  Gow's (2012) dual lexicon model suggests that the primary purpose of words is
to mediate the mappings between acoustic-phonetic input and other forms of
linguistic representation. Motivated by evidence from functional imaging,
aphasia, and behavioral results, the model argues for the existence of two
parallel wordform stores: the dorsal and ventral processing streams. In this
paper, we tested the hypothesis that the complex, but systematic mapping
between sound and articulation in the dorsal stream poses different
computational pressures on feature sets than the more arbitrary mapping between
sound and meaning. To test this hypothesis, we created two deep convolutional
neural networks (CNNs). While the dorsal network was trained to identify
individual spoken words, the ventral network was trained to map them onto
semantic classes. We then extracted patterns of network activation from the
penultimate level of each network and tested how well features generated by the
network supported generalization to linguistic categorization associated with
the dorsal versus ventral processing streams. Our preliminary results showed
both models successfully learned their tasks. Secondary generalization testing
showed the ventral CNN outperformed the dorsal CNN on a semantic task:
concreteness classification, while the dorsal CNN outperformed the ventral CNN
on articulation tasks: classification by onset phoneme class and syllable
length. These results are consistent with the hypothesis that the divergent
processing demands of the ventral and dorsal processing streams impose
computational pressures for the development of multiple lexica.
