Category vigilance (biological and computational models)

Todd I. Stark stark at
Wed Nov 3 18:54:18 EST 1993

bwhite at (William E. White ) writes...
>In the process of investigating "self-training" models, I learned about
>ART networks; however I found one potential problem which I wanted to ask
>about.  It occurs to me that, while the vigilance of the network can vary
>depending on success or failure, it varies "globally".  However, it seems
>that human vigilance varies "locally"; for example, a person among one
>race of people finds it easier to distinguish people of that race than
>of others.  Similar examples can be drawn, I'm sure.
>What this amounts to is that there needs to be a way of disambiguating
>closely-related but different stimuli, while still correctly categorizing
>noisy, far-related stimuli.  ART seems unable to do this.
>Is there currently any self-training model, derived either from biological
>research or from AI, which provides for this?  The only thing that I could
>think of was inserting layers between F1 and F2 which would selectively
>disambiguate, but then we're back to the problem of how to train them.

I vaguely recall that former immunologist Gerald Edelman was working on a 
model of category vigilance based on his theory of neural selection.  I don't 
have the cites with me, though.  Don't know how far he got with it, either.

							kind regards,

| Todd I. Stark				  stark at           |
| Digital Equipment Corporation		             (215) 542-3573           |
| Philadelphia, Pa. USA                                                       |

More information about the Neur-sci mailing list