Littérature scientifique sur le sujet « Kanizsa illusion »

Visual illusion is the fallacious perception of reality or some actually existing object. In this paper, we imitate the mechanism of Ehrenstein illusion, neon color spreading illusion, watercolor illusion, Kanizsa illusion, shifted edges illusion, and hybrid image illusion using the Open Source Computer Vision Library (OpenCV). We also imitate these illusions using Cellular Neural Networks (CNNs). These imitations suggest that some illusions are processed by high-level brain functions. We next apply the morphological gradient operation to anomalous motion illusions. The processed images are classified into two kinds of images, which correspond to the central drift illusion and the peripheral drift illusion, respectively. It demonstrates that the contrast of the colors plays an important role in the anomalous motion illusion. We also imitate the anomalous motion illusions using both OpenCV and CNN. These imitations suggest that some visual illusions may be processed by the illusory movement of animations.

Every day, we experience a rich and complex visual world. Our brain constantly translates meaningless fragmented input into coherent objects and scenes. However, our attentional capabilities are limited, and we can only report the few items that we happen to attend to. So what happens to items that are not cognitively accessed? Do these remain fragmentary and meaningless? Or are they processed up to a level where perceptual inferences take place about image composition? To investigate this, we recorded brain activity using fMRI while participants viewed images containing a Kanizsa figure, an illusion in which an object is perceived by means of perceptual inference. Participants were presented with the Kanizsa figure and three matched nonillusory control figures while they were engaged in an attentionally demanding distractor task. After the task, one group of participants was unable to identify the Kanizsa figure in a forced-choice decision task; hence, they were “inattentionally blind.” A second group had no trouble identifying the Kanizsa figure. Interestingly, the neural signature that was unique to the processing of the Kanizsa figure was present in both groups. Moreover, within-subject multivoxel pattern analysis showed that the neural signature of unreported Kanizsa figures could be used to classify reported Kanizsa figures and that this cross-report classification worked better for the Kanizsa condition than for the control conditions. Together, these results suggest that stimuli that are not cognitively accessed are processed up to levels of perceptual interpretation.

The strength of the Poggendorff illusion has been determined by a nulling method for the classical as well as other configurations of the central inducing region. Compared to a uniform field, an inducing rectangle with very low contrast produces a marked illusion, which saturates at a Michelson contrast of about 0.1. With virtual borders of the Kanizsa type there is a weak illusion and this effect is attenuated when the ‘pacman’ sectors are occluded. Texture borders without luminance contrast induce a stronger illusion. These results are discussed in relation to earlier data for contrast dependence of Vernier acuity and for the orientation discrimination and tilt illusion with real and virtual borders.

An object phenomenally shrinks in its horizontal dimension when shown on a 2-D plane as if the central portion of the object were partially occluded by another vertical one in 3-D space (the Kanizsa amodal shrinkage). We examined the predictions of the correcting-mechanism hypothesis proposed by Ohtsuka and Ono (2002, Proceedings of SPIE4864 167 – 174), which states that an inappropriate operation of the mechanism that corrects a phenomenal increase in monocularly visible areas accompanied by a stereoscopic occluder gives rise to the illusion. In this study we measured the perceived width (or height in experiment 3) of a square seen behind a rectangle, while controlling other factors which potentially influence the illusion, such as the division of space or depth stratification. The results of five experiments showed that (a) the perceived width was not influenced when the occluder had a relatively large binocular disparity, but was underestimated when the occluder did not have disparity, and (b) the shrinkage diminished when the foreground rectangle was transparent, was horizontally oriented, or contained no pictorial occlusion cues. These results support the hypothesis that the correcting mechanism, triggered by pictorial occlusion cues, contributes to the Kanizsa shrinkage.

