Date on Master's Thesis/Doctoral Dissertation


Document Type

Doctoral Dissertation

Degree Name

Ph. D.


Psychological and Brain Sciences

Degree Program

Experimental Psychology, PhD

Committee Chair

DeMarco, Paul

Committee Co-Chair (if applicable)

Petry, Heywood

Committee Member

Petry, Heywood

Committee Member

Zahorik, Pavel

Committee Member

He, Zijian

Committee Member

McCall, Maureen

Author's Keywords

electroretinogram; retinal pathways; vision science; electrophysiology


The current work assessed some of the key hypotheses behind the generation of the pattern electroretinogram (PERG) response. The first of these hypotheses states that the PERG response is the result of linear cancellation of simultaneous increment and decrement retinal responses, as generated by the retinal ON- and OFF-pathways. Experiment 1 evaluated the possibility of simulating the PERG by summing the ERG responses elicited by increment and decrement flashes, and found that it was indeed possible to simulate the PERG from these responses. However, only the steady-state PERG could be modeled consistently. The second hypothesis evaluated a theory that the retinal ganglion cells (RGCs) which generate the PERG response should be sensitive to spatial scaling of the PERG stimulus, and that an optimal spatial stimulus can be constructed based on the density of RGCs as a function of eccentricity. Validity of this claim was assessed by comparing the spatial tuning from uniform checkerboard stimuli to spatially-scaled gratings that mimicked the continuous change in RGC receptive field size. Spatial tuning was only found in response to uniform checkerboard stimuli. Experiment 3 tested the validity of the results from Experiment 1 in a population of glaucoma patients. Both the N95 and steady-state amplitudes from simulations could be modeled in patients and age-similar controls. While the PERG response and the simulated PERG both appear to track perimetric data, the sample size was too small to address the predictive validity of the PERG modeling as a tool for tracking disease progression.