A NEW APPROACH TO ALGORITHM-ORIENTED VISUAL PSYCHOPHYSICS
type: CreativeWork
description: The goal of visual neuroscience is to understand how visual systems are able to reconstruct scenes, infer scene properties, and make inferences about the natural environment from a pair of retinal images. Some approaches use psychophysical methods, which allow for controlled sensory presentation and perceptual measurement. Theory holds that through principled application of psychophysics and perceptual modelling, the mechanisms of the visual system can be uncovered. The most common approach has been to measure and model behavioral responses to simple stimuli. However, contemporary neuroscience continues to reveal a more holistic and complex visual system than initially expected. In this work, I argue that these findings suggest behavioral responses to simple stimuli may not as directly correspond to specific visual processing mechanisms, nor provide as powerful an assessment of mechanistic models as originally assumed. Algorithmic theory provides mathematical support for why this is the case and provides a prescription for how psychophysics and modelling can more efficiently explore the space of visual mechanisms. Here, I propose the use of natural or naturalistic images and repeated-measure experimental design to meet this prescription. Not only do natural images provide a diversity of stimuli---an effective substrate for exploring the space of visual mechanisms---but, are also relevant to the overarching goals of vision science---to understand how vision works in the real world. Last, I present the empirically based portion of this work that acts as as a proof of concept for this framework. In this study I investigate human stereo-depth discrimination performance using naturalistic images and repeated-measures design. I develop broadly applicable methods that can be used to make powerful model assessments based upon the diversity of stimuli. Additionally, the methods and procedures developed provide highly interpretable results: these results show how natural imag…
abstract: The goal of visual neuroscience is to understand how visual systems are able to reconstruct scenes, infer scene properties, and make inferences about the natural environment from a pair of retinal images. Some approaches use psychophysical methods, which allow for controlled sensory presentation and perceptual measurement. Theory holds that through principled application of psychophysics and perceptual modelling, the mechanisms of the visual system can be uncovered. The most common approach has been to measure and model behavioral responses to simple stimuli. However, contemporary neuroscience continues to reveal a more holistic and complex visual system than initially expected. In this work, I argue that these findings suggest behavioral responses to simple stimuli may not as directly correspond to specific visual processing mechanisms, nor provide as powerful an assessment of mechanistic models as originally assumed. Algorithmic theory provides mathematical support for why this is the case and provides a prescription for how psychophysics and modelling can more efficiently explore the space of visual mechanisms. Here, I propose the use of natural or naturalistic images and repeated-measure experimental design to meet this prescription. Not only do natural images provide a diversity of stimuli---an effective substrate for exploring the space of visual mechanisms---but, are also relevant to the overarching goals of vision science---to understand how vision works in the real world. Last, I present the empirically based portion of this work that acts as as a proof of concept for this framework. In this study I investigate human stereo-depth discrimination performance using naturalistic images and repeated-measures design. I develop broadly applicable methods that can be used to make powerful model assessments based upon the diversity of stimuli. Additionally, the methods and procedures developed provide highly interpretable results: these results show how natural imag…
author: White, David, Nathan
language: en
keywords: Neuroscience and Neurobiology, Psychology, Ecology and Evolutionary Biology
date published: 2024
date modified: 2026-07-24T03:45:26Z
provider: University of Pennsylvania
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Pennsylvania