Abstract
AbstractDynamical models of cognition have played a central role in recent cognitive science. In this paper, we consider a common strategy by which dynamical models describe their target systems neither as purely static nor as purely dynamic, but rather using a hybrid approach. This hybridity reveals how dynamical models involve representational choices that are important for understanding the relationship between dynamical and non-dynamical representations of a system.
Highlights
Timothy van Gelder’s seminal paper, “What might cognition be if not computation?” (van Gelder, 1995) was an important salvo in the debate between those who take the mind to be a digital computer and those seeking alternative characterizations
Dynamical modeling of cognitive processes has subsequently become a significant research area and has inspired philosophical developments both in accounts of cognition (Clark, 1998) and explanation (Bechtel, 1998; Zednik, 2011). We argue that these discussions have neglected a key aspect of dynamical modeling that is important for assessing dynamicist claims about cognition
We initially refer to representations employing this device as hybrid representations, though in section 3.3 we will characterize this phenomenon more precisely using the concept of a “quasistatic approximation” (Beer and Williams, 2015). We argue that this hybridity provides a lens through which to understand the ability of the modeled systems to perform tasks such as discrimination and learning, as it sheds light on the representational choices involved in designing and analyzing dynamical models to capture the systems’ behaviors
Summary
Timothy van Gelder’s seminal paper, “What might cognition be if not computation?” (van Gelder, 1995) was an important salvo in the debate between those who take the mind to be a digital computer and those seeking alternative characterizations. There has been considerable discussion in the philosophical literature about whether dynamicist cognitive science excludes representational accounts of mind (Bechtel, 1998) and whether it provides an alternative view of computation or is incompatible with computational theories of mind (e.g., Wheeler, 2005) Authors in this debate have been concerned with questions such as whether dynamical models are genuinely explanatory or merely descriptive of target systems, and if explanatory, whether they conform to patterns of mechanistic explanation, causal explanation or something else (see, e.g., Clark, 1998; Bechtel, 1998; Wheeler, 2005; Chemero and Silberstein, 2008; Wilkenfeld, 2014; Kaplan, 2015). A virtue of the present discussion is that it creates a potential bridge between work on dynamical systems in cognitive science and the more general study of how dynamical models function across the sciences
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