The study of illusory brightness and contour phenomena has become an important tool in modern brain research. Gestalt, cognitive, neural, and computational approaches are reviewed and their explanatory powers are discussed in the light of empirical data. Two well-known phenomena of illusory form are dealt with, the Ehrenstein illusion and the Kanizsa triangle. It is argued that the gap between the different levels of explanation, bottom—up versus top—down, creates scientific barriers which have all too often engendered unnecessary debate about who is right and who is wrong. In this review of the literature we favour an integrative approach to the question of how illusory form is derived from stimulus configurations which provide the visual system with seemingly incomplete information. The processes that can explain the emergence of these phenomena range from local feature detection to global strategies of perceptual organisation. These processes may be similar to those that help us restore partially occluded objects in everyday vision. To understand better the Ehrenstein and Kanizsa illusions, it is proposed that different levels of analysis and explanation are not mutually exclusive, but complementary. Theories of illusory contour and form perception must, therefore, take into account the underlying neurophysiological mechanisms and their possible interactions with cognitive and attentional processes.

Goldfish (Carassius auratus) were trained to discriminate triangles and squares using a two choice procedure. In the first experiment, three goldfish were trained with food reward on a black outline triangle on a white background, while a black outline square was shown for comparison. In transfer tests, a Kanizsa triangle and a Kanizsa square were presented, perceived by humans as an illusory triangle- or square-shaped surface of slightly higher brightness than the background. The choice behavior in this situation indicates that goldfish are able to discriminate between both figures in almost the same way as in the training situation. In control experiments goldfish did not discriminate between shapes in which humans do not perceive the illusion. A series of generalization experiments was performed indicating the similarity between the tested shapes and the training triangle. From all these findings we conclude that goldfish are able to perceive an illusory triangle or square within the Kanizsa figures. In a second experiment, four goldfish were trained on a white outline triangle versus a white outline square, both on black background with white diagonal lines. In transfer tests in which the shapes were replaced by gaps within the white diagonal lines, goldfish were clearly able to discriminate between the two patterns based on the illusory contours. This was not the case in tranfer tests with phase shifted abutting lines.

Westheimer, Gerald and Wu Li. Classifying illusory contours: edges defined by “pacman” and monocular tokens. J. Neurophysiol. 77: 731–736, 1997. Thresholds for the discrimination of orientation were measured in the human fovea for figures and borders delineated by solid lines and by “pacman” tokens as introduced by Kanizsa, as well as by contours induced by monocular tokens giving a stereoscopic depth illusion of a knife edge. Orientation discrimination of these illusory contours is poorer by a factor of ∼2 than that of equivalent contours made of solid lines and is not much better than that for their supporting structures if taken alone. It is concluded that these kinds of illusory borders do not address the “border” or “edge” mechanism in the same way as real lines. Orientation discrimination and simultaneous orientation contrast (tilt illusion) were compared for a variety of illusory borders. The more robust the borders, i.e., the more sensitive to changes in orientation, the less their susceptibility to the tilt illusion.

Visual illusions and perceptual grouping phenomena offer an invaluable tool for probing the computational mechanism of low-level visual processing. Some illusions, like the Kanizsa figure, reveal illusory contours that form edges collinear with the inducing stimulus. This kind of illusory contour has been modeled by neural network models by way of cells equipped with elongated spatial receptive fields designed to detect and complete the collinear alignment. There are, however, other illusory groupings which are not so easy to account for in neural network terms. The Ehrenstein illusion exhibits an illusory contour that forms a contour orthogonal to the stimulus instead of collinear with it. Other perceptual grouping effects reveal illusory contours that exhibit a sharp corner or vertex, and still others take the form of vertices defined by the intersection of three, four, or more illusory contours that meet at a point. A direct extension of the collinear completion models to account for these phenomena tends towards a combinatorial explosion, because it would suggest cells with specialized receptive fields configured to perform each of those completion types, each of which would have to be replicated at every location and every orientation across the visual field. These phenomena therefore challenge the adequacy of the neural network approach to account for these diverse perceptual phenomena. I have proposed elsewhere an alternative paradigm of neurocomputation in the harmonic resonance theory (Lehar 1999, see website), whereby pattern recognition and completion are performed by spatial standing waves across the neural substrate. The standing waves perform a computational function analogous to that of the spatial receptive fields of the neural network approach, except that, unlike that paradigm, a single resonance mechanism performs a function equivalent to a whole array of spatial receptive fields of different spatial configurations and of different orientations, and thereby avoids the combinatorial explosion inherent in the older paradigm. The present paper presents the directional harmonic model, a more specific development of the harmonic resonance theory, designed to account for specific perceptual grouping phenomena. Computer simulations of the directional harmonic model show that it can account for collinear contours as observed in the Kanizsa figure, orthogonal contours as seen in the Ehrenstein illusion, and a number of illusory vertex percepts composed of two, three, or more illusory contours that meet in a variety of configurations.

