The Time Course of Segmentation and Cue-Selectivity in the Human Visual Cortex

Texture discontinuities are a fundamental cue by which the visual system segments objects from their background. The neural mechanisms supporting texture-based segmentation are therefore critical to visual perception and cognition. In the present experiment we employ an EEG source-imaging approach in order to study the time course of texture-based segmentation in the human brain. Visual Evoked Potentials were recorded to four types of stimuli in which periodic temporal modulation of a central 3° figure region could either support figure-ground segmentation, or have identical local texture modulations but not produce changes in global image segmentation. The image discontinuities were defined either by orientation or phase differences across image regions. Evoked responses to these four stimuli were analyzed both at the scalp and on the cortical surface in retinotopic and functional regions-of-interest (ROIs) defined separately using fMRI on a subject-by-subject basis. Texture segmentation (tsVEP: segmenting versus non-segmenting) and cue-specific (csVEP: orientation versus phase) responses exhibited distinctive patterns of activity. Alternations between uniform and segmented images produced highly asymmetric responses that were larger after transitions from the uniform to the segmented state. Texture modulations that signaled the appearance of a figure evoked a pattern of increased activity starting at ∼143 ms that was larger in V1 and LOC ROIs, relative to identical modulations that didn't signal figure-ground segmentation. This segmentation-related activity occurred after an initial response phase that did not depend on the global segmentation structure of the image. The two cue types evoked similar tsVEPs up to 230 ms when they differed in the V4 and LOC ROIs. The evolution of the response proceeded largely in the feed-forward direction, with only weak evidence for feedback-related activity

Relative advantage of touch over vision in the exploration of texture

Texture segmentation is an effortless process in scene analysis, yet its mechanisms have not been sufficiently understood. Several theories and algorithms exist for texture discrimination based on vision. These models diverge from one another in algorithmic approaches to address texture imagery using spatial elements and their statistics. Even though there are differences among these approaches, they all begin from the assumption that texture segmentation is a visual task. However, considering that texture is basically a surface property, this assumption can at times be misleading. An interesting possibility is that since surface properties are most immediately accessible to touch, texture perception may be more intimately associated with texture than with vision (it is known that tactile input can affect vision). Coincidentally, the basic organization of the touch (somatosensory) system bears some analogy to that of the visual system. In particular, recent neurophysiological findings showed that receptive fields for touch resemble that of vision, albeit with some subtle differences. The main novelty and contribution of this thesis is in the use of tactile receptive field responses for texture segmentation. Furthermore, we showed that touch-based representation is superior to its vision-based counterpart when used in texture boundary detection. Tactile representations were also found to be more discriminable (LDA and ANOVA). We expect our results to help better understand the nature of texture perception and build more powerful texture processing algorithms. The results suggest that touch has an advantage over vision in texture processing. Findings in this study are expected to shed new light on the role of tactile perception of texture and its interaction with vision, and help develop more powerful, biologically inspired texture segmentation algorithms

