Abstract

Eco-cognitive computationalism is a cognitive science perspective that views computing in context, focusing on embodied, situated, and distributed cognition. It emphasizes the role of Turing in the development of the Logical Universal Machine and the concept of machines as “domesticated ignorant entities”. This perspective explains how machines can be dynamically active in distributed physical entities, allowing data to be encoded and decoded for appropriate results. In this perspective, we can clearly see that the concept of computation evolves over time due to historical and contextual factors, and it allows for the emergence of new types of computations that exploit new substrates. Taking advantage of this eco-cognitive framework I will also illustrate the concepts of “locked and unlocked strategies in deep learning systems, indicating different inference routines for creative results. Locked abductive strategies are characterized by poor hypothetical creative cognition due to the lack of what I call eco-cognitive openness, while unlocked human cognition involves higher kinds of creative abductive reasoning. This special kind of “openness” is physically rooted in the fundamental character of the human brain as an open system constantly coupled with the environment (that is an “open” or dissipative system): its activity is the uninterrupted attempt to achieve the equilibrium with the environment in which it is embedded, and this interplay can never be switched off without producing severe damage to the brain. Brain cannot be conceived as deprived of its physical quintessence which is its openness. In the brain, contrary to the computational case, ordering is not derived from the outside thanks to what I have called in a recent book “computational domestication of ignorant entities”, but it is the direct product of an “internal” open dynamical process of the system.