Age Against the Machine: How Age Relates to Listeners' Ability to Recognize Emotions in Robots' Semantic-Free Utterances

Semantic-Free Utterances (SFUs, sounds conveying intention without using words) are being increasingly adopted for human-robot interaction (HRI) to communicate affect. In healthcare, where older adults are overrepresented, affective robotics are becoming more common to reduce healthcare professionals' workload. Hence, understanding how older adults perceive and communicate with robots is crucial. Although previous studies have demonstrated a decline in older adults' ability to categorize emotions, it remains unclear how this impacts their comprehension of SFUs used in HRI. This paper investigates the effect of age (and other factors) on listeners' ability to categorize emotions in SFUs designed for HRI. Additionally, we explore listeners' preferences of SFUs for a healthcare robot. Listeners indicated that SFUs' similarities to natural language, the need for a distinction between human and robot, and their expectations of how a hospital robot should sound like, influenced their preferences. Furthermore, we conducted an online emotion categorization task to investigate how age, emotion category, type of SFU (with varying degrees of robot-likeness), listeners' gender, and their experience with robots relate to listeners' ability to categorize emotions. Results confirm that as age increases, there is a decline in emotion categorization performance of SFUs varying by emotion category and type of SFU.