The general publications and the academic research about “lie detection” (differentiation between truth and lie) and credibility analysis (plausibility of a statement or utterance) are very vast and different approach methods are available in these fields of behaviour analysis.
The main steps in the scientific approach to getting to the truth are well established and the PEEVR model (planning, preparation, engagement, exploration, verification, and resolution), described by Lansley (2017) as a generic and versatile framework, that allows for adaptations and tailoring, is very useful as well accepted.
The author also encourages users to apply the framework as a default model and to modify and develop more specific approaches. In this sense, no clear research has been done to establish the different methods to generate hypotheses in the exploration phase of the PEEVR model.
The Facial Expression channel (“F”) was chosen to be studied in the generation of hypotheses process, as this is a very important and decisive step of this model. More systematic methods, scientific or not, to generate hypotheses about Facial Expression in getting to the truth are worth being researched and proposed.
The potential impact of this study is to define a more clear and precise methodology to generate the possible hypotheses to explain the points of interest (PINs) related to the Facial Expression coding. These hypotheses can then be tested (whenever possible) in the lie detection and credibility analysis.
This method of generating hypotheses could also be modified and developed to other specific settings. It is important to answer this need because the maximum hypotheses we can formulate about a case being analysed, the higher the chances of getting to the truth.