Re: two important articles on cognition and implications for KR&R for AI

The problem isn't one of reconciling different approaches, whether they be philosophical, psychological or computational, but of visualization. And the most effective visualization is that found in Buddhist philosophy.
Imagine "nothingness". When we try to conceptualize this we causally create models, whether infinite dimensional spaces in which we can apply specialized forms of topology, hypergraphs or vector spaces, or algebras. These are extracted, abstracted from reasoning about percepts.
The point I am trying to make is that all three levels of cognition exist in the same "space", because from research on brain architecture, and "models" defined by psychologists and philosophers, there is no space outside our own mind, or better yet our mind does not exist outside this space.

Which hints at Godel-Skolem, and uncertainties found in computability, information theory and signal processing all playing a role in limiting our understanding and conceptualization of this highest level of cognition.
The current problems in (theoretical) (quantum) physics and cosmology arise from the fact that we cannot have awareness hovering above awareness. And the human brain, which uses visualizations and models, excels in coming up with processes that do neural networks more than one better, by few shot learning and adapting.
Thomas Kuhn is right in his book The Structure of Scientific Revolutions about how we arrive at new scientific insights, and it seems eve more plausible that the practice of science mimics or mirrors how our brain arrives at new reasoning processes by learning through outside the box thinking.
There is no one single model describing all three levels of cognition, but many and our brain continuously tries to come up with new ones, hence the ever evolving mind.

Finding the fundamental processes in the brain responsible for these three levels of cognition is the really hard problem.

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    On Tuesday, November 15, 2022 at 06:22:29 AM AST, Dave Raggett <dsr@w3.org> wrote:  
 
 

On 15 Nov 2022, at 06:00, ProjectParadigm-ICT-Program <metadataportals@yahoo.com> wrote:
A New Brain Model Could Pave the Way for Conscious AI
https://scitechdaily.com/a-new-brain-model-could-pave-the-way-for-conscious-ai/


The model in the above work is not dissimilar to what I’ve been exploring:
In the first stage, evolution ensures that animals can select appropriate actions in response to the immediate environment and their internal drives.  This involves complicated systems for perception and action, e.g. driving development of the cerebellum for real-time sensory-motor control, as well as hierarchies of perception that can detect when things aren’t behaving as expected, which entails learning how to predict behaviour at different levels of abstraction, with the help of the cerebellum.
The second stage is the evolution of System 1 cognition that can operate on memory as well as on perception, freeing the mind from fixating on the present. This allows for cognition about the past as well as potential futures, albeit only at an intuitive level that is not open to introspection.
The third stage is the evolution of System 2 cognition enabling deliberative, analytical cognition that is open to introspection, and works in tandem with System 1, as noted by Daniel Kahneman. This allows humans to create complex plans and to solve problems that other species have great difficulties with.
What I am now struggling with is how to mix inferential, i.e. logical everyday thought, as per Alan Collins, with model building, as per Philip Johnson-Laird, who showed that humans mostly reason in terms of mental models rather than logic and statistics. I have implementations for both approaches, but it isn’t yet clear how they fit together along with metacognition.
On the one hand I have a (logical) notation for plausible knowledge with properties, relationships and implications, and on the other hand I have a different notation for chunks and rules, inspired by John Anderson’s work on ACT-R.  Chunk rules are procedural with the means to manipulate knowledge expressed as chunks, including live models generated by perception, and to delegate actions to a separate system capable of real-time coordination. This poses a strong contrast between logical and procedural reasoning.
Metacognition is at a higher level, and deals with reasoning about reasoning, including reasoning how to learn to reason. This points the way to how to integrate logical and procedural reasoning. The next step is to gather some examples that can shine a light on the details. Working implementations then provide an important means to test the consistency and effectiveness of theories. You can’t find this stuff in existing work, though, which is at the same time frustrating and exciting. 
Best regards,
Dave Raggett <dsr@w3.org>