Pooling and suppression in human spatial vision

A distinct feature of several recent models of contrast masking is that detecting mechanisms are divisively inhibited by a broadly tuned ‘gain pool’ of narrow-band spatial pattern mechanisms. The contrast gain control provided by this ‘cross-channel’ architecture achieves contrast normalisation of early pattern mechanisms, which is important for keeping them within the non-saturating part of their biological operating characteristic. These models superseded earlier ‘within-channel’ models, which had supposed that masking arose from direct stimulation of the detecting mechanism by the mask. To reveal the extent of masking, I measured the levels produced with large ranges of pattern spatial relationships that have not been explored before. Substantial interactions between channels tuned to different orientations and spatial frequencies were found. Differences in the masking levels produced with single and multiple component mask patterns provided insights into the summation rules within the gain pool. A widely used cross-channel masking model was tested on these data and was found to perform poorly. The model was developed and a version in which linear summation was allowed between all components within the gain pool but with the exception of the self-suppressing route typically provided the best account of the data. Subsequently, an adaptation paradigm was used to probe the processes underlying pooled responses in masking. This delivered less insight into the pooling than the other studies and areas were identified that require investigation for a new unifying model of masking and adaptation. In further experiments, levels of cross-channel masking were found to be greatly influenced by the spatio-temporal tuning of the channels involved. Old masking experiments and ideas relying on within-channel models were re-elevated in terms of contemporary cross-channel models (e.g. estimations of channel bandwidths from orientation masking functions) and this led to different conclusions than those originally arrived at. The investigation of effects with spatio-temporally superimposed patterns is focussed upon throughout this work, though it is shown how these enquiries might be extended to investigate effects across spatial and temporal position.

Spatial patterns of TDP-43 neuronal cytoplasmic inclusions (NCI) in fifteen cases of frontotemporal lobar degeneration with TDP-43 proteinopathy (FTLD-TDP)

Neuronal cytoplasmic inclusions (NCI) immunoreactive for transactive response DNA-binding protein (TDP-43) are the pathological hallmark of frontotemporal lobar degeneration with TDP-43 proteinopathy (FTLD-TDP). We studied the spatial patterns of the TDP-43 immunoreactive NCI in the frontal and temporal cortex of 15 cases of FTLD-TDP. The NCI were distributed parallel to the tissue boundary predominantly in regular clusters 50-400 µm in diameter. In five cortical areas, the size of the clusters approximated to the cells of the cortico-cortical pathways. In most regions, cluster size was smaller than 400 µm. There were no significant differences in spatial patterns between familial and sporadic cases. Cluster size of the NCI was not correlated with disease duration, brain weight, Braak stage, or disease subtype. The spatial pattern of the NCI was similar to that of neuronal inclusions in other neurodegenerative diseases and may reflect a common pattern of degeneration involving the cortico-cortical projections.

The Riesz transform and simultaneous representations of phase, energy and orientation in spatial vision

To represent the local orientation and energy of a 1-D image signal, many models of early visual processing employ bandpass quadrature filters, formed by combining the original signal with its Hilbert transform. However, representations capable of estimating an image signal's 2-D phase have been largely ignored. Here, we consider 2-D phase representations using a method based upon the Riesz transform. For spatial images there exist two Riesz transformed signals and one original signal from which orientation, phase and energy may be represented as a vector in 3-D signal space. We show that these image properties may be represented by a Singular Value Decomposition (SVD) of the higher-order derivatives of the original and the Riesz transformed signals. We further show that the expected responses of even and odd symmetric filters from the Riesz transform may be represented by a single signal autocorrelation function, which is beneficial in simplifying Bayesian computations for spatial orientation. Importantly, the Riesz transform allows one to weight linearly across orientation using both symmetric and asymmetric filters to account for some perceptual phase distortions observed in image signals - notably one's perception of edge structure within plaid patterns whose component gratings are either equal or unequal in contrast. Finally, exploiting the benefits that arise from the Riesz definition of local energy as a scalar quantity, we demonstrate the utility of Riesz signal representations in estimating the spatial orientation of second-order image signals. We conclude that the Riesz transform may be employed as a general tool for 2-D visual pattern recognition by its virtue of representing phase, orientation and energy as orthogonal signal quantities.

