Albert, M.S., & Lafleche, G. (1991).  Neuroimaging in Alzheimer's disease.
    The Psychiatric Clinics of North America, 14, 443-459.
Anderson, J.A., Silverstein, J.W., Ritz, S.A., & Jones, R.S. (1977).
    Distinctive features, categorical perception, and probability learning:
    Some applications of a neural model.  Psychological Review, 84, 413-451
Ballard, D. (1986).  Cortical structures and parallel processing: Structure
    and function.  Behavioural and Brain Sciences, 9, 67-120.
Barlow, H.B. (1972).  Single units and sensation:  A neuron doctrine for
    perceptual psychology.  Perception, 1, 371-394.
Bechtel, W., & Abrahamsen, A. (1991).  Connectionism and the mind.  Cambridge,
    MA:  Basil Blackwell.
Berkeley, I.S.N., Dawson, M.R.W., Medler, D.A., & Schopflocher, D.P. (1995).
    Density plots of hidden value units reveal interpretable bands.  Neural
    Computation, in press.
Chambers, J.M., Cleveland, W.S., Kleiner, B., & Tukey, P.A. (1983).  Graphical
    methods for data analysis.  Belmont, CA:  Wadsworth International Group.
Dawson, M.R.W. (1987).  Moving contexts do affect the perceived direction of
    apparent motion in motion competition displays.  Vision Research, 27,
Dawson, M.R.W. (1988).  The cooperative application of multiple natural
   constraints to the motion correspondence problem.  In R. Goebel (Ed.)
   Proceedings of the Seventh Canadian Conference On Artificial Intelligence.
   Edmonton, AB: University of Alberta Printing Services.
Dawson, M.R.W. (1989).  Constraining tag-assignment from above and below.
   Behavioral and Brain Sciences, 12, 400-402.
Dawson, M.R.W. (1990a).  Apparent motion and element connectedness.  Spatial
   Vision, 4, 241-251.
Dawson, M.R.W. (1990b).  Training networks of value units:  Learning in PDP
   systems with nonmonotonic activation functions.  Canadian Psychology, 31(4),
   391. (Invited abstract.)
Dawson, M.R.W. (1991).  The how and why of what went where in apparent
   motion: Modeling solutions to the motion correspondence problem.
   Psychological Review, 98, 569-603.
Dawson, M.R.W., & Berkeley, I. (1993).  Making a middling mousetrap.
   Behavioural and Brain Sciences,16, 454-455.
Dawson, M.R.W., Dobbs, A., Hooper, H.R., McEwan, A.J.B., Triscott, J., &
   Cooney, J. (1994).  Artificial neural networks that use SPECT to identify
   patients with probably Alzheimers disease.  European Journal of Nuclear
   Medicine, 21, 1303-1311.
Dawson, M.R.W., Kremer, S., & Gannon, T. (1994).  Identifying the trigger
   features for PDP models of the early visual pathway.  In r. Elio (Ed.)
   Proceedings of the Tenth Canadian Conference On Artificial Intelligence.
   Palo Alto, CA: Morgan Kaufman (refereed).
Dawson, M.R.W., Nevin-Meadows, N., & Wright, R.D. (1994).  Polarity matching
   in the Ternus configuration.  Vision Research, 34,3347-3359.
Dawson, M.R.W., & Pylyshyn, Z. (1988).  Natural constraints on apparent
   motion.  In Z.W. Pylyshyn (Ed.) Computational processes in human vision.
   Norwood, NJ: Ablex.
Dawson, M.R.W., & Wright, R.D. (1989).  The consistency of element motion
   affects the visibility but not the direction of apparent movement.  Spatial
   Vision, 4, 17-29.
Dawson, M.R.W., & Wright, R.D. (1994).  Simultaneity in the Ternus
   configuration:  psychophysical data and a computer model.  Vision Research,
   34, 397-407.
Dawson, M.R.W. & Schopflocher, D.P. (1992b).  Autonomous processing in PDP
   networks.  Philosophical Psychology, 5, 199-219.
Dawson, M.R.W. & Schopflocher, D.P. (1992a).  Modifying the generalized delta
   rule to train networks of nonmonotonic processors for pattern classification.
   Connection Science, 4, 19-31.
Dawson, M.R.W., Schopflocher, D.P., Kidd, J., & Shamanski, K.S. (1992).
   Training networks of value units. Proceedings of the Ninth Canadian
   Conference on Artificial Intelligence. (pp. 244-250).
Dawson, M.R.W., & Shamanski, K.S. (1994).  Connectionism, confusion and
   cognitive science.  Journal of Intelligent Systems, 4, 215-262.
Dawson, M.R.W., Shamanski, K.S., & Medler, D.A. (1993).  From connectionism
   to cognitive science. In L. Goldfarb (Ed.)  Proceedings of the Fifth
   University of New Brunswick Symposium On Artificial Intelligence,
   Fredericton, NB: UNB press (pp. 295-305). (refereed).
Getting, P.A. (1989).  Emerging principles governing the operation of neural
   networks.  Annual Review of Neuroscience, 12, 185-204.
McCloskey, M. (1991).  Networks and theories:  The place of connectionism in
   cognitive science.  Psychological Science, 2, 387-395.
Medler, D.A., & Dawson, M.R.W. (1994).  Training redundant artificial neural
   networks:  Imposing biology on technology.  Psychological research, 57,
Mozer, M.C., & Smolensky, P. (1989).  Using relevance to reduce network size
   automatically.  Connection Science, 1, 3-16.
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986).  Learning
   representations by backpopagating errors.  Nature, 323, 533-536.
Shamanski, K.S., Dawson, M.R.W., & Berkeley, I.S.N. (1994).  The effect of
   linear separability on learning speed an generalization in monotonic and
   nonmonotonic PDP networks.  Connection science, under editorial review.
Smolensky, P. (1989).  On the proper treatment of connectionism.  Behavioural
   and Brain Sciences, 11, 1-74.
Ullman, S. (1979).  The interpretation of visual motion.  Cambridge, MA: MIT

[About the BCP | BCP Home Page]
Last Modified : 10 / 07 / 98