scholarly journals Predictive coding as a unifying principle for explaining a broad range of brightness phenomena

2020 ◽  
Author(s):  
Alejandro Lerer ◽  
Hans Supèr ◽  
Matthias S.Keil
Keyword(s):  
Visual System ◽  
Computational Model ◽  
Predictive Coding ◽  
Gain Control ◽  
Physical World ◽  
Input Image ◽  
Visual Illusions ◽  
Contrast Effects ◽  
Dynamic Filtering ◽  
The Brain

AbstractThe visual system is highly sensitive to spatial context for encoding luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining the perception of luminance (brightness). Here we propose a novel computational model for estimating the brightness of many visual illusions. We hypothesize that many aspects of brightness can be explained by a predictive coding mechanism, which reduces the redundancy in edge representations on the one hand, while non-redundant activity is enhanced on the other (response equalization). Response equalization is implemented with a dynamic filtering process, which (dynamically) adapts to each input image. Dynamic filtering is applied to the responses of complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is successful in predicting many challenging visual illusions, including contrast effects, assimilation, and reverse contrast.Author summaryWe hardly notice that what we see is often different from the physical world “outside” of the brain. This means that the visual experience that the brain actively constructs may be different from the actual physical properties of objects in the world. In this work, we propose a hypothesis about how the visual system of the brain may construct a representation for achromatic images. Since this process is not unambiguous, sometimes we notice “errors” in our perception, which cause visual illusions. The challenge for theorists, therefore, is to propose computational principles that recreate a large number of visual illusions and to explain why they occur. Notably, our proposed mechanism explains a broader set of visual illusions than any previously published proposal. We achieved this by trying to suppress predictable information. For example, if an image contained repetitive structures, then these structures are predictable and would be suppressed. In this way, non-predictable structures stand out. Predictive coding mechanisms act as early as in the retina (which enhances luminance changes but suppresses uniform regions of luminance), and our computational model holds that this principle also acts at the next stage in the visual system, where representations of perceived luminance (brightness) are created.

scholarly journals Dynamic decorrelation as a unifying principle for explaining a broad range of brightness phenomena

2021 ◽  
Vol 17 (4) ◽  
pp. e1007907
Author(s):  
Alejandro Lerer ◽  
Hans Supèr ◽  
Matthias S. Keil
Keyword(s):  
Gain Control ◽  
Context Sensitivity ◽  
Input Image ◽  
Visual Illusions ◽  
Model Parameters ◽  
Contrast Effects ◽  
Model Complex ◽  
Dynamic Filter ◽  
Dynamic Filtering ◽  
The One

The visual system is highly sensitive to spatial context for encoding luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining the perception of luminance (brightness). Here we propose a novel computational model for estimating the brightness of many visual illusions. We hypothesize that many aspects of brightness can be explained by a dynamic filtering process that reduces the redundancy in edge representations on the one hand, while non-redundant activity is enhanced on the other. The dynamic filter is learned for each input image and implements context sensitivity. Dynamic filtering is applied to the responses of (model) complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is successful in predicting many challenging visual illusions, including contrast effects, assimilation, and reverse contrast with the same set of model parameters.

A Computational Model for Color Perception

2012 ◽  
Vol 8 (4) ◽  
pp. 387-415
Author(s):  
Marc Ebner
Keyword(s):  
Visual System ◽  
Computational Model ◽  
Coordinate System ◽  
Color Perception ◽  
Human Observer ◽  
Average Color ◽  
Local Space ◽  
Color Constant ◽  
Spatially Varying ◽  
The Brain

ABSTRACT Color is not a physical quantity of an object. It cannot be measured. We can only measure reflectance, i.e. the amount of light reflected for each wavelength. Nevertheless, we attach colors to the objects around us. A human observer perceives colors as being approximately constant irrespective of the illuminant which is used to illuminate the scene. Colors are a very important cue in everyday life. They can be used to recognize or distinguish different objects. Currently, we do not yet know how the brain arrives at a color constant or approximately color constant descriptor, i.e. what computational processing is actually performed by the brain. What we need is a computational description of color perception in particular and color vision in general. Only if we are able to write down a full computational theory of the visual system then we have understood how the visual system works. With this contribution, a computational model of color perception is presented. This model is much simpler compared to previous theories. It is able to compute a color constant descriptor even in the presence of spatially varying illuminants. According to this model, the cones respond approximately logarithmic to the irradiance entering the eye. Cells in V1 perform a change of the coordinate system such that colors are represented along a red-green, a blue-yellow and a black-white axis. Cells in V4 compute local space average color using a resistive grid. The resistive grid is formed by cells in V4. The left and right hemispheres are connected via the corpus callosum. A color constant descriptor which is presumably used for color based object recognition is computed by subtracting local space average color from the cone response within a rotated coordinate system.

