This paper discusses the phenomenological dimension of social understanding. The author’s general hypothesis is that complex forms of social understanding that biological agents especially humans show are based on two mechanisms: 1 the bodily, experiential dynamics of emphatic resonance and 2 the biographic reconstruction of a communication situation. The latter requires the agent’s bodily experiences as the point of reference for the reconstruction process. This hypothesis is derived from discussions in philosophy, natural sciences, and cognitive science on the social embodiment of cognition and understanding. Evidence comes from studies on social cognition in primates, infants, and autistic people that are interpreted in terms of the “mind-experiencing” hypothesis. The second part of the paper sketches an '‘interactive’’ experiment that investigates the dynamic coupling of a robot with its environment. This example is used to discuss the role of the human observer and designer as an active, embodied agent who is biased toward interpreting the world in terms of intentionality and explanation. The paper describes how this aspect can influence the processes of understanding and interpretation of the behavior of autonomous robotic agents. The author concludes by stressing the need to overcome the distinction between computationalism and phenomenology in order to develop complex artificial systems.

A framework for understanding and exploiting embodiment is presented which is not dependent on any specific ontological context. This framework is founded on a new definition of embodiment, based on the relational dynamics that exist between biological organisms and their environments, and inspired by the structural dynamics of the bacterium Escherichia coli. Full recognition is given to the role played by physically instantiated bodies, but in such a way that this can be meaningfully abstracted within the constraints implied by the term ‘embodiment’, and applied in a variety of operational contexts. This is illustrated by ongoing experimental work in which the relational dynamics that exist between E. coli and its environment are applied in a variety of software environments, using Cellular Automata (CA) with artificial’ sensory’ and’ effector’ surfaces, producing qualitatively similar’ chemotactic’ behaviours in a variety of operational domains.