Effective affective communication is essential for collaborative human-robot interaction, as emotional expressions can enhance trust and collaboration with robots. This paper addresses this need by investigating whether a musically-informed sound design or a color-based display is more effective for emotional expression in a non-anthropomorphic kitchen assistant robot. Using a within-subjects VR experiment (N=14), participants completed three collaborative cooking tasks while the robot expressed happiness, sadness, and anger via synthesized sounds or colored lights. Results show that effectiveness is emotion-dependent: sound significantly outperformed color for happiness and sadness, while color (red) was more effective for anger. Sound also yielded higher believability, likeability, animacy, and perceived intelligence. Qualitative findings suggest sound’s advantage stems from both its functional benefit in visually demanding tasks and its capacity to create social presence. A key emergent finding is that contextual appropriateness shapes emotion interpretation, indicating that expression design must account for social expectations in collaborative settings.
Ayden Janssen, Rachel Ringe, Lisa Hesselbarth et al.· Message Understanding Confer...· 0 citations
Metacognition has gained significant attention for its potential to enhance autonomy and adaptability of artificial agents but remains a fragmented field: diverse theories, terminologies, and design choices have led to disjointed developments and limited comparability across systems. Existing overviews remain at a conceptual level that is undiscerning to the underlying algorithms, representations, and their respective effectiveness. We address this gap by performing a systematic scoping review. Reports were included if they described techniques enabling Computational Metacognitive Architectures (CMAs) to model, store, remember, and process their episodic metacognitive experiences, one of Flavell’s (1979) three foundational components of metacognition. Searches were conducted in 16 databases between December 2023 and June 2024. Data were charted using a 20-item framework considering pertinent aspects and analysed via structured narrative synthesis. A total of 101 reports on 35 distinct CMAs were included. Our findings show that metacognitive experiences may boost system performance and explainability, e.g., via self-repair. However, lack of standardisation and limited evaluations may hinder progress: only 17% of CMAs were quantitatively evaluated regarding this review’s focus, and significant terminological inconsistency limits cross-architecture synthesis. Systems also varied widely in memory content, data types, and employed algorithms. Limitations include the non-iterative nature of the search query, heterogeneous data availability, and an under-representation of sub-symbolic CMAs. Future research should focus on standardisation and evaluation, e.g., via community-driven challenges, and on transferring promising principles to emergent systems.
Robin Nolte, M. Pomarlan, Ayden Janssen et al.· Artificial Intelligence Revi...· 0 citations
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