"Edwina Taborsky" <tabor...@primus.ca>
wrote:
Helmut - no, I don't think such a set-up would function very well.
The way you've set it up, - indeed, in either set up - you've got the mediative function of the Representamen operating in a mode of either 1-1 or 2-2. And you've got the three Interpretants functioning in various modes of Thirdness. You can't have the Interpretants as MORE complex in organization than the mediative Representamen.
Take a look at the ten classes of Signs; 2.236 and on. You'll see that the Interpretants are never in a more complex mode than the Representamen. They can be the same or less complex - but never higher in complexity. After all - one would have to ask, where do they get that extra information to function in such an increased complexity?
And the only class with the Interpretants in Thirdness - is a pure syllogism, the Argument Symbolic Legisign, where ALL nodes of the triadic Sign - are in the mode of Thirdness.
Edwina
On Tue 02/04/19 2:24 PM , "Helmut Raulien" h.raul...@gmx.de sent:
Edwina, list,you wrote:"Peirce provided us with an analytic infrastructure than enables us to examine the complexity within these actions. That is, his basic informational format is the semiosic triad of O-R-I, BUT, this triad is further broken down into more intricate 'nodal sites', and we end up with six: DO-IO-R-II-DI-FI. Such a framework enables more information transformation at each nodal site.
In addition - Peirce provided the three categories of Firstness, Secondness and Thirdness - which are modes of organization of data/information. BUT again, he increased the complexity capacity of these three modes by introducing their so-called 'degenerate' forms: So- we have 1-1, 2-2 AND 2-1. Then, we have 3-3 AND 3-1 and 3-2. Note that Thirdness, the action of knowledge storage has THREE methods to carry out this action: iconic, indexical and symbolic. That's a powerful tool."
Now please forgive me for assuming, that "DO-IO-R-II-DI-FI", written "R-IO-DO-II-DI-FI" is in accord with "(1.1), (2.1), (2.2), (3.1), (3.2), (3.3)" as more than a coincidence. It would be quite a coincidence.
Best,
Helmut
01. April 2019 um 19:35 Uhr
"Edwina Taborsky"
List:
I'm continuing with my interest in the pragmatics of Peircean semiosis; that is, the use of the Peircean analytic infrastructure to examine the dynamic operations within the organic chemical, the biological, the societal [economic systems, population behaviour] - and the cognitive [which includes AI].
Basically, it's all about 'information processing' , which includes the self-organization of an organism's capacity and actions of knowledge development and maintenance, , adaptation of knowledge and behaviour, anticipation tactics, entropy problems and so on.
Peirce provided us with an analytic infrastructure than enables us to examine the complexity within these actions. That is, his basic informational format is the semiosic triad of O-R-I, BUT, this triad is further broken down into more intricate 'nodal sites', and we end up with six: DO-IO-R-II-DI-FI. Such a framework enables more information transformation at each nodal site.
In addition - Peirce provided the three categories of Firstness, Secondness and Thirdness - which are modes of organization of data/information. BUT again, he increased the complexity capacity of these three modes by introducing their so-called 'degenerate' forms: So- we have 1-1, 2-2 AND 2-1. Then, we have 3-3 AND 3-1 and 3-2. Note that Thirdness, the action of knowledge storage has THREE methods to carry out this action: iconic, indexical and symbolic. That's a powerful tool.
Then, there are the ten basic classes of Signs - [2:254] - which explain the triads from the simple ''feeling' to the complex cognitive. Put this all together and I maintain that Peirce has provided a powerful analytic framework for examining the dynamics - and it IS a dynamical operation - of information generation, adaptation, evolution and storage. These can, I suggest, be moved into the broader scientific world - and would be, I think, of great benefit.
I'd like to refer to two articles as examples of how this Peircean framework could be put to use. I provide examples from two reputable journals: Biosystems and Entropy. I note that neither deal with self-published works; the articles must go through a peer-review and revision process.
The first article, from Biosystems, refers to the analogy between the biological realm and the work being done in AI.The focus is on 'Anticipation' - which is an ability generated by the mode of Thirdness. Understanding this mode and that there are THREE modes of Thirdness [which I have elsewhere referred to as strong and weak anticipation] would be, I suggest, of great benefit in the development of AI.
The second article, from Entropy, also refers to the realm of Thirdness - to enable 'Interpretants/Understanding'. Again, this work sets up the act of 'anticipation' - and again, is focused on the development of AI.
Essentially, my suggestion is that the complex framework of Peircean semiosis - with those Six nodal sites, those Six modal actions and ten classes - provides a powerful tool for the examination of complex processes in the real pragmatic world.
Edwina
1]Anticipation: Beyond synthetic biology and cognitive roboticsopen access2] The Understanding Capacity and Information Dynamics in the Human BrainVirtual Structures Research, Inc., Potomac, MD 20854, USAReceived: 23 December 2018 / Revised: 8 March 2019 / Accepted: 15 March 2019 / Published: 21 March 2019(This article belongs to the Special Issue Information Dynamics in Brain and Physiological Networks)Abstract
This article proposes a theory of neuronal processes underlying cognition, focusing on the mechanisms of understanding in the human brain. Understanding is a product of mental modeling. The paper argues that mental modeling is a form of information production inside the neuronal system extending the reach of human cognition “beyond the information given” (Bruner, J.S., Beyond the Information Given, 1973). Mental modeling enables forms of learning and prediction (learning with understanding and prediction via explanation) that are unique to humans, allowing robust performance under unfamiliar conditions having no precedents in the past history. The proposed theory centers on the notions of self-organization and emergent properties of collective behavior in the neuronal substrate. The theory motivates new approaches in the design of intelligent artifacts (machine understanding) that are complementary to those underlying the technology of machine learning. View Full-Text
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