@article {5685708,
	title = {Mechanistic and non-mechanistic varieties of dynamical models in cognitive science: explanatory power, understanding, and the {\textquoteright}mere description{\textquoteright} worry},
	journal = {Synthese},
	volume = {192},
	number = {1},
	year = {2014},
	pages = {43{\textendash}66},
	abstract = {<p>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.</p>},
	doi = {http://dx.doi.org/10.1007/s11229-014-0548-5},
	author = {Gervais, Raoul}
}
