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Poster E83

A neural mechanistic model of auditory tri-stability

Poster Session E - Monday, April 15, 2024, 2:30 – 4:30 pm EDT, Sheraton Hall ABC

Jiaqiu Sun1,2 (, Zeyu Jin3, James Rankin4, John Rinzel2,3; 1Division of Arts and Sciences, New York University Shanghai, 1555 Century Avenue, Shanghai, 200122, China, 2Center for Neural Science, New York University, New York, New York, United States of America, 3Courant Institute of Mathematical Sciences, New York University, New York, New York, United States of America, 4Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QJ, UK

The human brain effortlessly deciphers ambiguous auditory signals, resulting in multi-stable perceptions. This phenomenon is traditionally investigated via the auditory streaming paradigm, employing ABA_ triplet sequences. The prevailing body of research has focused on perceptual bi-stability. It interprets the perceptions either as a single integrated stream or as two simultaneous distinct streams. Our study extends this inquiry to include tri-stable perceptions. We collected empirical data from participants engaged in a tri-stable auditory task, utilizing this dataset to refine a neural mechanistic model that had successfully reproduced multiple features of auditory bi-stability. Remarkably, the model successfully emulated basic statistical characteristics of tri-stability without substantial modification. This model also allows us to demonstrate a parsimonious approach to account for individual variability by adjusting the parameter of either the noise level or the neural adaptation strength.

Topic Area: PERCEPTION & ACTION: Audition


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April 13–16  |  2024