Axis-alignment affects perceptual grouping: evidence from simultanagnosia

We examined the effect of grouping by the alignment of implicit axes on the perception of multiple shapes, using a patient (GK) who shows simultanagnosia as part of Blint's syndrome. Five experiments demonstrated that: (1) GK was better able to judge the orientation of a global configuration if the constituent local shapes were aligned with their major axes than if they were aligned with their edges; (2) this axis information was used implicitly, since GK was unable to discriminate between configurations of axis-aligned and edge-aligned shapes; (3) GK's sensitivity to axis-alignment persisted even when the orientations of local shapes were kept constant, indicating some form of cooperative effect between the local elements; (4) axis-alignment of shapes also facilitated his ability to discriminate single-item from multi-item configurations; (5) the effect of axis-alignment could be attributed, at least partially, to the degree to which there was matching between the orientations of local shapes and the global configuration. Taken together, the results suggest that axis-based grouping can support the selection of multiple objects.

Axis-based grouping reduces visual extinction

We examined the effects on extinction of grouping by collinearity of edges and grouping by alignment of internal axes of shapes, in a patient (GK) with simultanagnosia following bilateral parietal brain damage. GK’s visual extinction was reduced when items (equilateral triangles and angles) could be grouped by base alignment (i.e., collinearity) or by axis alignment, relative to a condition in which items were ungrouped. These grouping effects disappeared when inter-item spacing was increased, though factors such as display symmetry remained constant. Overall, the results suggest that, under some conditions, grouping by alignment of axes of symmetry can have an equal beneficial effect on visual extinction as edge-based grouping; thus, in the extinguished field, there is derivation of axis-based representations from the contours present.

Neural networks for perpetual grouping

A number of researchers have investigated the application of neural networks to visual recognition, with much of the emphasis placed on exploiting the network's ability to generalise. However, despite the benefits of such an approach it is not at all obvious how networks can be developed which are capable of recognising objects subject to changes in rotation, translation and viewpoint. In this study, we suggest that a possible solution to this problem can be found by studying aspects of visual psychology and in particular, perceptual organisation. For example, it appears that grouping together lines based upon perceptually significant features can facilitate viewpoint independent recognition. The work presented here identifies simple grouping measures based on parallelism and connectivity and shows how it is possible to train multi-layer perceptrons (MLPs) to detect and determine the perceptual significance of any group presented. In this way, it is shown how MLPs which are trained via backpropagation to perform individual grouping tasks, can be brought together into a novel, large scale network capable of determining the perceptual significance of the whole input pattern. Finally the applicability of such significance values for recognition is investigated and results indicate that both the NILP and the Kohonen Feature Map can be trained to recognise simple shapes described in terms of perceptual significances. This study has also provided an opportunity to investigate aspects of the backpropagation algorithm, particularly the ability to generalise. In this study we report the results of various generalisation tests. In applying the backpropagation algorithm to certain problems, we found that there was a deficiency in performance with the standard learning algorithm. An improvement in performance could however, be obtained when suitable modifications were made to the algorithm. The modifications and consequent results are reported here.

Auditory grouping in cochlear implant listeners

This thesis describes a series of experiments investigating both sequential and concurrent auditory grouping in implant listeners. Some grouping cues used by normal-hearing listeners should also be available to implant listeners, while others (e.g. fundamental frequency) are unlikely to be useful. As poor spectral resolution may also limit implant listeners’ performance, the spread of excitation in the cochlea was assessed using Neural Response Telemetry (NRT) and the results were related to those of the perceptual tasks. Experiment 1 evaluated sequential segregation of alternating tone sequences; no effect of rate or evidence of perceptual ambiguity was found, suggesting that automatic stream segregation had not occurred. Experiment 2 was an electrode pitch-ranking task; some relationship was found between pitch-ranking judgements (especially confidence scores) and reported segregation. Experiment 3 used a temporal discrimination task; this also failed to provide evidence of automatic stream segregation, because no interaction was found between the effects of sequence length and electrode separation. Experiment 4 explored schema-based grouping using interleaved melody discrimination; listeners were not able to segregate targets and distractors based on pitch differences, unless accompanied by substantial level differences. Experiment 5 evaluated concurrent segregation in a task requiring the detection of level changes in individual components of a complex tone. Generally, large changes were needed and abrupt changes were no easier to detect than gradual ones. In experiment 6, NRT testing confirmed substantially overlapping simulation by intracochlear electrodes. Overall, little or no evidence of auditory grouping by implant listeners was found.

