A mixture model to analyse synesthetic experience in museums using multi-point semantic differential scales

Arts and cultural places have a relevant role in our society, and it is fundamental to address the decisions of institutions taken to engage visitors and improve their experience. Statistical methods and techniques can be used to reach this objective. In the project Data Science for Brescia - Arts and Cultural Places we investigated the sensations evoked by the colourful rooms of the Art Gallery of the city of Brescia, Italy. We used the concepts of synesthesia and ideasthesia to evaluate the visitors’ sensations and asked them to answer a questionnaire. To assess the synesthetic experience evoked by colours, we used twelve items with a multi-point Semantic Differential response scale to investigate different aspects related to four senses: sight, touch, smell, and taste. The data were analyzed with two models belonging to the CUB class: the basic CUB (Combination of discrete Uniform and shifted Binomial random variable) model, originally proposed to analyse rating data, and the CUM (Combination of discrete Uniform and linearly transformed Multinomial random variable) model, which is particularly suitable to analyse data coming from the type of response scales we used. Our study showed interesting results about the visitor multisensory experience which can be integrated with other aspects of the museum experience, such as satisfaction. These insights can be leveraged to enhance the overall quality of experiences for both citizens and tourists.