Abstract
In the literature on dynamical models in cognitive science, two issues have recently caused controversy. First, what is the relation between dynamical and mechanistic models? I will argue that dynamical models can be upgraded to be mechanistic as well, and that there are mechanistic and non-mechanistic dynamical models. Second, there is the issue of explanatory power. Since it is uncontested the mechanistic models can explain, I will focus on the non-mechanistic variety of dynamical models. It is often claimed by proponents of mechanistic explanations that such models do not really explain cognitive phenomena (the ‘mere description’ worry). I will argue against this view. Although I agree that the three arguments usually offered to vindicate the explanatory power of non-mechanistic dynamical models (predictive power, counterfactual support, and unification) are not enough, I consider a fourth argument, namely that such models provide understanding. The Voss strong anticipation model is used to illustrate this.
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Notes
Of course, the question here is whether dynamical models are necessarily non-explanatory, not whether there are examples of dynamical models that happen to be non-explanatory—even those who answer the former question negatively can admit that there might be bad instances of dynamical models.
In the words of Zednik: “As long as dynamical explanation is viewed as a form of covering-law explanation [...] the mere description worry looms” (2011, p. 246). Although, as we will see, dynamical models do have CL-type properties, ultimately, they fall far short of Hempel and Oppenheim’s (1948, p. 137) strict requirements. In particular, the generalities employed do not meet the traditional requirements for lawhood. Therefore, I will stick to the term ‘predictivism’.
Although it is not denied that such non-explanatory dynamical models may have other virtues (Kaplan and Bechtel 2011, p. 443).
It may be that these positions apply to other domains, but the focus here is on dynamical and mechanistic models in cognitive science.
Here, the worry may arise that it is possible for all dynamical models to be upgraded in a way that makes them mechanistic, so that the distinction between mechanistic and non-mechanistic models collapses entirely. I address this worry at the end of Sect. 5.
It should be noted however, that Zednik himself would probably not agree with this reading, as he says that the examples of mechanistic dynamical models he considers “resemble mechanistic explanations rather than covering-law explanations” (2011, p. 245, my emphasis). As he also thinks that Kelso’s dynamical model of bimanual coordination is a predictivist (or in his terms, a CL) explanation (Ibid, p. 244), it seems that Zednik subscribes to position \(E3\) as defined above. However, in discussing Walmsley’s (2008) analysis of Thelen et al.’s (2001) dynamical field theory of infant perseverative reaching (a theory I shall briefly consider later in this article), he does admit that it can be used to “derive, by way of deductive inferences, predictions about goal-directed reaching in actual and counterfactual circumstances” (Zednik 2011, p. 250), although he claims that emphasizing this particular feature of the model over its mechanistic features is misleading (Ibid, p. 250). In my view however, regardless of what feature one chooses to emphasize, to vindicate the non-exclusivity thesis (and thus show that either position NE1, NE2 or NE3 must be correct) it is enough that both are present. The difference can be brought out clearly when we consider carefully what Zednik says of his examples: “In each case, the explanation proceeds by identifying the component parts and operations of a mechanism and by showing how the organized activity of these parts and operations produces the phenomenon being explained. Therefore, Thelen et al. and Beer each provide a counterexample to the received view of dynamical explanation: some dynamical explanations are mechanistic explanations rather than covering-law explanations” (Ibid, p. 255, my emphasis). As I endorse position NE1, on my account, Zednik’s conclusion does not follow.
Like in the case of dynamical systems, the various accounts offered in the literature do vary somewhat in their details. To give just two examples, Bechtel and Abrahamsen (2005) define a mechanism as a “structure performing a function in virtue of its component parts, component operations, and their organization. The orchestrated functioning of the mechanism is responsible for one or more phenomena.” (p. 423). According to Craver (2007), a mechanism is “a set of entities and activities organized such that they exhibit the phenomenon to be explained (p. 5). Despite the differences, all these accounts include the ingredients of entities/component parts, activities/components operations, organization, and overall behavior/phenomenon. More importantly for our present purposes however, all view mechanisms as complex systems.
Van Gelder also offers another argument (1998, p. 625), namely that dynamical systems theory is also used to study systems in many other domains besides cognitive science, so that rejecting dynamical models of cognitive systems as non-explanatory would have the consequence of rendering all these models non-explanatory too. I will not consider this argument in detail, but as Walmsley (2008, p. 337) rightly points out, being a reductio, it offers no help against someone willing to deny the explanatory power of dynamical models in general.
It needs to be said though, that this ontic conception of explanation has not gone unchallenged, even within the community of philosophers who concern themselves with mechanistic explanation. In particular Bechtel (2008), along with Wright (2012) has rejected such an ontic reading in favor of an epistemic one (see Illari 2013 for an reconciliatory overview of the arguments on both sides). Nevertheless, even on the epistemic reading, the objections still stand, and ‘understanding’ requires some measure of success in understanding the world, rather than simply a feeling of satisfaction (Waskan 2011).
In claiming that these skills are necessary, I move somewhat beyond de Regt, who uses the term ‘crucial’ instead (2009, p. 588).
As this example shows, CIT is not just a weaker form of predictivism. Although it is concerned with recognizing consequences of a theory or model, the focus is on the ability to use the theory or model, not on a Hempelian notion of expectability (that the explanandum ‘was to be expected’).
I use the term ‘correct’ instead of ‘true’ to allow for models containing idealizations (and hence are literally false) to be explanatory (cf. Strevens 2013).
One might object that even in the hypothetical case, the understanding is only provided by the reference to mechanistic details, not by the regularities—I shall consider this objection at the end of Sect. 5.
Stepp et al. introduce these features specifically as challenges to representational accounts of circadian rhythms (2011, p. 428), but that need not concern us here.
See De Regt 2013 for various views pro and contra.
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Gervais, R. Mechanistic and non-mechanistic varieties of dynamical models in cognitive science: explanatory power, understanding, and the ‘mere description’ worry. Synthese 192, 43–66 (2015). https://doi.org/10.1007/s11229-014-0548-5
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DOI: https://doi.org/10.1007/s11229-014-0548-5