The Web in the Spider: Associative Learning Theory

This chapter provides an overview of basic learning mechanisms proposed within associationist learning theory: error-driven learning, Hebbian learning, and chunking. It takes the complementary learning systems perspective, which is contrasted with a Bayesian perspective in which the learner is an ‘ideal observer’. The discussion focuses on two issues. First, what is a learning mechanism? It is argued that two brain areas implement two different learning mechanisms if they would learn different things from the same input. The available data from neuroscience suggests that the brain contains multiple learning mechanisms in this sense but each learning mechanism is domain-general in applying to many different types of input. Second, what are the sources of bias that influence what a learner acquires from a certain experience? Bayesian theorists have distinguished between inductive bias implemented in prior beliefs and channel bias implemented in the translation from input to intake and output to behaviour. Given the intake and prior beliefs, belief updating in Bayesian models is unbiased, following Bayes Theorem. However, biased belief updating may be another source of bias in biological learning mechanisms.