|From:||FERENC KOVACS <f.kovacs@xxxxxxxxxxxxxx>|
|Date:||Thu, 10 Dec 2009 14:06:26 +0000 (GMT)|
My interest in the neurocognition is from a secure distance. I am more interested in the fact that knowledge representations on the internet and in libraries are sorted on form (morphological features) that are thought to be indicative of content. In other words we have alpha indexes, keywords, tag-clouds, library classification systems. Nevertheless we are interested in the associated content, so most f our repeated searches are wasted.
On the othe hand, when we have the job of saving our knowledge representations, e.g. our email, papers, etc. we create or use a system of locations tagged by the very same descriptors. Subject to the number of items to be stored we have generic terms which would need to be changed/extended and the corresponding items be moved about.
This routine is the same as in a library, doing indexing and classification, and it is usually the first step of any search op.
I assume that more often than not we are after a content that can be incorporated in our works as reference either to something we refuse or something we accept. The bottom line is that we build novel representations that are constructed by using suportive citations
But this content is not directly available through morphological search, becasue the nominal character of the indices, keywords and descriptors would never allow that. And concocting them does not improve the situation significantly either.
Therefore there must be a way to identify and record content that allows sorting in a non-morphologival manner. So far the attemtps are shown in thesauri, or numeric classifications, such as the UDC or Dewey's. The relations represneetd there, however are mostly "contained in" spatial type, which is fine with noun phrases, but no good for verbs that I reckon to be carrying realtions. For a verb to be complete you need to specifí time and grammar persons, ion addition to objects as complements. All that calls for a different structure and approach extended to house more than one element in focus, unlike the current network (e.g. wordnet) constructions.
From: John F. Sowa <sowa@xxxxxxxxxxx>
To: [ontolog-forum] <ontolog-forum@xxxxxxxxxxxxxxxx>
Sent: Thursday, 10 December, 2009 13:28:02
Subject: [ontolog-forum] Brainweb (was http...)
That's an interesting paper:
FK> a link for those who are interested in the interpretation of the
> physical network in the brain with respect to the importance of
> the exposition of relations in FO
Following is the title, authors, and opening paragraph:
> THE BRAINWEB: PHASE SYNCHRONIZATION AND LARGE-SCALE INTEGRATION
> Francisco Varela*, Jean-Philippe Lachaux*, Eugenio Rodriguez‡ and Jacques Martinerie*
> The emergence of a unified cognitive moment relies on the coordination of scattered mosaics of
> functionally specialized brain regions. Here we review the mechanisms of large-scale integration
> that counterbalance the distributed anatomical and functional organization of brain activity to
> enable the emergence of coherent behaviour and cognition. Although the mechanisms involved
> in large-scale integration are still largely unknown, we argue that the most plausible candidate is
> the formation of dynamic links mediated by synchrony over multiple frequency bands.
1. Activity in many "scattered" brain regions is involved in
a "unified cognitive moment".
2. Synchrony of the frequencies of the activations in different
brain regions is a significant criterion for showing that
they are doing related operations.
3. But the caveat is that "the mechanisms involved in large-scale
integration are still largely unknown."
4. And the conclusions are at best "plausible."
There has been an enormous amount of research on neural mechanisms
in the past 20 years, and a great deal has been learned. But note
how little is actually known about what those brain regions are
doing and what kind of data they are processing.
Suppose that somebody did an analysis of the Internet and showed
that there were significant correlations in the frequencies of
activations of different regions. That would be interesting.
But we would immediately ask what kinds of data were those
regions processing and what transformations on the data were
Those questions are ones that every neuroscientist would love
to answer, but nobody knows how to decode the signals. It's
possible to find correlations of activity in different regions
with different subjects that people are viewing or talking about.
But that is like saying "This pattern of computer activity shows
that the user is viewing a PDF file." That information may be
useful, but it doesn't tell us how to decode a PDF file.
In short, the neural evidence is interesting, since it might give
us some useful hypotheses to explore. But there is a long way
to go before it can tell us what people are thinking, feeling,
reasoning, acting -- or how we could simulate what they do.
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