The Development of a Multi-Modality Emotion Recognition Test Presented via a Mobile Application.

Area of Science:

• Psychology
• Neuroscience
• Computer Science

Background:

• Emotion recognition is crucial for social interaction and identifying neurological changes.
• Current assessment tools are limited by single modality, paper-based formats, and Western-centric stimuli.
• Multimodality and cultural context significantly impact emotion recognition.

Purpose of the Study:

• To develop and validate a novel multi-modality emotion recognition mobile application (MMER app).
• To address the limitations of existing emotion recognition assessment tools.
• To provide a culturally sensitive and versatile tool for evaluating emotion recognition.

Main Methods:

• Development of the MMER app utilizing a published database and tablet interface.
• Recruitment of 169 healthy adult participants.
• Application of Rasch analysis, factor analysis, and psychometric evaluations.

Main Results:

• The MMER app exhibited excellent internal consistency (Cronbach alpha = 0.94) and test-retest reliability (0.85).
• Factor analysis identified three underlying factors of emotion recognition.
• An adjusted score formula was developed for clinical application.

Conclusions:

• The MMER app possesses robust psychometric properties, indicating its suitability for assessing emotion recognition.
• The app offers a promising, accessible tool for research and clinical practice.
• Further investigations into the app's applications are warranted.