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Abstract:
Traditionally, cognitive neuroscientists have represented
long-term memory in terms of the structure of a neural network's
connections and short-term memory in terms of the patterns of
activation across the network (e.g., Hebb, 1949; Caianiello, 1961).
However, recent neural-network models of short-term verbal working
memory (VWM) have used modifiable structural connections to encode
item and order information (Hartley & Houghton, 1996; Burgess
& Hitch, 1999). In these models, words are stored by changing
the connection weights between linguistic units, and phenomena
related to VWM are modeled with long-term memory structures and
mechanisms. Although it may be possible for neural connections to
change rapidly (e.g., see Jensen et al., 1996), this latter
approach to modeling VWM does not appear to be motivated by
neurobiology. Consequently, we have formulated a neural-network
model of VWM that stores and maintains item and order information
as patterns of activation within a fixed-structure network whose
connections remain constant. Performance with this network is
affected by many of the standard factors that influence short-term
VWM, such as phonological similarity, articulatory duration, and
word frequency. Our work demonstrates that the implementation of
short-term memory based on patterns of network activation is
feasible, parsimonious, and merits more investigation. Furthermore,
from our work, it appears that both the neurobiology and psychology
of specific mental processes must be understood more fully before
neural networks are deemed "neurally plausible".
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