Introduction

1980 ◽  
Vol 290 (1038) ◽  
pp. 3-4 ◽  
Keyword(s):  
Visual System ◽  
Visual Processing ◽  
Royal Society ◽  
Computational Models ◽  
Shape Recognition ◽  
External World ◽  
Physical World ◽  
The Subject ◽  
Contrast Discrimination ◽  
The Brain

There is a long-standing tradition of research on vision in Great Britain that goes back at least as far as Newton. The Royal Society is therefore a most suitable venue for a conference on the Psychology of Vision, and it is no accident that two of our distinguished guests from North America are British subjects. In the first 30 years of this century the Gestalt movement brought about a revolution in our ways of thinking about vision, but the subject then remained rather stagnant for two decades. In more recent years, dramatic discoveries and radical new insights have been forthcoming from three different directions. First, neurophysiologists have laid bare some of the highly systematic wiring that subserves the early stages of the processing of the visual input. Secondly, psychologists and psychophysiologists have uncovered some of the intricacies of the mechanisms that underlie such functions as acuity, contrast discrimination, motion detection and stereopsis. It is becoming possible to put together results from these two directions and to show how mechanisms inferred from psychophysical observations are instantiated in known neurophysiological circuits. The two sets of results indicate that visual processing is both more complex and more elegant than had been suspected 50 years ago. Thirdly, the advent of the digital computer has made it possible to build rigorous computational models of the visual system, to explore and to specify more adequately the nature of the task that the visual system must perform, and to demonstrate precisely how the constraints imposed by the nature of the physical world and of its optics make it possible for the brain to use the patterns of light impinging on the retinae to form a useful representation of the external world. Although this last enterprise may strike some as speculative, it has already led to insights into the nature of vision that have changed our ways of looking at the problems and have made the theories of shape recognition put forward in the 1950s and 1970s, including those of one of us, look extremely superficial.

scholarly journals Spontaneous Beta Band Rhythms in the Predictive Coding of Natural Stimuli

2020 ◽  
pp. 107385842092898 ◽  
Author(s):  
Viviana Betti ◽  
Stefania Della Penna ◽  
Francesco de Pasquale ◽  
Maurizio Corbetta
Keyword(s):  
Predictive Coding ◽  
Physical World ◽  
Brain Regions ◽  
Temporal Coding ◽  
Strong Interactions ◽  
Beta Band ◽  
Cognitive Representations ◽  
Band Power ◽  
The Brain

The regularity of the physical world and the biomechanics of the human body movements generate distributions of highly probable states that are internalized by the brain in the course of a lifetime. In Bayesian terms, the brain exploits prior knowledge, especially under conditions when sensory input is unavailable or uncertain, to predictively anticipate the most likely outcome of upcoming stimuli and movements. These internal models, formed during development, yet still malleable in adults, continuously adapt through the learning of novel stimuli and movements. Traditionally, neural beta (β) oscillations are considered essential for maintaining sensorimotor and cognitive representations, and for temporal coding of expectations. However, recent findings show that fluctuations of β band power in the resting state strongly correlate between cortical association regions. Moreover, central (hub) regions form strong interactions over time with different brain regions/networks (dynamic core). β band centrality fluctuations of regions of the dynamic core predict global efficiency peaks suggesting a mechanism for network integration. Furthermore, this temporal architecture is surprisingly stable, both in topology and dynamics, during the observation of ecological natural visual scenes, whereas synthetic temporally scrambled stimuli modify it. We propose that spontaneous β rhythms may function as a long-term “prior” of frequent environmental stimuli and behaviors.

