Eye movements in response to different cognitive activities measured by eyetracking: a prospective study on some of the neurolinguistics programming theories

The eyes are in constant movement to optimize the interpretation of the visual scene by the brain. Eye movements are controlled by complex neural networks that interact with the rest of the brain. The direction of our eye movements could thus be influenced by our cognitive activity (imagination, internal dialogue, memory, etc.). A given cognitive activity could then cause the gaze to move in a specific direction (a brief movement that would be instinctive and unconscious). Neuro Linguistic Programming (NLP), which was developed in the 1970s by Richard Bandler and John Grinder (psychologist and linguist respectively), issued a comprehensive theory associating gaze directions with specific mental tasks. According to this theory, depending on the visual path observed, one could go back to the participant's thoughts and cognitive processes. Although NLP is widely used in many disciplines (communication, psychology, psychotherapy, marketing, etc), to date, few scientific studies have examined the validity of this theory. Using eye tracking, this study explores one of the hypotheses of this theory, which is one of the pillars of NLP on visual language. We created a protocol based on a series of questions of different types (supposed to engage different brain areas) and we recorded by eye tracking the gaze movements at the end of each question while the participants were thinking and elaborating on the answer. Our results show that 1) complex questions elicit significantly more eye movements than control questions that necessitate little reflection, 2) the movements are not random but are oriented in selected directions, according to the different question types, 3) the orientations observed are not those predicted by the NLP theory. This pilot experiment paves the way for further investigations to decipher the close links between eye movements and neural network activities in the brain

A Machine Learning Approach to Identify the Preferred Representational System of a Person

Whenever people think about something or engage in activities, internal mental processes will be engaged. These processes consist of sensory representations, such as visual, auditory, and kinesthetic, which are constantly being used, and they can have an impact on a person’s performance. Each person has a preferred representational system they use most when speaking, learning, or communicating, and identifying it can explain a large part of their exhibited behaviours and characteristics. This paper proposes a machine learning-based automated approach to identify the preferred representational system of a person that is used unconsciously. A novel methodology has been used to create a specific labelled conversational dataset, four different machine learning models (support vector machine, logistic regression, random forest, and k-nearest neighbour) have been implemented, and the performance of these models has been evaluated and compared. The results show that the support vector machine model has the best performance for identifying a person’s preferred representational system, as it has a better mean accuracy score compared to the other approaches after the performance of 10-fold cross-validation. The automated model proposed here can assist Neuro Linguistic Programming practitioners and psychologists to have a better understanding of their clients’ behavioural patterns and the relevant cognitive processes. It can also be used by people and organisations in order to achieve their goals in personal development and management. The two main knowledge contributions in this paper are the creation of the first labelled dataset for representational systems, which is now publicly available, and the use of machine learning techniques for the first time to identify a person’s preferred representational system in an automated way

Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing

Neuro Linguistic Programming (NLP) is a methodology used for recognition of human behavioural patterns and modification of the behaviour. A significant part of this process is influenced by the theory of representational systems which equates to the five main senses. The preferred representational system of an individual can explain a large part of exhibited behaviours and characteristics. There are different methods to recognise the representational systems, one of which is to investigate the sensory based words in the used language during the conversation. However, there are difficulties during this process since there is not a single reference method used for identification of representational systems and existing ones are subject to human interpretations. Some human errors like lack of experience, personal judgment, different levels of skill and personal mistakes may also affect the accuracy and reliability of the existing methods. This research aims to apply a new approach that is to automate the identification process in order to remove human errors thereby increasing the accuracy and precision. Natural Language Processing has been used for automating this process and an intelligent software has been developed able to identify the preferred representational system with increased accuracy and reliability. This software has been tested and compared to human identification of representational systems. The results of the software are similar to a NLP practitioner and the software responds more accurately than a human practitioner in various parts of the process. This novel methodology will assist the NLP practitioners to obtain an improved understanding of their clients’ behavioural patterns and the associated cognitive and emotional processes

Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing

Neuro Linguistic Programming (NLP) is a methodology used for recognition of human behavioral patterns and modification of the behavior. A significant part of this process is influenced by the theory of representational systems which equates to the five main senses. The preferred representational system of an individual can explain a large part of exhibited behaviors and characteristics. There are different methods to recognize the representational systems, one of which is to investigate the sensory-based words in the used language during the conversation. However, there are difficulties during this process since there is not a single reference method used for identification of representational systems and existing ones are subject to human interpretations. Some human errors like lack of experience, personal judgment, different levels of skill and personal mistakes may also affect the accuracy and reliability of the existing methods. This research aims to apply a new approach that is to automate the identification process in order to remove human errors, thereby increasing the accuracy and precision. Natural Language Processing has been used for automating this process, and an intelligent software has been developed to identify the preferred representational system with increased accuracy and reliability. This software has been tested and compared to human identification of representational systems. The results of the software are similar to a NLP practitioner, and the software responds more accurately than a human practitioner in various parts of the process. This novel methodology will assist the NLP practitioners to obtain an improved understanding of their clients’ behavioral patterns and the associated cognitive and emotional processes

