Towards hybrid primary intersubjectivity: a neural robotics library for human science

TL;DR

This paper explores primary intersubjectivity in human-robot interaction using neural robotics, proposing a new open-source library and demonstrating its potential for advancing research in human science and social cognition.

Contribution

It introduces the neural robotics library (NRL), an open-source methodology for studying low-level cognitive interactions in human-robot dyads based on active inference.

Findings

Demonstrated real-time interaction with a virtual robot

Proposed a framework linking active inference to intersubjectivity

Discussed applications in psychology and education

Abstract

Human-robot interaction is becoming an interesting area of research in cognitive science, notably, for the study of social cognition. Interaction theorists consider primary intersubjectivity a non-mentalist, pre-theoretical, non-conceptual sort of processes that ground a certain level of communication and understanding, and provide support to higher-level cognitive skills. We argue this sort of low level cognitive interaction, where control is shared in dyadic encounters, is susceptible of study with neural robots. Hence, in this work we pursue three main objectives. Firstly, from the concept of active inference we study primary intersubjectivity as a second person perspective experience characterized by predictive engagement, where perception, cognition, and action are accounted for an hermeneutic circle in dyadic interaction. Secondly, we propose an open-source methodology named \textit{neural robotics library} (NRL) for experimental human-robot interaction, and a demonstration program for interacting in real-time with a virtual Cartesian robot (VCBot). Lastly, through a study case, we discuss some ways human-robot (hybrid) intersubjectivity can contribute to human science research, such as to the fields of developmental psychology, educational technology, and cognitive rehabilitation.