Reading skills are related to global, but not local, acoustic pattern perception

Although reading ability has been related to the processing of simple pitch features such as isolated transitions or continuous modulation spoken language also contains complex patterns of pitch changes that are important for establishing stress location and for segmenting the speech stream. These aspects of spoken language processing depend critically on pitch pattern (global structure) rather than on absolute pitch values (local structure). Here we show that the detection of global structure, and not local structure, is predictive of performance on measures of phonological skill and reading ability, which supports a critical importance of pitch contour processing in the acquisition of literacy.

Using directional curvatures to visualize folding patterns of the GTM projection manifolds

In data visualization, characterizing local geometric properties of non-linear projection manifolds provides the user with valuable additional information that can influence further steps in the data analysis. We take advantage of the smooth character of GTM projection manifold and analytically calculate its local directional curvatures. Curvature plots are useful for detecting regions where geometry is distorted, for changing the amount of regularization in non-linear projection manifolds, and for choosing regions of interest when constructing detailed lower-level visualization plots.

Hierarchical GTM: constructing localized non-linear projection manifolds in a principled way

It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets, and therefore a hierarchical visualization system is desirable. In this paper we extend an existing locally linear hierarchical visualization system PhiVis ¸iteBishop98a in several directions: bf(1) We allow for em non-linear projection manifolds. The basic building block is the Generative Topographic Mapping (GTM). bf(2) We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. bf(3) Using tools from differential geometry we derive expressions for local directional curvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the ancestor visualization plots which are captured by a child model. We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set and apply our system to two more complex 12- and 18-dimensional data sets.

Hierarchical GTM: Constructing localized non-linear projection manifolds in a principled way

It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets, and therefore a hierarchical visualization system is desirable. In this paper we extend an existing locally linear hierarchical visualization system PhiVis ¸iteBishop98a in several directions: bf(1) We allow for em non-linear projection manifolds. The basic building block is the Generative Topographic Mapping. bf(2) We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. bf(3) Using tools from differential geometry we derive expressions for local directional curvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the parent visualization plot which are captured by a child model. We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set and apply our system to two more complex 12- and 19-dimensional data sets.

A principled approach to interactive hierarchical non-linear visualization of high-dimensional data

Hierarchical visualization systems are desirable because a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex high-dimensional data sets. We extend an existing locally linear hierarchical visualization system PhiVis [1] in several directions: bf(1) we allow for em non-linear projection manifolds (the basic building block is the Generative Topographic Mapping -- GTM), bf(2) we introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree, bf(3) we describe folding patterns of low-dimensional projection manifold in high-dimensional data space by computing and visualizing the manifold's local directional curvatures. Quantities such as magnification factors [3] and directional curvatures are helpful for understanding the layout of the nonlinear projection manifold in the data space and for further refinement of the hierarchical visualization plot. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. We demonstrate the visualization system principle of the approach on a complex 12-dimensional data set and mention possible applications in the pharmaceutical industry.

Control of pattern formation by time-delay feedback with global and local contributions

We consider the suppression of spatiotemporal chaos in the complex GinzburgLandau equation by a combined global and local time-delay feedback. Feedback terms are implemented as a control scheme, i.e., they are proportional to the difference between the time-delayed state of the system and its current state. We perform a linear stability analysis of uniform oscillations with respect to space-dependent perturbations and compare with numerical simulations. Similarly, for the fixed-point solution that corresponds to amplitude death in the spatially extended system, a linear stability analysis with respect to space-dependent perturbations is performed and complemented by numerical simulations. © 2010 Elsevier B.V. All rights reserved.

Local luminance amplitude modulates the interpretation of shape-from-shading in textured surfaces

The pattern of illumination on an undulating surface can be used to infer its 3-D form (shape-from-shading). But the recovery of shape would be invalid if the luminance changes actually arose from changes in reflectance. So how does vision distinguish variation in illumination from variation in reflectance to avoid illusory depth? When a corrugated surface is painted with an albedo texture, the variation in local mean luminance (LM) due to shading is accompanied by a similar modulation in local luminance amplitude (AM). This is not so for reflectance variation, nor for roughly textured surfaces. We used depth mapping and paired comparison methods to show that modulations of local luminance amplitude play a role in the interpretation of shape-from-shading. The shape-from-shading percept was enhanced when LM and AM co-varied (in-phase) and was disrupted when they were out of phase or (to a lesser degree) when AM was absent. The perceptual differences between cue types (in-phase vs out-of-phase) were enhanced when the two cues were present at different orientations within a single image. Our results suggest that when LM and AM co-vary (in-phase) this indicates that the source of variation is illumination (caused by undulations of the surface), rather than surface reflectance. Hence, the congruence of LM and AM is a cue that supports a shape-from-shading interpretation. © 2006 Elsevier Ltd. All rights reserved.

