Pseudoneglect and visual attention networks

Pseudoneglect represents the tendency for healthy individuals to show a slight but consistent bias in favour of stimuli appearing in the left visual field. The bias is often measured using variants of the line bisection task. An accurate model of the functional architecture of the visuospatial attention system must account for this widely observed phenomenon, as well as for modulation of the direction and magnitude of the bias within individuals by a variety of factors relating to the state of the participant and/or stimulus characteristics. To date, the neural correlates of pseudoneglect remain relatively unmapped. In the current thesis, I employed a combination of psychophysical measurements, electroencephalography (EEG) recording and transcranial direct current stimulation (tDCS) in an attempt to probe the neural generator(s) of pseudoneglect. In particular, I wished to utilise and investigate some of the factors known to modulate the bias (including age, time-on-task and the length of the to-be-bisected line) in order to identify neural processes and activity that are necessary and sufficient for the lateralized bias to arise. Across four experiments utilising a computerized version of a perceptual line bisection task, pseudoneglect was consistently observed at baseline in healthy young participants. However, decreased line length (experiments 1, 2 and 3), time-on-task (experiment 1) and healthy aging (experiment 3) were all found to modulate the bias. Specifically, all three modulations induced a rightward shift in subjective midpoint estimation. Additionally, the line length and time-on-task effects (experiment 1) and the line length and aging effects (experiment 3) were found to have additive relationships. In experiment 2, EEG measurements revealed the line length effect to be reflected in neural activity 100 – 200ms post-stimulus onset over source estimated posterior regions of the right hemisphere (RH: temporo-parietal junction (TPJ)). Long lines induced a hemispheric asymmetry in processing (in favour of the RH) during this period that was absent in short lines. In experiment 4, bi-parietal tDCS (Left Anodal/Right Cathodal) induced a polarity-specific rightward shift in bias, highlighting the crucial role played by parietal cortex in the genesis of pseudoneglect. The opposite polarity (Left Cathodal/Right Anodal) did not induce a change in bias. The combined results from the four experiments of the current thesis provide converging evidence as to the crucial role played by the RH in the genesis of pseudoneglect and in the processing of visual input more generally. The reduction in pseudoneglect with decreased line length, increased time-on-task and healthy aging may be explained by a reduction in RH function, and hence contribution to task processing, induced by each of these modulations. I discuss how behavioural and neuroimaging studies of pseudoneglect (and its various modulators) can provide empirical data upon which accurate formal models of visuospatial attention networks may be based and further tested.

Integrated visual perception architecture for robotic clothes perception and manipulation

This thesis proposes a generic visual perception architecture for robotic clothes perception and manipulation. This proposed architecture is fully integrated with a stereo vision system and a dual-arm robot and is able to perform a number of autonomous laundering tasks. Clothes perception and manipulation is a novel research topic in robotics and has experienced rapid development in recent years. Compared to the task of perceiving and manipulating rigid objects, clothes perception and manipulation poses a greater challenge. This can be attributed to two reasons: firstly, deformable clothing requires precise (high-acuity) visual perception and dexterous manipulation; secondly, as clothing approximates a non-rigid 2-manifold in 3-space, that can adopt a quasi-infinite configuration space, the potential variability in the appearance of clothing items makes them difficult to understand, identify uniquely, and interact with by machine. From an applications perspective, and as part of EU CloPeMa project, the integrated visual perception architecture refines a pre-existing clothing manipulation pipeline by completing pre-wash clothes (category) sorting (using single-shot or interactive perception for garment categorisation and manipulation) and post-wash dual-arm flattening. To the best of the author’s knowledge, as investigated in this thesis, the autonomous clothing perception and manipulation solutions presented here were first proposed and reported by the author. All of the reported robot demonstrations in this work follow a perception-manipulation method- ology where visual and tactile feedback (in the form of surface wrinkledness captured by the high accuracy depth sensor i.e. CloPeMa stereo head or the predictive confidence modelled by Gaussian Processing) serve as the halting criteria in the flattening and sorting tasks, respectively. From scientific perspective, the proposed visual perception architecture addresses the above challenges by parsing and grouping 3D clothing configurations hierarchically from low-level curvatures, through mid-level surface shape representations (providing topological descriptions and 3D texture representations), to high-level semantic structures and statistical descriptions. A range of visual features such as Shape Index, Surface Topologies Analysis and Local Binary Patterns have been adapted within this work to parse clothing surfaces and textures and several novel features have been devised, including B-Spline Patches with Locality-Constrained Linear coding, and Topology Spatial Distance to describe and quantify generic landmarks (wrinkles and folds). The essence of this proposed architecture comprises 3D generic surface parsing and interpretation, which is critical to underpinning a number of laundering tasks and has the potential to be extended to other rigid and non-rigid object perception and manipulation tasks. The experimental results presented in this thesis demonstrate that: firstly, the proposed grasp- ing approach achieves on-average 84.7% accuracy; secondly, the proposed flattening approach is able to flatten towels, t-shirts and pants (shorts) within 9 iterations on-average; thirdly, the proposed clothes recognition pipeline can recognise clothes categories from highly wrinkled configurations and advances the state-of-the-art by 36% in terms of classification accuracy, achieving an 83.2% true-positive classification rate when discriminating between five categories of clothes; finally the Gaussian Process based interactive perception approach exhibits a substantial improvement over single-shot perception. Accordingly, this thesis has advanced the state-of-the-art of robot clothes perception and manipulation.