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

Area of Science:

• Psychology
• Neuroscience
• Artificial Intelligence
• Affective Computing

Background:

• The scientific study of affect suffers from a lack of terminological consensus, leading to conceptual fragmentation.
• This ambiguity hinders the development of artificial intelligence (AI) systems for recognizing human emotions and impacts affective computing.
• Current AI models struggle with accurately recognizing emotions due to unclear definitions and the ability of individuals to mask or feign affective states.

Purpose of the Study:

• To introduce the Walla Emotion Model (also known as the ESCAPE model) as a neurobiologically grounded framework to resolve terminological ambiguity in emotion research.
• To establish clear, non-overlapping definitions for affective processing, feelings, and emotions.
• To provide a theoretical foundation for more precise research and ethically sound affective computing, including AI-driven emotion recognition.

Main Methods:

• The paper proposes a theoretical framework, the Walla Emotion Model.
• It distinguishes between subcortical affective processing (valence evaluation, neural activity), subjective experience (feeling), and external communication (emotion).
• The model facilitates the detection of incongruences between internal states and external signals, offering a new terminology.

Main Results:

• The Walla Emotion Model provides a clear, neurobiologically grounded framework for understanding and defining affect, feelings, and emotions.
• It enables the detection of masked or feigned affective states by analyzing incongruences between internal states and external signals.
• The model offers a path toward more precise research and ethically sound affective computing by establishing distinct terminology.

Conclusions:

• The Walla Emotion Model offers a novel terminology and framework to address the persistent lack of consensus in emotion research.
• This model is crucial for advancing affective computing and AI-driven emotion recognition by providing clear, distinct definitions.
• Establishing clear communication about emotion across disciplines is essential for future progress in the field.