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Computationalism in a Dynamic and Distributed Eco-Cognitive Perspective

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Eco-Cognitive Computationalism

Part of the book series: Cognitive Systems Monographs ((COSMOS,volume 43))

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Abstract

The concepts of information, computation, and cognition are variously interpreted and explained and still lead to ambiguous results.

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Notes

  1. 1.

    It is well-known that this view is instead consistent with the classical view of cognition as the manipulation of linguistic/sentential entities.

  2. 2.

    Catastrophe theory is a branch of mathematics that uses topology to explain events (such as a volcano eruption or an economical crisis) characterized by very sudden changes in behavior arising from relatively small changes in circumstances. It is part of bifurcation theory in the area of research on dynamical systems.

  3. 3.

    More details about this concept proposed by Turing are illustrated below, at the end of this section.

  4. 4.

    Longo further observes: “Note that we are not claiming here that the brain is a dynamical system: also Turing refers to the brain as, at least, a dynamical, highly sensitive, system [...]. To stay within his image, take a turbulent system that is at the same time very stable and very unstable, very ordinate and very inordinate; insert it sandwich-style between different levels of organization that regulate it and that it integrates. You will then have a very pale physical image of a biological entity. Among these entities, quite material, soulless and without software distinct from the hardware (the modern dualism of the cognitivism of the formal rule and of the program), you will also find bodies with nervous systems that integrate and regulate them (as networks of exchange and communication), within which they integrate themselves (as organs) and by which they are regulated (by hormonal cascades, for example)” (Longo 2009a, footnote 13, p. 390).

  5. 5.

    “Of course, there may be some endowed indeterminacy (the machine can make steps which lead to an arbitrary element of a finite set of possible discrete states, instead of leading to a single one—we are then dealing with an non-deterministic DSM), but it consists of probabilistic type of abstract indeterminacy, already well studied by Laplace, and which is not the same mathematical concept as the unpredictability of deterministic dynamical systems, in the modern sense” (Longo 2009, p. 380).

  6. 6.

    On the related debate concerning the conflicting recent views on the relationships between computation and representation in cognitive neuroscience (and on computationalism about the brain, that is the view that the brain literally performs computations) cf. a recent special issue of the journal Minds an Machines (Piccinini 2018). I think this rich debate, mainly aiming at philosophical/ontological and definitory ambitions, still shows how the meaning of the concepts of representation and computation evolves depending on theory and practice.

  7. 7.

    Abduction is a cognitive process that leads to hypotheses, both diagnostic—such as in medical reasoning—and creative, such as in the case of scientific discovery. I have richly illustrated the concept of abduction in some of my recent studies (Magnani 2009, 2017).

  8. 8.

    Fresco (2013) fruitfully aims at disambiguating the concept of digital computation in contemporary cognitive science by illustrating how digital computation is implemented in physical systems, without ending up in pancomputationalism, that is, the view that every physical system is a digital computing system and can be explained in computational terms.

  9. 9.

    Even if we have to remember that some connectionists considered cognition in terms of neural networks that perform computations defined over strings of digits.

  10. 10.

    An analogous perspective has recently been illustrated by Raja (2018) exploiting the well-known Gibson’s ecological psychology (Gibson 1979): the animal perceives its environment by “resonating” to information in energy patterns.

  11. 11.

    Further insight on the semi-encapsulated character of cognition can be found taking advantage of the analysis of the several cultural forces that also transform cognitive actions, as illustrated by Nisbett (2003).

  12. 12.

    To describe the emergence of pregnances Thom adopts the Pavlovian perspective. Other perspectives take advantage of Hebbian (1949) and other more adequate learning principles and models, cf. for example (Loula et al. 2010).

  13. 13.

    On the infant cortex as an unorganized machine see Sect. 1.3.3.

  14. 14.

    On the related evidence from cognitive paleanthropology about the so-called “disembodiment of the mind” and on what I called semiotic brains, as brains that become capable to make up a series of signs and that are engaged in making or manifesting or reacting to a series of signs, see (Magnani 2009, Chap. 3).

  15. 15.

    I address the readers interested in the psychological aspects of evolving cognitive processes from child to adults to the rich handbook (Goswami 2010) and, more oriented to theoretical and cognitive aspects, to Karmiloff-Smith et al. (2018).

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  1. Department of Humanities, Philosophy Section and Computational Philosophy Laboratory, University of Pavia, Pavia, Italy

    Lorenzo Magnani

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Magnani, L. (2022). Computationalism in a Dynamic and Distributed Eco-Cognitive Perspective. In: Eco-Cognitive Computationalism. Cognitive Systems Monographs, vol 43. Springer, Cham. https://doi.org/10.1007/978-3-030-81447-2_1

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  • DOI: https://doi.org/10.1007/978-3-030-81447-2_1

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