Étude du traitement visuel simple et complexe chez les enfants autistes

Les personnes ayant un trouble du spectre autistique (TSA) manifestent des particularités perceptives. En vision, des travaux influents chez les adultes ont mené à l’élaboration d’un modèle explicatif du fonctionnement perceptif autistique qui suggère que l’efficacité du traitement visuel varie en fonction de la complexité des réseaux neuronaux impliqués (Hypothèse spécifique à la complexité). Ainsi, lorsque plusieurs aires corticales sont recrutées pour traiter un stimulus complexe (e.g., modulations de texture; attributs de deuxième ordre), les adultes autistes démontrent une sensibilité diminuée. À l’inverse, lorsque le traitement repose principalement sur le cortex visuel primaire V1 (e.g., modulations locales de luminance; attributs de premier ordre), leur sensibilité est augmentée (matériel statique) ou intacte (matériel dynamique). Cette dissociation de performance est spécifique aux TSA et peut s’expliquer, entre autre, par une connectivité atypique au sein de leur cortex visuel. Les mécanismes neuronaux précis demeurent néanmoins méconnus. De plus, on ignore si cette signature perceptuelle est présente à l’enfance, information cruciale pour les théories perceptives de l’autisme. Le premier volet de cette thèse cherche à vérifier, à l’aide de la psychophysique et l’électrophysiologie, si la double dissociation de performance entre les attributs statiques de premier et deuxième ordre se retrouve également chez les enfants autistes d’âge scolaire. Le second volet vise à évaluer chez les enfants autistes l’intégrité des connexions visuelles descendantes impliquées dans le traitement des textures. À cet effet, une composante électrophysiologique reflétant principalement des processus de rétroaction corticale a été obtenue lors d’une tâche de ségrégation des textures. Les résultats comportementaux obtenus à l’étude 1 révèlent des seuils sensoriels similaires entre les enfants typiques et autistes à l’égard des stimuli définis par des variations de luminance et de texture. Quant aux données électrophysiologiques, il n’y a pas de différence de groupe en ce qui concerne le traitement cérébral associé aux stimuli définis par des variations de luminance. Cependant, contrairement aux enfants typiques, les enfants autistes ne démontrent pas une augmentation systématique d’activité cérébrale en réponse aux stimuli définis par des variations de texture pendant les fenêtres temporelles préférentiellement associées au traitement de deuxième ordre. Ces différences d’activation émergent après 200 ms et engagent les aires visuelles extrastriées des régions occipito-temporales et pariétales. Concernant la connectivité cérébrale, l’étude 2 indique que les connexions visuelles descendantes sont fortement asymétriques chez les enfants autistes, en défaveur de la région occipito-temporale droite. Ceci diffère des enfants typiques pour qui le signal électrophysiologique reflétant l’intégration visuo-corticale est similaire entre l’hémisphère gauche et droit du cerveau. En somme, en accord avec l’hypothèse spécifique à la complexité, la représentation corticale du traitement de deuxième ordre (texture) est atypiquement diminuée chez les enfants autistes, et un des mécanismes cérébraux impliqués est une altération des processus de rétroaction visuelle entre les aires visuelles de haut et bas niveau. En revanche, contrairement aux résultats obtenus chez les adultes, il n’y a aucun indice qui laisse suggérer la présence de mécanismes supérieurs pour le traitement de premier ordre (luminance) chez les enfants autistes.Atypical perceptual information processing is commonly described in Autism Spectrum Disorders (ASD). In the visual modality, influential work with autistic adults suggests altered connectivity within specialized local networks defining the response properties of stimulus-driven mechanisms. This has led to the development of a hypothesis that stipulates that the efficiency of autistic visual perception is contingent on the complexity of the neural network involved (Complexity-specific hypothesis). When several cortical areas must communicate with each other (as in texture-defined perception, also called second-order), reduced sensitivity to visual input is observed in autistic individuals. In contrast, when visual processing predominately relies on the primary visual cortex V1 (as in luminance-defined perception, also called first-order), their sensitivity is either enhanced (stationary stimuli) or intact (moving stimuli). This dissociation in performance is unique to ASD and suggests atypical connectivity within their visual cortex. The precise type of neural alteration remains unknown, however. In addition, studies focusing on younger individuals are needed to define the developmental trajectories of perceptual abilities in autism. This issue is crucial for perceptual theories of ASD. The first experiment aims to investigate whether the dissociation regarding first- and second-order spatial vision is also present in school-aged children with autism. We combined the use of behavioural (psychophysics) and neuroimaging (visual evoked potentials: VEPs) methods. The second experiment was designed to assess the integrity of one type of neural connections that are known to be involved in texture processing: feedback processes from extrastriate areas towards lower hierarchical levels (V1). As such, we used a visual texture segregation task and isolated a texture-segregation specific VEP component that mainly reflects feedback modulation in the visual cortex. Behavioural measures from the first experiment do not reveal differences in visual thresholds between typically developing and autistic children for both luminance- and texture-defined stimuli. With respect to electrophysiology, there is no group difference in brain activity associated with luminance-defined stimuli. However, unlike typical children, autistic children do not reliably show reliable enhancements of brain activity in response to texture-defined stimuli during time-windows more closely associated with second-order processing. These differences emerge after 200 msec post-stimulation and mainly involve extrastriate areas located over occipito-temporal and parietal scalp areas. Regarding the second experiment, the texture-segregation specific VEP component is found to be greatly diminished over the right as compared to the left occipito-lateral cortex in autism, while it shows no hemispheric asymmetry in typically developing children. In summary, in line with the complexity-specific hypothesis, cortical representation of second-order attributes (texture) is atypically reduced in autistic children. This thesis further reveals that altered feedback from extrastriate visual areas to lower areas (V1) is one of the neuronal mechanisms involved in atypical texture processing. In contrast, contrary to the results obtained in adults with autism, first-order vision (luminance) is not found to be superior in autistic children

