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Cited by 18Social Robot Interaction and HRIPsychology of Social InfluenceChild and Animal Learning Development

Social Influence under uncertainty in interaction with peers, robots and computers

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Zonca, Joshua, Folsø, Anna, Sciutti, Alessandra·2023·International Journal of Social Robotics
DOI 10.1007/s12369-022-00959-x
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Abstract

Taking advice from others requires confidence in their competence. This is important for interaction with peers, but also for collaboration with social robots and artificial agents. Nonetheless, we do not always have access to information about others’ competence or performance. In these uncertain environments, do our prior beliefs about the nature and the competence of our interacting partners modulate our willingness to rely on their judgments? In a joint perceptual decision making task, participants made perceptual judgments and observed the simulated estimates of either a human participant, a social humanoid robot or a computer. Then they could modify their estimates based on this feedback. Results show participants’ belief about the nature of their partner biased their compliance with its judgments: participants were more influenced by the social robot than human and computer partners. This difference emerged strongly at the very beginning of the task and decreased with repeated exposure to empirical feedback on the partner’s responses, disclosing the role of prior beliefs in social influence under uncertainty. Furthermore, the results of our functional task suggest an important difference between human–human and human–robot interaction in the absence of overt socially relevant signal from the partner: the former is modulated by social normative mechanisms, whereas the latter is guided by purely informational mechanisms linked to the perceived competence of the partner.

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  • Lack of transparency may lead humans to overreact to AI errors, inferring AI is generally unreliable and incompetent, leading to mistrust and misuse.

    “Third, the lack of transparency may lead humans to overreact to AI errors: if humans cannot understand the cause of an AI error or if they consider it anomalous compared to typical human errors, they may be tempted to infer that the AI is generally unreliable and incompetent, leading to mistrust and misuse of the AI system itself [71 , 72] .”

    From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [72]

    Section 7.5 (page not provided) says people who believe a robot is making mistakes may rapidly lose trust in its competence and stop following its advice. The paper does not link that reaction to a lack of transparency, an inability to explain the error, or errors perceived as anomalous compared with human errors. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Zonca, J., Folsø, A., & Sciutti, A.
Affiliations
  • Italian Institute of Technology (IT)
  • University of Genoa (IT)
Corresponding author
Zonca, Joshua
ORCID
Venue
International Journal of Social Robotics
Publisher
Springer Science and Business Media LLC
Published
2023-01-11
Volume
15
Issue
2
Pages
249-268
ISSN
  • 1875-4791 (ISSN-L)
  • 1875-4805
DOI
10.1007/s12369-022-00959-x
OpenAlex
W4315649350
Cited by
18
References
8
Citations by year
2026: 3 · 2025: 7 · 2024: 6 · 2023: 1 · 2022: 1
FWCI
4.20
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Social Robotics (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • BOA (University of Milano-Bicocca) (submittedVersion, cc-by)
  • arXiv (Cornell University)
Funders
  • H2020 European Research Council (ERC Starting Grant 804388)
  • Istituto Italiano di Tecnologia
Topics
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Social Sciences › Sociology and Political Science › Psychology of Social Influence
  • Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
Keywords
social influence, uncertainty, peer influence, social robots, human-robot interaction, perceived competence, prior beliefs, informational influence, normative influence, perceptual decision making, shared decision-making, human-computer interaction

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