A peak-clustering method for MEG group analysis to minimise artefacts due to smoothness

Magnetoencephalography (MEG), a non-invasive technique for characterizing brain electrical activity, is gaining popularity as a tool for assessing group-level differences between experimental conditions. One method for assessing task-condition effects involves beamforming, where a weighted sum of field measurements is used to tune activity on a voxel-by-voxel basis. However, this method has been shown to produce inhomogeneous smoothness differences as a function of signal-to-noise across a volumetric image, which can then produce false positives at the group level. Here we describe a novel method for group-level analysis with MEG beamformer images that utilizes the peak locations within each participant's volumetric image to assess group-level effects. We compared our peak-clustering algorithm with SnPM using simulated data. We found that our method was immune to artefactual group effects that can arise as a result of inhomogeneous smoothness differences across a volumetric image. We also used our peak-clustering algorithm on experimental data and found that regions were identified that corresponded with task-related regions identified in the literature. These findings suggest that our technique is a robust method for group-level analysis with MEG beamformer images.

Clustering web documents using hierarchical representation with multi-granularity

Web document cluster analysis plays an important role in information retrieval by organizing large amounts of documents into a small number of meaningful clusters. Traditional web document clustering is based on the Vector Space Model (VSM), which takes into account only two-level (document and term) knowledge granularity but ignores the bridging paragraph granularity. However, this two-level granularity may lead to unsatisfactory clustering results with “false correlation”. In order to deal with the problem, a Hierarchical Representation Model with Multi-granularity (HRMM), which consists of five-layer representation of data and a twophase clustering process is proposed based on granular computing and article structure theory. To deal with the zero-valued similarity problemresulted from the sparse term-paragraphmatrix, an ontology based strategy and a tolerance-rough-set based strategy are introduced into HRMM. By using granular computing, structural knowledge hidden in documents can be more efficiently and effectively captured in HRMM and thus web document clusters with higher quality can be generated. Extensive experiments show that HRMM, HRMM with tolerancerough-set strategy, and HRMM with ontology all outperform VSM and a representative non VSM-based algorithm, WFP, significantly in terms of the F-Score.

Clustering algorithm based on minimal path loss ratio for vehicular communication

Emerging vehicular comfort applications pose a host of completely new set of requirements such as maintaining end-to-end connectivity, packet routing, and reliable communication for internet access while on the move. One of the biggest challenges is to provide good quality of service (QoS) such as low packet delay while coping with the fast topological changes. In this paper, we propose a clustering algorithm based on minimal path loss ratio (MPLR) which should help in spectrum efficiency and reduce data congestion in the network. The vehicular nodes which experience minimal path loss are selected as the cluster heads. The performance of the MPLR clustering algorithm is calculated by rate of change of cluster heads, average number of clusters and average cluster size. Vehicular traffic models derived from the Traffic Wales data are fed as input to the motorway simulator. A mathematical analysis for the rate of change of cluster head is derived which validates the MPLR algorithm and is compared with the simulated results. The mathematical and simulated results are in good agreement indicating the stability of the algorithm and the accuracy of the simulator. The MPLR system is also compared with V2R system with MPLR system performing better. © 2013 IEEE.

Some clarifications on the DEA clustering approach

This paper clarifies the role of alternative optimal solutions in the clustering of multidimensional observations using data envelopment analysis (DEA). The paper shows that alternative optimal solutions corresponding to several units produce different groups with different sizes and different decision making units (DMUs) at each class. This implies that a specific DMU may be grouped into different clusters when the corresponding DEA model has multiple optimal solutions. © 2011 Elsevier B.V. All rights reserved.

