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This chapter compares amodal completion and the perception of visual illusions in birds and primates. Amodal completion concerns the perception of partly occluded figures. Visual illusions, in theory, represent cases in which perceptual systems that are adaptively suited to some situations may suffer in other situations. The examples discussed in this chapter suggest that the visual perceptual system of a particular species may be tuned to its ecological niche: where its members live, how they move, what they eat, etc.

In Phenomenology of Perception, Merleau–Ponty holds that sensory consciousness of place exhibits an indeterminacy that shows it is, in a sense, non-representational. But he thinks this does not preclude its having a kind of intentionality. This chapter offers an interpretation and defense of this view. Directing visual attention involves changes to the phenomenal character of experience that cannot be specified by attributing verbal or imagistic content to it. In that sense the character of experience is non-representational. And partly because our anticipation of future experience is not to be construed in terms of beliefs incorporating sensorimotor conditionals, these attentional changes in experience are not conceivable independently of the exercise of sensorimotor skills. However, experience is assessable as correct or illusory in virtue of having such character. Thus, visual experience has, in virtue of its phenomenal character, a distinctively sensorimotor kind of intentionality.

This chapter provides a discussion on illusions in neglect. The data shows that the pathological bias producing the line-length effect applies not only to physical but also to perceived represented length. In general, the available empirical evidence concerning the processing of visual illusions in right-brain-damaged patients with left neglect consistently indicates that the levels of representation where these visual phenomena arise are largely preserved. Moreover, illusory effects are disrupted in patients with left neglect when the lesions extend from the posterior-inferior parietal region, which represents the main anatomical correlate of this disorder, more caudally to the occipital regions. The view that multiple representations of lateral extension may exist in the brain, within different reference frames, is compatible with the finding that a related perceptual disorder has been observed in right-brain-damaged patients with unilateral neglect — a tendency to ‘overvalue’ the size of ipsilesional drawings and ‘size distortion’.

This chapter shows how visual illusions arise from neural processes that play an adaptive role in achieving the remarkable perceptual capabilities of advanced brains. It clarifies that many visual percepts are visual illusions, in the sense that they arise from active processes that reorganize and complete perceptual representations from the noisy data received by retinas. Some of these representations look illusory, whereas others look real. The chapter heuristically summarizes explanations of illusions that arise due to completion of perceptual groupings, filling-in of surface lightnesses and colors, transformation of ambiguous motion signals into coherent percepts of object motion direction and speed, and interactions between the form and motion cortical processing streams. A central theme is that the brain is organized into parallel processing streams with computationally complementary properties, that interstream interactions overcome these complementary deficiencies to compute effective representations of the world, and how these representations generate visual illusions.

In the last decades, cognitive Psychology has provided researchers with a powerful background and the rigor of experimental methods to better understand why so many people believe in pseudoscience, paranormal phenomena and superstitions. According to recent evidence, those irrational beliefs could be the unintended result of how the mind evolved to use heuristics and reach conclusions based on scarce and incomplete data. Thus, we present visual illusions as a parallel to the type of fast and frugal cognitive bias that underlies pseudoscientific belief. In particular, we focus on the causal illusion, which consists of people believing that there is a causal link between two events that coincide just by chance. The extant psychological theories that can account for this causal illusion are described, as well as the factors that are able to modulate the bias. We also discuss that causal illusions are adaptive under some circumstances, although they often lead to utterly wrong beliefs. Finally, we mention several debiasing strategies that have been proved effective in fighting the causal illusion and preventing some of its consequences, such as pseudoscientific belief.

