes of this dissociation. We found that the dissociation (a) generalized to industrial robots, surgical robots, and self-driving cars; (b) replicated with structurally aligned direct and indirect measures of competence; and (c) is at least partially explained by the diagnosticity of the evidence. We discuss implications for social cognition and human-robot interaction research.
Data availability
All data, materials, and code can be found at: https://osf.io/zwued/?view_only=9bc9367ab70e4935b278972efb662a1c.
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Acknowledgements
We thank John Bargh, Margaret Clark, Julian Jara-Ettinger, Yarrow Dunham, the Implicit Social Cognition Lab at Yale, OpenAI’s ChatGPT, and Anthropic’s Claude for helpful comments on an earlier version of the manuscript. We also thank Malte Jung and Wen-Ying Lee for their software support, and the Office of Naval Research for funding this research (Award Number: N00014-19-1-2299).
Funding
This research was funded by the Office of Naval Research (Award Number: N00014-19-1-2299) and Yale University.
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N.S. and M.F. equally contributed to project conceptualization and methodology. N.S. led data collection and analyses, and drafted the initial manuscript text. M.F. supervised, acquired funding, and revised the manuscript text. Both authors reviewed the manuscript.
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Surdel, N., Ferguson, M.J. The role of diagnosticity in judging robot competence. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35375-y
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DOI: https://doi.org/10.1038/s41598-026-35375-y