Performance, Development, and Analysis of Tactile vs. Visual Receptive Fields in Texture Tasks

Texture segmentation is an effortless process in scene analysis, yet its neural mechanisms are not sufficiently understood. A common assumption in most current approaches is that texture segmentation is a vision problem. However, considering that texture is basically a surface property, this assumption can at times be misleading. One interesting possibility is that texture may be more intimately related with touch than with vision. Recent neurophysiological findings showed that receptive fields (RFs) for touch resemble that of vision, albeit with some subtle differences. To leverage on this, here I propose three ways to investigate the tactile receptive fields in the context of texture processing: (1) performance, (2) development, and (3) analysis. For performance, I tested how such distinct properties in tactile receptive fields can affect texture segmentation performance, as compared to that of visual receptive fields. Preliminary results suggest that touch has an advantage over vision in texture segmentation. These results support the idea that texture is fundamentally a tactile (surface) property. The next question is what drives the two types of RFs, visual and tactile, to become different during cortical development? I investigated the possibility that tactile RF and visual RF emerge based on the same cortical learning process, where the only difference is in the input type, natural-scene-like vs. texture-like. The main result is that RFs trained on natural scenes develop RFs resembling visual RFs, while those trained on texture resemble tactile RFs. These results again suggest a tight link between texture and the tactile modality, from a developmental context. To investigate further the functional properties of these RFs in texture processing, the response of tactile RFs and visual RFs were analyzed with manifold learning and with statistical approaches. The results showed that touch-based manifold seems more suitable for texture processing and desirable properties found in visual RF response can carry over to those in the tactile domain. These results are expected to shed new light on the role of tactile perception of texture; help develop more powerful, biologically inspired texture segmentation algorithms; and further clarify the differences and similarities between touch and vision

Edge- and region-based processes of 2nd-order vision

The human visual system is sensitive to 2nd-order image properties (often called texture properties). Spatial gradients in certain 2nd-order properties are edge-based, in that contours are effortlessly perceived through a rapid segmentation process. Others, however, are region-based, in that they require regional integration in order to be discriminated. The five studies reported in this thesis explore these mechanisms of 2nd-order vision, referred to respectively as segmentation and discrimination. Study one compares the segmentation and discrimination of 2nd-order stimuli and uses flicker-defined-form to demonstrate that the former may be subserved by phase-insensitive mechanisms. In study two, through testing of a neuropsychological patient, it is shown that 2nd-order segmentation is achieved relatively early in the visual system and, contrary to some claims, does not require the region termed human “V4”. Study three demonstrates, through selective adaptation aftereffects, that orientation variance (a 2nd-order regional property) is encoded by a dedicated mechanism tuned broadly to high and low variance and insensitive to low-level pattern information. Furthermore, the finding that the variance-specific aftereffect is limited to a retinotopic (not spatiotopic) reference frame, and that a neuropsychological patient with mid- to high-level visual cortical damage retains some sensitivity to variance, suggests that this regional property may be encoded at an earlier cortical site than previously assumed. Study four examines how cues from different 2nd-order channels are temporally integrated to allow cue-invariant segmentation. Results from testing a patient with bilateral lateral occipital damage and from selective visual field testing in normal observers suggest that this is achieved prior to the level of lateral occipital complex, but at least at the level of V2. The final study demonstrates that objects that are segmented rapidly by 2nd-order channels are processed at a sufficiently high cortical level as to allow object-based attention without those objects ever reaching awareness

Investigating the effect of texture edges in figure-ground segregation using psychophysical and eye tracking experiments

