Negative affective state mimics effects of perceptual load on spatial perception

Recent electrophysiological evidence has shown that perceptual load and negative affective state can produce very similar, early-attention gating effects in early visual areas, modulating the processing of peripheral stimuli. Here we assessed the question of whether or not these modulatory effects of perceptual load and negative affect (NA) lead to comparable changes in spatial perception abilities, which could be captured at the behavioral level. High perceptual load at fixation impaired the precise spatial localization of peripheral textures, relative to a low perceptual load condition. By contrast, the coarse spatial encoding of these peripheral stimuli was not load-dependent, under neutral affective conditions. The transient experience of NA was induced in an independent sample of participants, who showed decreased performance in the localization task, even at a low perceptual-load level. These results were observed in the absence of any systematic eye movement toward the peripheral textures. These findings suggest that spatial location perception is an attention-dependent, as well as state-dependent process, in the sense that NA, very much like load, can dynamically shape early spatial perceptual abilities. Although NA mimics load during spatial localization, we discuss the possibility that these two effects likely depend upon nonoverlapping brain networks

Selective Control of Surface Spin Current in Topological Materials based on Pyrite-type OsX2 (X = Se, Te) Crystals

Topological materials host robust surface states, which could form the basis for future electronic devices. As such states have spins that are locked to the momentum, they are of particular interest for spintronic applications. Understanding spin textures of the surface states of topologically nontrivial materials, and being able to manipulate their polarization, is therefore essential if they are to be utilized in future technologies. Here we use first-principles calculations to show that pyrite-type crystals OsX2 (X= Se, Te) are a class of topological material that can host surface states with spin polarization that can be either in-plane or out-of-plane. We show that the formation of low-energy states with symmetry-protected energy- and direction-dependent spin textures on the (001) surface of these materials is a consequence of a transformation from a topologically trivial to nontrivial state, induced by spin orbit interactions. The unconventional spin textures of these surface states feature an in-plane to out-of-plane spin polarization transition in the momentum space protected by local symmetries. Moreover, the surface spin direction and magnitude can be selectively filtered in specific energy ranges. Our demonstration of a new class of topological material with controllable spin textures provide a platform for experimentalists to detect and exploit unconventional surface spin textures in future spin-based nanoelectronic devices

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

Single-picture reconstruction and rendering of trees for plausible vegetation synthesis

State-of-the-art approaches for tree reconstruction either put limiting constraints on the input side (requiring multiple photographs, a scanned point cloud or intensive user input) or provide a representation only suitable for front views of the tree. In this paper we present a complete pipeline for synthesizing and rendering detailed trees from a single photograph with minimal user effort. Since the overall shape and appearance of each tree is recovered from a single photograph of the tree crown, artists can benefit from georeferenced images to populate landscapes with native tree species. A key element of our approach is a compact representation of dense tree crowns through a radial distance map. Our first contribution is an automatic algorithm for generating such representations from a single exemplar image of a tree. We create a rough estimate of the crown shape by solving a thin-plate energy minimization problem, and then add detail through a simplified shape-from-shading approach. The use of seamless texture synthesis results in an image-based representation that can be rendered from arbitrary view directions at different levels of detail. Distant trees benefit from an output-sensitive algorithm inspired on relief mapping. For close-up trees we use a billboard cloud where leaflets are distributed inside the crown shape through a space colonization algorithm. In both cases our representation ensures efficient preservation of the crown shape. Major benefits of our approach include: it recovers the overall shape from a single tree image, involves no tree modeling knowledge and minimal authoring effort, and the associated image-based representation is easy to compress and thus suitable for network streaming.Peer ReviewedPostprint (author's final draft

Positive emotion broadens attention focus through decreased position-specific spatial encoding in early visual cortex: evidence from ERPs

Recent evidence has suggested that not only stimulus-specific attributes or top-down expectations can modulate attention selection processes, but also the actual mood state of the participant. In this study, we tested the prediction that the induction of positive mood can dynamically influence attention allocation and, in turn, modulate early stimulus sensory processing in primary visual cortex (V1). High-density visual event-related potentials (ERPs) were recorded while participants performed a demanding task at fixation and were presented with peripheral irrelevant visual textures, whose position was systematically varied in the upper visual field (close, medium, or far relative to fixation). Either a neutral or a positive mood was reliably induced and maintained throughout the experimental session. The ERP results showed that the earliest retinotopic component following stimulus onset (C1) strongly varied in topography as a function of the position of the peripheral distractor, in agreement with a near-far spatial gradient. However, this effect was altered for participants in a positive relative to a neutral mood. On the contrary, positive mood did not modulate attention allocation for the central (task-relevant) stimuli, as reflected by the P300 component. We ran a control behavioral experiment confirming that positive emotion selectively impaired attention allocation to the peripheral distractors. These results suggest a mood-dependent tuning of position-specific encoding in V1 rapidly following stimulus onset. We discuss these results against the dominant broaden-and-build theory