  This last point applies to Dynamic Systems Theory approaches to cognition as well (Thelen &amp; Smith 1994; Port &amp; van Gelder 1995). Dynamicists hold that human behavior should be explained in terms of sets of differential equations that represent a subject’s trajectory in real time through a space of possible total cognitive-behavioral states. Because they, like anti-Classical Connectionists, reject the Classical paradigm’s commitment to symbol manipulation and computation, they also avoid the Nativist consequences of that view. But neither Connectionists nor Dynamicists are in principle anti-Nativist. However we model an organisms cognitive processes—as executing a Classical Von Neumann style program, as reassigning weights to nodes in line with a Connectionist back-propagation algorithm, or as moving through a Dynamicist state space as described by a set of differential equations—the question remains: what are the built in initial biases of the system and what role do they play in determining the steady state. One can construct a Connectionist system that is antecedently tuned to converge on a specific steady-state, and as such will have a significant Nativist element (Hummel &amp; Biederman 1992 presents such a system for shape recognition). The same seems true of Dynamic Systems models. The oft-used Dynamicist example of a pendulum is ‘innately specified’ to reach a specific steady state (its point attractor) despite wide variability in its inputs. If very young children do indeed distinguish helpers from hinderers, for example, then this capacity will need to figure in the Dynamicist model. It will be appropriate to then ask about the role that the child’s initial structure or configuration played in its coming to have this capacity.