Spectral pattern, grouping, and the pitches of complex tones and their components

Harmonically related components are typically heard as a unified entity with a rich timbre and a pitch corresponding to the fundamental frequency. Mistuning a component generally has four consequences: (i) the global pitch of the complex shifts in the same direction as the mistuning; (ii) the component makes a reduced contribution to global pitch; (iii) the component is heard out as a separate sound with a pure timbre; (iv) its pitch differs from that of a pure tone of equal frequency in a small but systematic way. Local interactions between neighbouring components cannot explain these effects; instead they are usually explained in terms of the global operation of a single harmonic-template mechanism. However, several observations indicate that separate mechanisms govern the selection of spectral components for perceptual fusion and for the computation of global pitch. First, an increase in mistuning causes a harmonic to be heard out before it begins to be excluded from the computation of global pitch. Second, a single even harmonic added to an odd-harmonic complex is typically more salient than its odd neighbours. Third, the mistuning of a component in frequency-shifted stimuli, or stimuli with a moderate spectral stretch, results in changes in salience and component pitch like those seen for harmonic stimuli. Fourth, the global pitch of frequency-shifted stimuli is predicted well by the weighted fit of a harmonic template, but, with the exception of the lowest component, the fusion of individual partials for shifted stimuli is best predicted by the common pattern of spectral spacing. Fifth, our sensitivity to spectral pattern is surprisingly resistant to random variations in component spacing induced by applying mistunings to several harmonics at once. These findings are evaluated in the context of an autocorrelogram model of the proposed pitch/grouping dissociation. © S. Hirzel Verlag · EAA.

The spatial pattern of senile plaques, neurofibrillary tangles and A4 deposits in Alzheimer's disease

The spatial patterns of senile plaques (SP) and neurofibrillary tangles (NFT) as visualised using the Gallyas stain and of discrete A4 protein deposits were determined in coronal serial sections from a variety of brain regions in six elderly patients with Alzheimer's disease (AD). These lesions showed clustering in virtually all tissues examined with many of the clusters being regularly spaced. These spatial patterns were compared with the clustering observed for SP and NFT stained by the Glees and Marsland method in the same tissues. The data suggest that on average, while the regular clusters of A4 deposits and NFT were of approximately the same mean diameter (3600 microns), clusters of both Glees and Gallyas SP were approximately half this diameter (1800 - 2000 microns). If SP develop in local areas of the brain where both A4 deposition and neurofibrillary changes have occurred, the data suggest that the SP clusters would represent the region of overlap of the A4 deposits and neurofibrillary changes. Various hypothese are advocated to explain the regular clsuetring of the A4 deposits.

Spatially extensive summation of contrast-energy is revealed by contrast detection of micro-pattern textures

Vision must analyze the retinal image over both small and large areas to represent fine-scale spatial details and extensive textures. The long-range neuronal convergence that this implies might lead us to expect that contrast sensitivity should improve markedly with the contrast area of the image. But this is at odds with the orthodox view that contrast sensitivity is determined merely by probability summation over local independent detectors. To address this puzzle, I aimed to assess the summation of luminance contrast without the confounding influence of area-dependent internal noise. I measured contrast detection thresholds for novel Battenberg stimuli that had identical overall dimensions (to clamp the aggregation of noise) but were constructed from either dense or sparse arrays of micro-patterns. The results unveiled a three-stage visual hierarchy of contrast summation involving (i) spatial filtering, (ii) long-range summation of coherent textures, and (iii) pooling across orthogonal textures. Linear summation over local energy detectors was spatially extensive (as much as 16 cycles) at Stage 2, but the resulting model is also consistent with earlier classical results of contrast summation (J. G. Robson & N. Graham, 1981), where co-aggregation of internal noise has obscured these long-range interactions.

