Sensitive Pictures: Emotional Interpretation in the Museum

Abstract

Museums are interested in designing emotional visitor experiences to complement traditional interpretations. HCI is interested in the relationship between Affective Computing and Affective Interaction. We describe Sensitive Pictures, an emotional visitor experience co-created with the Munch art museum. Visitors choose emotions, locate associated paintings in the museum, experience an emotional story while viewing them, and self-report their response. A subsequent interview with a portrayal of the artist employs computer vision to estimate emotional responses from facial expressions. Visitors are given a souvenir postcard visualizing their emotional data. A study of 132 members of the public (39 interviewed) illuminates key themes: designing emotional provocations; capturing emotional responses; engaging visitors with their data; a tendency for them to align their views with the system's interpretation; and integrating these elements into emotional trajectories. We consider how Affective Computing can hold up a mirror to our emotions during Affective Interaction

Citation

Benford, S., Sundnes Løvlie, A., Ryding, K., Rajkowska, P., Bodiaj, E., Paris Darzentas, D., …Spanjevic, B. (2022). Sensitive Pictures: Emotional Interpretation in the Museum. In CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491102.3502080

• Conference Name: CHI '22: CHI Conference on Human Factors in Computing Systems
• Conference Location: New Orleans, LA, USA
• Start Date: Apr 29, 2022
• End Date: May 5, 2022
• Acceptance Date: Apr 5, 2022
• Online Publication Date: Apr 29, 2022
• Publication Date: Apr 29, 2022
• Deposit Date: Jul 4, 2022
• Publicly Available Date: Jul 5, 2022
• Publisher: Association for Computing Machinery (ACM)
• Book Title: CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
• ISBN: 9781450391573
• DOI: https://doi.org/10.1145/3491102.3502080
• Public URL: https://nottingham-repository.worktribe.com/output/8848735
• Publisher URL: https://dl.acm.org/doi/10.1145/3491102.3502080