Utilizing the Walla Emotion Model to Standardize Terminological Clarity for AI-Driven "Emotion" Recognition.

The scientific study of affect has been historically characterized by a profound lack of terminological consensus, leading to a state of conceptual fragmentation that persists in psychology, neuroscience and many other fields. This ambiguity is not merely an academic concern; it has significant consequences for the development of artificial intelligence (AI) systems designed to recognize and respond to human "emotions". In fact, it has an influence on the entire field of affective computing. The problem is obvious. Without a distinct definition of "emotion" it is difficult to train an algorithm to recognize it. The Walla Emotion Model, also known as the ESCAPE (Emotions Convey Affective Processing Effects) model, provides a potentially helpful and neurobiologically grounded framework to resolve this impasse and to improve any discourse about it, for businesses and even lawmakers aiming at healthy societies. By establishing clear, non-overlapping definitions for affective processing, feelings, and emotions, this model offers a path toward more precise research and more ethically sound affective computing including AI-driven "emotion" recognition. It introduces a concept that allows for the detection of incongruences between internal states and external signals with a very clear terminology supporting understandable communication. This is critical for identifying feigned or socially masked inner affective states, a challenge that traditional "face-reading" AI models frequently fail to address. Even tone of voice and body postures as well as gestures can be and are often voluntarily modified. Through the separation of subcortical affective processing (evaluation of valence; neural activity) from subjective experience (feeling) and external communication (emotion), the Walla model provides a helpful framework for AI-designs meant to have the capacity to infer an internal affective state from collected signals in the wild bypassing verbal self-report. This paper is purely theoretical; it does not provide any algorithm models or other distinct suggestions to train a software package. Its main purpose is the introduction of a new emotion model, particularly a new terminology that is considered helpful in order to proceed with this endeavor. It is considered important to first enable the clearest-possible form of communication about anything related to the term emotion across all disciplines dealing with it. Only then can progress be made.