L'ambiente naturale in cui gli animali sono costretti a sopravvivere modella il loro cervello e il modo in cui si comportano per adattarsi e superare le pressioni naturali. Queste pressioni selettive potrebbero aver determinato l'emergere di un antico sistema neurale adatto a estrarre rapidamente informazioni astratte da set di oggetti, come la loro numerosità, per prendere decisioni informate fondamentali per la sopravvivenza e l'adattamento. La teoria del "Senso numerico" rappresenta il modello neurale più influente che tiene conto delle prove neuropsicologiche e psicofisiche negli esseri umani e negli animali. Tuttavia, questo modello è ancora ampiamente dibattuto a causa delle difficoltà metodologiche nell'isolare i segnali neurali relativi all'elaborazione della numerosità astratta "discreta" (cioè il numero reale di oggetti in un set) da quelli relativi ad altre caratteristiche correlate o confuse con la numerosità nell’input sensoriale grezzo (p. es., area, densità, frequenza spaziale, ecc.). La presente tesi si proponeva di indagare quali meccanismi potrebbero essere alla base delle rappresentazioni della numerosità visiva, superando le difficoltà nell'isolare le caratteristiche discrete da quelle continue. Dopo aver esaminato i principali modelli teorici e i risultati della letteratura (Capitolo 1 e 2), nel Capitolo 3 abbiamo presentato un paradigma psicofisico in cui le linee dei contorni illusori (IC) simili alla famosa illusione di Kanizsa, sono state utilizzate per manipolare la connessione (ad es., grouping) degli elementi nel set, controllando tutte le caratteristiche continue attraverso i livelli di connessione. Abbiamo mostrato che la numerosità era sottostimata quando le connessioni aumentavano, suggerendo che la numerosità si basa su oggetti percettivi segmentati piuttosto che su caratteristiche grezze di basso livello. Nel Capitolo 4, abbiamo controllato la luminosità illusoria che accompagna gli ICs. Sfruttando le proprietà percettive dell'illusione di Kanizsa generata con induttori con contrasto inverso, abbiamo scoperto che la sottostima era insensibile alla direzione del contrasto dell'induttore, suggerendo che l'effetto era specificamente indotto da un raggruppamento di bordi indipendente dalla polarità del contrasto e non dovuto alla luminosità percepita. Nel Capitolo 5, abbiamo manipolato contemporaneamente il raggruppamento con le linee IC e la dimensione percepita dei set numerici usando illusioni di dimensioni classiche (Ponzo Illusion). Utilizzando una combinazione di illusioni visive, abbiamo dimostrato che la percezione della numerosità non si basa su segnali continui percepiti, nonostante il segnale continuo possa influenzare la percezione numerica. Nel capitolo 6 abbiamo affrontato la questione con un approccio fisico diretto: utilizzando l'analisi di Fourier per equalizzare la frequenza spaziale (SF) negli stimoli, abbiamo mostrato che l'energia dello stimolo non è coinvolta nella rappresentazione della numerosità. Piuttosto la segmentazione degli elementi e l'organizzazione percettiva hanno spiegato i nostri risultati principali. Nel capitolo 7 abbiamo anche mostrato che l'effetto del rapporto, un importante segno distintivo della codifica Weber-like della percezione numerica, non è principalmente spiegato dall'energia dello stimolo o SF. Infine, nel Capitolo 8, abbiamo anche fornito la prima evidenza empirica che la numerosità non simbolica è rappresentata spazialmente indipendentemente dal contenuto fisico (SF) degli stimoli. Nel complesso, questa tesi supporta l'idea che l'elaborazione della numerosità non si basa semplicemente su caratteristiche di basso livello, ma suggerisce piuttosto direttamente che le informazioni discrete sono al centro del senso del numero.
The natural environment in which animals are forced to survive shapes their brain and the way in which they behave to adapt and overcome natural pressures. These selective pressures may have determined the emergence of an evolutionary ancient neural system suited to rapidly extract abstract information from collections, such as their numerosity, to take informed decisions pivotal for survivance and adaptation. The “Number Sense” theory represents the most influential neural model accounting for neuropsychological and psychophysical evidence in humans and animals. However, this model is still largely debated because of the methodological difficulties in isolating neural signals related to “discrete” (i.e., the real number of objects in a collection) abstract numerosity processing from those related to other features correlated or confounded with numerosity in the raw sensory input (e.g., visual area, density, spatial frequency, etc). The present thesis aimed to investigate which mechanisms might be at the basis of visual numerosity representations, overcoming the difficulties in isolating discrete from continuous features. After reviewing the main theoretical models and findings from the literature (Chapter 1 and 2), in the Chapter 3 we presented a psychophysical paradigm in which Kanizsa-like illusory contours (ICs) lines were used to manipulate the connectedness (e.g., grouping strength) of the items in the set, controlling all the continuous features across connectedness levels. We showed that numerosity was underestimated when connections increased, suggesting that numerosity relies on segmented perceptual objects rather than on raw low-level features. In Chapter 4, we controlled for illusory brightness confounds accompanying ICs. Exploiting perceptual properties of the reverse-contrast Kanizsa illusion, we found that underestimation was insensitive to inducer contrast direction, suggesting that the effect was specifically induced by a sign invariant boundary grouping and not due to perceived brightness confounds. In Chapter 5, we concurrently manipulated grouping with ICs lines and the perceived size of the collections using classic size illusions (Ponzo Illusion). By using a combination of visual illusions, we showed that numerosity perception is not based on perceived continuous cues, despite continuous cue might affect numerical perception. In Chapter 6 we tackled the issue with a direct physical approach: using Fourier analysis to equalize spatial frequency (SF) in the stimuli, we showed that stimulus energy is not involved in numerosity representation. Rather segmentation of the items and perceptual organization explained our main findings. In Chapter 7 we also showed that the ratio effect, an important hallmark of Weber-like encoding of numerical perception, is not primarily explained by stimulus energy or SF. Finally, in Chapter 8, we also provided the first empirical evidence that non-symbolic numerosity are represented spatially regardless of the physical SF content of the stimuli. Overall, this thesis strongly supports the view that numerosity processing is not merely based on low-level features, and rather strongly suggests that discrete information is at the core of the Number Sense.

