Number 5 is alive! Attribution of knowledge and intention in human-robot interactions

Robots will become increasingly common in many fields of work, from health care to fabrication to logistics. However, because robots look and act differently than humans, they may not trigger the same mental state attribution process that otherwise promote fluent social interactions. This can severely undermine their interactions with and acceptance by other people, especially in fields where anxiety-free cooperation is crucial (e.g., health care). This interdisciplinary research project combines Bach's expertise on human social interaction and perception with Giannaccini's expertise in engineering and robotics to reveal the extent to which people spontaneously attribute the same mental states to robots as to other humans, on which morphological (e.g., eyes) and behavioural features (e.g., biological motion, efficient goal seeking) such attributions rely, and whether manipulating these features can either encourage or discourage people from seeing robots as human-like interaction partners. It relies on two well-established tasks developed by Bach, which robustly measure two central components of social sense-making in independent research streams: (1) how people predict another actor's behaviour from the intentions they attribute to them, and (2) how they derive another actor's knowledge from the particular visual perspective this actor has upon it. By varying the robots' morphological features and behaviours, we will be able to measure these two central types of mental state attribution and link them to explicit ratings of interaction quality and mind perception. In doing so, this project will not only provide new insights into how mental states are attributed to robots - and other humans - but also provide novel methods to give robots characteristics that allow people to "see" them in a mentalistic way and cooperate with them more confidently.