  We offer a general theoretical framework for brain and behavior that is
evolutionarily and computationally plausible. The brain in our abstract model
is a network of nodes and edges. Although it has some similarities to standard
neural network models, as we show, there are some significant differences. Both
nodes and edges in our network have weights and activation levels. They act as
probabilistic transducers that use a set of relatively simple rules to
determine how activation levels and weights are affected by input, generate
output, and affect each other. We show that these simple rules enable a
learning process that allows the network to represent increasingly complex
knowledge, and simultaneously to act as a computing device that facilitates
planning, decision-making, and the execution of behavior. By specifying the
innate (genetic) components of the network, we show how evolution could endow
the network with initial adaptive rules and goals that are then enriched
through learning. We demonstrate how the developing structure of the network
(which determines what the brain can do and how well) is critically affected by
the co-evolved coordination between the mechanisms affecting the distribution
of data input and those determining the learning parameters (used in the
programs run by nodes and edges). Finally, we consider how the model accounts
for various findings in the field of learning and decision making, how it can
address some challenging problems in mind and behavior, such as those related
to setting goals and self-control, and how it can help understand some
cognitive disorders.
