Imagine, when a newbie (using cricket as an example) batsman or bats-person stands in posture…
(feet together positioned adjacent and about 1/2 meter away from the wickets, cricket bat held in both hands, with the tip of the bat resting on the ground in front of the toes, forcing the bending of the back with the bats-person’s head twisted through 90 degrees to face the attention of the bowler’s oncoming piece d’ resistance)
…the learner is to face a ball made from leather which is hard enough to cause serious injury. The ball is then bowled in a manner depicted of correct cricket; that is, it is swung with the arm behind the bowler in a forward arc and released at around the 10.00 o’clock position towards the bats-person with the speed ranging anything from 20 -120 miles per hour. (Hopefully the former for the leaner).
At this point, if the ball is travelling too fast the learner will do the honourably wise thing and dive out of the way of the oncoming ball; or if more tenacious and spirited in attempt; (in that great English tradition of good cricket), hit the ball.
Depending upon the trajectory of the ball, the bats-person’s innate skills and abilities of hand eye co-ordination to oncoming solid objects at speed, there are a number of outcomes that could happen, (not least of all, head-butting the ball), including batting the ball or the ball meeting it’s target and knocking off the wooden bails from the stumps.
“Purpose of Modelling is both in the replication as much as the efficacy”
Take this through to the learner; (whom so far has not been scared off by solid objects flying through the air towards them) having spent weeks, months and perhaps years of ‘trial and error’ in learning the different non-verbal cues the bowler will signal to the bats-person through these unconscious minimal non-verbal cues as to the type of bowling action that he or she will use to either spin the ball at the top or bottom, aim the ball off wicket, on wicket, straight at the wicket or bounce off the grass etc.
Mix this with the learnt unconscious signals of the firmness of the ground to the feet of the bats-person, indicating the likelihood of how the ball will respond to bouncing off the grass, with the unconscious signals of the wind and how that effects the trajectory of the ball etc… starting to get the picture? The whole plethora of skills that a professional cricketer has learnt through trial and error has been accumulated through years of tenacious practice and trial and error to which they finally arrive at their customary game of excellence.
Years of this learning culminates in the, the bats-person’s created intuitive patterns of behaviours, set within the boundaries of the scope and range of his or her behaviours. These are reflective to the diverse bowling styles, cues and environmental conditions, that result in (accordingly to our imaginary model of genius) a set of commensurate skills of excellence.
“We are able to perform near exacting behaviours to the model and achieve near exacting responses”
Now, supposing we model this person, and after ‘X period of time, we arrive at the conclusive evidence that our ability to replicate the defined behaviour in our said ‘context’ (this being a definitive variable worthy of note when Modelling) meets the criterion for NLP Modelling. That is we are able to perform near exacting behaviours to the model and achieve near exacting responses. As in our example cricket, hit the ball and achieve either the ball out of the boundary for 6 points or rolling out of the boundary for 4 etc. This depends upon the initial criteria set up for modelling the person of excellence, that is What is my outcome for modelling? i.e. What specifically do I wish to model and replicate as a skill?
Now we have the ability to replicate the models behaviours and achieve our desired result. Set within the confines of a) our own physical abilities and b) the constraints of the context of the Modelling project.
Context: just a note here. Context is an important variable within the Modelling project. This defines the scope and boundaries of the project, that is, what portion of the models behaviour are we going to model. For instance – All of it, or partial, or specific, – All of it, we could say, includes, different weather conditions, different pitches (grass, grounds, stadiums), different bowlers, top spinners, bottom spinners etc. different cricket bats… and the list goes on. Partial – any subset or division of All. Specific – a specific subset of behaviours that come from Partial.
So the division of these sets would be driven by the modellers own way in partitioning the sets of experience called cricket.
Example of partition = Modelling batting against top spinner bowlers. Which could include, the positioning of bats-person on the pitch to when the bowler makes their run and then bowls, to the extradition of the ball from the bats-person’s cricket bat to the other side of the pitch.
Example of Specific = Modelling the positioning of the bats-person on the batting line.
