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There are five main distortions in NLP seen in the Meta model.
They are:
We are all guilty of mind reading I know you are! A mind read is categorised by knowing what someone else is thinking or feeling but without any information to support that thought. Mind reading can come in lots of different forms. One of them which is very common is knowing what a person is thinking or feeling. Another is how others should know how we think and feel. And lastly predicting the future or proper sizing what is going to happen.
Whenever you hear mind reading type statements you can begin to ask questions using 'how' and 'what.'
Examples:
Meta model challenges to those statements
Possible Reply
Recognising a cause-and-effect Meta model violation is when you spot and X causes why configurations in the language. so much of our life is in a cause-and-effect format that has a tendency to make connections where there is none. "If you do your homework, then you can stay up late on Friday." "If you pass all of your grades at school, you will get a good job."
So in the Meta model for cause and effect, we are looking for violations where someone associates X causes Y.
Whenever you spot this cause-and-effect violation it's time to begin asking more questions to find out how specifically do they know that X causes Y?
Examples:
Meta Model Challenges
Recognising a lost performative is when you hear a person say something like, "We will end up living on Mars by 2050" or "Only weak people stay in relationships."
The structure of a lost performative is an opinion stated as a fact and a value judgement that does not say who had that value.
You can begin to challenge lost performative type statements by asking "who says, according to whom and how do you know that?" type of questions.
Examples:
Meta model Challenges to those statements
Possible replies to the challenges
A complex equivalence is very similar to cause-and-effect. Just as we, cause-and-effect is when X causes Y. And complex equivalence can be recognised by hearing a statement that says X means Y.
"The fact that you're reading watching or listening to this means that you'll find something new!"
it's amazing how often you will hear people attach meaning to something very different.
"The fact that you're changing your job means that you are outgrowing your family"
Whenever you hear convex equivalent type statements you can challenge them by asking. "So does X have to always equal Y? Could it mean anything else? Does it always have to mean that?"
Examples:
Meta model challenges
Possible replies
If you've been looking into NLP you have heard the word presupposition quite frequently. A presupposition is simply a part of the statement has to be presupposed to be true in order for that sentence to make sense.
We challenge presuppositions to allow us to gain more specific detail and get closer to the truth. Let's look at some working examples of presuppositions in NLP.
Examples
Meta model challenges
You can use any suitable meta model question to dive more deeply into the intention behind any presupposition.
with everything, practice makes permanent. To save this and come back to this article many times and really begin to notice these distortions in NLP in other people's language and even yours.
Have a play around with some of the Meta model challenges and begin to notice how you're able to really open up a person's map to allow you to understand more richly and more deeply.
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There are five main distortions in NLP seen in the Meta model.
They are:
We are all guilty of mind reading I know you are! A mind read is categorised by knowing what someone else is thinking or feeling but without any information to support that thought. Mind reading can come in lots of different forms. One of them which is very common is knowing what a person is thinking or feeling. Another is how others should know how we think and feel. And lastly predicting the future or proper sizing what is going to happen.
Whenever you hear mind reading type statements you can begin to ask questions using 'how' and 'what.'
Examples:
Meta model challenges to those statements
Possible Reply
Recognising a cause-and-effect Meta model violation is when you spot and X causes why configurations in the language. so much of our life is in a cause-and-effect format that has a tendency to make connections where there is none. "If you do your homework, then you can stay up late on Friday." "If you pass all of your grades at school, you will get a good job."
So in the Meta model for cause and effect, we are looking for violations where someone associates X causes Y.
Whenever you spot this cause-and-effect violation it's time to begin asking more questions to find out how specifically do they know that X causes Y?
Examples:
Meta Model Challenges
Recognising a lost performative is when you hear a person say something like, "We will end up living on Mars by 2050" or "Only weak people stay in relationships."
The structure of a lost performative is an opinion stated as a fact and a value judgement that does not say who had that value.
You can begin to challenge lost performative type statements by asking "who says, according to whom and how do you know that?" type of questions.
Examples:
Meta model Challenges to those statements
Possible replies to the challenges
A complex equivalence is very similar to cause-and-effect. Just as we, cause-and-effect is when X causes Y. And complex equivalence can be recognised by hearing a statement that says X means Y.
"The fact that you're reading watching or listening to this means that you'll find something new!"
it's amazing how often you will hear people attach meaning to something very different.
"The fact that you're changing your job means that you are outgrowing your family"
Whenever you hear convex equivalent type statements you can challenge them by asking. "So does X have to always equal Y? Could it mean anything else? Does it always have to mean that?"
Examples:
Meta model challenges
Possible replies
If you've been looking into NLP you have heard the word presupposition quite frequently. A presupposition is simply a part of the statement has to be presupposed to be true in order for that sentence to make sense.
We challenge presuppositions to allow us to gain more specific detail and get closer to the truth. Let's look at some working examples of presuppositions in NLP.
Examples
Meta model challenges
You can use any suitable meta model question to dive more deeply into the intention behind any presupposition.
with everything, practice makes permanent. To save this and come back to this article many times and really begin to notice these distortions in NLP in other people's language and even yours.
Have a play around with some of the Meta model challenges and begin to notice how you're able to really open up a person's map to allow you to understand more richly and more deeply.
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