Computational Cognition

Artificial Intelligence (AI) is a domain of computer science focused on devices that are able to take some level of agency in achieving novel goals. Beyond that detail, the scope of AI is disputed, in part because “novel goals” is a moving target … as once-unimaginable feats become routine. Commonly mentioned aspects include abilities to collect relevant information about the environment, draw inferences based on that information, and apply those inferences flexibly.

Coherence Discourses regard distinctions and descriptions as useful devices to make sense of the complex dynamics of learning, but they caution that such devices are mere heuristic conveniences. Coherence Discourses suggest that truths do not exist independently or outside of a system – which is a commentary on humans’ understanding of reality, not a commentary on reality. Most Coherence Discourses employ biological and ecological metaphors, with dynamics framed in evolutionary terms.

Machine Learning encompasses all efforts and strategies to structure and program machines to improve their performances on specific tasks. In these applications, “learning” is usually defined in operational rather than cognitive terms – that is, in terms of measurable improvements in performance, not any sort of imagined “thinking.” Many strategies are under development, employing different computer architectures and a range of logics.

Computationalism is more a philosophical positioning than a practical theory. Its grounding premise is that the mind is an information-processing system, and so perception, thought, consciousness, and so are all forms of computation. By implication, learning is seen as a matter of rule-based symbolic manipulations within neural networks.