Social norm learning from non-human agents can induce a persistent perceptual bias: A diffusion model approach

Abstract

Seminal studies on social influence (Asch, 1956; Sherif, 1935) were based on face-to-face interactions between humans. Nowadays, computer-mediated communication is steadily becoming ubiquitous, and we are increasingly influenced by non-human agents, such as algorithms, robots, and chatbots. The present research aimed to answer two important questions: Can non-human agents exert social influence in a persistent manner and, thus, contribute to the emergence of social norms? And if this is the case, is social influence exerted by non-human agents mediated by the same or by different cognitive mechanisms as social influence exerted by human agents? To answer these questions, we used an online version of an established paradigm in research on social norm learning. To examine the cognitive underpinnings of social influence, we used a diffusion model approach. Our results provide strong evidence for the notion that non-human agents can induce persistent social influence outside an immediate group context and, hence, can contribute to the emergence of social norms. Furthermore, results from our diffusion model analyses support the notion that social influence exerted by non-human agents is mainly mediated by the same cognitive mechanisms as social influence exerted by human agents.

Keywords: Diffusion model; Human-robot interaction; Non-human agents; Social influence; Social norms.