Advancing affective stimuli databases: challenges and solutions.

Area of Science:

• Psychology
• Neuroscience
• Affective Science

Background:

• Affective stimulus databases are crucial for controlled emotional induction in research.
• Existing databases suffer from methodological limitations impacting study comparability and reproducibility.

Purpose of the Study:

• To review modern affective stimulus databases across various domains (visual, auditory, textual, multimodal).
• To identify key challenges and propose strategies for improving future affective databases.

Main Methods:

• Comprehensive literature review of current affective stimulus databases.
• Analysis of database attributes, including standardization, validation, cultural specificity, and assessment methods.
• Examination of stimulus diversity, duration control, and ecological validity.

Main Results:

• Identified significant challenges: inconsistent standardization and validation, cultural specificity, and limited emotion assessment methods.
• Noted issues with stimulus diversity, duration control, and ecological validity affecting psychophysiological studies.
• Highlighted variability in visual, auditory, textual, and multimodal affective databases.

Conclusions:

• Future affective databases require standardized evaluation methodologies and culturally diverse stimuli.
• Integration of virtual reality and machine learning can enhance stimulus control and ecological validity.
• Improved database structure and consistent methodologies will enhance emotion research reliability and applicability.