A Hybrid Human-Neurorobotics Approach to Primary Intersubjectivity via Active Inference

Abstract

Interdisciplinary efforts from developmental psychology, phenomenology, and philosophy of mind, have studied the rudiments of social cognition and conceptualized distinct forms of intersubjective communication and interaction at human early life. 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 the study of human/neurorobot interaction consists in a unique opportunity to deepen understanding of underlying mechanisms in social cognition through synthetic modeling, while allowing to examine a second person experiential (2PP) access to intersubjectivity in embodied dyadic interaction. Concretely, we propose the study of primary intersubjectivity as a 2PP experience characterized by predictive engagement, where perception, cognition, and action are accounted for an hermeneutic circle in dyadic interaction. From our interpretation of the concept of active inference in free-energy principle theory, we propose an open-source methodology named neural robotics library (NRL) for experimental human/neurorobot interaction, wherein a demonstration program named virtual Cartesian robot (VCBot) provides an opportunity to experience the aforementioned embodied interaction to general audiences. Lastly, through a study case, we discuss some ways human-robot primary intersubjectivity can contribute to cognitive science research, such as to the fields of developmental psychology, educational technology, and cognitive rehabilitation.

The Bayesian brain and cooperative communication in schizophrenia

“…Psycholinguistic studies in psychosis generally focus on lexical or sentence-level processing rather than dialogic interactions; novel insights on communication emerge when natural conversation is studied, even at a single-brain level (for example see Castellucci and colleagues 67 ). Second-person paradigms (e.g., hyperscanning to J Psychiatry Neurosci 2022;47(1) capture interpersonal interactions 68 and brain-computer inter faces 69 ) offer tantalizing possibilities in this regard. Indeed, emerging insights on "interbrain synchrony" of neur al dynamics from healthy individuals have opened the door for second-person neuroscience in psychiatry.…”

Design Principles for Neurorobotics

“…Jun Tani's group has developed several predictive robot controllers using recurrent neural networks (Tani, 2016 ; Ahmadi and Tani, 2019 ; Chame et al, 2020 ). For example, they trained a hierarchy of continuous time recurrent neural networks (CTRNN) to learn different movements.…”

“…Similarly, the primary visual cortex sends sensory information and prediction errors to the slower parietal cortex, which sends top-down predictions for the primary visual cortex. In this group's recent work, they show the potential for prototyping robotics agents, modeled after active inference from the free energy principle theory (Friston, 2010 ), for human-robot interaction and socially assistive robotics (Chame et al, 2020 ).…”

Active Inference in Robotics and Artificial Agents: Survey and Challenges

“…General introduction and tutorial [50], [5], Derivations for robot control [13], [17] Relationship with classical control [51], [18] Relationship with optimal control [52], [53] Discrete Active Inference [54] RL and active inference [55], [25] State estimation [56], [9] Predictive processing [57], [58] Human Robot Interaction [45] Neuroscientific foundations [1], [59] Fig. 2.…”

“…After the learning process has converged, action generation can be conducted. The basic architecture described above has been extended and implemented in various robotic experimental tasks including human-robot imitative interaction [84], [85], dyadic robot imitative interaction [86], goal-directed planning for robot object manipulation [87], and goal-directed planning using active inference and reinforcement learning in navigation [29].…”