Mind, Body, World- Foundations of Cognitive Science, 2013a

in contiguity with the UR, the CS becomes capable of eliciting the UR on its own.
When this occurs, the UR is then known as the conditioned response (CR).
Classical conditioning is a very basic kind of learning, but experiments revealed
that the mechanisms underlying it were more complex than the simple law of contiguity.
For example, one phenomenon found in classical conditioning is blocking
(Kamin, 1968). Blocking involves two conditioned stimuli, CS A
and CS B
. Either
stimulus is capable of being conditioned to produce the CR. However, if training
begins with a phase in which only CS A
is paired with the US and is then followed by
a phase in which both CS A
and CS B
are paired with the US, then CS B
fails to produce
the CR. The prior conditioning involving CS A
blocks the conditioning of CS B
, even
though in the second phase of training CS B
is contiguous with the UR.
The explanation of phenomena such as blocking required a new model of associative
learning. Such a model was proposed in the early 1970s by Robert Rescorla
and Allen Wagner (Rescorla & Wagner, 1972). This mathematical model of learning
has been described as being cognitive, because it defines associative learning in
terms of expectation. Its basic idea is that a CS is a signal about the likelihood that
a US will soon occur. Thus the CS sets up expectations of future events. If these
expectations are met, then no learning will occur. However, if these expectations
are not met, then associations between stimuli and responses will be modified.
“Certain expectations are built up about the events following a stimulus complex;
expectations initiated by that complex and its component stimuli are then only
modified when consequent events disagree with the composite expectation” (p. 75).
The expectation-driven learning that was formalized in the Rescorla-Wagner
model explained phenomena such as blocking. In the second phase of learning in the
blocking paradigm, the coming US was already signaled by CS A
. Because there was
no surprise, no conditioning of CS B
occurred. The Rescorla-Wagner model has had
many other successes; though it is far from perfect (Miller, Barnet, & Grahame, 1995;
Walkenbach & Haddad, 1980), it remains an extremely influential, if not the most
influential, mathematical model of learning.
The Rescorla-Wagner proposal that learning depends on the amount of surprise
parallels the notion in supervised training of networks that learning depends
on the amount of error. What is the relationship between Rescorla-Wagner learning
and perceptron learning?
Proofs of the equivalence between the mathematics of Rescorla-Wagner
learning and the mathematics of perceptron learning have a long history. Early
proofs demonstrated that one learning rule could be translated into the other
(Gluck & Bower, 1988; Sutton & Barto, 1981). However, these proofs assumed that
the networks had linear activation functions. Recently, it has been proven that if
when it is more properly assumed that networks employ a nonlinear activation
Elements of Connectionist Cognitive Science 191