Document Type



Connectionist networks may provide useful models of
stimulus equivalence and transfer of function phenomena. Such
models have been applied to a range of behavioral tasks and have
demonstrated transfers of function via equivalence relations
following appropriate training, with networks accurately simulating
the behavior of human subjects. In the current study, a
connectionist network was pretrained on a series of equivalence
and sequence tasks to simulate the preexperimental experience of
an adult subject. It was then exposed to the equivalent of six
conditional discriminations, and was tested for the formation of
three 3-member equivalence classes (corresponding to A 1-A2-A3,
B1-B2-B3, C1-C2-C3). It was subsequently trained to produce a
pair of four part sequences (corresponding to B1 -+B2-+Ct1 -+B3
and B3-+B2-+Ct2-+B1 , where Ct1 and Ct2 represented contextual
cues) before being tested for transfer, through equivalence, of the
sequence responses to the C stimuli. Following appropriate
pretraining, the network showed the formation of three
equivalence classes and a transfer of sequence function to the
nontrained C stimuli (producing the novel sequences C1 -+C2-.
Ct1 -+C3 and C3-+C2-+Ct2-+C1). A control network, which was
not exposed to conditional discrimination training, failed to
demonstrate equivalence and the transfer of sequence function,
as predicted by findings from experimental demonstrations with
human participants. Network performance was analyzed as a
function of amount of pretraining and a number of psychologically
plausible training methods are presented. The data suggest that
connectionist networks may provide accurate and plausible
models of stimulus equivalence and transfer of function
phenomena in natural language.