Understanding each other: Defining a conceptual space for cognitive modeling

Understanding each other: Defining a conceptual space for cognitive modeling Robert West (robert_west@carleton.ca) & David Pierre Leibovitz (dpleibovitz@ieee.org) Institute of Cognitive Science, 2201 DT, Carleton University 1125 Colonel By Drive, Ottawa, Ontario, K1S 5B6 Canada Abstract Cognitive modeling is a complex endeavor so it is not surprising that the goals and intentions of modelers are often misunderstood, even by other modelers. To try to clarify this we have attempted to map out the various philosophical and theoretical commitments that one makes when creating a cognitive model or architecture. The goal of this is to avoid misunderstandings between the adherents of different modeling systems and between cognitive modelers and the rest of the scientific community. Keywords: cognitive modeling. Introduction In the 1990s there was movement to contrast mainstream cognitive modeling, which was labeled as cognitivist, with alternative approaches which were asserted to represent a fundamentally different paradigm. These alternatives included situated cognition, distributed cognition, dynamicism, embodied cognition and subsumption architectures. However, Vera and Simon (1993) argued that these theories represented progress and innovation but not an alternative approach. They did this by arguing that critics of the mainstream view had mistakenly assumed that the practices, strategies, and short cuts of mainstream modelers represented their actual philosophical and theoretical commitments. We argue that Vera & Simon’s argument was a legitimate response and more generally that philosophical and theoretical commitments cannot be determined solely by analyzing systems and practices associated with a method of modeling. Full understanding requires explicating the philosophical and theoretical commitments of the modeler. Today all of the alternatives to mainstream modeling discussed in Vera and Simon (1993) are accepted in the main stream. That is to say, they are broadly recognized as important contributions. At this time it would be fair to say that there is no widely accepted, main stream approach to modeling. However, despite efforts to understand different modeling systems as alternative approaches, each with their own strengths (e.g., McClelland, 2009) there are still numerous attempts to vilify modeling systems by critics who do not fully understand the goals and intentions of the system creators and users. In our view, the idea that a modeling system can be dismissed based on a principle or a philosophical argument is, in fact, a philosophical mistake. We argue that pigeon holing modeling systems according to broad philosophical and theoretical distinctions (e.g., cognitivist vs. anti-cognitivist, computational vs. non- computational, representational vs. anti-representational, etc.) is misleading and counterproductive. In place of this, we advocate a multidimensional approach to characterize modeling systems along numerous dimensions, including the beliefs and motivations of the modeler. Thus in our system, two modelers can use the same computational code but actually have very little in common. Likewise two modelers could use very different codes (e.g., the ACT-R symbolic/subsymbolic code, J. R. Anderson & Lebiere, 1998; and the NENGO spiking neuron code, Eliasmith & Anderson, 2003) and still be completely on the same page. To illustrate this approach we will use the controversial example of the symbol throughout the paper, although each dimension can be applied to any modeling construct. Due to limited space we have focused on dimensions that we believe are important. AI and Useful fictions The strong AI hypothesis says that if the functions of the human mind can be correctly simulated on a computer then there will be no difference between the human mind and the computer mind. It is important to note that this hypothesis is silent about the level of abstraction or embodiment required for success. It could involve high level algorithms realized as software, or it could involve a brain made of highly realistic mechanical neurons embodied in a lifelike humanoid robot and raised as a human infant. Therefore, if we apply strong AI to symbols it means that the symbols in a model are a valid way of representing what is taking place in the brain. Alternatively, symbols can be viewed as useful fictions. That is, the brain does not process symbols but there is something about the processing of symbols that is analogous to how a brain works and it is therefore useful or expedient to model it in this way. Metaphysics In mainstream western philosophy there are substances and processes that act on the substances. For example, logical formalisms can be used to act on symbolic representations about the state of the world. We will refer to this as substance philosophy. In process philosophy (see Bickhard, 2010; Whitehead & Griffin, 1931) there are no modular substances, only interacting processes. What appear to be substances are temporary emergent properties of ongoing processes. According to Quantum Physics, process philosophy is true for physical objects. For example, the chair across the room is a temporary quantum process not an independent object. Likewise, when you close your eyes and