Advancing affective stimuli databases: challenges and solutions

Affective stimulus databases are integral elements in psychological and neuroscientific research, enabling the controlled induction of emotional states. However, despite significant progress, existing databases face methodological limitations that interfere with cross- study comparability and reproducibility. This review thoroughly examines modern affective stimulus databases across visual, auditory, textual, and multimodal domains, presenting their positive attributes and deficiencies. Key challenges include variability in stimulus standardization, inconsistencies in validation procedures, cultural specificity, and reliance on either categorical or dimensional emotion assessment methods. Additionally, issues related to stimulus diversity, duration control, and ecological validity further complicate the interpretation of results in psychophysiological studies. To address these challenges, we propose strategies for improving future databases, including the integration of standardized evaluation methodologies, the expansion of multimodal and culturally diverse stimuli, and the implementation of advanced technological solutions such as virtual reality and machine learning. Improving the structure of databases and maintaining consistent methodologies will increase the reliability and applicability of emotion research, ultimately contributing to a more comprehensive understanding of affective processes across different fields.