The distinction between seeing and knowing, and why our brains even bother to see, are discussed using vivid perceptual examples, including image features without visible qualia that can nonetheless be consciously recognized, The work of Helmholtz and Kanizsa exemplify these issues, including examples of the paradoxical facts that “all boundaries are invisible”, and that brighter objects look closer. Why we do not see the big holes in, and occluders of, our retinas that block light from reaching our photoreceptors is explained, leading to the realization that essentially all percepts are visual illusions. Why they often look real is also explained. The computationally complementary properties of boundary completion and surface filling-in are introduced and their unifying explanatory power is illustrated, including that “all conscious qualia are surface percepts”. Neon color spreading provides a vivid example, as do self-luminous, glary, and glossy percepts. How brains embody general-purpose self-organizing architectures for solving modal problems, more general than AI algorithms, but less general than digital computers, is described. New concepts and mechanisms of such architectures are explained, including hierarchical resolution of uncertainty. Examples from the visual arts and technology are described to illustrate them, including paintings of Baer, Banksy, Bleckner, da Vinci, Gene Davis, Hawthorne, Hensche, Matisse, Monet, Olitski, Seurat, and Stella. Paintings by different artists and artistic schools instinctively emphasize some brain processes over others. These choices exemplify their artistic styles. The role of perspective, T-junctions, and end gaps are used to explain how 2D pictures can induce percepts of 3D scenes.

This chapter presents a review of the evidence supporting the notion that motor responses may resist visual illusions. It asks the question of whether actions will be affected by illusions. The review is selective, as dictated by space constraints, and highly critical because the interpretation of the relevant evidence remains controversial. The conclusions underscore the theoretical relevance, as well as the heuristic value, of studies of illusion effects on action responses. At the same time, however, they offer a warning concerning past interpretations of this literature and suggest that the proposed immunity of actions from visual illusions may be much more limited than proposed in earlier accounts.

The limited ability to estimate properly the linear extent or spatial separation of objects is one of the well-tested and documented features of visual perception. However, despite a large amount of experimental data collected in various studies of the Müller-Lyer illusion and related visual illusions of extent, the generally accepted view concerning the origin of this phenomenon is still absent. This chapter addresses a possible role of the perceptual positional shifts of the stimulus parts in occurrence of the illusions. It also discusses the most important features of the computational model based on the hypothesis of positional coding via centroids.

A suite of three related visual illusions of size and shape are described—the occlusion illusion (OI), configural shape illusion (CSI), and shrinkage illusion (SI)—along with the relations among them. All can be produced by simple geometric arrangements of two overlapping rectangular surfaces. They differ in the direction of their effects, however, with the OI and CSI making regions appear larger and the SI making them appear smaller. Evidence is also described suggesting that different mechanisms underlie them, with the OI caused by partial modal filling-in along an occluding edge and the CSI and SI by assimilation of edge positions to configually relevant borders.

This chapter addresses some obvious objections to color adverbialism which arise from considering the dominant theories within the philosophy of perception, such as the problem of perceptual error. The motivations for color adverbialism stem largely from consideration of the theoretical commitments and conceptual needs of perceptual science. As such, the theory is not intended as an ontology which will make sense of ordinary color discourse, or as an analysis of color visual experience. However, it is still beneficial to see how it stands with respect to such doctrines in the philosophy of perception as representationalism and naïve realism. In this chapter it is argued that color adverbialism is compatible with versions of representational and naïve realist theories, and that it has the resources to distinguish examples usually classified as color illusions from instances of veridical perception.

While bottom-up visual processing is important, the brain integrates this information with top-down, generative expectations from very early on in the visual processing hierarchy. Indeed, our brain should not be viewed as a classification system, but rather as a generative system, which perceives something by integrating sensory evidence with the available, learned, predictive knowledge about that thing. The involved generative models continuously produce expectations over time, across space, and from abstracted encodings to more concrete encodings. Bayesian information processing is the key to understand how information integration must work computationally – at least in approximation – also in the brain. Bayesian networks in the form of graphical models allow the modularization of information and the factorization of interactions, which can strongly improve the efficiency of generative models. The resulting generative models essentially produce state estimations in the form of probability densities, which are very well-suited to integrate multiple sources of information, including top-down and bottom-up ones. A hierarchical neural visual processing architecture illustrates this point even further. Finally, some well-known visual illusions are shown and the perceptions are explained by means of generative, information integrating, perceptual processes, which in all cases combine top-down prior knowledge and expectations about objects and environments with the available, bottom-up visual information.

