Models of Cognition: Neurological Possiblity Does Not Indicate Neurological Plausibility
Models of Cognition: Neurological possibility does not indicate neurological plausibility. Peter R. Krebs (peterk@cse.unsw.edu.au) Cognitive Science Program School of History & Philosophy of Science The University of New South Wales Sydney, NSW 2052, Australia Abstract level cognitive functions like the detection of syntactic and semantic features for words (Elman, 1990, 1993), learning the past tense of English verbs (Rumelhart and McClelland, 1996), or cognitive development (McLeod et al., 1998; Schultz, 2003). SRNs have even been suggested as a suitable platform “toward a cognitive neurobiology of the moral virtues” (Churchland, 1998). While some of the models go back a decade or more, there is still great interest in some of these ‘classics’, and similar models are still being developed, e.g. Rogers and McClelland (2004). I argue that many models in this class explain little at the neurological level about the theories they are designed to support, however I do not intend to offer a critique of connectionism following Fodor and Pylyshyn (1988). Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as ma- jor contributors in the quest for understanding the hu- man mind. Computational models serve as objects of experimentation, and results from these virtual experi- ments are tacitly included in the framework of empiri- cal science. Cognitive functions, like learning to speak, or discovering syntactical structures in language, have been modeled and these models are the basis for many claims about human cognitive capacities. Artificial neu- ral nets (ANNs) have had some successes in the field of Artificial Intelligence, but the results from experiments with simple ANNs may have little value in explaining cognitive functions. The problem seems to be in re- lating cognitive concepts that belong in the ‘top-down’ approach to models grounded in the ‘bottom-up’ con- nectionist methodology. Merging the two fundamentally different paradigms within a single model can obfuscate what is really modeled. When the tools (simple artifi- cial neural networks) to solve the problems (explaining aspects of higher cognitive functions) are mismatched, models with little value in terms of explaining functions of the human mind are produced. The ability to learn functions from data-points makes ANNs very attractive analytical tools. These tools can be developed into valu- able models, if the data is adequate and a meaningful interpretation of the data is possible. The problem is, that with appropriate data and labels that fit the desired level of description, almost any function can be modeled. It is my argument that small networks offer a univer- sal framework for modeling any conceivable cognitive theory, so that neurological possibility can be demon- strated easily with relatively simple models. However, a model demonstrating the possibility of implementation of a cognitive function using a distributed methodol- ogy, does not necessarily add support to any claims or assumptions that the cognitive function in question, is neurologically plausible. Input nodes Hidden nodes Output nodes Figure 1. Feed forward network architecture Introduction Several classes of computational model and simulation (CMS) used in Cognitive Science share common ap- proaches and methods. One of these classes involves artificial neural nets (ANNs) with small numbers of nodes, particularly feed forward networks (Fig. 1) and simple recurrent networks (SRNs) 1 (Fig. 2). Both of these architectures have been employed to model high SRNs have a set of nodes that feed some or all of the previous states of the hidden nodes back. The nodes are often described as context nodes. They provide a kind of Instead, this paper concerns models where ANNs act merely as mathematical, or analytical, tools. The fact that mathematical functions can be extracted from a given set of data, and that these functions can be success- fully approximated by an ANN (neurological possibility), does not provide any evidence that these functions are capable of being realized in similar fashion inside human brains (neurological plausibility). Bridging the Paradigms Theories in Cognitive Science fall generally into two dis- tinct categories. Some theories are offered as explana- tions of aspects of human cognition in terms of what ‘short term memory’ that becomes part of the input in the next step of the simulation.