Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Texture Segregation By Visual Cortex: Perceptual Grouping, Attention, and Learning

A neural model is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model brings together five interacting processes: region-based texture classification, contour-based boundary grouping, surface filling-in, spatial attention, and object attention. The model shows how form boundaries can determine regions in which surface filling-in occurs; how surface filling-in interacts with spatial attention to generate a form-fitting distribution of spatial attention, or attentional shroud; how the strongest shroud can inhibit weaker shrouds; and how the winning shroud regulates learning of texture categories, and thus the allocation of object attention. The model can discriminate abutted textures with blurred boundaries and is sensitive to texture boundary attributes like discontinuities in orientation and texture flow curvature as well as to relative orientations of texture elements. The model quantitatively fits a large set of human psychophysical data on orientation-based textures. Object boundar output of the model is compared to computer vision algorithms using a set of human segmented photographic images. The model classifies textures and suppresses noise using a multiple scale oriented filterbank and a distributed Adaptive Resonance Theory (dART) classifier. The matched signal between the bottom-up texture inputs and top-down learned texture categories is utilized by oriented competitive and cooperative grouping processes to generate texture boundaries that control surface filling-in and spatial attention. Topdown modulatory attentional feedback from boundary and surface representations to early filtering stages results in enhanced texture boundaries and more efficient learning of texture within attended surface regions. Surface-based attention also provides a self-supervising training signal for learning new textures. Importance of the surface-based attentional feedback in texture learning and classification is tested using a set of textured images from the Brodatz micro-texture album. Benchmark studies vary from 95.1% to 98.6% with attention, and from 90.6% to 93.2% without attention.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

The assessment of visual behaviour and depth perception in surgery

Imperial Users onl

Multi-feature Bottom-up Processing and Top-down Selection for an Object-based Visual Attention Model

Artificial vision systems can not process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. However, inspired by biological perception systems, it is possible to develop an artificial attention model able to select only the relevant part of the scene, as human vision does. This paper presents an attention model which draws attention over perceptual units of visual information, called proto-objects, and which uses a linear combination of multiple low-level features (such as colour, symmetry or shape) in order to calculate the saliency of each of them. But not only bottom-up processing is addressed, the proposed model also deals with the top-down component of attention. It is shown how a high-level task can modulate the global saliency computation, modifying the weights involved in the basic features linear combination.Ministerio de Economía y Competitividad (MINECO), proyectos: TIN2008-06196 y TIN2012-38079-C03-03. Campus de Excelencia Internacional Andalucía Tech

Perceptual organization and its influence upon attention

Humans are able to control so much of their environment not through brute strength or enhanced sensory receptors, but through our ability to understand the world around us. In order to make sense of the world around us we need to organize the information that our sensory systems receive. One of the most fundamental steps in this organizational process lies in the construction of objects. By breaking down our sensory input into objects the mind provides a basis upon which it can begin to scaffold our understanding of the world. This thesis therefore explores the basic stages at which the visual system organizes our sensory input into distinct objects. It explores these stages by exploiting the fact that the brain’s limited processing resources can be selectively allocated on the basis of ‘object-hood’. This allocation of processing resources, or attention, on the basis of these early stages of segmentation is commonly referred to as ‘object based attention’. ‘Object based attention’ and perceptual organisation are explored in three sections in this thesis: Understanding the Phenomenon of Object Based Attention. The first three chapters of this thesis seeks to further our understanding of the phenomenon of ‘object based attention’, for example, chapter 3 explores whether the visual system can simultaneously parse several objects as potential units of attention, or whether it can only segment one or two objects at a time. Object Based Attention, a Tool to Explore the Nature of Perceptual Organisation The second section of this thesis attempts to use the phenomenon of ‘object based attention’ as a tool to explore the nature of perceptual representations, for example chapter 5 tests whether different modalities (in particular vision and touch) are able to directly share information about objects in order to build up an integrated model of the external world. Object Based Attention, Perceptual Organisation and Shape Processing Area LO. In the final section of this thesis the nature of perceptual organization is explored in a patient with a very specific form of brain damage that enables us to ask what areas of the brain are critically required for different aspects of perceptual organization