Author ORCID Identifier
Date of Graduation
Doctor of Philosophy (PhD)
Estimation of perceptual variables is imprecise and prone to errors. Although the properties of these perceptual errors are well characterized, the physiological basis for these errors is unknown. One previously proposed explanation for these errors is the trial-by-trial variability of the responses of sensory neurons that encode the percept. Initially, it would seem that a complicated electrophysiological experiment would need to be performed to test this hypothesis. However, using a strong theoretical framework, I demonstrate that it is possible to determine statistical characteristics of the physiological mechanism responsible for perceptual errors solely from a behavioral experiment. The basis for this theoretical framework is that different stochastic distributions (e.g., Poisson, Gaussian, etc.) will behave differently under temporal constraints. The results of this model connect easily with existing psychophysical techniques; additionally, I extend the theory here and show that it can generate realistic tuning curves that can predict perceptual acuity as a function of stimulus magnitude and duration. Following the analytical work, I performed the necessary experiments to test the model. I demonstrate that the physiological basis of perceptual error has a constant level of noise (i.e., independent of stimulus intensity and duration). By comparing these results to previous physiological measurements, I show that perceptual errors cannot be due to the variability during the encoding stage. Further, I show a very close fit between the theoretically generated tuning curve and the behavioral results, which gives more insight into the error generation mechanism. Finally, I find that the time window over which perceptual evidence is integrated lasts no more that ~230ms. I discuss these results and others, and speculate on sources of error that may be consistent with my behavioral measurements.
contrast sensitivity, encoding/decoding, neuron, noise, detection/discrimination
Available for download on Thursday, November 02, 2017