This chapter draws an analogy between computing mechanisms and autopoietic systems, focusing on the non-representational status of both kinds of system (computational and autopoietic). It will be argued that the role played by input and output components in a computing mechanism closely resembles the relationship between an autopoietic system and its environment, and in this sense differs from the classical understanding of inputs and outputs. The analogy helps to make sense of why we should think of computing mechanisms as non- representational, and might also facilitate reconciliation between computational and autopoietic/enactive approaches to the study of cognition.

In order to construct scientifically reasoning artifacts we not only have to close the loop between hypothesis generation and evaluation but also to make the system embodied. To genuinely understand scientific insights, “robot scientists” need to represent scientific knowledge within their own representational structure rather than in terms of a priori defined logical propositions. Two main features of such systems are identified: projective constructivism that reverses the flow of information processing, and cognitive canalization that reduces computational requirements.