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 Sun^1,2 (js11247@nyu.edu), Zeyu Jin³, James Rankin⁴, John Rinzel^2,3; 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