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 penny for your thoughts! Patterns of fMRI activity reveal the content and the spatial topography of visual mental images

Visual mental imagery is a complex process that may be influenced by the content of mental images. Neuropsychological evidence from patients with hemineglect suggests that in the imagery domain environments and objects may be represented separately and may be selectively affected by brain lesions. In the present study, we used functional magnetic resonance imaging (fMRI) to assess the possibility of neural segregation among mental images depicting parts of an object, of an environment (imagined from a first-person perspective), and of a geographical map, using both a mass univariate and a multivariate approach. Data show that different brain areas are involved in different types of mental images. Imagining an environment relies mainly on regions known to be involved in navigational skills, such as the retrosplenial complex and parahippocampal gyrus, whereas imagining a geographical map mainly requires activation of the left angular gyrus, known to be involved in the representation of categorical relations. Imagining a familiar object mainly requires activation of parietal areas involved in visual space analysis in both the imagery and the perceptual domain. We also found that the pattern of activity in most of these areas specifically codes for the spatial arrangement of the parts of the mental image. Our results clearly demonstrate a functional neural segregation for different contents of mental images and suggest that visuospatial information is coded by different patterns of activity in brain areas involved in visual mental imagery. Hum Brain Mapp 36:945-958, 2015.

Peripheral vision and pattern recognition : a review

We summarize the various strands of research on peripheral vision and relate them to theories of form perception. After a historical overview, we describe quantifications of the cortical magnification hypothesis, including an extension of Schwartz's cortical mapping function. The merits of this concept are considered across a wide range of psychophysical tasks, followed by a discussion of its limitations and the need for non-spatial scaling. We also review the eccentricity dependence of other low-level functions including reaction time, temporal resolution, and spatial summation, as well as perimetric methods. A central topic is then the recognition of characters in peripheral vision, both at low and high levels of contrast, and the impact of surrounding contours known as crowding. We demonstrate how Bouma's law, specifying the critical distance for the onset of crowding, can be stated in terms of the retinocortical mapping. The recognition of more complex stimuli, like textures, faces, and scenes, reveals a substantial impact of mid-level vision and cognitive factors. We further consider eccentricity-dependent limitations of learning, both at the level of perceptual learning and pattern category learning. Generic limitations of extrafoveal vision are observed for the latter in categorization tasks involving multiple stimulus classes. Finally, models of peripheral form vision are discussed. We report that peripheral vision is limited with regard to pattern categorization by a distinctly lower representational complexity and processing speed. Taken together, the limitations of cognitive processing in peripheral vision appear to be as significant as those imposed on low-level functions and by way of crowding.

Different molecular pathologies result in similar spatial patterns of cellular inclusions in neurodegenerative disease:a comparative study of eight disorders

Recent research suggests cell-to-cell transfer of pathogenic proteins such as tau and α-synuclein may play a role in neurodegeneration. Pathogenic spread along neural pathways may give rise to specific spatial patterns of the neuronal cytoplasmic inclusions (NCI) characteristic of these disorders. Hence, the spatial patterns of NCI were compared in four tauopathies, viz., Alzheimer's disease, Pick's disease, corticobasal degeneration, and progressive supranuclear palsy, two synucleinopathies, viz., dementia with Lewy bodies and multiple system atrophy, the 'fused in sarcoma' (FUS)-immunoreactive inclusions in neuronal intermediate filament inclusion disease, and the transactive response DNA-binding protein (TDP-43)-immunoreactive inclusions in frontotemporal lobar degeneration, a TDP-43 proteinopathy (FTLD-TDP). Regardless of molecular group or morphology, NCI were most frequently aggregated into clusters, the clusters being regularly distributed parallel to the pia mater. In a significant proportion of regions, the regularly distributed clusters were in the size range 400-800 μm, approximating to the dimension of cell columns associated with the cortico-cortical pathways. The data suggest that cortical NCI in different disorders exhibit a similar spatial pattern in the cortex consistent with pathogenic spread along anatomical pathways. Hence, treatments designed to protect the cortex from neurodegeneration may be applicable across several different disorders. © 2012 Springer-Verlag.

