Foundations of Cognitive Science:

 Continuity, Dynamics, and Representation

 

Chris Eliasmith

University of Waterloo

Depts. of Philosophy and Systems Design Engineering

celiasmith@uwaterloo.ca

 

Since its inception, the theoretical terms of ‘continuity’, ‘dynamics’, and ‘representation’ have been central to work in cognitive science. The relation between these terms is an interesting one, with various combinations of commitment to them representing the major approaches to understanding cognitive systems. Consider, as three central exemplars, symbolicism, connectionism, and dynamicism. Symbolicism (i.e., ‘classical’ cognitive science) can be characterized as a rejection of the relevance of continuity, a dismissal or downplaying of dynamics, and a strong commitment to representation (Fodor and Pylyshyn 1988; Newell 1990). Connectionism generally entails an acceptance of the importance of all three (Smolensky 1988; Churchland 1995). Lastly, dynamicism consists of a strong emphasis on continuity and dynamics and argues from those, to a rejection of representation (Thelen and Smith 1994; van Gelder 1995).

In this paper, I describe a number of recent results from work in computational neuroscience that helps shed light on the nature of, and relation between, these three terms. I begin by presenting evidence that neural systems themselves can only be properly characterized as discrete systems (i.e. Turing machines). This blocks dynamicist arguments from the purported continuity of neural systems to anti-representationalism and anti-computationalism (van Gelder 1998), and connectionist arguments from continuity to special (i.e. non-Turing describable) computational properties of brains (Churchland 1995). Nevertheless, I argue that the kind of discreteness found in neural systems does not support the symbolicist view of cognitive systems either. In particular, it does not establish the centrality of symbolic psychological-level representation nor does it provide a defense against the strong dynamicist criticisms of symbolicist ‘atemporal’ (i.e. non-, or anti-dynamic) commitments.

By further examining these and related results from computational neuroscience, I present a positive view of neural function  which excludes continuity, but unifies dynamics and representation. On this view, which I call Cognitive Neural Control Theory (CNCT), representation is rigourously defined by encoding and decoding relations  which can hold at various levels of description. The variables identified at higher levels can be considered state variables in control theoretical descriptions of neural dynamics. I argue that, given the generality of control theory and representation so defined, this approach is sufficiently powerful to unify descriptions of cognitive systems from the neural to  the psychological levels. CNCT thus avoids the lack of a proper dynamical characterization of cognitive systems characteristic of symbolicism, and shows how, contrary to dynamicist arguments, representation and dynamics can be part of a consistent approach in cognitive science.

 

 

References

 

Churchland, P. (1995). The engine of reason, the seat of the soul: a philosophical journey into the brain. Cambridge, MA, MIT Press.

Fodor, J. and Z. Pylyshyn (1988). “Connectionism and cognitive architecture: A critical analysis.” Cognition 28: 3-71.

Newell, A. (1990). Unified theories of cognition. Cambridge, MA, Harvard University Press.

Smolensky, P. (1988). “On the proper treatment of connectionism.” Behavioral and Brain Sciences 11(1): 1-23.

Thelen, E. and L. B. Smith (1994). A dynamic systems approach to the development of cognition and action. Cambridge, MIT Press.

van Gelder, T. (1995). “What might cognition be, if not computation?” The Journal of Philosophy XCI(7): 345-381.

van Gelder, T. (1998). “The dynamical hypothesis in cognitive science.” Behavioral and Brain Sciences 21(5): 615-665.