Evolutionary Connectionism and Mind/Brain Modularity

Brain/mind modularity is a contentious issue in cognitive science. Cognitivists tend to conceive of the mind as a set of distinct specialized modules and they believe that this rich modularity is basically innate. Cognitivist modules are theoretical entities which are postulated in “boxes-and-arrows” models used to explain behavioral data. On the other hand, connectionists tend to think that the mind is a more homogeneous system that basically genetically inherits only a general capacity to learn from experience and that if there are modules they are the result of development and learning rather than being innate. In this chapter we argue for a form of connectionism which is not anti-modularist or antiinnatist. Connectionist modules are anatomically separated and/or functionally specialized parts of a neural network and they may be the result of a process of evolution in a population of neural networks. The new approach, Evolutionary Connectionism, does not only allow us to simulate how genetically inherited information can spontaneously emerge in populations of neural networks, instead of being arbitrarily hardwired in the neural networks by the researcher, but it makes it possible to explore all sorts of interactions between evolution at the population level and learning at the level of the individual that determine the actual phenotype. Evolutionary Connectionism shares the main goal of Evolutionary Psychology, that is, to develop a psychology informed by the importance of evolutionary process in shaping the inherited architecture of human mind, but differs from Evolutionary Psychology for three main reasons: (1) it uses neural networks rather than cognitive models for interpreting human behavior; (2) it adopts computer simulations for testing evolutionary scenarios; (3) it has a less pan-adaptivistic view of evolution and it is more interested in the rich interplay between genetically inherited and experiential information. We present two examples of evolutionary connectionist simulations that show how modular architectures can emerge in evolving populations of neural networks.

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