By David E. Meyer (ed.), Sylvan Kornblum (ed.)

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X- K- . W . -G-- . M-- . B- . . O *D­ N- . - 0--- 6- . . - 3. . c- -p Z -- . Y- . - J . --7-- . . Q-- . - 4 . . :2 • • - -- - - 8--. 9-� 1 . - -- -- . 19 Re p resentations developed by a similarity-judging n etwork for Morse code signals, with the two features plotted on the two axes and showing the locations generalized to for five previously unseen codes (starred). logistic activation functions used), the features came out directly from the two hidden lU1its without the need for rotation.

Ungerleider. L. • 498. and Mishkin. M. (1982). Two cortical visual systems. In D. Goodale. and R. J. W. ). Analysis of visual behavior. MIT Press. 35 Visual Information Processing Copyrighted Material 549-586. J. Ingle. M. A. Cambridge. MA: 2 Visual Information Processing: A Perspective Michael H. Van Kleeck and Stephen M. Kosslyn The study of visual infonnation processing has produced remarkable insights over the past twenty-five years. Researchers have discovered a host of phe­ nomena that seem to reflect basic principles of vision and the use of visual infonnation in cognition.

We trained the network on these two facts about this new creature as we did for the sparrow, by sending error signals back only from the bird and fly output units and changing only the weights on the connections from the new emu input unit to the representation hidden units. We found that the network discovered ostrich. Therefore ostrich: it said that emu is large, has feathers, has wings, is-an animal, is-a living-thing, and so on. a representation for emu that was exactly the same as that for its responses to all queries were also the same as those for an Since the ostrich was the only example of a flightless bird that the network knew about, it simply assimilated emu to ostrich and thereby gave the ri ght answers to essentially every query about emus.

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