Although it happens infrequently in the natural world, the human visual system is able to perceive objects defined solely by a difference in texture i.e. with no accompanying change in colour or luminance. However, studies on texture perception have frequently used figure-ground patterns with abrupt texture variations, with few studies using patterns with smooth texture variations. The work presented in this thesis considers the contribution of the edge and centre regions of a texture figure to orientation-based texture segregation. To this end, we used psychophysical (Chapters 4 – 6) and eye tracking techniques (Chapters 8 – 10) to investigate texture detection and segmentation. For the psychophysics experiments, the primary goal was to investigate if either an edge-based or region-based mechanism can account for both smooth and abrupt texture variations. We first examined this in Chapter 4, where we studied the effects of texture edges on figure-ground segregation. Lower thresholds were found when the texture figure had orientation contrast information at the edge and centre of the figure. Data modeling supports the notion that texture segregation involves a large-scale second-order texture filter i.e. akin to a region-based mechanism, but where information is extracted over a large albeit fixed-size region. Various studies were also conducted to determine what aspects of a texture figure would change the size of the second-stage filter. The size and aspect ratio of the figure were manipulated (Chapter 5), and also the spatial frequency of the texture pattern, age of the participants, and the viewing distance (Chapter 6). We found that higher spatial frequencies resulted in larger integration regions i.e. feeds into large second-stage filters, but age, viewing distance, figure size and aspect ratio did not influence the size of the integration region. For the eye tracking studies, the general aim was to investigate what information of a texture target is extracted in order to produce signals for eye movement control. We measured eye movements made by participants while they searched for a texture figure embedded in a background. We found that irrespective of the types of orientation profiles, area-normalized data were that the centre region of a figure was looked at most often, and for longer durations. However, figures with information of orientation contrast at both the edge and centre of figure were easier to localise (Chapter 7), and produced the highest level of saliency in attracting eye movements (Chapter 10). In Chapter 9, we demonstrate that the visual system is also able to efficiently segregate a texture figure from the ground to accurately plan a saccade to the target figure, and these saccades are planned based on the representation of the whole figure shape as opposed to local salient regions. More specifically, saccades were directed to the centre of gravity of the target, with some degree of undershoot. Finally, the similar size of the integration region for the eye tracking (Chapter 10) and psychophysics experiments implies that the saccadic system receives input from the mechanism that segregates figure-ground texture stimuli

Investigating the effect of texture edges in figure-ground segregation using psychophysical and eye tracking experiments

Although it happens infrequently in the natural world, the human visual system is able to perceive objects defined solely by a difference in texture i.e. with no accompanying change in colour or luminance. However, studies on texture perception have frequently used figure-ground patterns with abrupt texture variations, with few studies using patterns with smooth texture variations. The work presented in this thesis considers the contribution of the edge and centre regions of a texture figure to orientation-based texture segregation. To this end, we used psychophysical (Chapters 4 – 6) and eye tracking techniques (Chapters 8 – 10) to investigate texture detection and segmentation. For the psychophysics experiments, the primary goal was to investigate if either an edge-based or region-based mechanism can account for both smooth and abrupt texture variations. We first examined this in Chapter 4, where we studied the effects of texture edges on figure-ground segregation. Lower thresholds were found when the texture figure had orientation contrast information at the edge and centre of the figure. Data modeling supports the notion that texture segregation involves a large-scale second-order texture filter i.e. akin to a region-based mechanism, but where information is extracted over a large albeit fixed-size region. Various studies were also conducted to determine what aspects of a texture figure would change the size of the second-stage filter. The size and aspect ratio of the figure were manipulated (Chapter 5), and also the spatial frequency of the texture pattern, age of the participants, and the viewing distance (Chapter 6). We found that higher spatial frequencies resulted in larger integration regions i.e. feeds into large second-stage filters, but age, viewing distance, figure size and aspect ratio did not influence the size of the integration region. For the eye tracking studies, the general aim was to investigate what information of a texture target is extracted in order to produce signals for eye movement control. We measured eye movements made by participants while they searched for a texture figure embedded in a background. We found that irrespective of the types of orientation profiles, area-normalized data were that the centre region of a figure was looked at most often, and for longer durations. However, figures with information of orientation contrast at both the edge and centre of figure were easier to localise (Chapter 7), and produced the highest level of saliency in attracting eye movements (Chapter 10). In Chapter 9, we demonstrate that the visual system is also able to efficiently segregate a texture figure from the ground to accurately plan a saccade to the target figure, and these saccades are planned based on the representation of the whole figure shape as opposed to local salient regions. More specifically, saccades were directed to the centre of gravity of the target, with some degree of undershoot. Finally, the similar size of the integration region for the eye tracking (Chapter 10) and psychophysics experiments implies that the saccadic system receives input from the mechanism that segregates figure-ground texture stimuli