Abstract
Computational explanations focus on information processing required in specific cognitive capacities, such as perception, reasoning or decision-making. These explanations specify the nature of the information processing task, what information needs to be represented, and why it should be operated on in a particular manner. In this article, the focus is on three questions concerning the nature of computational explanations: (1) What type of explanations they are, (2) in what sense computational explanations are explanatory and (3) to what extent they involve a special, “independent” or “autonomous” level of explanation. In this paper, we defend the view computational explanations are genuine explanations, which track non-causal/formal dependencies. Specifically, we argue that they do not provide mere sketches for explanation, in contrast to what for example Piccinini and Craver (Synthese 183(3):283–311, 2011) suggest. This view of computational explanations implies some degree of “autonomy” for the computational level. However, as we will demonstrate that does not make this view “computationally chauvinistic” in a way that Piccinini (Synthese 153:343–353, 2006b) or Kaplan (Synthese 183(3):339–373, 2011) have charged it to be.
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Notes
See Piccinini (2011) for discussion.
To be clear, we do not claim that the marrian sense is the “correct” way to use the term, or that computational explanations would be more useful, or better than the other types of explanations. In contrast, what we claim is simply that there is a set of explanations in current computational science which can be characterized as “marrian”.
Moreover, they are not ultimate explanations. For instance, for the human and the robot the computational explanation for a given computational task may be exactly the same i.e. the computational analysis can be the same for widely different algorithms and implementations, regardless of one’s hypotheses concerning causal history (e.g. evolutionary history, intelligent design).
We thank Oron Shagrir for this remark. According to Milkowski (2013), the computational explanations consists of three levels of organization: contextual, isolated, and constitutive. However, according to Milkowski, who defends the mechanistic view, the main foci of computational explanations are the “isolated” computational processes.
Actually, Egan (1995, p. 189ff) does discuss these at length, and considers them essential for computational explanation of cognitive processes—she just does not consider the ecological grounds for computational adequacy an essential part of the computational characterization of the system. Egan also discusses the adequacy conditions’ relation to content. We want to emphasize that our discussion, above, remains agnostic on the role that causal or correspondence relations between brain states and the world plays in determining representational content.
In Marr’s terminology these adequacy conditions are “natural constraints” (Marr 1982).
In his recent writings Piccinini has mentioned the possibility of “adequate” non-mechanistic computational explanations. For instance, see Boone and Piccinini, under evaluation.
It is not clear, whether all neurophysiological or neuromolecular explanations are descriptions of mechanisms. Moreover, it is not clear, whether all algorithmic neurocognitive explanations are mechanistic. For instance, Chater and colleagues have raised the possibility that there are some universal, law-like principles of cognition, such as the “principle of simplicity”, “universal law of generalization” or the “principle of scale-variance” (Chater and Brown 2008; Chater and Vitanyi 2003). Chater and colleagues argue the mechanistic models of these phenomena may actually be derived from these general principles, and explanations that appeal to these general principles provide “deeper” explanations than the mechanistic explanations (Chater and Brown 2008).
Of course in the longer run the pursuit of computational neuroscience—considered as a research enterprise—cannot remain “autonomous” from the rest of science even in this weak sense. What we are discussing here is formulation of individual computational hypotheses.
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Acknowledgments
We thank the anonymous referees of this paper for their incisive and fruitful comments. Moreover, we wish to thank Petri Ylikoski and Oron Shagrir for discussions and commenting on earlier drafts of this paper.
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Anna-Mari Rusanen and Otto Lappi have contributed equally to this work.
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Rusanen, AM., Lappi, O. On computational explanations. Synthese 193, 3931–3949 (2016). https://doi.org/10.1007/s11229-016-1101-5
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DOI: https://doi.org/10.1007/s11229-016-1101-5