“Again this is part of the context of Modelling What do you want to model?”
Let us now take it to the point in question, (Trial and Error learning), Consider all the errors and learning the cricketer (model of excellence) has taken throughout their journey to arrive at excellence and all the ‘great lessons’ they have learnt along the way.
Firstly, ‘Great lessons’ is a personal criteria judgement based upon their own interpretation of their experiences. ‘One persons seat is another’s mile stone’ or ‘one person’s food is another’s poison’
Yes, the model would have undoubtedly learnt many things, including what to do when a ball is flying at 120mph towards their head, and how to avoid it, (hopefully) but again this is part of the context of Modelling What do you want to model?
When there are the examples of – “what to do when…?” or “what if x happens, then what…?” these are all the consequential parts of different Modelling partitions within the larger frame of the Modelling project, (if they are desired outcomes).
However as a modeller, you have the choice to arrange and set the contextual partition as you see fit for your own purpose. However one of the criteria that maybe outside of your control, is the level of access you have to the model in question. If you have a limited amount of access, this may narrow and filter / streamline the desired area to be modelled.
So these ‘great lessons’ are a) subjective b) contextual c) relegated to classes of behaviour that do not perform and achieve the required outcome
Or to offer another example – I raise my arm from my leg to my midriff. I have spent weeks trying to get it to move in a straight line, after many many attempts at moving it in a straight line I finally achieve it. I now have a set of strategies that I go through to achieve this behaviour. Throughout this process I have relegated all of the trial and error processes and kept all those meet my outcome, until at such a point the differences between a certain number of moves are ironed out to achieve the one move that meets my criteria.
So what happens to all of the other moves…in one way we could say they are all excellent moves for achieving some other outcome but not the one in question. So if someone were to model the ‘moving of the arm’ what is the point in learning other sets of behaviour that are great at achieving another outcome. Surly my outcome as a modeller is to model the outcome I want, that is the movement of the arm to be level to the midriff and not all of the other movements that will lead my arm to a, b, c, and not X.
“why model non-excellence?”
When we think of modelling it is based upon successful achievements and not unsuccessful attempts. A modeller is interested in the successes of the person they model and not the trial and errors, the ‘great things’ they have learnt which may or may not be useful. But the point is, as a modeller you are after what works, not what they either think works or does not work.
It is easy to confuse the logical sets between one and the other when modelling. The set of achieved outcomes become the refined behaviour, the set of unsuccessful attempts (the error phase, from trial and error) are not inclusive within the set of ‘keen skill’ (skill that works). To explicate, set Y = keen skill, set X = unsuccessful attempts. Set X does not = set Y. Set X = all other behaviours that are not part of set Y.
Therefore any Modelling should be done within the set of Y (skill that works) and not X (unsuccessful attempts during the trial and error).
Unless, a member of the set X falls into the set of Z which could be the larger context of the Modelling project, that is the ‘what ifs…’ or another behaviour. Where both set X and Set Y are a subset of Z.
Why model – non-excellence (or another set of behaviours that don’t achieve the outcome), if what you want is excellence.
The final part is, once the model has been patterned, the behaviours implemented, the code explicated, the model refined to the least amount of points to achieve the same outcome.. . Where else will it work? In what other contexts? Can it be cross fertilized into other contexts, areas and disciplines? Now begins the process of generalisation – Is the pattern robust to be cross-disciplinary? Is it a prime pattern or model?. Where doesn’t it work? etc.
For instance, can the pattern (in the context of cricket) be used against a top spin bowler as well as a bottom spin, what adjustments have to be made if any, to accommodate both types of bowlers and so on.
Trial and error learning is great if you have an unlimited amount of years to learn a specific skill, but if you wish to learn the key elements of a skill and apply them to your own life, NLP modelling provides the tools to replicate the skills or set of skills efficiently and effectively.
A lesson from Music College:- Tutor “learn it our way, through years of Modelling other musicians we know that our way works, when you can do it our way you have the choice to go back to how you learnt it initially or if our way is better, then you can continue to use it.” Context: ‘how to hold a plectrum’.
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