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Understanding the sense of self through robotics.

Tony J Prescott, Kai Vogeley, Agnieszka Wykowska

Science robotics October 30, 2024 DOI: 10.1126/scirobotics.adn2733 via PubMed

Summary

AI-generated from the abstract

Robotics contributes to the scientific understanding of the self by building embodied models and using robots as experimental probes. This review covers research on the minimal self, the extended self, and disorders of the self, highlighting how key phenomena of the self can be generated in robots with suitable sensor and actuator systems and a layered cognitive architecture involving predictive models. The approach allows testing hypotheses about the self's development, behavioral manifestation, and diversity across humans, animals, and potentially machines. Future directions and open challenges in understanding the self through constructing its components in artificial systems are discussed.

Study at a glance

Characteristics Review Peer reviewed
Citations 11
Key finding Key phenomena of the self can be generated in robots with suitably configured sensor and actuator systems and a layered cognitive architecture involving networks of predictive models.

Abstract

Robotics can play a useful role in the scientific understanding of the sense of self, both through the construction of embodied models of the self and through the use of robots as experimental probes to explore the human self. In both cases, the embodiment of the robot allows us to devise and test hypotheses about the nature of the self, with regard to its development, its manifestation in behavior, and the diversity of selves in humans, animals, and, potentially, machines. This paper reviews robotics research that addresses the topic of the self-the minimal self, the extended self, and disorders of the self-and highlights future directions and open challenges in understanding the self through constructing its components in artificial systems. An emerging view is that key phenomena of the self can be generated in robots with suitably configured sensor and actuator systems and a layered cognitive architecture involving networks of predictive models.

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