title: Model for stochastic-resonance-type behavior in sensory perception.
abstract: Recently it was found that noise could help improve human detection of sensory stimuli via stochastic-resonance-type behavior. Specifically, the ability of an individual to detect a weak tactile stimulus could be enhanced by adding a certain amount of noise. Here we propose, from the perspective of classical signal detection theory, a simple and general model to elucidate the mechanism underlying this phenomenon. We demonstrate that noise-mediated enhancements and decrements in human sensation can be well reproduced by our model. The predicted upper bound of the performance improvement by adding noise is also consistent with the experimental data. We suggest additional experiments to further test the model.
authors.affiliation: Center for Neurobiology and Behavior, Columbia University, 722 West 168th Street, New York, New York 10032, USA.
authors.forename: Yunfan
authors.initials: Y
authors.lastname: Gong
authors.forename: Nestor
authors.initials: N
authors.lastname: Matthews
authors.forename: Ning
authors.initials: N
authors.lastname: Qian
mesh_terms: D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009622:Noise; D010465:Perception / Q000502:physiology*