Introducing ART: A new method for testing auditory memory with circular reproduction tasks

• Published in: Behavior research methods
• Main Authors: Karabay, Aytaç, Nijenkamp, Rob, Sarampalis, Anastasios, Fougnie, Daryl
• Format: Journal Article
• Language: English
• Published: United States 09.09.2024
• Subjects: Auditory working memory; Auditory delayed estimation paradigm; Shepard tone complex
• ISSN: 1554-3528; 1554-3528
• DOI: 10.3758/s13428-024-02477-2

Theories of visual working memory have seen significant progress through the use of continuous reproduction tasks. However, these tasks have mainly focused on studying visual features, with limited examples existing in the auditory domain. Therefore, it is unknown to what extent newly developed memory models reflect domain-general limitations or are specific to the visual domain. To address this gap, we developed a novel methodology: the Auditory Reproduction Task (ART). This task utilizes Shepard tones, which create an infinite rising or falling tone illusion by dissecting pitch chroma and height, to create a 1-360° auditory circular space. In Experiment 1, we validated the perceptual circularity and uniformity of this auditory stimulus space. In Experiment 2, we demonstrated that auditory working memory shows similar set size effects to visual working memory-report error increased at a set size of 2 relative to 1, caused by swap errors. In Experiment 3, we tested the validity of ART by correlating reproduction errors with commonly used auditory and visual working memory tasks. Analyses revealed that ART errors were significantly correlated with performance in both auditory and visual working memory tasks, albeit with a stronger correlation observed with auditory working memory. While these experiments have only scratched the surface of the theoretical and computational constraints on auditory working memory, they provide a valuable proof of concept for ART. Further research with ART has the potential to deepen our understanding of auditory working memory, as well as to explore the extent to which existing models are tapping into domain-general constraints.