Spatial patterns of the tau pathology in progressive supranuclear palsy

Progressive supranuclear palsy (PSP) is characterized neuropathologically by neuronal loss, gliosis, and the presence of tau-immunoreactive neuronal and glial cell inclusions affecting subcortical and some cortical regions. The objectives of this study were to determine (1) the spatial patterns of the tau-immunoreactive pathology, viz., neurofibrillary tangles (NFT), oligodendroglial inclusions (GI), tufted astrocytes (TA), and Alzheimer's disease-type neuritic plaques (NP) in PSP and (2) to investigate the spatial correlations between the histological features. Post-mortem material of cortical and subcortical regions of eight PSP cases was studied. Spatial pattern analysis was applied to the NFT, GI, TA, NP, abnormally enlarged neurons (EN), surviving neurons, and glial cells. NFT, GI, and TA were distributed either at random or in regularly distributed clusters. The EN and NP were mainly randomly distributed. Clustering of NFT and EN was more frequent in the cortex and subcortical regions, respectively. Variations in NFT density were not spatially correlated with the densities of either GI or TA, but were positively correlated with the densities of EN and surviving neurons in some regions. (1) NFT were the most widespread tau-immunoreactive pathology in PSP being distributed randomly in subcortical regions and in regular clusters in cortical regions, (2) GI and TA were more localized and exhibited a regular pattern of clustering in subcortical regions, and (3) neuronal and glial cell pathologies were not spatially correlated. © 2012 Springer-Verlag.

Interactions between landcover pattern and geospatial processing methods:effects on landscape metrics and classification accuracy

Remote sensing data is routinely used in ecology to investigate the relationship between landscape pattern as characterised by land use and land cover maps, and ecological processes. Multiple factors related to the representation of geographic phenomenon have been shown to affect characterisation of landscape pattern resulting in spatial uncertainty. This study investigated the effect of the interaction between landscape spatial pattern and geospatial processing methods statistically; unlike most papers which consider the effect of each factor in isolation only. This is important since data used to calculate landscape metrics typically undergo a series of data abstraction processing tasks and are rarely performed in isolation. The geospatial processing methods tested were the aggregation method and the choice of pixel size used to aggregate data. These were compared to two components of landscape pattern, spatial heterogeneity and the proportion of landcover class area. The interactions and their effect on the final landcover map were described using landscape metrics to measure landscape pattern and classification accuracy (response variables). All landscape metrics and classification accuracy were shown to be affected by both landscape pattern and by processing methods. Large variability in the response of those variables and interactions between the explanatory variables were observed. However, even though interactions occurred, this only affected the magnitude of the difference in landscape metric values. Thus, provided that the same processing methods are used, landscapes should retain their ranking when their landscape metrics are compared. For example, highly fragmented landscapes will always have larger values for the landscape metric "number of patches" than less fragmented landscapes. But the magnitude of difference between the landscapes may change and therefore absolute values of landscape metrics may need to be interpreted with caution. The explanatory variables which had the largest effects were spatial heterogeneity and pixel size. These explanatory variables tended to result in large main effects and large interactions. The high variability in the response variables and the interaction of the explanatory variables indicate it would be difficult to make generalisations about the impact of processing on landscape pattern as only two processing methods were tested and it is likely that untested processing methods will potentially result in even greater spatial uncertainty. © 2013 Elsevier B.V.

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.

