Natural Perspective: Mapping Visual Space with Art and Science

Varieties of pictorial vision

Pictorial awareness is addressed through experimental phenomenology involving over 90 naïve participants. Since one can't look at the "same" picture twice the study uses one-shot trials. The participant's fascination for the duration of a session is held through the artistic principle of theme and variation. Six variations focus on the theme of pictorial geometry, both two-dimensional and three-dimensional. Major findings are: Idiosyncratic deviations from veridical are huge as compared to common textbook "effects." Observers wield arbitrary heuristics for tasks that are "formally related." The assumption of a common formal framework is apparently unsound. The notion of "inverse optics" is misleading. A fair fraction of the population appears to lack monocular stereopsis as intuitive awareness. It suggests an as-yet unrecognized, but perhaps common variety of aphantasia.

Toward a theory of perspective perception in pictures

This paper reviews projection models and their perception in realistic pictures, and proposes hypotheses for three-dimensional (3D) shape and space perception in pictures. In these hypotheses, eye fixations, and foveal vision play a central role. Many past theories and experimental studies focus solely on linear perspective. Yet, these theories fail to explain many important perceptual phenomena, including the effectiveness of nonlinear projections. Indeed, few classical paintings strictly obey linear perspective, nor do the best distortion-avoidance techniques for wide-angle computational photography. The hypotheses here employ a two-stage model for 3D human vision. When viewing a picture, the first stage perceives 3D shape for the current gaze. Each fixation has its own perspective projection, but, owing to the nature of foveal and peripheral vision, shape information is obtained primarily for a small region of the picture around the fixation. As a viewer moves their eyes, the second stage continually integrates some of the per-gaze information into an overall interpretation of a picture. The interpretation need not be geometrically stable or consistent over time. It is argued that this framework could explain many disparate pictorial phenomena, including different projection styles throughout art history and computational photography, while being consistent with the constraints of human 3D vision. The paper reviews open questions and suggests new studies to explore these hypotheses.

Being alive to the world: an artist's perspective on predictive processing

I consider predictive processing (PP) from the perspective of an artist who also conducts scientific research into art and perception. This paper presents artworks I have made and statements from other artists that exemplify some of PP's core principles. But it also raises questions about the extent to which current applications of PP theory provide a comprehensive account of art experience. Prediction error minimization, a key mechanism of PP, has been proposed as a cause of positive aesthetic affect because artworks offer opportunities for reward through disambiguation and learning. However, there are many cases where prediction errors proliferate in art experiences in a way that enhances aesthetic affect. Here I suggest the inability of our perceptual systems to minimize prediction errors when beholding certain artworks can evoke heightened states of fascination and exhilaration. Moreover, powerful artworks provide opportunities for maximizing prediction errors, within certain bounds, by evoking states of paradox, contradiction and illogicality. I conclude that beholding such artworks can intensify our sense of being by making us more alive to the world. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Non-metric distance judgements are influenced by image projection geometry and field of view

Despite its mathematical simplicity and ubiquity in imaging technology, there has long been doubt about the ability of linear perspective to best represent human visual space, especially at wide-angle fields of view under natural viewing conditions. We investigated whether changes to image geometry had an impact on participants' performance, specifically in terms of non-metric distance estimates. Our multidisciplinary research team developed a new open-source image database to study distance perception in images by systematically manipulating target distance, field of view, and image projection using non-linear natural perspective projections. The database consists of 12 outdoor scenes of a virtual three-dimensional urban environment in which a target ball is presented at increasing distance, visualised using both linear perspective and natural perspective images, rendered, respectively, with three different fields of view: 100°, 120°, and 140° horizontally. In the first experiment (N = 52), we tested the effects of linear versus natural perspective on non-metric distance judgements. In the second experiment (N = 195), we investigated the influence of contextual and previous familiarity with linear perspective, and individual differences in spatial skills on distance estimations. The results of both experiments showed that distance estimation accuracy improved in natural compared with linear perspective images, particularly at wide-angle fields of view. Moreover, undertaking a training session with only natural perspective images led to more accurate distance judgements overall. We argue that the efficacy of natural perspective may stem from its resemblance to the way objects appear under natural viewing conditions, and that this can provide insights into the phenomenological structure of visual space.

