This blog is about gamut relativity, which is a new theory of surface perception and computational vision that I recently introduced into the scientific literature (read about this blog and its author here). I am currently further developing the theory in my capacity as Research Fellow in the Computational and Theoretical Neuroscience Laboratory at the Institute for Telecommunications Research, University of South Australia. My research was recently featured in a story posted at ABC Science News online. You can find links to several published research articles on gamut relativity at my university homepage here. Below I explain some key aspects of the theory in a manner intended for a general audience.
- What does gamut relativity aim to explain?
When we look at the world, we perceive things like a frosted white glass, a glossy black car, or a brightly lit moon through a grey cloud bank. That is, we experience surfaces as varying in appearance along different dimensions, such as black to white (lightness), matte to glossy (glossiness), and opaque to transparent (transparency). These experiences are partly based on the way that physical surfaces reflect and transmit light and partly on the way the brain interprets the patterns of light reaching the eye. How the brain constructs our experience of different surface dimensions from these light patterns has long remained a mystery. Gamut relativity aims to explain the nature of the brain representations and computations that underlie these experiences.
- What does the term gamut relativity mean?
A gamut in the theory is a range of perceivable grey shades between a shade of black and a shade of white. These grey shades are represented as ‘blackness’ and ‘whiteness’ co-ordinates in a two-dimensional perceptual space that I have termed blackness-whiteness space (see picture below). The relativity part of the theory describes the dependence of the grey-scale gamut on how transparent or glossy a surface appears. Different gamuts correspond to different ‘slices’ of blackness-whiteness space, each represented by a different line joining points on the blackness and whiteness axes. These lines correspond to different levels of surface transparency and/or gloss.
- Why is the theory potentially revolutionary?
Gamut relativity questions the widespread assumption in psychology and neuroscience that the dimensions of visual perception correspond to the dimensions of the physical world. This assumption is called reification and is related to a well known logical fallacy. Classical theories of surface perception suppose that the brain rather literally ‘represents’ the physical dimensions of surfaces as dimensions, like building a miniature version of the world inside the brain. According to these theories, lightness, gloss and transparency constitute perceptual dimensions corresponding to the physical dimensions of matte reflectance (the tendency of surfaces to reflect certain amounts of light equally in all directions), specular reflectance (the tendency of surfaces to reflect more light from certain viewing angles) and transmittance (the tendency of surfaces to transmit light rather than reflect it), respectively. If gamut relativity is correct, and the dimensions of perception do not correspond to the dimensions of physics, the theory is likely to have wide ranging ramifications for psychology and neuroscience.
- How then does gamut relativity explain lightness, transparency and gloss?
The new insight in gamut relativity is to show how brain computations can give rise to the experience of surfaces as varying along different physical dimensions, without literally representing those dimensions in the brain. Gamut relativity thus avoids some of the philosophical traps that have plagued conventional theories of vision, and in so doing provides the first unified account of how we see glossy and transparent surfaces. Gamut relativity predicts that the properties of lightness, gloss and transparency all emerge naturally from brain computations that function to represent surfaces, illumination and atmospheric media. According to the theory, glossy and transparent surfaces form a type of ‘mirror image’ representation in blackness-whiteness space: gamut lines lying below a ‘standard’ gamut line represent different surface transparency levels and gamut lines lying above the ‘standard’ gamut line represent different surface gloss levels. Lightness emerges in terms of invariant relationships between points lying on different gamut lines, enabling the familiar experience of lightness constancy. Illumination properties (shadows, shading and highlights) are represented in a similar manner. Until now, different theories had been needed to explain the apparently different properties of lightness, gloss and transparency. Gamut relativity now provides the first unified description of how the brain represents these surface properties. This type of unification is familiar in the ‘harder’ sciences, such as physics and chemistry, but is rare in computational vision science.
- If reification is wrong, what is the right way to think about perceptual dimensions?
The theory predicts that whiteness and blackness compose the perceptual dimensions underlying surface perception, and that these dimensions are related to the organizational feature of the visual brain known as the ON and OFF channels. These channels transmit signals encoding increases and decreases in light intensity levels from the eye to the brain. The visual ON and OFF channels have been studied for more than 50 years but their functional purpose has yet to be fully elucidated. The theory thus sheds new light on the functional purpose of these channels in terms of the computations that underlie surface perception. More generally, the theory suggests that perceptual dimensions are more closely related to the computational organization of the visual brain (blackness-whiteness space) than to the dimensions of the physical world.
- How does the theory work?
Blackness-whiteness space can be understood as a specialized form of representation that enables the computation of surface, illumination and atmospheric properties. These computations take the form of the familiar operations of vector arithmetic, such as vector addition and decomposition. Such vector computations can be thought of as natural expressions of the computational organization of the visual brain (blackness-whiteness space).
- What’s the current status of the theory?
Gamut relativity suggests novel solutions to many outstanding problems of surface perception, explaining a wide range of perceptual phenomena and data not previously explained in a unified way by existing theories. The theory also makes a wide range of quantitative predictions about surface perception in humans, and is likely to lead to applications in the design of engineered vision systems, such as computer, robotic and bionic vision systems. A great deal of work needs to be done, however, to fully account for the huge variety of perceptual phenomena pertaining to human surface perception and to develop algorithms capable of computing surface properties given an arbitrary image.
- Can I get involved in this exciting new research area?
Yes. I am currently recruiting motivated students to undertake Honours or PhD research on gamut relativity or related topics in computational vision science. I’m also interested in collaborating with experienced researchers across several disciplines, ranging from psychology and neuroscience to engineering and the arts.