Pikatza Huerga, AmaiaMatanzas de Luis, PabloFernandez De Retana Uribe, MiguelPeña Lasa, JavierZulaika Zurimendi, UnaiAlmeida, Aitor2025-11-052025-11-052025Pikatza-Huerga, A., Matanzas de Luis, P., Uribe, M. F.-D.-R., Lasa, J. P., Zulaika, U., & Almeida, A. (2025). Analysing the Impact of Images and Text for Predicting Human Creativity Through Encoders. International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE - Proceedings, 15-24. https://doi.org/10.5220/0013203600003938978989758743610.5220/0013203600003938https://hdl.handle.net/20.500.14454/4271Ponencia presentada en la 11th International Conference on Information and Communication Technologies for Ageing Well and e-Health, celebrada en Porto, entre el 6 y el 8 de abril de 2025This study explores the application of multimodal machine learning techniques to evaluate the originality and complexity of drawings. Traditional approaches in creativity assessment have primarily focused on visual analysis, often neglecting the potential insights derived from accompanying textual descriptions. The research assesses four target features: drawings’ originality, flexibility and elaboration level, and titles’ creativity, all labelled by expert psychologists. The research compares different image encoding and text embeddings to examine the effectiveness and impact of individual and combined modalities. The results indicate that incorporating textual information enhances the predictive accuracy for all features, suggesting that text provides valuable contextual insights that images alone may overlook. This work demonstrates the importance of a multimodal approach in creativity assessment, paving the way for more comprehensive and nuanced evaluations of artistic expression.engArtistic expressionCreativity assessmentEEGMachine learningOriginality evaluationText and image analysisconference paper2025-11-052184-4984