The vast majority of contemporary models of bistable perception are neurochemechanistic in nature (e.g., Rankin, Sussman, & Rinzel, 2015). However, quantum approaches challenge this rather antiquated mechanistic Newtonian working hypothesis.
Rankin, J., Sussman, E., & Rinzel, J.. (2015). Neuromechanistic Model of Auditory Bistability. PLoS Computational Biology
Plain numerical DOI: 10.1371/journal.pcbi.1004555
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“Sequences of higher frequency a and lower frequency b tones repeating in an aba- triplet pattern are widely used to study auditory streaming. one may experience either an inte- grated percept, a single aba-aba- stream, or a segregated percept, separate but simulta- neous streams a-a-a-a- and -b—b–. during minutes-long presentations, subjects may report irregular alternations between these interpretations. wecombine neuromechanistic modeling and psychoacoustic experiments to study these persistent alternations and to characterize the effects of manipulating stimulus parameters. unlike many phenomenologi- cal models with abstract, percept-specific competition and fixed inputs, our network model comprises neuronal units with sensory feature dependent inputs that mimic the pulsatile- like a1 responses to tones in the aba- triplets. it embodies a neuronal computation for per- cept competition thought to occur beyond primary auditory cortex (a1). mutual inhibition, adaptation and noise are implemented.we include slow ndmarecurrent excitation for local temporal memory that enables linkage across sound gaps from one triplet to the next. percepts in our model are identified in the firing patterns of the neuronal units.we predict with the model thatmanipulations of the frequency difference between tones a and b should affect the dominance durations of the stronger percept, the one dominant a larger fraction of time, more than those of the weaker percept—a property that has been previ- ously established and generalized across several visual bistable paradigms. we confirm the qualitative prediction with our psychoacoustic experiments and use the behavioral data to further constrain and improve the model, achieving quantitative agreement between experimental andmodeling results. our work and model provide a platform that can be extended to consider other stimulus conditions, including the effects of context and volition.”