arXiv subjects

Ernest Greene

Publications and source records attributed to Ernest Greene.

8 recordsLinked to original sources

arXiv2025

Anomalous contrast as an adaptive violation of the Talbot-Plateau law

Purpose: To better understand anomalous contrast mechanisms that allow flicker-fused stimuli to be visible even when they provide the same average luminance as background. Method: Stimulus flicker was used to elicit differential activation of ON and OFF retinal channels at frequencies above the flicker-fusion threshold. Providing balanced light energy to ON and OFF channels will normally cause the stimulus to vanish into the background. Results: We used ultra-brief bright pulses, combined with ultra-long dark pulses, to elicit "anomalous contrast" that rendered the stimulus visible, even though it had the same average luminance as the background. The duration and intensity of flicker components were varied to gain insight into the conditions that would elicit this effect. Conclusions: Anomalous contrast displays violated the Talbot-Plateau law, but in doing so, provided an adaptive way to register and signal contours that matched background luminance. These findings contribute additional details about this visual adaptation, and we discuss how the retinal circuitry provides for stimulus visibility.

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arXiv2024

Neuromorphic Dual-channel Encoding of Luminance and Contrast

There is perceptual and physiological evidence that the retina registers and signals luminance and luminance contrast using dual-channel mechanisms. This process begins in the retina, wherein the luminance of a uniform zone and differentials of luminance in neighboring zones determine the degree of brightness or darkness of the zones. The neurons that process the information can be classified as "bright" or "dark" channels. The present paper provides an overview of these retinal mechanisms along with evidence that they provide brightness judgments that are log-linear across roughly seven orders of magnitude.

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arXiv2023

Probing the limits of the Talbot-Plateau law

The Talbot-Plateau law specifies what combinations of flash frequency, duration, and intensity will yield a flicker-fused stimulus that matches the brightness of a steady stimulus. It has proven to be remarkably robust in its predictions, and here we provide addition support though the use of a contrast discrimination task. However, we also find that the visual system can register contrast when the combination of frequency and duration is relatively low. Flicker-fused letters are recognized even though they have the same physical luminance as background. We propose that this anomalous result is produced by large disparities in the duration of bright and dark components of the flash cycle, which brings about unexpected differential activation of ON and OFF retinal channels.

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arXiv2023

Evaluating the Talbot-Plateau Law

The Talbot-Plateau law asserts that when the flux (light energy) of a flicker-fused stimulus equals the flux of a steady stimulus, they will appear equal in brightness. To be perceived as flicker-fused, the frequency of the flash sequence must be high enough that no flicker is perceived, i.e., it appears to be a steady stimulus. Generally, this law has been accepted as being true across all brightness levels, and across all combinations of flash duration and frequency that generate the matching flux level. Two experiments that were conducted to test the law found significant departures from its predictions, but these were small relative to the large range of flash intensities that were tested.

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arXiv2020

An evolutionary perspective on the design of neuromorphic shape filters

A substantial amount of time and energy has been invested to develop machine vision using connectionist (neural network) principles. Most of that work has been inspired by theories advanced by neuroscientists and behaviorists for how cortical systems store stimulus information. Those theories call for information flow through connections among several neuron populations, with the initial connections being random (or at least non-functional). Then the strength or location of connections are modified through training trials to achieve an effective output, such as the ability to identify an object. Those theories ignored the fact that animals that have no cortex, e.g., fish, can demonstrate visual skills that outpace the best neural network models. Neural circuits that allow for immediate effective vision and quick learning have been preprogrammed by hundreds of millions of years of evolution and the visual skills are available shortly after hatching. Cortical systems may be providing advanced image processing, but most likely are using design principles that had been proven effective in simpler systems. The present article provides a brief overview of retinal and cortical mechanisms for registering shape information, with the hope that it might contribute to the design of shape-encoding circuits that more closely match the mechanisms of biological vision.

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arXiv2018

Scan transcription of two-dimensional shapes as an alternative neuromorphic concept

Selfridge, along with Sutherland and Marr provided some of the earliest proposals for how to program computers to recognize shapes. Their emphasis on filtering for contour features, especially the orientation of boundary segments, was reinforced by the Nobel Prize winning work of Hubel & Wiesel who discovered that neurons in primary visual cortex selectively respond as a function of contour orientation. Countless investigators and theorists have continued to build on this approach. These models are often described as neuromorphic, which implies that the computational methods are based on biologically plausible principles. Recent work from the present lab has challenged the emphasis on orientation selectivity and the use of neural network principles. The goal of the present report is not to relitigate those issues, but to provide an alternative concept for encoding of shape information that may be useful to neuromorphic modelers.

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arXiv2018

What constitutes elemental shape information for biological vision?

We do not yet understand how the vertebrate visual system provides for recognition of ob- jects. Countless experiments have been performed to examine the contribution of cues such as color, texture, and shadowing, but the most important cues are the con- tours of the outer boundary. Most objects that we can name can be identified as a silhouette, or equally well as a line drawing of the boundary. This has long been ap- preciated, so it is somewhat surprising that after more than a century of experimental research, we have not yet established how our visual system encodes this shape in- formation.

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arXiv2018

Rapid de novo shape encoding: a challenge to connectionist modeling

Neural network (connectionist) models are designed to encode image features and provide the building blocks for object and shape recognition. These models generally call for: a) initial diffuse connections from one neuron population to another, and b) training to bring about a functional change in those connections so that one or more high-tier neurons will selectively respond to a specific shape stimulus. Advanced models provide for translation, size, and rotation invariance. The present discourse notes that recent work on human perceptual skills has demonstrated immediate encoding of unknown shapes that were seen only once. Further, the perceptual mechanism provided for translation, size, and rotation invariance. This finding represents a challenge to connectionist models that require many training trials to achieve recognition and invariance.

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