Stochastic resonance (SR) is a phenomenon wherein the response of a nonlinear system to a weak input signal is optimized by the presence of a particular, nonzero level of noise. Our objective was to demonstrate cross-modality SR in human sensory perception. Specifically, we were interested in testing the hypothesis that the ability of an individual to detect a subthreshold mechanical cutaneous stimulus can be significantly enhanced by introducing a particular level of electrical noise. Psychophysical experiments were performed on 11 healthy subjects. The protocol consisted of the presentation of: (a) a subthreshold mechanical stimulus plus electrical noise, or (b) no mechanical stimulus plus electrical noise. The intensity of the electrical noise was varied between trials. Each subject's ability to identify correctly the presence of the mechanical stimulus was determined as a function of the noise intensity. In 9 of the 11 subjects, the introduction of a particular level of electrical noise significantly enhanced the subject's ability to detect the subthreshold mechanical cutaneous stimulus. In 2 of the 11 subjects, the introduction of electrical noise did not significantly change the subject's ability to detect the mechanical stimulus. These findings indicate that input electrical noise can serve as a negative masker for subthreshold mechanical tactile stimuli, i.e., electrical noise can increase the detectability of weak mechanical signals. Thus, for SR-type effects to be observed in human sensory perception, the noise and stimulus need not be of the same modality. From a bioengineering and clinical standpoint, this work suggests that an electrical noise-based technique could be used to improve tactile sensation in humans when the mechanical stimulus is around or below threshold. (c) 1998 American Institute of Physics.
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Sci Rep
January 2025
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Laboratoire PHENIX, Sorbonne Université, CNRS, (Physico-Chimie des Electrolytes et Nanosystèmes Interfaciaux), 4 Place Jussieu, 75005 Paris, France.
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View Article and Find Full Text PDFSci Rep
January 2025
Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA.
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