Abstract
It has been argued that only those models that describe the actual mechanisms responsible for a given cognitive capacity are genuinely explanatory. On this account, descriptive accuracy is necessary for explanatory power. This means that mechanistic models, which include reference to the components of the actual mechanism responsible for a given capacity, are explanatorily superior to functional models, which decompose a capacity into a number of sub-capacities without specifying the actual realizers. I argue against this view by considering models in engineering contexts. Here, other considerations besides descriptive accuracy play a role. Often, the goal of performance trumps that of accuracy, and researchers are interested in how cognitive capacities as such can be realized, rather than how it is realized in a given system.
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Notes
- 1.
Throughout this paper, the term ‘model’ is used in a loose sense, to encompass any schema that mimics a certain pattern of behaviour that constitutes the explanandum. Of course, not all such models are scientifically or even philosophically interesting. However, in what follows, some specific types of models that are of interest will be considered in more detail.
- 2.
Of course, this is not to say that models cannot be causal in themselves, or that we cannot model causes. Rather, the difference is that the explanation of an event, occurrence or state of affairs typically refers to the cause of that event, occurrence or state of affairs, while the explanation of a capacity refers to a model, which may include descriptions or simulations of causes, but not the actual cause responsible for the capacity. In the former case, the explanans is located in reality, in the latter, it is a description or simulation of the cause, not the cause itself that does the explaining.
- 3.
This is not to say that one cannot ask how-questions about events, or why-questions about capacities (evolutionary explanations of biological traits provide examples of the latter strategy). The point is simply that in the cognitive sciences, explaining how a capacity comes about by constructing a model is simply a very prominent research strategy, which makes it philosophically interesting.
- 4.
See for example Machamer et al., who write that a mechanistic explanation typically starts by providing a mechanism sketch, which is “…an abstraction for which bottom out entities and activities cannot (yet) be supplied or which contains gaps in its stages. The productive continuity from one stage to the next has missing pieces, black boxes, which we do not yet know how to fill in” (Machamer et al. 2000, p. 18).
- 5.
Another way to put the difference is that mechanistic explanations, besides decomposition, also involve localization, where the latter notion is understood as the identification of activities with parts (Bechtel and Richardson 1993).
- 6.
Note that this question does not fall into the category of Craver’s how-possibly questions (Craver 2006). For Craver, how-possibly questions are loose inquiries that are made in the early stages of an investigation, in which a lot of data is still missing: they are attempts to put some initial constraints on the explanandum, prior to constructing a more informed (how-plausibly), and ultimately ideally complete description (how-actually). Nevertheless, how-possibly questions in Craver’s sense are still asked with respect to a capacity as it is performed by some system. The question under consideration differs because it is asked about a capacity as such, regardless of any particular realization.
- 7.
Also, think of animal testing: here we continue to drop constraints until the capacity is described in such a way as to apply across species. Again, S can be any system, natural or artificial.
- 8.
Examples of such constraints are: the materials available, convenience of use and time considerations (we want the calculator to perform calculations rapidly—within a timeframe that is of use to us, that is).
- 9.
As the debate currently stands though, connectionist networks are considered to be highly idealized models too—but still more plausible than classic computationalist architectures.
- 10.
And in fact, with the example of face recognition systems we considered earlier, this is beginning to happen right now; see the results from the 2006 Face Recognition Vendor Test (available for download at: http://www.frvt.org/).
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Acknowledgements
The research for this paper was supported by the Research Fund Flanders (FWO) through project nr. G.0031.09.
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Gervais, R. (2014). Explaining Capacities: Assessing the Explanatory Power of Models in the Cognitive Sciences. In: Weber, E., Wouters, D., Meheus, J. (eds) Logic, Reasoning, and Rationality. Logic, Argumentation & Reasoning, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9011-6_3
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