Peripheral vision and pattern recognition:a review

We summarize the various strands of research on peripheral vision and relate them to theories of form perception. After a historical overview, we describe quantifications of the cortical magnification hypothesis, including an extension of Schwartz's cortical mapping function. The merits of this concept are considered across a wide range of psychophysical tasks, followed by a discussion of its limitations and the need for non-spatial scaling. We also review the eccentricity dependence of other low-level functions including reaction time, temporal resolution, and spatial summation, as well as perimetric methods. A central topic is then the recognition of characters in peripheral vision, both at low and high levels of contrast, and the impact of surrounding contours known as crowding. We demonstrate how Bouma's law, specifying the critical distance for the onset of crowding, can be stated in terms of the retinocortical mapping. The recognition of more complex stimuli, like textures, faces, and scenes, reveals a substantial impact of mid-level vision and cognitive factors. We further consider eccentricity-dependent limitations of learning, both at the level of perceptual learning and pattern category learning. Generic limitations of extrafoveal vision are observed for the latter in categorization tasks involving multiple stimulus classes. Finally, models of peripheral form vision are discussed. We report that peripheral vision is limited with regard to pattern categorization by a distinctly lower representational complexity and processing speed. Taken together, the limitations of cognitive processing in peripheral vision appear to be as significant as those imposed on low-level functions and by way of crowding.

Perception of global image contrast is predicted by the same spatial integration model of gain control as detection and discrimination

How are the image statistics of global image contrast computed? We answered this by using a contrast-matching task for checkerboard configurations of ‘battenberg’ micro-patterns where the contrasts and spatial spreads of interdigitated pairs of micro-patterns were adjusted independently. Test stimuli were 20 × 20 arrays with various sized cluster widths, matched to standard patterns of uniform contrast. When one of the test patterns contained a pattern with much higher contrast than the other, that determined global pattern contrast, as in a max() operation. Crucially, however, the full matching functions had a curious intermediate region where low contrast additions for one pattern to intermediate contrasts of the other caused a paradoxical reduction in perceived global contrast. None of the following models predicted this: RMS, energy, linear sum, max, Legge and Foley. However, a gain control model incorporating wide-field integration and suppression of nonlinear contrast responses predicted the results with no free parameters. This model was derived from experiments on summation of contrast at threshold, and masking and summation effects in dipper functions. Those experiments were also inconsistent with the failed models above. Thus, we conclude that our contrast gain control model (Meese & Summers, 2007) describes a fundamental operation in human contrast vision.

Spatial patterns of neuronal cytoplasmic inclusions in the tauopathies: A comparative study of five disorders

The tauopathies are a major molecular group of neurodegenerative disorders characterised by the deposition of abnormal cellular aggregates of the microtubule associated protein (MAP) tau in the form of neuronal cytoplasmic inclusions (NCI). Recent research suggests that cell to cell propagation of pathogenic tau may be involved in the neurodegeneration of these disorders. If pathogenic tau spreads along anatomical pathways it may give rise to specific spatial patterns of the NCI in brain tissue. To test this hypothesis, the spatial patterns of NCI in cerebral cortical regions were compared in tissue sections taken from five major tauopathies: (1) argyrophilic grain disease (AGD), (2) Alzheimer's disease (AD), (3) corticobasal degeneration (CBD), (4) Pick's disease (PiD), and (5) progressive supranuclear palsy (PSP). In the cerebral cortex of these disorders, NCI were frequently aggregated into clusters and the clusters were regularly distributed parallel to the pia mater. In a significant proportion of regions, the mean size of the regularly distributed clusters of NCI was in the range 400 – 800 m, measured parallel to the pia mater, approximating to the dimension of cell columns associated with the cortico-cortical anatomical pathways. Hence, the data suggest that cortical NCI in the tauopathies exhibit a spatial pattern in the cortex which could result from the spread of pathogenic tau along anatomical pathways. Treatments designed to protect the cortex from tau propagation may therefore be applicable across several different disorders within this molecular group.