A machine learning approach to identify stochastic resonance in human perceptual thresholds.

BACKGROUND: Stochastic resonance (SR) is achieved when a faint signal is improved with the addition of the appropriate amount of white noise. Perceptual thresholds are expected to follow a characteristic performance improvement curve as a function of the white noise level added (i.e., thresholds are reduced with an optimal amount of added white noise, beyond which excessive white noise is no longer beneficial). Since SR exhibition in perceptual thresholds is defined by a shape rather than a statistical difference, the presence of SR is typically identified qualitatively. The current state-of-the-art is for blinded human judges to categorize the presence of SR by visually examining data. While categorizations are made with subject data intermixed within a balanced, simulated dataset, which accounts for false positives, this method is still subjective and prone to human error. NEW METHOD: We use a logistic regression (LR) trained on engineered features in order to quantitatively classify exhibition of SR. The LR was trained on datasets simulated from a model for SR performance enhancement. RESULTS: We implemented the algorithmic classification process in 6 perceptual threshold test cases, informed by the literature and parameters were defined by experimental subject data. Comparison to Existing Method(s): We report algorithmic classifications of SR exhibition, considering the 6 test cases, that outperform existing subjective methods in accuracy (p < 0.05). CONCLUSIONS: We demonstrate that algorithmic classification can effectively identify SR in perceptual thresholds, providing a rigorous, objective, and quantitative approach to identifying the presence of SR.

Galvanic Vestibular Stimulation Produces Cross-Modal Improvements in Visual Thresholds.

BACKGROUND: Stochastic resonance (SR) refers to a faint signal being enhanced with the addition of white noise. Previous studies have found that vestibular perceptual thresholds are lowered with noisy galvanic vestibular stimulation (i.e., "in-channel" SR). Auditory white noise has been shown to improve tactile and visual thresholds, suggesting "cross-modal" SR. OBJECTIVE: We investigated galvanic vestibular white noise (nGVS) (n = 9 subjects) to determine the cross-modal effects on visual and auditory thresholds. METHODS: We measured auditory and visual perceptual thresholds of human subjects across a swath of different nGVS levels in order to determine if some individual-subject determined best nGVS level elicited a reduction in thresholds as compared the no noise condition (sham). RESULTS: We found improvement in visual thresholds (by an average of 18%, p = 0.014). Subjects with higher (worse) visual thresholds with no stimulation (sham) improved more than those with lower thresholds (p = 0.04). Auditory thresholds were unchanged by vestibular stimulation. CONCLUSION: These results are the first demonstration of cross-modal improvement with galvanic vestibular stimulation, indicating galvanic vestibular white noise can produce cross-modal improvements in some sensory channels, but not all.