sfp@stc06.ornl.gov (SPELT P F) (11/10/90)
From: Phil Spelt, Oak Ridge National Laboratory
Oak Ridge, TN 37831-6364 USA
About a month ago, I posted in this BB a request for feedback from
people using Artificial Neural Networks (ANNs) in the area of Human Factors
research. I promised, at that time, to post a summary of the replies.
Below are excerpts from messages from those who responded to that
posting. The overall result is that there will be a paper session at
the 2nd Neural Network Workshop at Auburn University, 11-13 FEbruary 1991.
There will also be a roundtable discussion about the uses of ANNs in Human
Factors research. A summary of all this will be published in the _Bulletin_
of the Human Factors Society sometime in mid 1991, and the papers wil
appear in the Proceedings of the Workshop.
My thanks to all who responded to the original posting. Phil Spelt
----------=========>>>>>Summaries:
From: NAME: Ray Eberts <eberts%ntthif.ntt.jp@relay.cs.net@UMCGATE@OAX>
To: sfp%stc06.ctd.ornl.gov@relay.cs.net@UMCGATE@OAX
I am responding to your newsgroup announcement about human factors
and neural nets. I have been working on several applications
(I tried to kill a previous message I sent you, I hope it got killed,
because I don't wish to divulge all my research at this time until I
can get some of it out). Let me only say, that I am working on using
neural nets for user interface design, modeling, and as simulations to
investigate training procedures. I have recently completed a survey
of neural net research and human factors for the Handbook on Industrial
Engineering chapter on human Information Processing. There is very little
research in the journals at this point. About the only things available
are Schneider and Detweiler in Human Factors on training for dual task
skills and the Sejnowski and Rosenberg work on massed vs. distributed
training. Some work has also been done on knowledge extraction which
could be a human factors domain. If my other message got through, please
do not send out that information to anybody else at this time.
I can be reached at the following address:
Ray Eberts
School of Industrial Engineering
Purdue University
West Lafayette, IN 47907
I am currently on sabbatical in Japan until December at which time
I will go off to USC for a semester. My e-mail address in Japan is:
eberts%ntthif.ntt.jp@relay.cs.net
At Purdue, my e-mail address is:
eberts@rainbow.ecn.purdue.edu
------------------------------------------------------------------
From: NAME: Steve Bryan <sxb@inel.gov@UMCGATE@OAX>
I have not yet considered the use of neural networks in human factors work,
but am intrigued with the possibilities. Please email a copy of the
responses that you get, if it would not be too much trouble.
[I asked Steve what kind of work he is doing -- NN's or HF. PFS]
I am doing neural networks research, but am always on the lookout for
interesting applications. I have applied neural networks to control
problems, and used them as pattern classifiers as parts of larger
projects.
Human factors is a pretty broad area for neural network application, but
I have seen no publications in neural networks as applied to human
factors per se. Did you have computer interfaces that learn individual
preferences in mind, or another application?
-Steve
--
____________________________________________________________________________
|Steven R. Bryan, Idaho National Engineering Lab. INTERNET: sxb@INEL.GOV |
|N7MPY POB 1625 M.S. 1206 Phone: (208) 526-0338 |
|_________________Idaho_Falls,_Id._83415___________FAX:___(208)_526-1419_____|
------------------------------------------------------------------------------
From: jfmcgrew@srv.pacbell.com@UMCGATE@OAX
I work for the Advanced System Group at Pacific Bell. We build
expert systems for inhouse use. I am one of the two human factors
specticialists in the group of 10. I do the user interface design and
knowledge engineering. I have found that the determining of the structure
and content of the users tast and knowledge engineering to have extensive
overlap. Because of this I am very interested in the subject of human
factors and neural nets.
We at present do not use neural nets in our human factors work, although
two areas are presently bumping against the technology. These areas are
the use of graph theory and matricies to determine the underlying structure
of a users knowledge and tasks, and mathimatical group theory to model
the users view of objects (this work is in very early stages of develop
ment).
I would like to know the outcome of your survey and to be kept on any
mailing list related to the subject that you develope.
Thanks!
