Abstract
As robots advance from the pages and screens of science fiction into our homes, hospitals, and schools, they are poised to take on increasingly social roles. Consequently, the need to understand the mechanisms supporting human-machine interactions is becoming increasingly pressing. We introduce a framework for studying the cognitive and brain mechanisms that support human-machine interactions, leveraging advances made in cognitive neuroscience to link different levels of description with relevant theory and methods. We highlight unique features that make this endeavour particularly challenging (and rewarding) for brain and behavioural scientists. Overall, the framework offers a way to study the cognitive science of human-machine interactions that respects the diversity of social machines, individuals' expectations and experiences, and the structure and function of multiple cognitive and brain systems.
Highlights
As robots advance from the pages and screens of science fiction into our homes, hospitals, and schools, they are poised to take on increasingly social roles
The modal approach to cognitive science-informed human–robot interaction research has been grounded in social cognition
We focus on human interactions with robots that have been designed to take on social roles
Summary
As robots advance from the pages and screens of science fiction into our homes, hospitals, and schools, they are poised to take on increasingly social roles. The framework offers a way to study the cognitive science of human–machine interactions that respects the diversity of social machines, individuals’ expectations and experiences, and the structure and function of multiple cognitive and brain systems. Machines designed to socially interact with humans are proliferating, our understanding of the mental processes supporting such interactions remains limited. As progress towards developing machines that take on increasingly sophisticated social roles continues apace, the cognitive and brain mechanisms that underpin social engagement with these machines remain largely unknown. It is critical to use this understanding of human–machine interaction to further our knowledge of the flexibility and limits of neurocognitive processes supporting human social behaviour towards both human and artificial agents.
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