A common rule for integration and suppression of luminance contrast across eyes, space, time, and pattern

Visual perception begins by dissecting the retinal image into millions of small patches for local analyses by local receptive fields. However, image structures extend well beyond these receptive fields and so further processes must be involved in sewing the image fragments back together to derive representations of higher order (more global) structures. To investigate the integration process, we also need to understand the opposite process of suppression. To investigate both processes together, we measured triplets of dipper functions for targets and pedestals involving interdigitated stimulus pairs (A, B). Previous work has shown that summation and suppression operate over the full contrast range for the domains of ocularity and space. Here, we extend that work to include orientation and time domains. Temporal stimuli were 15-Hz counter-phase sine-wave gratings, where A and B were the positive and negative phases of the oscillation, respectively. For orientation, we used orthogonally oriented contrast patches (A, B) whose sum was an isotropic difference of Gaussians. Results from all four domains could be understood within a common framework in which summation operates separately within the numerator and denominator of a contrast gain control equation. This simple arrangement of summation and counter-suppression achieves integration of various stimulus attributes without distorting the underlying contrast code.

Pattern integration in the normal and abnormal human visual system

The processing conducted by the visual system requires the combination of signals that are detected at different locations in the visual field. The processes by which these signals are combined are explored here using psychophysical experiments and computer modelling. Most of the work presented in this thesis is concerned with the summation of contrast over space at detection threshold. Previous investigations of this sort have been confounded by the inhomogeneity in contrast sensitivity across the visual field. Experiments performed in this thesis find that the decline in log contrast sensitivity with eccentricity is bilinear, with an initial steep fall-off followed by a shallower decline. This decline is scale-invariant for spatial frequencies of 0.7 to 4 c/deg. A detailed map of the inhomogeneity is developed, and applied to area summation experiments both by incorporating it into models of the visual system and by using it to compensate stimuli in order to factor out the effects of the inhomogeneity. The results of these area summation experiments show that the summation of contrast over area is spatially extensive (occurring over 33 stimulus carrier cycles), and that summation behaviour is the same in the fovea, parafovea, and periphery. Summation occurs according to a fourth-root summation rule, consistent with a “noisy energy” model. This work is extended to investigate the visual deficit in amblyopia, finding that area summation is normal in amblyopic observers. Finally, the methods used to study the summation of threshold contrast over area are adapted to investigate the integration of coherent orientation signals in a texture. The results of this study are described by a two-stage model, with a mandatory local combination stage followed by flexible global pooling of these local outputs. In each study, the results suggest a more extensive combination of signals in vision than has been previously understood.

Sub-national responses to climate change in England:evidence from local area agreements

The machinery of governance to address climate change at the sub-national level in England continues to evolve. Drawing on documentary evidence and the views of civil servants and local authority officials from the English West Midlands, this article explores the process through an examination of the inclusion of climate change indicators in the recent round of Local Area Agreements (LAAs), negotiated between central government and local authorities and Local Strategic Partnerships. Considerable popularity has been accorded these indicators nationally, but there are important variations in the pattern of take up. Moreover, significant uncertainties surround the contribution of local measures to reduce CO2 emissions and the targets attached to measures to adapt to climate change are seen as undemanding. Conversely, the impending Carbon Reduction Commitment will act as a powerful incentive for public bodies to cut CO2 emissions from their estates. Although potentially contributing to greater coherence in tackling climate change, achieving collective action through LAAs will prove problematic.

The impact of context on pattern category learning and representation

Context traditionally has been regarded in vision research as a determinant for the interpretation of sensory information on the basis of previously acquired knowledge. Here we propose a novel, complementary perspective by showing that context also specifically affects visual category learning. In two experiments involving sets of Compound Gabor patterns we explored how context, as given by the stimulus set to be learned, affects the internal representation of pattern categories. In Experiment 1, we changed the (local) context of the individual signal classes by changing the configuration of the learning set. In Experiment 2, we varied the (global) context of a fixed class configuration by changing the degree of signal accentuation. Generalization performance was assessed in terms of the ability to recognize contrast-inverted versions of the learning patterns. Both contextual variations yielded distinct effects on learning and generalization thus indicating a change in internal category representation. Computer simulations suggest that the latter is related to changes in the set of attributes underlying the production rules of the categories. The implications of these findings for phenomena of contrast (in)variance in visual perception are discussed.