John F. McGrew
----------------------------------------------------------------------------
From: NAME: Yong Kim <yjkim@milton.u.washington.edu@UMCGATE@OAX>
Can I get the reply? Thanks!! yjkim@milton.u.washington.edu
----------------------------------------------------------------------------
From: jones@nprdc.navy.mil@UMCGATE@OAX
I currently have a small 6.2 grant to study the feasibility of
applying neural net techniques to the analysis of cortical evoked potential
data. The general theme of the division that I work in at the Navy Personnel
R&D Center in San Diego is the prediction of job performance from EP data
collected on cognitive tasks, with the primary application being in selection
and classification. Tradition statistical techniques, such as discriminant
analysis and multiple regression have been less than satisfactory, presumably
because the relationship are complex and nonlinear. In general, psychometric
tests are also not very good at predicting job performance in our sample of
subjects (Marines). The initial thrust of this project is to predict rifle and
pistol marksmanship from data collected from our subjects on several cognitive
tasks. I don't know whether or not this would fit in with your planned
workshop, but I would be interested in participating or discussing it further
if you desire. My postal address is David L. Ryan-Jones, Navy Personnel R&D
Center, Code 141, San Diego, CA 92152. David Ryan-Jones
----------------------------------------------------------------------------
From: lr@chmsr.gatech.edu@UMCGATE@OAX
I am currently working on a visual perception problem in a dynamic
environment using the back-propagation network. It involves simulating human
perceptual learning through mimicking similar inputs into the network. I have
done work in the neural nets field in the past, but the human factors area is
relatively new to me. I hope to have some results available by the end of the
fall quarter (i.e. around January).
Ling Rothrock (Grad / Ga Tech / Human-Machine Systems)
[ Also, the faculty contact at Ga. Tech is:
Alex Kirlik: kirlik@chmsr.gatech.edu
snail-mail: Center for Human-Machine Systems Research
School of Industrial and Systems Engineering
Ga. Inst. Tech.
Atlanta, GA 30332-0205 PFS ]
---------------------------------------------------------------------------
From: janet@minster.york.ac.uk@UMCGATE@OAX
I've been using associative memories in particular in HCI for some time -
two main (related) applications: automatic user modelling - training the
memory on examples of user behaviour and so classifying user types for
adaptive interface/intelligent help type applications; and using similar
techniques to recognise patterns in protocols in order to aid evaluation.
These two applications are reported in the papers listed below and are the
subject of my DPhil thesis (recently submitted).
I'll happily provide more information if you're interested.
User Modelling By Classification: A Neural-Based Approach
authors: R Beale, J Finlay, J Austin, M Harrison
in: New Developments in Neural Computing
editors: J G Taylor & C L T Mannion
abstract:
A neural-based system is used for a classification task in the field of
user-modelling. The properties associated with neural methods, such as
learning by example and generalisation are exploited to provide a system that
overcomes the limitations of traditional approaches, removing the need for an
extensive, explicit knowledge base, and exhiiting the desirable properties of
domain indepedence, noise tolerance and resource efficiency."
[The other references had no abstracts. I can provide them if needed. PFS]
Janet Finlay, Human Computer Interaction Group,
Dept. of Computer Science, University of York, Heslington,
YORK. YO1 5DD. UK. Tel: [044] (0904) 432765
janet@uk.ac.york.minster JANET connexions
janet%minster.york.ac.uk@nsfnet-relay.ac.uk ARPA connexions
..!mcsun!ukc!minster!janet UUCP connexions
janet@minster.york.ac.uk eab mail
-----------------------------------------------------------------------
From: NAME: Jim R.Chen <jchen@apple.com@UMCGATE@OAX>
Two years ago I did a project with the Human Interface Group at Apple
Computer on the use of neural network to aid MacIntosh desktop color
selection. In that project I used back-propagation to recall input
activations in a layered network which simulates an analog
associative memory with asymmetric connections. That was reported in
Chen, James R., Richard K. Belew, and Gitta B. Salomon, "A Connectionist
Network for Color Selection", Proceedings of the International Joint
Conference on Neural Networks, Jan. 1990, 467-470.
I am currently working on a neural network hypertext interface at
the Human Factors Center at IBM Santa Teresa Lab. The project is
still at an early stage and I don't have much to report yet.
Jim Chen
chen@cs.ucsd.edu
(408) 463-2469 (day)
(408) 741-5348