The Oxford Compendium of Visual Illusions

2017 ◽  
Keyword(s):  
Visual Perception ◽  
Da Vinci ◽  
Physical World ◽  
Color Images ◽  
Visual Illusions ◽  
The Other ◽  
The Past ◽  
Full Color ◽  
The One ◽  
The Brain

Visual illusions cut across academic divides and popular interests: on the one hand, illusions provide entertainment as curious tricks of the eye; on the other hand, scientific research related to illusory phenomena has given generations of scientists and artists deep insights into the brain and principles of mind and consciousness. Numerous thinkers (including Aristotle, Descartes, Da Vinci, Escher, Goethe, Galileo, Helmholtz, Maxwell, Newton, and Wittgenstein) have been lured by the apparent simplicity of illusions and the promise that illusory phenomena can elucidate the puzzling relationship between the physical world and perceptual reality. Over the past thirty years, advances in imaging and electrophysiology have dramatically expanded the range of illusions and enabled new forms of analysis, thereby creating new and exciting ways to consider how the brain constructs the perceptual world. The Oxford Compendium of Visual Illusions is a collection of over one hundred chapters about illusions, displayed and discussed by the researchers who invented and conducted research on the illusions. Chapters include full-color images, associated videos, and extensive references. The book is divided into eleven sections: first, a presentation of general history and viewpoints on illusions, followed by sections on geometric, color, motion, space, faces, and cross-category illusions. The book will be of interest to vision scientists, neuroscientists, psychologists, physicists, philosophers, artists, designers, advertisers, and educators curious about applied aspects of visual perception and the brain.

scholarly journals Cerebral hierarchies: predictive processing, precision and the pulvinar

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140169 ◽  
Author(s):  
Ryota Kanai ◽  
Yutaka Komura ◽  
Stewart Shipp ◽  
Karl Friston
Keyword(s):  
Prediction Error ◽  
Message Passing ◽  
Predictive Coding ◽  
Gain Control ◽  
Predictive Processing ◽  
Special Focus ◽  
First Order ◽  
Neuronal Populations ◽  
Neurobiological Substrates ◽  
The Brain

This paper considers neuronal architectures from a computational perspective and asks what aspects of neuroanatomy and neurophysiology can be disclosed by the nature of neuronal computations? In particular, we extend current formulations of the brain as an organ of inference—based upon hierarchical predictive coding—and consider how these inferences are orchestrated. In other words, what would the brain require to dynamically coordinate and contextualize its message passing to optimize its computational goals? The answer that emerges rests on the delicate (modulatory) gain control of neuronal populations that select and coordinate (prediction error) signals that ascend cortical hierarchies. This is important because it speaks to a hierarchical anatomy of extrinsic (between region) connections that form two distinct classes, namely a class of driving (first-order) connections that are concerned with encoding the content of neuronal representations and a class of modulatory (second-order) connections that establish context—in the form of the salience or precision ascribed to content. We explore the implications of this distinction from a formal perspective (using simulations of feature–ground segregation) and consider the neurobiological substrates of the ensuing precision-engineered dynamics, with a special focus on the pulvinar and attention.

Author response for "The brain of the African wild dog. IV . The visual system"

2020 ◽  
Author(s):  
Samson Chengetanai ◽  
Adhil Bhagwandin ◽  
Mads F. Bertelsen ◽  
Therese Hård ◽  
Patrick R. Hof ◽  
...  
Keyword(s):  
Visual System ◽  
Author Response ◽  
African Wild Dog ◽  
Wild Dog ◽  
The Brain

Neuro-Optometric Treatment of Complications from a Cerebellar Astrocytoma

2020 ◽  
pp. 304-312
Keyword(s):  
Visual System ◽  
Common Site ◽  
Us Army ◽  
Cranial Nerve Palsies ◽  
Pilocytic Astrocytomas ◽  
The Us ◽  
Extent Of Damage ◽  
Functional Components ◽  
The Brain

Background: Insult to the brain, whether from trauma or other etiologies, can have a devastating effect on an individual. Symptoms can be many and varied, depending on the location and extent of damage. This presentation can be a challenge to the optometrist charged with treating the sequelae of this event as multiple functional components of the visual system can be affected. Case Report: This paper describes the diagnosis and subsequent ophthalmic management of an acquired brain injury in a 22 year old male on active duty in the US Army. After developing acute neurological symptoms, the patient was diagnosed with a pilocytic astrocytoma of the cerebellum. Emergent neurosurgery to treat the neoplasm resulted in iatrogenic cranial nerve palsies and a hemispheric syndrome. Over the next 18 months, he was managed by a series of providers, including a strabismus surgeon, until presenting to our clinic. Lenses, prism, and in-office and out-of-office neurooptometric rehabilitation therapy were utilized to improve his functioning and make progress towards his goals. Conclusions: Pilocytic astrocytomas are the most common primary brain tumors, and the vast majority are benign with excellent surgical prognosis. Although the most common site is the cerebellum, the visual pathway is also frequently affected. If the eye or visual system is affected, optometrists have the ability to drastically improve quality of life with neuro-optometric rehabilitation.

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