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

stronger, or more intense, or faster behavior.” Researchers evaluate the Rescorla-
Wagner model (Miller, Barnet, & Grahame, 1995; Walkenbach & Haddad, 1980) by
agreeing that associations will eventually lead to behaviour, without actually stating
how this is done. In the Rescorla-Wagner model, learning comes first and behaviour
comes later—maybe.
Using perceptrons to study classical conditioning paradigms contributes to the
psychological understanding of such learning in three ways. First, at the computational
level, it demonstrates equivalences between independent work on learning
conducted in computer science, electrical engineering, and psychology (Dawson,
2008; Gluck & Bower, 1988; Sutton & Barto, 1981).
Second, the results of training perceptrons in these paradigms raise issues that
lead to a more sophisticated understanding of learning theories. For instance, the
perceptron paradox led to the realization that when the Rescorla-Wagner model is
typically used, accounts of converting associations into behaviour are unspecified.
Recall that one of the advantages of computer simulation research is exposing tacit
assumptions (Lewandowsky, 1993).
Third, the activation functions that are a required property of a perceptron
serve as explicit theories of behaviour to be incorporated into the Rescorla-Wagner
model. More precisely, changes in activation function result in changes to how the
perceptron responds to stimuli, indicating the importance of choosing a particular
architecture (Dawson & Spetch, 2005). The wide variety of activation functions that
are available for artificial neural networks (Duch & Jankowski, 1999) offers a great
opportunity to explore how changing theories of behaviour—or altering architectures—affect
the nature of associative learning.
The preceding paragraphs have shown how the perceptron can be used to
inform theories of a very old psychological phenomenon, classical conditioning.
We now consider how perceptrons can play a role in exploring a more modern
topic, reorientation, which was described from a classical perspective in Chapter 3
(Section 3.12).
4.16 Connectionist Reorientation
In the reorientation task, an agent learns that a particular place—usually a corner
of a rectangular arena—is a goal location. The agent is then removed from the
arena, disoriented, and returned to an arena. Its task is to use the available cues
to relocate the goal. Theories of reorientation assume that there are two types of
cues available for reorienting: local feature cues and relational geometric cues.
Studies indicate that both types of cues are used for reorienting, even in cases
where geometric cues are irrelevant (Cheng & Newcombe, 2005). As a result, some
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