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In sensory perception, stochastic resonance (SR) refers to the application of noise to enhance information transfer, allowing for the sensing of lower-level stimuli. Previously, subjective-assessments identified SR in vestibular perceptual thresholds, assessed using a standard two alternative (i.e., binary), forced-choice task, when applying noisy Galvanic Vestibular Stimulation (nGVS). However, this required extensive testing of at least 100 binary trials to yield sufficiently precise thresholds at each of several nGVS amplitudes, leading to confounds of fatigue, sleepiness, learning, etc. stalling the study of vestibular SR. To mitigate this, we explore confidence reporting, which via a confidence signal detection (CSD) model may much more efficiently identify SR (i.e., with fewer trials), if SR exists in CSD thresholds. To test this, Y-translation thresholds were tested with 100 trials at each nGVS amplitude (0 or sham, 0.1, 0.2, 0.3 and 0.4 mA peak-to-peak). To objectively identify SR, we applied a machine learning classification algorithm trained on simulated datasets. We found significant evidence of SR exhibition using CSD thresholds (p = 0.0025), with six of 10 subjects classified as exhibiting SR. Next, we considered fewer trials, finding the false positive rate of SR identification to be better using CSD thresholds with as few as 50 trials, when compared to 100 binary trials. Applying the CSD model to our subject’s data with a subset of their trials found similar classifications of SR exhibition as with 100 binary trials. We demonstrate CSD thresholds exhibit SR, proving a means of better and much more efficiently identifying SR. [ABSTRACT FROM AUTHOR] more...