KodaliVilkhuJolly
Introduction
The human eye is always moving: usually either large movements to smoothly track a target in a scene or small ballistic movements that occur even when fixated at a single point in a scene. While the exact purpose of the small, continuous (fixational) eye movements is still an area of active research, literature agrees that the primary purpose is to counter perceptual image fading [1]. Perceptual fading occurs when a static image is projected onto the retina and higher-level, cognitive function results in a perceived fading of the image over time due to the lack of change of stimulus (see Appendix I for an optimal illusion demonstrating this effect). Therefore, the purpose of fixational eye movements is to provide a mechanical "refresh" of the visual system to have continual neural responses even to static scenes.
Specifically, for the scope of the Psych 221 final project, we set out to explore how fixational eye movements contribute to human visual acuity. This is a tangible problem that allows us to leverage the ISETBio toolkit [2] and our understanding of how to interpret Modulation Transfer Function (MTF) curves.
Background
The primary inspiration of the study was from a paper out of Michele Rucci's lab, which illustrated the importance of fixational eye movements such as drift and saccades to accurately model the primate (macaque) visual system [3]. The study focused on determining the contrast sensitivity as a function of the retinal ganglion cell (RGC) output of macaque retina. This output was measured both with and without fixational eye movements (which was included as a parameter in a modeled retinal neuron), and the key finding was that the presence of eye movements was critical to replicating the observed contrast behavior demonstrated by past behavioral experiments (De Valois et al. 1974). This is illustrated in the figure below (from the paper), which clearly shows that the model with drift better matches the findings of behavioral experiments and actually leads to higher observed contrast sensitivity. For further details on the exact neural model used in the paper, please consult the Materials and Methods section of the referenced paper. To briefly summarize for the purposes of this wiki, Rucci's lab used real eye movement data from 5 human participants and used the data to augment how visual stimuli was fed into a neural model of RGC cells, the output of which was used to determine contrast sensitivity.
Based on the findings of this study, we were able to formulate a hypothesis that the presence of fixational eye movements improves human contrast sensitivity. This is the claim we plan to validate/contradict based upon simulating eye movements in the ISETBio toolkit.
Background into Fixational Eye Movements:
Fixational eye movement (FEM) comprises of three distinct components: drift, microsaccades, and tremor. Drift is considered as oculomotor noise and is visible as a net change in position of the eye fixation point over time. Microsaccades are small-amplitude motions within the drift component that move towards the fixation point; they are characterized by their “ballistic” and sudden movement pattern. Like microsaccades, tremor is also a small-amplitude motion but is oscillatory. Not all eye-tracking devices are able to record the tremor properly, so it is not accounted for in all eye movement models. [1] The figure below shows the three components in FEM [4].
Methods
The ISETBio Fixational Eye Movement model was used to generate different eye movement patterns. The model successfully captures drift and microsaccades but does not account for tremor. Drift component was modeled as an implementation of a delayed random walk model from previous literature. [1] The drift was represented as the sum of an autoregressive term for excitatory burst neuron response, a baseline noise component for the tonic unit neuron response, and a negative feedback component for movement stabilization. The drift model is illustrated in the figure below [5]. The microsaccade model used statistical properties from the inter-saccade interval to create a micro-saccade jump [2]. Both the drift and microsaccade submodels had several parameters which could be utilized as degrees of freedom to generate different eye movement patterns.
Although several parameters are available for the drift / microsaccade submodels and a fine-grained exploration of FEM patterns could be interesting, it would likely yield a more detailed analysis of the FEM model itself and its limitations. In order to have a higher level understanding and understand the contributions of different FEM subcomponents, the following FEM patterns were generated with the ISETBIO FEM model:
1. No drift, No microsaccade (fixed point)
2. Yes drift, No microsaccade
3. Yes drift, Yes microsaccade
Drift was removed by setting the autoregressive gamma parameter was set to 1, the noise to 0, and the feedback steepness ∈ to 0. Note that a FEM pattern without drift and with microsaccade could not be generated; since microsaccade is embedded in the drift component, no drift - regardless of whether a microsaccade is present - would result in a stationary point.
In addition to the aforementioned ISETBIO FEM model-generated patterns, custom eye movement patterns were explored. The team is interested in retinal and visual prosthetics, and these systems are not limited by biological constraints of drift or microsaccades. Novel movements can be generated in these systems to facilitate or even augment vision. As a simple exploration of custom movements, the following patterns were generated:
4. Rapid large horizontal movement
5. Rapid large vertical movement
6. Rapid large positive slope movement
7. Rapid large negative slope movement
All the evaluated FEM patterns are summarized in the following table:
| Eye Movement Pattern | Model | Description |
|---|---|---|
| 1 | ISETBio FEM | No drift, No microsaccade |
| 2 | ISETBio FEM | Yes drift, No microsaccade |
| 3 | ISETBio FEM | Yes drift, Yes microsaccade |
| 4 | Custom | Rapid large horizontal |
| 5 | Custom | Rapid large vertical |
| 6 | Custom | Rapid large positive slope |
| 7 | Custom | Rapid large negative slope |
A simulation environment was based upon the ISETBio eye movement tutorial. A slanted bar visual stimuli was created and modeled going through a standard human eyeball to generate an optical image. The optical image was then projected on a cone mosaic of a specified cone type spatial density. The photoreceptor absorption is recorded from the model and ISO12233 is utilized to generate a contrast reduction plot, a version of a modular transfer function plot. Figure Z summarizes the environment pipeline. The code was modified to allow multiple trials to be executed and plotted for each unique eye movement pattern shown in table 1.
Results
- Relevant graphs and/or images.
- Make sure you draw the reader's attention to the key element of figures
-Clear figure captions.
Analysis
From the figures above, it is evident that different eye patterns led to different levels of absorption at the photoreceptors. However, due to the amount of variance between and within trials, it was difficult to conduct relevant quantitative analysis on the data. Nonetheless...
Conclusions
- Describe what you learned.
- What worked? What didn't? Why?
- What should someone next year try?
References
[1] Mergenthaler et. al. "Modeling the Control of Fixational Eye movements with Neurophysiological Delays." Physical Review Letters, 30 Mar. 2007, doi:10.1103/PhysRevLett.98.138104.
[2] ISETBio Toolkit: https://github.com/isetbio/isetbio
[3] Casile, Antonino, et al. “Contrast Sensitivity Reveals an Oculomotor Strategy for Temporally Encoding Space.” ELife: Computational and Systems Biology, Neuroscience, 8 Jan. 2019, doi:10.7554/eLife.40924.
[4] Makarava, N., et al. "Bayesian estimation of the scaling parameter of fixational eye movements." Europhysics Letters, 3 Dec. 2012, doi:10.1209/0295-5075/100/40003.
[5] Eye movement model: https://github.com/isetbio/isetbio/wiki/Eye-movements.
Appendix I
- Extra test images, etc, and give a description of each link.
Appendix II
Paul Jolly worked on writing the MATLAB script to generate SFR curves given various eye movement parameters. Sreela Kodali performed a thorough analysis of the Mergenthaler and Engbert model used in the ISETBio toolkit to determine how/which parameters to adjust in the MATLAB script for varying movement patterns. Raman Vilkhu helped with the analysis of the data in the context of the Rucci research study. All group members worked together on the final interpretation of the results, the creation of the slides, and the creation of the wiki page.