Neuro-Linguistic Programming (NLP) and Innovation

Neuro-Linguistic Programming (NLP) provides principles and specific processes for supporting innovative thinking. Even though many of the original models and their constituent patterns found in NLP developed from the process of ‘modelling’ experts and their expert performance in the field of psychotherapy, innovative thinking by the two main co-creators, John Grinder and Richard Bandler, played a significant role in the development of the field of NLP.

Innovation and the context within which NLP was created

NLP was created and developed in Santa Cruz, California. Dr. John Grinder was an assistant professor of linguistics at the University of California, Santa Cruz (UCSC), Richard Bandler was a fourth year psychology student at the same institution; and Frank Pucelik, the third creator of NLP, was a psychology student. 

UCSC is an unusual university, unlike many others in the US, in having a collegiate system and a policy of encouraging cross-disciplinary projects and research. In the culture of the time in Northern California, there were many people exploring various forms of psychotherapy and personal development. Santa Cruz is just up the coast from Big Sur, where the Esalen Institute resides, an institute at the heart of the exploration of many diverse and non-traditional forms of psychotherapy, including Gestalt therapy. The father of Gestalt therapy, Fritz Pearls, resided at the Esalen Institute for a time and ran Gestalt programmes there. 

Bandler worked part time for the publisher Science and Behaviour Books, a publisher that specialised in books on psychotherapy. His job was to listen to hours of audio recordings of Pearls doing Gestalt and to transcribe relevant passages for the Pearls final book. 

The renowned anthropologist, Gregory Bateson also lived in Santa Cruz, was a regent of UCSC and was one of the researchers at the Mental Research Institute in Palo Alto. Bateson’s groundbreaking work in the field of communication and cybernetics influenced the institute’s approach to therapy and its emphasis on understanding the patterns and dynamics of human interaction. 

Bateson had an interesting background and history. He came from a family of natural historians, and was exposed to the natural history approach to thinking about the natural world. Interestingly, his father, William Bateson, coined the word genetics. Rather than becoming a biologist, Gregory Bateson became an anthropologist. With his family background in natural history, he drew on that way of thinking in his work as an anthropologist. He was involved with the Macy conference in cybernetics in the 1950’s and later took patterns from cybernetics into his study of cultures. 

Bateson later introduced Bandler and Grinder to the psychiatrist Milton H. Erickson, an innovative psychotherapist in the application of hypnosis, whom they modelled, producing a number of useful models, including the ‘Milton model’ of communication.

Grinder was able to draw on his expertise as a linguist as a significant part of the project of creating explicit models of expertise in psychotherapy, and in the initial project, he was able to create an explicit model of Bandler’s intuitions in doing Gestalt therapy. 

Some patterns supporting Innovation

One pattern that supports innovation is having a context that supports collaboration between people from different fields. This supports cross-fertilisation of patterns and models from multiple fields to the project. Bateson drew on patterns from one field and applied them to another, making contributions not only to anthropology but also to psychiatry, family therapy, systems thinking and ecology. Bandler and Grinder drew on patterns from psychology, both cognitive and behavioural, linguistics and automata theory and applied them to the project of creating models of expertise for personal change with its application to therapy, coaching and organisational change. 

The new world of artificial intelligence provides another example of this pattern found in innovation. The development of artificial intelligence using neural networks has in its origins the intersection of neuroscience and software engineering. Drawing on understandings of how neurons in the brain are linked in layers, for example, to do shape recognition, software engineers have used this as the basis for designing neural networks in computing. This is an example of taking a pattern from one field and applying it (with modification) to another to produce something new.

One of the key ideas in NLP borrowed from Bateson’s work is the idea of creating multiple descriptions of the world. For example, in Grinder’s Perceptual Positions format in NLP, we are creating a ‘triple description’ by using first, second and third positions when examining a relationship. Anyone who has applied perceptual positions to an interpersonal relationship, will note that resources that would make a positive difference in the relationship emerge and become apparent as a by-product of the exercise. When applying perceptual positions, information is generated through the relationships between the different descriptions (first, second and third positions) that lead to the generation of the resource (innovation) required to transform the relationship. 

The content of NLP is a set of models of human expertise (and their constituent patterns), including models for finding new patterns. The unit of study in NLP is pattern. In contrast, the unit of study in behavioural psychology is behaviour. The importance of pattern is apparent in the definition of NLP.

“NLP is a meta-discipline which focuses on the discovery and coding of patterns which distinguish the most capable of the practitioners of some particular discipline (managerial practice, medical practice, sports, therapy…) from the average practitioner. These distinguishing patterns are the substance of NLP” – John Grinder, from the NLP Field Guide part 1: A reference manual of practitioner level patterns (2001)

What is a Pattern?

A pattern is a repeating sequence where, if you detect the first part of the sequence, you can then predict the next part. For example, I have a pet cat who will only drink water from a running tap. The first part of the sequence is a particular call that she makes, the second part of the sequence is myself or my partner going to the tap and holds it open for her. The third part of the sequence is she leans her head under the tap and begins to drink. The pattern is one that includes two players, person and cat, each with their own role for completing the entire pattern.

Everything we do is made up of patterns, from how we eat breakfast, to driving a car, negotiating with another, making a decision or interacting with a partner. Patterns occur in a context and include an outcome. The content of our lives is processed through patterns, some more useful than others in achieving outcomes. Even the operation of our states (emotions) are patterned, and thereby potentially changeable. Change the pattern that generates the state and you change the state.

Patterns of innovation

The above examples are all examples of one key pattern found in innovation, where one or more people take patterns of thinking from one or more fields that they are already familiar with and apply those patterns to another context. Bateson used the patterns of thinking about the world as a natural historian and applied them to anthropology, and later patterns from cybernetics to anthropology.

Grinder took patterns from Linguistics and applied them to Bandler’s use of language when doing Gestalt therapy to create the first model developed in NLP, the meta model. Neural networks, the basis of artificial intelligence, was first developed in Cognitive Science, itself a field that is cross-disciplinary. Cognitive science draws on research from anthropology, cognitive psychology, computing science, automata theory and linguistics. Neural networks, as the name implies, are modelled from models of how neurons are organised in brains.

To cultivate innovation, one needs to be aware of patterns, be able to detect patterns, preferably in multiple contexts, and be proactive in applying and testing patterns in other contexts, especially the desired context where you want innovation.

One of the key competencies that is developed during our 10970NAT Graduate Certificate in Neuro-Linguistic Programming is pattern detection and utilisation.

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