  To interact seamlessly with robots, users must infer the causes of a robot's
behavior and be confident about that inference. Hence, trust is a necessary
condition for human-robot collaboration (HRC). Despite its crucial role, it is
largely unknown how trust emerges, develops, and supports human interactions
with nonhuman artefacts. Here, we review the literature on trust, human-robot
interaction, human-robot collaboration, and human interaction at large. Early
models of trust suggest that trust entails a trade-off between benevolence and
competence, while studies of human-to-human interaction emphasize the role of
shared behavior and mutual knowledge in the gradual building of trust. We then
introduce a model of trust as an agent's best explanation for reliable sensory
exchange with an extended motor plant or partner. This model is based on the
cognitive neuroscience of active inference and suggests that, in the context of
HRC, trust can be cast in terms of virtual control over an artificial agent. In
this setting, interactive feedback becomes a necessary component of the
trustor's perception-action cycle. The resulting model has important
implications for understanding human-robot interaction and collaboration, as it
allows the traditional determinants of human trust to be defined in terms of
active inference, information exchange and empowerment. Furthermore, this model
suggests that boredom and surprise may be used as markers for under and
over-reliance on the system. Finally, we examine the role of shared behavior in
the genesis of trust, especially in the context of dyadic collaboration,
suggesting important consequences for the acceptability and design of
human-robot collaborative systems.
