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Adding noise to a sensory signal generally decreases human performance.However noise can improve performance too, due to a process called stochasticresonance (SR). This paradoxical effect may be exploited in psychophysicalexperiments, to provide additional insights into how the sensory system dealswith noise. Here, I develop a model for stochastic resonance to study theinfluence of noise on human perception, in which the biological parameter of`lapse rate' was included. I show that the inclusion of lapse rate allows forthe occurrence of stochastic resonance in terms of the performance metric d'.At the same time, I show that high levels of lapse rate cause stochasticresonance to disappear. It is also shown that noise generated in the brain(i.e., internal noise) may obscure any effect of stochastic resonance inexperimental settings. I further relate the model to a standard equivalentnoise model, the linear amplifier model, and show that the lapse rate canfunction to scale the threshold versus noise (TvN) curve, similar to theefficiency parameter in equivalent noise (EN) models. Therefore, lapse rateprovides a psychophysical explanation for reduced efficiency in EN paradigms.Furthermore, I note that ignoring lapse rate may lead to an overestimation ofinternal noise in equivalent noise paradigms. Overall, describing stochasticresonance in terms of signal detection theory, with the inclusion of lapserate, may provide valuable new insights into how human performance depends oninternal and external noise.

Modelling stochastic resonance in humans: the influence of lapse rate