Social Influence Under Uncertainty in Interaction with Peers, Robots and Computers

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.

Self-Repairing and/or Buoyant Trust in Artificial Intelligence

“…At a more mechanical level, these findings are based entirely on rough and subjective scales of trust, administered in a retrospective fashion. The retrospective reporting may be subject to memory and recall issues, where numerical ratings may exhibit a regression to the mean (Zonca et al, 2021). Similarly, the "quick and dirty" measure of trust lacks formal validation, although these types of rough measures such as the System Usability Scale have proven to be incredibly useful in different applications (Brooke, 1996).…”