Russell Meyer

Enactivists frequently argue their account heralds a revolution in cognitive science: enactivism will unseat cognitivism as the dominant paradigm. We examine the lines of reasoning enactivists employ in stirring revolt, but show that none of these prove compelling reasons for cognitivism to be replaced by enactivism. First, we examine the hard sell of enactivism: enactivism reveals a critical explanatory gap at the heart of cognitivism. We show that enactivism does not meet the requirements to incite a paradigm shift in the Kuhnian sense—there is no internal crisis in cognitivism. Nor does it provide inherently better explanations of cognition as some have claimed. Second, we consider the soft sell of enactivism: enactivism provides a more attractive, parsimonious, or clear-eyed lens on cognition. This move proves to boil down to a misunderstanding of how theories are selected in science. Instead we lend support to a broader and more desirable way to conceive of enactivism, the recent proposal that enactivism is a philosophy of nature. We explain how a philosophy of nature does more than support a single research paradigm by integrating scientific questions into a cohesive picture.

Mechanistic explanations, according to one prominent account, are derived from objective explanations. Mechanistic standards of explanation are in turn pulled from nature, and are thereby insulated from the values of investigators, since explanation is an objectively defined achievement grounded in the causal structure of the world. This results in the closure of mechanism’s explanatory standards—it is insulated from the values, norms and goals of investigators. I raise two problems with this position. First, it relies on several ontological claims which, while plausible, fail to guarantee the objectivity of mechanistic explanatory standards to the degree of certainty required. Second, Craver’s position itself introduces a value–laden explanatory standard—the 3M requirement —which undermines the closure of explanatory standards. I show how in practice mechanistic explanation is in part guided by explanatory taste, shorthand for background contextual values that influence our standards of explanation. Mechanism often has a particular pragmatically-oriented taste for control, and gerrymanders explanatory standards in order to obtain it. I conclude by arguing that objectivity, rather than being obtained through the right set of explanatory standards, is better thought of as the result of processes of intersubjective criticism, which renders visible the contextual values of communities of investigators and allows them to be controlled for.

This article demonstrates that non-mechanistic, dynamical explanations are a viable approach to explanation in the special sciences. The claim that dynamical models can be explanatory without reference to mechanisms has previously been met with three lines of criticism from mechanists: the causal relevance concern, the genuine laws concern, and the charge of predictivism. I argue, however, that these mechanist criticisms fail to defeat non-mechanistic, dynamical explanation. Using the examples of Haken et al.’s model of bimanual coordination, and Thelen et al.’s dynamical field model of infant perseverative reaching, I show how each mechanist criticism fails once the standards of Woodward’s interventionist framework are applied to dynamical models. An even-handed application of Woodwardian interventionism reveals that dynamical models are capable of producing genuine explanations without appealing to underlying mechanistic details. 1Introduction2Interventionism and Mechanistic Explanation 2.1Causal relevance and ideal interventions2.2Invariance2.3Explanation3Covering-Laws and Dynamical Explanation 3.1Dynamical models3.2Covering-law explanation3.3Prediction4Causal Relevance 4.1The causal relevance concern4.2Intervening on dynamical models4.3Test case I: The Haken–Kelso–Bunz model4.4Test case II: Dynamical field model5Genuine Laws 5.1The genuine laws concern5.2Using invariance in place of laws5.3Test case I: The Haken–Kelso–Bunz model5.4Test case II: Dynamical field model6Prediction 6.1Predictivism6.2Crude and invariant prediction7Interventionist Criticism of the Haken–Kelso–Bunz Model8Dynamical Explanation8Conclusion

Mechanistic explanations are often said to explain because they reveal the causal structure of the world. Conversely, dynamical models supposedly lack explanatory power because they do not describe causal structure. The only way for dynamical models to produce causal explanations is via the 3M criterion: the model must be mapped onto a mechanism. This framing of the situation has become the received view around the viability of dynamical explanation. In this paper, I argue against this position and show that dynamical models can themselves reveal causal structure and consequently produce nonmechanistic, dynamical explanations. Taking the example of cell fates from systems biology, I show how dynamical models, and specifically the attractor landscapes they describe, identify the causes of cell differentiation and explain why cells select particular fates. These dynamical features of the system better fit Woodward’s (2010, 2018) criteria of specificity and proportionality and make them the best candidate causes of cell fates than mechanisms. I also show how these causes are irreducible and inaccessible to mechanistic models, making 3M unworkable and counterproductive in this case. Dynamical models can reveal dynamical causes and thereby provide causal explanations.

In line with proponents of 4E cognition, Gallagher [2019] is concerned that many cognitive phenomena are not amenable to decomposition strategies since their very nature is to be constituted extensively. By contrast the received view on causal explanation—the mechanistic account [Craver 2007]—emphasises the necessity for decomposition in explaining natural phenomena and insists on a sharp distinction between causal versus constitutive relations. I propose that removing the requirement that constitutive relations cannot also be causes helps to ease this tension between explanation and Gallagher’s non-decompositional stance, per Leuridan [2012]. Further, I draw on Froese et al.’s [2013] minimal model of social cognition to argue that constitutive relations can and do act as causal difference-makers in the Woodwardian sense [Woodward 2003].

This paper demonstrates that nonmechanistic, dynamical explanations are a viable approach to explanation in the special sciences. The claim that dynamical models can be explanatory without reference to mechanisms has previously been met with three lines of criticism from mechanists: the causal relevance concern, the genuine laws concern, and the charge of predictivism. I argue, however, that these mechanist criticisms fail to defeat nonmechanistic, dynamical explanation. Using the examples of Haken et al.’s ([1985]) HKB model of bimanual coordination, and Thelen et al.’s ([2001]) dynamical field model of infant perseverative reaching, I show how each mechanist criticism fails once the standards of Woodward’s ([2003]) interventionist framework are applied to dynamical models. An even-handed application of Woodwardian interventionism reveals that dynamical models are capable of producing genuine explanations without appealing to underlying mechanistic details.

In this paper, we offer reasons to justify the explanatory credentials of dynamical modeling in the context of the metaplasticity thesis, located within a larger grouping of views known as 4E Cognition. Our focus is on showing that dynamicism is consistent with interventionism, and therefore with a difference-making account at the scale of system topologies that makes sui generis explanatory differences to the overall behavior of a cognitive system. In so doing, we provide a general overview of the interventionist approach. We then argue that recent mechanistic attempts at reducing dynamical modeling to a merely descriptive enterprise fail given that the explanatory standard in dynamical modeling can be shown to rest on interventionism. We conclude that dynamical modeling captures features of nested and developmentally plastic cognitive systems that cannot be explained by appeal to underlying mechanisms alone.