I think that model is too linear to be analogous. — 180 Proof
Well, how about an extension of the IPO model, that is more in-line with current computer systems and their various network topologies (star, bus, ring, mesh and fully connected mesh.) Do you have any knowledge of network topologies and the workings of stand alone operating systems/networked operating systems?
If we mimic a brain model such as the triune brain model of R-complex, Limbic system and Cortex, then we could compare this with three separate core's on the same CPU.
We can then use the IPO model to include parallel processing. Would this not alleviate your concern, regarding the linearity of the IPO model, when applied to a single 'stand alone' computer?
. Are you familiar with Douglas Hofstadter's writings on 'tangled hierarchies' model of cognition (e.g. Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought)? — 180 Proof
No, but I will follow your link and become a little more familiar with it. I have found your suggested links in the past, to be useful and informative. I will comment after I have read the material your link offers.
Artificial neural networks seem to me much closer analogues to the processing of (meta)cognition than von Neumann architecture 'programs'. — 180 Proof
What neural network examples are you referring to? One based on biological components or ones based only on electronic components?
Considering wiki's description of:
A neural network can refer to either a neural circuit of biological neurons (sometimes also called a biological neural network), or a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1.
The major aspect seems to me, to be the weight system applied and how that mimics human credence/confidence level, that a human might assign to a particular thought. I agree that is of significant value, BUT I think a biological system might involve sensation's/feelings, that affect the 'weight' assigned to a neural net output, whereas an electronic/artificial neural net, uses probability calculations to provide a weighting to an output.
I think the IPO model is still of good use here, as a neural net still complies with a serial and parallel notion of the three stages of input, process and output. The Von Neumann architecture merely adds the concept of the stored program and stored data files to the IPO model, along with connecting communication/carrier channels.
In the 'sketch' at the bottom of my last post I use bidirectional arrows to simplistically suggest nonlinear relationships (i.e. self-recursion / self-referencing) among the 'nodes'. — 180 Proof
to begin with. I see perceptual cognition something like this: phenomena —> data —> experience <——> memory traces <——> information (signal:noise) ... etc. — 180 Proof
But bidirectional is not non-linear in this sense. For example, the data bus inside a CPU is bidirectional as data can be sent to and retrieved from storage, but it's still linear, the data bus is made up of parallel communication channels, etched in silicone.
Phenomena described in philosophy, as the object of a person's perception. would produce personal data, yes, but could a persons personal interpretation of inputed data, not also produce personal phenomena? Could the arrow between the two not also be bidirectional?
I think we are still ONLY modelling mechanistic aspects of consciousness in the brain and we are still no closer to the notion of 'me' or 'you.' Would you, at this stage, assign any credence to the proposal that the mechanics of a working electronic neural net IS a very low level feature of consciousness?
As I delve deeper into such models, I keep returning to consciousness as an emergent feature of component parts. I keep returning to the 'more than the sum of its parts,' idea.
I've found it most informative and insightful in the last fifteen or so years – the monumental Being No One (or it's nontechnical summary The Ego Tunnel (re phenomenal self model)) by Thomas Metzinger. I highly recommend his work if you're not familiar with it. — 180 Proof
No again, I am not familiar with it. You are assigning me too much homework sir! I already have a large reading list, but I will do my best to find some time to peruse Mr Metzingers work.
I want to stress that while I appreciate that perceptual cognition, etc in primate brains is computational, I'm also convinced that these brains are not computers in the (mostly) linear 'IPO' sense – just as David Deutsch points out that it does not follow from the computability of fundamental physical laws (re: constructor theory) that the universe is a computer simulation. — 180 Proof
I agree that a purely computational model of the human cannot be the full story of human consciousness. I currently assign almost 0 credence to simulation theory as the 'reality' of our universe.