Posts filed under ‘evolution’
There are several underlying problems with cognition which are different from what most expect.
The primary issue is due to perception where too much emphasis is attributes to the human senses (primarily sight and sound) – which as I’ve mentioned before – are just inputs. As you’ll know from physics – you’ll often see simple patterns repeated in many different fields – it’s unlikely that cognitive processes will be any different when dealing with sound/sight and thought.
The next issue is that many fall foul of attempting to describe the system in terms they can understand – a natural approach but essentially it boils down to the pushing of grammar parsers and hand lexers with too much forward weighting to identify external grammar (essentially pre-weighting the lexers with formal grammar). An approach that can produce interesting results but isn’t cognition and fails as an end game for achieving it. Essentially this is the approach used in current machine translation processes in it’s various forms.
The key fundamental issue is much simpler and related to issues around: pattern, reduction & relationship. An area that had some activity a while ago in various forms (cellular networks, etc) but fell to the wayside generally due to poor conceptual reference frameworks and the over-emphasis on modelling approaches used in nature (neural networks).
Now comes the time of definitions – a vehicle to ensure we’re on the same page 🙂
Cognitive processes thrive on them – and it’s one of the main drivers behind how it perceives, processes and responds to information. There’s a constant search to find similarities between what is perceived and what is known. It’s a fuzzy matching system that is rewarded, in the sense that it promotes change or adaptation, as much by differences as it is with finding similarities. When thinking about similarities – a handy term is to think about something being true or false. Don’t confuse true/false as the general definitions of the terms – it’s more about the sense of confidence. If something has a high confidence of being valid then it is true. The threshold of confidence is something that evolves and adapts within the cognition over time (essentially as a result of experience).
The development of patterns is both external (due to an external perception or input) and internal. To avoid turning this comment into something massive (and boring you 🙂 ) – think along the lines of the human cognitive process and the subconscious or dreams.
Reduction happens at several key stages – essentially it’s when a domain of experience breaches a threshold. It’s a way of reducing the processing required to a more automatic response. Think along the lines of short-circuit expressions. It’s a fundamental part of the cognitive process. From a human cognitive perspective you have probably seen it in your climbing and in your learning of the trumpet. We often express it as “having the knack” or “getting the hang” of something.
It’s important for 2 reasons: a) it means it has gained knowledge about a domain; b) it allows the cognitive process to further explore a domain. While Reduction is a desirable end-game – it is not The End from a cognitive process perspective. The meta information for this node of Reduction combines again and again with Pattern and Relationship allowing the process to reuse both the knowledge itself but more importantly the lessons learned when achieving reduction.
Relationship is really a meta process for drawing together apparently unrelated information into something that’s cohesive and is likely to either help with identifying patterns or for bringing about Reduction. Relationship at first looks very similar to Pattern but differs in it’s ability to ask itself “what if” and by being able to adjust things (facts, perception, knowledge, Pattern, Reduction and versions of these[versions are actually quite important]) to suit the avenue that it being explored. When expressed in human cognitive terms think of Relationship as the subconscious, dreams or the unfolding of events in thought. The unfolding of events is an example of versions. Essentially Relationship is a simulation that allows the testing of something.
I stumbled over an interesting post on another site (http://www.ioremap.net/node/283) by zbr, a very bright guy which prompted a long comment. I wanted to repost it here to further expand upon later.
NLP based on a grammatical rules engine, while an interesting toy, is essentially a dead-end when it comes to developing an approach to cognition. Language is a complex system that has evolved over time and continues to evolve each and every day. Grammar is an artificial construct that we have developed as a vehicle to describe language but describing something doesn’t mean you understand it or that it can be used to extract knowledge or understanding from what it attempts to describe.
Take the example from Cyc (http://www.cyc.com/cyc/technology/whatiscyc_dir/whatsincyc):
* Fred saw the plane flying over Zurich.
* Fred saw the mountains flying over Zurich.
Grammar itself will help develop a weighted tree of the sentences and you’ll be able to describe the scene – but the system will lack enough reference to be able to respond. In such a situation what is the proper response?
To answer we need a reference model – which luckily we have all around us everyday – people. What do people do when they encounter a phrase and don’t have enough information to process it? They ask a question. What question would they ask? Who’s fred? What’s a plane? What’s Zurich? or would they laugh out loud as they exclaim (and picture) the mountains flying? (in itself a valid hypothesis)
Knowledge is obtained from the answer to the question – as it provided an addendum – a relationship between the phrase, the question and the answer. Additionally the question itself often gets corrected – providing a short-circuit feedback loop to the knowledge acquisition process. The description of the answer also provides information about the relationship of items in the phrase to other information stored within the system.
What’s Zurich? Zurich is the name of a city in a country called Switzerland.
(assuming that there is some information about what a plane is or that there is some relationship that interprets plans as machines like a car)
What color is the planes? Planes are all shapes and colors but this plane is bright green.
(note in this example the question indicates the singular but uses the plural – which is corrected in the answer)
The question provides insight into the internal state of the system we are interacting with (be it a computer program, a child we’re reading a story to or a colleague we are interacting with). Inherent in any interaction is feedback, correction, elucidation of terms and phrases to assist understanding with those we are interacting with. Often it happens in a subconscious way and tends to be in the style of continuous correcting feedback (the same approach we use when we reach down to pick up an object off of a surface).
A system needs to adapt & correct, to provide feedback (both to itself and with the other party it is interacting with) in a way that’s more than just updating state – but that also affects the very rules that make up the system itself. This, however, is where many people tend to start going wrong. A common pitfall is that the rules are considered to be the weightings between nodes of information or its relationships. This however means that the underlying reference system (often implemented as grammar rules) rarely changes – which in essence lobotomizes the system. It’s an indicator that you’ve put too much forward knowledge into the system.
Take how children learn – not the mechanic but the approach that’s used and not just for language or understanding (which is what we are trying to replicate when we implement the system) but with everything they do. Nature, bless her cotton socks, is frugal with how she expends energy – so she reuses as much as possible (in essence cutting things down to their most common denominator). You’ll see the same approach being uses for walking, talking, breathing, looking and following objects – in everything that we see, do or think. Over time the system specializes domains of knowledge – further compartmentalizing – but also reusing that which has been learned and found to be valid in the domain. Which in turn allows for further specialization and compartmentalization.