Psychometric function

Detector

Linguistics

Cognitive psychology

Philosophy

Neuroscience

Somatosensory system

Sensory system

Telecommunications

Computer science

Stimulus (psychology)

Communication noise

Psychology

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Sensory threshold

Psychophysics

Artificial intelligence

Perception

Noise (video)

Detection theory

Neurophysiology

Frontiers in Neuroscience

Reliable determination of sensory thresholds is the holy grail of signal detection theory. However, there exists no assumption-independent gold standard for the estimation of thresholds based on neurophysiological parameters, although a reliable estimation method is crucial for both scientific investigations and clinical diagnosis. Whenever it is impossible to communicate with the subjects, as in studies with animals or neonates, thresholds have to be derived from neural recordings or by indirect behavioral tests. Whenever the threshold is estimated based on such measures, the standard approach until now is the subjective setting – either by eye or by statistical means – of the threshold to the value where at least a "clear" signal is detectable. These measures are highly subjective, strongly depend on the noise, and fluctuate due to the low signal-to-noise ratio near the threshold. Here we show a novel method to reliably estimate physiological thresholds based on neurophysiological parameters. Using surrogate data we demonstrate that fitting the responses to different stimulus intensities with a hard sigmoid function, in combination with subsampling, provides a robust threshold value as well as an accurate uncertainty estimate. This method has no systematic dependence on the noise and does not even require samples in the full dynamic range of the sensory system. We prove that this method is universally applicable to all types of sensory systems, ranging from somatosensory stimulus processing in the cortex to auditory processing in the brain stem.

Objective Estimation of Sensory Thresholds Based on Neurophysiological Parameters