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Behavioural Explanation in the Realm of Non-mental Computing Agents

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Abstract

Recently, many philosophers have been inclined to ascribe mentality to animals (including some insects) on the main grounds that they possess certain complex computational abilities. In this paper I contend that this view is misleading, since it wrongly assumes that those computational abilities demand a psychological explanation. On the contrary, they can be just characterised from a computational level of explanation, which picks up a domain of computation and information processing that is common to many computing systems but is autonomous from the domain of psychology. Thus, I propose that it is possible to conceive insects and other animals as mere computing agents, without having any commitment to ascribe mentality to them. I conclude by sketching a proposal about how to draw the line between mere computing and genuine mentality.

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Notes

  1. In addition to mental states that play a causal role in behaviour, the mind is normally understood as involving consciousness. But for the purposes of this paper—and following many philosophers persuaded by the computational theory of mind, I shall assume that consciousness is not essential to psychological explanation, and that important progress can be made on the nature of the mind without addressing the phenomenal character of mental states.

  2. I believe it innocuous to describe symbolic structures at the computational level for two reasons. First, computational theory is inherently symbolic. Second, I adopt an informational approach to symbols according to which computational structures can in some way refer to environmental properties by virtue of carrying and using information about them, without necessarily having fully-fledged mental content. I explain these points in Sects. 4 and 5.1.

  3. It has been proposed that associative conditioning can be accommodated within a computational framework. For example, Gallistel and Gibbon (2001) suggest that associative learning can be better explained in terms of computational operations such as extracting temporal regularities between information-bearing structures. If this is the case, associationist explanations would be part of the computational level. But insofar as they can be formulated without appeal to psychological notions, this approach is compatible with the argument put forward in this paper.

  4. Of course, the Mars rovers receive instructions from earth. But since those instructions take some time to reach the rover, they are designed to carry out many tasks in a rather autonomous way, such as self-monitoring, navigating and making some decisions without human intervention.

  5. A second motivation can be internalism, the (more general) view that representational notions cannot play any genuine role in a scientific psychology (Kim 1982; Stich 1983). But for the purposes of this paper I assume externalism, according to which it is plausible to formulate psychological explanations that advert to representational contents.

  6. To satisfy the demands of explaining how concrete computing agents behave in real environments, the computational level might have to be supplemented by computational frameworks distinct from Turing models, such as interactive computation or hypercomputation (see Dodic-Crnkovic 2011 for a review). Nothing in this paper depends crucially on which computational framework we adopt.

  7. When discussing a Fodorian approach (called the “semantic account of computation”) Piccinini (2012) arrives at a similar conclusion: the dependency of this account on a psychological notion of content, makes it unfit for providing computational explanations beyond philosophy of mind, e.g. computer science.

  8. Burge understands the term “representation” as strictly psychological, meaning that it can be equated with what I call “mental symbols” in this paper.

  9. The personal-subpersonal distinction was first formulated by Dennett (1969) and since then it has had considerable influence within the philosophy of pscyhology literature (see e.g. McDowell 1994; Hornsby 2000; Bermúdez 2005; Frankish and Evans 2009).

  10. To determine which animals might actually satisfy these computational constraints exceeds the scope of this paper. However, I believe that these conditions are rather demanding and cast doubt about whether computing agents such as insects could satisfy them. For example, honeybees might fail to satisfy the generality constraint due to their massively modular computational architecture (Aguilera 2011; for a dissenting view, see Carruthers 2009).

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Acknowledgments

The author would like to thank Stephen Laurence, Dominic Gregory, Asa Cusack and anonymous referees for helpful comments on earlier drafts of this paper.

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  1. University of Chile, Santiago, Chile

    Bernardo Aguilera

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Aguilera, B. Behavioural Explanation in the Realm of Non-mental Computing Agents. Minds & Machines 25, 37–56 (2015). https://doi.org/10.1007/s11023-015-9362-1

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