An overview is provided of multiple book themes. A critical one is explaining how and where conscious states of seeing, hearing, feeling, and knowing arise in our minds, why they are needed to choose effective actions, yet how unconscious states also critically influence behavior. Other themes include learning, expectation, attention, imagination, and creativity; differences between illusion and reality, and between conscious seeing and recognizing, as embodied within surface-shroud resonances and feature-category resonances, respectively; roles of visual boundaries and surfaces in understanding visual art, movies, and TV; different legacies of Helmholtz and Kanizsa towards understanding vision; how stable opaque percepts and bistable transparent percepts are explained by the same laws; how solving the stability-plasticity dilemma enables brains to learn quickly without catastrophically forgetting previously learned but still useful knowledge; how we correct errors, explore novel experiences, and develop individual selves and cumulative cultural accomplishments; how expected vs. unexpected events are regulated by interacting top-down and bottom-up processes, leading to either adaptive resonances that support fast and stable new learning, or hypothesis testing whereby to learn about novel experiences; how variations of the same cooperative and competitive processes shape intelligence in species, cellular tissues, economic markets, and political systems; how short-term memory, medium-term memory, and long-term memory regulate adaptation to changing environments on different time scales; how processes whereby we learn what events are causal also support irrational, superstitious, obsessional, self-punitive, and antisocial behaviors; how relaxation responses arise; and how future acoustic contexts can disambiguate conscious percepts of past auditory and speech sequences that are occluded by noise or multiple speakers.

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.