Models of Cognition: Neurological possibility does not indicate neurological plausibility.



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Models of Cognition:
Neurological possibility does not indicate neurological plausibility.

Peter R. Krebs ([email protected])
Cognitive Science Program

School of History & Philosophy of Science
The University of New South Wales
Sydney, NSW 2052, Australia

Abstract

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.

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

1 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
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).

Figure 1. Feed forward network architecture

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.

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