Abstract

In discussions regarding models of cognition, the very mention of “computationalism” often incites reactions against the insufficiency of the Turing machine model, its abstractness, determinism, the lack of naturalist foundations, triviality and the absence of clarity. None of those objections, however, concerns models based on natural computation or computing nature, where the model of computation is broader than symbol manipulation or conventional models of computation. Computing nature consists of physical structures that form layered computational architecture, with computation processes ranging from quantum to chemical, biological/cognitive and social-level computation. It is argued that, on the lower levels of information processing in the brain, finite automata or Turing machines may still be adequate models, while, on the higher levels of whole-brain information processing, natural computing models are necessary. A layered computational architecture of the mind based on the intrinsic computing of physical systems avoids objections against early versions of computationalism in the form of abstract symbols manipulation.

Highlights

  • In discussions regarding models of cognition, the very mention of “computationalism” often incites reactions against the insufficiency of the Turing machine model, its abstractness, determinism, the lack of naturalist foundations, triviality and the absence of clarity

  • George Kampis describes the problem in the following way: “If somebody writes a tricky language that goes beyond the capabilities of LISP and changes its own interpreter as well, and perhaps it changes the operating system, and so on, we find ourselves at the level of the processor chip of the computer that carries out the machine code instructions

  • Naturalizing computationalism through ideas of computing nature brings clarity as well as plausibility in computationalism. It aids in understanding how embodied and situated systems can be modeled computationally, as well as how self-reflective systems in biology with different levels of cognition and mind can be understood as computational networks organized on different levels of computation

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Summary

Critique of Classical Computationalism and a New Understanding of Computation

Computationalism has been accused of many sins [1,2,3]. In what follows I would like to answer the following three concerns about computationalism put forward by Mark Sprevak: Philosophies 2016, 1. “I have already endorsed the importance of recognizing neurons as ‘complex self-modifying’ agents, but the (ultra-)plasticity of such units can and should be seen as the human brain’s way of having something like the competence of a silicon computer to take on an unlimited variety of temporary cognitive roles, ‘implementing’ the long-division virtual machine, the French-speaking virtual machine, the flying-a-plane virtual machine, the sightreading-Mozart virtual machine and many more These talents get ‘installed’ by various learning processes that have to deal with the neurons’ semi-autonomous native talents, but once installed, they can structure the dispositions of the whole brain so strongly that they create higher levels of explanation that are both predictive and explanatory” [52]. For more details on taxonomy of computation, see [53]

Mind as a Process and Computational Architecture of Mind
Conclusions
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