The Missing Link: Enhancing Mediation Success Using Neuro-Linguistic Programming

What is it that separates the best from the rest? Generally speaking, the highly coveted litigators and mediators draw people to them over and over again because of that something extra they possess. In Neuro-Linguistic Programming (NLP), that something extra is often referred to as the difference that makes the difference. Outstanding performers in any field instinctively know the difference that makes the difference. Successful trial lawyers, for example, have a keen knack for connecting with the jury and persuading them to follow their lead in support of the client\u27s case. Similarly, parties prefer some mediators over others in large part because they are able to move people away from their entrenched positions and toward a more flexible mindset needed to settle cases. Although litigating and mediating require quite different skill sets, a review of those who demonstrate excellence in either of these fields will yield certain common denominators, which can be identified using NLP

Frequency Value Grammar and Information Theory

I previously laid the groundwork for Frequency Value Grammar (FVG) in papers I submitted in the proceedings of the 4th International Conference on Cognitive Science (2003), Sydney Australia, and Corpus Linguistics Conference (2003), Lancaster, UK. FVG is a formal syntax theoretically based in large part on Information Theory principles. FVG relies on dynamic physical principles external to the corpus which shape and mould the corpus whereas generative grammar and other formal syntactic theories are based exclusively on patterns (fractals) found occurring within the well-formed portion of the corpus. However, FVG should not be confused with Probability Syntax, (PS), as described by Manning (2003). PS is a corpus based approach that will yield the probability distribution of possible syntax constructions over a fixed corpus. PS makes no distinction between well and ill formed sentence constructions and assumes everything found in the corpus is well formed. In contrast, FVG’s primary objective is to distinguish between well and ill formed sentence constructions and, in so doing, relies on corpus based parameters which determine sentence competency. In PS, a syntax of high probability will not necessarily yield a well formed sentence. However, in FVG, a syntax or sentence construction of high ‘frequency value’ will yield a well-formed sentence, at least, 95% of the time satisfying most empirical standards. Moreover, in FVG, a sentence construction of ‘high frequency value’ could very well be represented by an underlying syntactic construction of low probability as determined by PS. The characteristic ‘frequency values’ calculated in FVG are not measures of probability but rather are fundamentally determined values derived from exogenous principles which impact and determine corpus based parameters serving as an index of sentence competency. The theoretical framework of FVG has broad applications beyond that of formal syntax and NLP. In this paper, I will demonstrate how FVG can be used as a model for improving the upper bound calculation of entropy of written English. Generally speaking, when a function word precedes an open class word, the backward n-gram analysis will be homomorphic with the information source and will result in frequency values more representative of co-occurrences in the information source

Judicial decision-making and extra-legal influences: Neurolinguistic Programming as a candidate framework to understand persuasion in the legal context

Jurister försöker påverka rättsliga beslutsprocesser med hjälp av övertalning, men den befintliga litteraturen om övertalning i rättssalen är förvånansvärt begränsad med fokus på enskilda tekniker i isolering; inga omfattande integrerade ramverk finns tillgängliga. Vi föreslår en populär kommersiell metod för övertalning, Neurolingvistisk Programmering (NLP), som startpunkt för att utveckla en modell som kan fylla detta gap. Först presenterar vi en bred analys av rättsliga beslutsprocesser och utomrättsliga faktorer som påverkar dem. Därefter utsätter vi centrala aspekter av NLP för noggrann granskning. Slutligen syntetiserar vi dessa trådar i en mångfacetterad bedömning av NLPs potentiella användbarhet som ett omfattande och integrerat ramverk för att förstå och beskriva juristers övertalningsprocesser i rättssalen. Vi hävdar att NLP kan beskriva dessa beteenden och strategier både genom en självreflexiv logik, som ett resultat av dess breda inflytande, men också för mer generella övertalningsprocesser tack vare ett stort antal överensstämmelser mellan NLP-begrepp och resultat från vetenskaplig litteratur. Även om dessa överensstämmelser är ytliga, tyder det faktum att NLP integrerar sina förenklade koncept i ett sammanhållet ramverk, som spänner argumentations- och presentations-dimensioner för övertalning, att det förhållandevis enkelt kan anpassas till en praktisk modell för att beskriva och förstå övertalning i rättssalen. Vidare forskning är indikerad.Trial advocates seek to influence the outcomes of judicial decision-making processes using persuasion, but the existing literature regarding persuasion in the courtroom is surprisingly piecemeal, focusing on individual techniques in isolation; no comprehensive frameworks for integrating these techniques, or for systematically analyzing advocates’ attempts to enact persuasion in the courtroom, have been developed. We propose a popular commercial technology for persuasion, Neurolinguistic Programming (NLP), as a candidate framework that might be modified and adapted to fill this gap. First we present a wide-ranging, discursive analysis of judicial decision-making processes and extra-legal factors that influence them. Next, core aspects of NLP theory are subjected to careful examination. Finally, these threads are synthesized into a multifaceted assessment of NLP’s potential utility as a comprehensive and integrative framework for understanding and describing how litigators enact persuasion in the courtroom. We argue that NLP can describe these behaviors and strategies both by way of a self-reflexive logic resulting from its popular influence, but also as a more general, context independent model by virtue of a large number of correspondences between NLP concepts and findings from the scholarly literature. Although these correspondences are superficial, the fact that NLP integrates its simplified, folk concepts into a coherent framework spanning argumentative and presentational dimensions of persuasion suggests that it might readily be adapted into a useful descriptive model for understanding persuasion in the courtroom. Further scholarly attention is indicated