This paper aims to offer a new view of the role of connectionist models in
the study of human cognition through the conceptualization of the history of
connectionism - from the simplest perceptrons to convolutional neural nets
based on deep learning techniques, as well as through the interpretation of
criticism coming from symbolic cognitive science. Namely, the connectionist
approach in cognitive science was the target of sharp criticism from the
symbolists, which on several occasions caused its marginalization and almost
complete abandonment of its assumptions in the study of cognition. Criticisms
have mostly pointed to its explanatory inadequacy as a theory of cognition or
to its biological implausibility as a theory of implementation, and critics
often focused on specific shortcomings of some connectionist models and
argued that they apply on connectionism in general. In this paper we want to
show that both types of critique are based on the assumption that the only
valid explanations in cognitive science are instances of homuncular
functionalism and that by removing this assumption and by adopting an
alternative methodology - exploratory mechanistic strategy, we can reject
most objections to connectionism as irrelevant, explain the progress of
connectionist models despite their shortcomings and sketch the trajectory of
their future development. By adopting mechanistic explanations and by
criticizing functionalism, we will reject the objections of explanatory
inadequacy, by characterizing connectionist models as generic rather than
concrete mechanisms, we will reject the objections of biological
implausibility, and by attributing the exploratory character to connectionist
models we will show that practice of generalizing current to general failures
of connectionism is unjustified.
Analytical thinking as a problem of the connectionist approach in cognitive science