Knowledge may be examined from the third-person perspective, as psychologists and sociologists do, or it may be examined from the first-person perspective, as each of us does when we reflect on what we ought to believe. This chapter takes the third-person perspective. One obvious source of knowledge is perception, and some general features of how our perceptual systems are able to pick up information about the world around us are highlighted. The role of the study of visual illusions in this research is an important focus of the chapter. Our ability to draw out the consequences of things we know by way of inference is another important source of knowledge, and some general features of how inference achieves its successes are discussed. Structural similarities between the ways in which perception works and the ways in which inference works are highlighted.

The Pinna illusion is the first case of visual illusion showing a rotating motion phenomenon. Squares, arranged in two concentric rings, show a strong counter-rotation effect. The inner ring of the squares appears to rotate counterclockwise and the outer ring clockwise when the observer’s head is slowly moved toward the figure while the gaze is kept fixed in the center of the stimulus pattern. The direction of rotation is reversed when the observer’s head moves away from the stimulus. The speed of the illusory rotation is proportional to the one of the motion imparted by the observer. While the way each individual check receives a local illusory motion signal can be explained by the response of direction-selective neurons at the earliest cortical stage of visual processing, the whole illusory rotational motion can be thought to be sensed by the higher cortical area, which collates all the signals provided by the local motion checks.

Some paintings, and other art forms, create vivid sensations of shimmering and movement, despite the fact that they are nothing more than simple static patterns of paint on a static canvas. This is known as a motion illusion. This chapter explores this type of visual illusion and explains why such motion sensations exist in static images. Understanding such phenomena requires the careful definition of stimulus conditions in terms of space and time, consideration of the visuomotor interaction, and the resulting space-time characteristics of the input to cortical processing networks, through modeling of a quantitative model for the neural networks that generate (in this case, illusory) perception

As a visual illusion, the Enigma illusion is a pattern that in its original version consists of 120 black radial lines on a white background intercepted by three bicolored annuli and a central disk. The main illusory effect in the Enigma (leading to its name) occurs during fixation of the center of the static image. Then, quite intense streaming motion may be perceived on the different annuli. It is characterized by a traveling wave or some subtle motion on the annuli that may not be described in more detail by the observer. Sometimes the observers call it “a feeling of motion”. This perceived (illusory) motion can occur in either direction; clockwise or counterclockwise. This example shows that such illusions are an important means to psychophysically investigate human motion perception and its limits.

A common misconception is that we see the world in accord with physical reality but are sometimes fooled in special circumstances that give rise to discrepancies between reality and what we perceive (visual illusions). Evidence accumulated over the past decade, however, indicates that all visual perceptions are at odds with the physical parameters of the world. Since biological visual systems cannot measure the world but must nonetheless afford the ability to behave in it, animals create biologically determined visual stimuli based on their value to reproductive success. In light of this evidence, the phrase “visual illusions” is misleading and should be abandoned.

Chapter 7 begins to puts REC positive story into action. It opens by questioning the value of appealing to a priori intuitions in trying to understand the character of perceiving. Focusing on explanatory concerns, it revisits Predictive Processing or PPC proposals about perceiving and defuses arguments that the explanatory punch of PPC requires characterizing perceptual processes and products in representational terms. Instead the chapter shows how REC can successfully appropriate the main apparatus of PPC to explain perception. It demonstrates that mental representations are not needed to explain how intramodal and intermodal forms of perceiving integrate. The chapter concludes by showing how contentless forms of perceiving can connect with contentful attitudes, enabling us to make sense of a range of perceptual phenomena – including our capacity to attune to optical effects and the ways in which we respond to visual illusions.