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A Mechanistic Account of Computational Explanation in Cognitive Science and Computational Neuroscience

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Computing and Philosophy

Part of the book series: Synthese Library ((SYLI,volume 375))

Abstract

Explanations in cognitive science and computational neuroscience rely predominantly on computational modeling. Although the scientific practice is systematic, and there is little doubt about the empirical value of numerous models, the methodological account of computational explanation is not up-to-date. The current chapter offers a systematic account of computational explanation in cognitive science and computational neuroscience within a mechanistic framework. The account is illustrated with a short case study of modeling of the mirror neuron system in terms of predictive coding.

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Notes

  1. 1.

    I am not using the word ‘computational’ here in the sense used by Marr to define one of the levels in his account.

  2. 2.

    In this chapter, I do not go into detail of how the notion of function is best understood in the case of computing mechanisms. See however (Miłkowski 2013).

  3. 3.

    The notion of information will be fully explicated in Sect. 13.1.3. Note that I do not endorse the view that digital computation cannot be understood in terms of Shannon information, which has been argued by Piccinini and Scarantino (2010). They qualify the view somewhat in a later paper (without mentioning that they change their earlier view), see (Piccinini and Scarantino 2011).

  4. 4.

    The distinction between data and the model of data has been rediscovered later by Bogen and Woodward (1988) in their distinction between data and phenomena.

  5. 5.

    For example, Newell claimed that the knowledge level, as related to rationality, is always idealized. These idealizations can prohibit mechanistic decomposition (Dennett 1987, pp. 75–76).

  6. 6.

    These distinctions were used by Craver (2007), but were unrelated to the distinction between the scaffolding and the explanatory focus.

  7. 7.

    As one of my reviewers noticed, this is a controversial claim. I do not deal with skepticism about physical computation here because all skeptical arguments either fail or involve skepticism about any empirical science, which does not make such skepticism particularly interesting; for my extended discussion with Putnam and Searle, see (Miłkowski 2012b, 2013, pp. 25–85); see also (Buechner 2008).

  8. 8.

    In analogue computing, the range of values in question need not be restricted, but may be continuous, i.e., infinite. Note that this is not a definition of analogue computation; there might be analogue computers that rely on, for example, potentiometers changing their values in a step-wise manner. I simply do not exclude the conceptual possibility of analogue hypercomputation (Siegelmann 1994). For a recent analysis of analog/digital and continuous/discrete distinctions see (Maley 2010).

  9. 9.

    Notice the caveat. In principle, it’s possible to create a formalism that would both specify the computation, for example as a computer program in LISP, and required machinery. There’s no contradiction involved. But, as a matter of fact, modelers don’t use such models in their practice, and prefer functional separation of different types of models.

  10. 10.

    For my purposes, it is quite irrelevant whether this account of MNS is correct or not (but see (Hickok 2014; Lingnau et al. 2009); for a recent review see (Kilner and Lemon 2013)). I am merely interested in how the model is vindicated by its authors and how it should be evaluated from the mechanistic standpoint.

  11. 11.

    It’s worth noticing that the interventionist framework is not committed to realism about physical causation. There are interventionists who defend a specific neo-Russellian view on causation, according to which causal explanations in special sciences are genuine, while there might be no genuine causal explanations in fundamental physics (Reutlinger 2013). While I do not espouse such scepticism about causation in physics, nothing in my argument depends on there being physical causation or not; there is also no reliance on causal determinism in my argument. The mathematical framework of interventionism is sufficient to specify the conditions that genuine computational explanations must satisfy to count as good explanations, and realism or anti-realism about causes is not a part of the mathematical framework.

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Acknowledgments

The author gratefully acknowledges the support of the Polish National Science Center Grant in the OPUS program under the decision DEC-2011/03/B/HS1/04563. An earlier version of this paper has been presented during the Cognitive Science Annual Conference 2013 in Berlin. The author also wishes to thank the reviewers of the draft of the paper for their helpful and detailed comments, in particular Nir Fresco and Marcin J. Schroeder.

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  1. Institute of Philosophy and Sociology, Polish Academy of Sciences, ul. Nowy Świat 72, 00-330, Warsaw, Poland

    Marcin Miłkowski

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Correspondence to Marcin Miłkowski .

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  1. Anatolia College/ACT, Thessaloniki, Greece

    Vincent C. Müller

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Miłkowski, M. (2016). A Mechanistic Account of Computational Explanation in Cognitive Science and Computational Neuroscience. In: Müller, V.C. (eds) Computing and Philosophy. Synthese Library, vol 375. Springer, Cham. https://doi.org/10.1007/978-3-319-23291-1_13

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