Drawing as a versatile cognitive tool

Drawing is a cognitive tool that makes the invisible contents of mental life visible. Humans use this tool to produce a remarkable variety of pictures, from realistic portraits to schematic diagrams. Despite this variety and the prevalence of drawn images, the psychological mechanisms that enable drawings to be so versatile have yet to be fully explored. In this Review, we synthesize contemporary work in multiple areas of psychology, computer science and neuroscience that examines the cognitive processes involved in drawing production and comprehension. This body of findings suggests that the balance of contributions from perception, memory and social inference during drawing production varies depending on the situation, resulting in some drawings that are more realistic and other drawings that are more abstract. We also consider the use of drawings as a research tool for investigating various aspects of cognition, as well as the role that drawing has in facilitating learning and communication. Taken together, information about how drawings are used in different contexts illuminates the central role of visually grounded abstractions in human thought and behaviour.

Craniofacial superimposition: a review of focus distance estimation methods and an extension to profile view photographs

Craniofacial superimposition concerns the photographic overlay of skulls and faces, for skeletal identification. As a phased method that depends on photographic optics first and anatomical comparisons second, superimposition is strongly underpinned by the physics of light travel through glass lenses. So that the downstream (and dependent) anatomical evaluations are not thwarted or erroneous identification decisions risked, it is critical that the optical prerequisites for valid image comparisons are met. As focus distance sets the perspective, the focus distance used for skull photography must be matched to that used at face photography, so that anatomically comparable 1:1 images are obtained. In this paper, we review the pertinent camera optics that set these nonnegotiable fundamentals and review a recently proposed method for focus distance estimation. We go beyond the original method descriptions to explain the mathematical justification for the PerspectiveX algorithm and provide an extension to profile images. This enables the first scientifically grounded use of profile view (or partial profile view) photographs in craniofacial superimposition. Proof of concept is provided by multiple worked examples of the focus distance estimation for frontal and profile view images of three of the authors at known focus distances. This innovation (1) removes longstanding trial-and-error components of present-day superimposition methods, (2) provides the first systematic and complete optical basis for image comparison in craniofacial superimposition, and (3) will enable anatomical comparison standards to be established from a valid grassroots basis where complexities of camera vantage point are removed as interfering factors.

The choices hidden in photography

Photography is often understood as an objective recording of light measurements, in contrast with the subjective nature of painting. This article argues that photography entails making the same kinds of choices of color, tone, and perspective as in painting, and surveys examples from film photography and smartphone cameras. Hence, understanding picture perception requires treating photography as just one way to make pictures. More research is needed to understand the effects of these choices on pictorial perception, which in turn could lead to the design of new imaging techniques.

Does Machine Understanding Require Consciousness?

This article addresses the question of whether machine understanding requires consciousness. Some researchers in the field of machine understanding have argued that it is not necessary for computers to be conscious as long as they can match or exceed human performance in certain tasks. But despite the remarkable recent success of machine learning systems in areas such as natural language processing and image classification, important questions remain about their limited performance and about whether their cognitive abilities entail genuine understanding or are the product of spurious correlations. Here I draw a distinction between natural, artificial, and machine understanding. I analyse some concrete examples of natural understanding and show that although it shares properties with the artificial understanding implemented in current machine learning systems it also has some essential differences, the main one being that natural understanding in humans entails consciousness. Moreover, evidence from psychology and neurobiology suggests that it is this capacity for consciousness that, in part at least, explains for the superior performance of humans in some cognitive tasks and may also account for the authenticity of semantic processing that seems to be the hallmark of natural understanding. I propose a hypothesis that might help to explain why consciousness is important to understanding. In closing, I suggest that progress toward implementing human-like understanding in machines—machine understanding—may benefit from a naturalistic approach in which natural processes are modelled as closely as possible in mechanical substrates.