TR Announcement

[James L. McClelland]

The following Technical Report is available both electronically from
our own FTP server or in hard copy form.  Instructions for obtaining 
copies may be found at the end of this post.

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     Stochastic Interactive Processing, Channel Separability, and
     Optimal Perceptual Inference: An Examination of Morton's Law

               Javier R. Movellan & James L. McClelland

                    Technical Report PDP.CNS.95.4
                            December 1995

In this paper we examine a regularity found in human perception,
called Morton's law, in which stimulus and context have independent
influences on perception.  This regularity has been used in the past
to argue that perception is a feed-forward, non-interactive process.
Building on earlier work by McClelland ( Cognitive Psychology, 1991)
we illustrate how Morton's law may emerge from stochastic interactions
between simple processing units.  To this end we consider the
properties of interactive diffusion networks, the continuous
stochastic limit of standard artificial neural models.  If, as we
believe, human information processing involves using noisy processing
elements to process potentially noisy inputs, such models may
ultimately serve as foundations for a theory of human information
processing.  We show that Morton's law emerges in recurrent diffusion
networks when the units are organized into separable channels,
feed-forward processing is not a necessary condition for Morton's law
to hold.  Failures to exhibit Morton's law provide evidence that the
information channels are not separable. This result can be used to
analyze cognitive models as well as actual brain structures. Finally,
we illustrate how diffusion networks can be organized to implement
optimal Bayesian perceptual inference.

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Retrieval information for pdp.cns TRs:

unix> ftp 128.2.248.152                 # hydra.psy.cmu.edu
Name: anonymous
Password: <email address>
ftp> cd pub/pdp.cns
ftp> binary
ftp> get pdp.cns.95.4.ps.Z              # gets this tr
ftp> quit
unix> zcat pdp.cns.95.4.ps.Z | lpr      # or however you print postscript

NOTE:  

The compressed file is 255910 bytes long.
Uncompressed, the file is 727359 byes long.

The printed version is 66 total pages long.

For those who do not have FTP access, physical copies can be requested from
Barbara Dorney <bd1q+ at andrew.cmu.edu>.

For a list of available PDP.CNS Technical Reports:

> get README

For the titles and abstracts:

> get ABSTRACTS