Human-machine Symbiosis: A Multivariate Perspective for Physically Coupled Human-machine Systems

TL;DR

This paper proposes a comprehensive multivariate framework for understanding human-machine symbiosis, emphasizing task coordination, interaction understanding, performance synergy, and user experience in physically coupled systems.

Contribution

It introduces a unified, multivariate perspective on human-machine symbiosis, integrating insights from multiple disciplines to define its core dimensions.

Findings

Defines four key dimensions of symbiosis: task, interaction, performance, and experience.

Provides a flexible framework applicable across diverse human-machine scenarios.

Bridges disciplinary gaps by formalizing symbiosis as a multivariate concept.

Abstract

The notion of symbiosis has been increasingly mentioned in research on physically coupled human-machine systems. Yet, a uniform specification on which aspects constitute human-machine symbiosis is missing. By combining the expertise of different disciplines, we elaborate on a multivariate perspective of symbiosis as the highest form of physically coupled human-machine systems. Four dimensions are considered: Task, interaction, performance, and experience. First, human and machine work together to accomplish a common task conceptualized on both a decision and an action level (task dimension). Second, each partner possesses an internal representation of own as well as the other partner's intentions and influence on the environment. This alignment, which is the core of the interaction, constitutes the symbiotic understanding between both partners, being the basis of a joint, highly coordinated and effective action (interaction dimension). Third, the symbiotic interaction leads to synergetic effects regarding the intention recognition and complementary strengths of the partners, resulting in a higher overall performance (performance dimension). Fourth, symbiotic systems specifically change the user's experiences, like flow, acceptance, sense of agency, and embodiment (experience dimension). This multivariate perspective is flexible and generic and is also applicable in diverse human-machine scenarios, helping to bridge barriers between different disciplines.