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Identifying and individuating cognitive systems: a task-based distributed cognition alternative to agent-based extended cognition

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Abstract

This article argues for a task-based approach to identifying and individuating cognitive systems. The agent-based extended cognition approach faces a problem of cognitive bloat and has difficulty accommodating both sub-individual cognitive systems (“scaling down”) and some supra-individual cognitive systems (“scaling up”). The standard distributed cognition approach can accommodate a wider variety of supra-individual systems but likewise has difficulties with sub-individual systems and faces the problem of cognitive bloat. We develop a task-based variant of distributed cognition designed to scale up and down smoothly while providing a principled means of avoiding cognitive bloat. The advantages of the task-based approach are illustrated by means of two parallel case studies: re-representation in the human visual system and in a biomedical engineering laboratory.

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Notes

  1. Clark and Chalmers argue that both cognition and mind are extended; the Otto case was originally used to argue that mind, in particular, is extended, but it can be used for either purpose. Our focus here is on the hypothesis of extended cognition; hence we will not be concerned with the claim that Otto’s beliefs are located in his notebook but rather with the claim that Otto and his notebook constitute a single cognitive system. See Huebner (2014) for a defense of distributed mentality. Our focus is also distinct from that of theorists who have argued for extended consciousness (Manzotti 2011; Honderich 2014), and we take no stand on the question of extended or distributed consciousness. A fuller treatment would, however, eventually have to deal with this question, especially in view of recent debates on cognitive phenomenology, many participants in which have argued that cognition has a distinctive conscious character (see, e.g., Strawson 2011; Smithies 2013.)

  2. But see Sprevak (2009) for an attempt to use the relationship between functionalism and the extended cognition hypothesis to ground a reductio of the latter.

  3. For an overview of the transition from first-wave to second-wave extended cognition theorizing, see Kirchhoff (2012).

  4. In fact, it is unclear to what extent it is compatible with the spirit of first-wave extended cognition to impose criteria in addition to functional isomorphism; in retrospect, the “trust-and-glue” conditions to some extent look like an ad hoc attempt to prevent cognitive bloat (Palermos 2011).

  5. Similarly, artificial intelligence researchers will sometimes perform “ablation experiments” on their complicated programs. These are analogous to lesioning experiments in animals in that they remove a part of the program and observe the resulting behavior, allowing the researcher to draw conclusions regarding the functions of specific parts of the program.

  6. We say “virtually” because there are exceptions, e.g., Tallis (2004). While most theorists agree that cognition is a matter of information processing, there is controversy over the nature of information itself. In a recent review, for example, Crnkovic and Hofkirchner (2011) list several senses of information endorsed by scholars. Our conception of information is meant to be very inclusive, neutral among the kinds of information listed.

  7. This raises the question of how cognitive tasks are to be individuated; see “Identifying cognitive systems” section below.

  8. This implies that cognition necessarily involves representations. While this view is compatible with most approaches to cognition, it is rejected by radical brands of antirepresentationalism (Beer 1990; Brooks 1999; Gelder 1995; Chemero 2009). Many dynamical systems theorists, for example, reject talk of representations. While this approach has found applications in certain domains in fields such as robotics, dynamical systems theorists have difficulty providing a general account of cognition that does not appeal to representations, since cognition includes, in addition to processes that might be explained in terms of interaction between the cognizer and its environment, processes that refer to entities beyond the cognizer’s environment (Clark 1998a). Consider, e.g., the difficulty of providing an account of mental time travel [i.e., remembering the past and imagining the future (Michaelian 2016; Michaelian et al. 2016)] without appealing to representations of past and future events.

  9. One might ask what additional explanatory power is gained by positing the existence of sub- and supra-individual cognitive systems; we return to this question in “Summing up” section.

  10. While we have chosen these cases because they involve analogous cognitive tasks performed at different scales, this should not be taken to suggest that we endorse the functionalist reasoning behind first-wave extended cognition.

  11. We use the term “proposition” here in the sense that it has in the knowledge representation literature: propositions are sentence-like representations, as opposed to, for example, images. We do not use it in the logical sense, in that, for example, propositions do not necessarily obey the law of excluded middle.

  12. Though re-interpretation is the major functional reason offered for mental imagery, empirical support for peoples’ ability to do it at all has been spotty. See Chambers and Reisberg (1985) and Slezak (1992) for some failed attempts, and Finks et al. (1989) for a response.

  13. Nersessian was the first to suggest that scientific laboratories were distributed cognitive systems. See Giere (2002) for a useful description of this approach.

  14. Aliases are used to protect the anonymity of respondents and their laboratories.

  15. After the results are collected, statistics are run on the data. Since software is used to run these analyses, it too is done by a DCS consisting of, at the very least, a person and a computer program.

  16. The architecture CLARION (Sun 2005) supports the modeling of multi-person cognition, though not (or at least not explicitly) of cognitive artifacts.

  17. Swarm intelligence (Panigrahi et al. 2011) provides one illustration of the insights that can be obtained when we are prepared to view processes unfolding at different scales—including the scale of multi-organism systems—as engaged in cognition.

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Acknowledgments

Thanks to Andrew Brook, Anthony J. Francis, Jeanette Bicknell, Eros Corazza, Wendy Newstetter, Elke Kurz-Milcke, Etienne Pelaprat, and especially Nancy J. Nersessian. The ethnographic research discussed in “Re-representation in a biomedical engineering laboratory” section was part of a project in which the first author was involved; the project was supported by a National Science Foundation ROLE Grant (REC0106773) to Nancy J. Nersessian (PI) and Wendy Newstetter (co-PI).

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Authors and Affiliations

  1. Institute of Cognitive Science, Carleton University, Ottawa, Canada

    Jim Davies

  2. Philosophy Department, University of Otago, Dunedin, New Zealand

    Kourken Michaelian

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  1. Jim Davies
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  2. Kourken Michaelian
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Correspondence to Jim Davies.

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Handling editor: John K. Tsotsos, York University, Toronto.

Reviewers: Iris van Rooij, Radboud University Nijmegen, Igor Aleksander, Imperial College, London.

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Davies, J., Michaelian, K. Identifying and individuating cognitive systems: a task-based distributed cognition alternative to agent-based extended cognition. Cogn Process 17, 307–319 (2016). https://doi.org/10.1007/s10339-016-0759-4

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