Formant-frequency variation and its effects on across-formant grouping in speech perception

How speech is separated perceptually from other speech remains poorly understood. In a series of experiments, perceptual organisation was probed by presenting three-formant (F1+F2+F3) analogues of target sentences dichotically, together with a competitor for F2 (F2C), or for F2+F3, which listeners must reject to optimise recognition. To control for energetic masking, the competitor was always presented in the opposite ear to the corresponding target formant(s). Sine-wave speech was used initially, and different versions of F2C were derived from F2 using separate manipulations of its amplitude and frequency contours. F2Cs with time-varying frequency contours were highly effective competitors, whatever their amplitude characteristics, whereas constant-frequency F2Cs were ineffective. Subsequent studies used synthetic-formant speech to explore the effects of manipulating the rate and depth of formant-frequency change in the competitor. Competitor efficacy was not tuned to the rate of formant-frequency variation in the target sentences; rather, the reduction in intelligibility increased with competitor rate relative to the rate for the target sentences. Therefore, differences in speech rate may not be a useful cue for separating the speech of concurrent talkers. Effects of competitors whose depth of formant-frequency variation was scaled by a range of factors were explored using competitors derived either by inverting the frequency contour of F2 about its geometric mean (plausibly speech-like pattern) or by using a regular and arbitrary frequency contour (triangle wave, not plausibly speech-like) matched to the average rate and depth of variation for the inverted F2C. Competitor efficacy depended on the overall depth of frequency variation, not depth relative to that for the other formants. Furthermore, the triangle-wave competitors were as effective as their more speech-like counterparts. Overall, the results suggest that formant-frequency variation is critical for the across-frequency grouping of formants but that this grouping does not depend on speech-specific constraints.

The multiple pheromone ant clustering algorithm and its application to real world domains

The Multiple Pheromone Ant Clustering Algorithm (MPACA) models the collective behaviour of ants to find clusters in data and to assign objects to the most appropriate class. It is an ant colony optimisation approach that uses pheromones to mark paths linking objects that are similar and potentially members of the same cluster or class. Its novelty is in the way it uses separate pheromones for each descriptive attribute of the object rather than a single pheromone representing the whole object. Ants that encounter other ants frequently enough can combine the attribute values they are detecting, which enables the MPACA to learn influential variable interactions. This paper applies the model to real-world data from two domains. One is logistics, focusing on resource allocation rather than the more traditional vehicle-routing problem. The other is mental-health risk assessment. The task for the MPACA in each domain was to predict class membership where the classes for the logistics domain were the levels of demand on haulage company resources and the mental-health classes were levels of suicide risk. Results on these noisy real-world data were promising, demonstrating the ability of the MPACA to find patterns in the data with accuracy comparable to more traditional linear regression models. © 2013 Polish Information Processing Society.

A multiple pheromone ant clustering algorithm

Ant colony optimisation algorithms model the way ants use pheromones for marking paths to important locations in their environment. Pheromone traces are picked up, followed, and reinforced by other ants but also evaporate over time. Optimal paths attract more pheromone and less useful paths fade away. The main innovation of the proposed Multiple Pheromone Ant Clustering Algorithm (MPACA) is to mark objects using many pheromones, one for each value of each attribute describing the objects in multidimensional space. Every object has one or more ants assigned to each attribute value and the ants then try to find other objects with matching values, depositing pheromone traces that link them. Encounters between ants are used to determine when ants should combine their features to look for conjunctions and whether they should belong to the same colony. This paper explains the algorithm and explores its potential effectiveness for cluster analysis. © 2014 Springer International Publishing Switzerland.

Clustering epileptiform discharges in the interictal electroencephalogram with topography preserving maps

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The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods

Biological experiments often produce enormous amount of data, which are usually analyzed by data clustering. Cluster analysis refers to statistical methods that are used to assign data with similar properties into several smaller, more meaningful groups. Two commonly used clustering techniques are introduced in the following section: principal component analysis (PCA) and hierarchical clustering. PCA calculates the variance between variables and groups them into a few uncorrelated groups or principal components (PCs) that are orthogonal to each other. Hierarchical clustering is carried out by separating data into many clusters and merging similar clusters together. Here, we use an example of human leukocyte antigen (HLA) supertype classification to demonstrate the usage of the two methods. Two programs, Generating Optimal Linear Partial Least Square Estimations (GOLPE) and Sybyl, are used for PCA and hierarchical clustering, respectively. However, the reader should bear in mind that the methods have been incorporated into other software as well, such as SIMCA, statistiXL, and R.

Rhythmic masking release:effects of asynchrony, temporal overlap, harmonic relations, and source separation on cross-spectral grouping

The rhythm created by spacing a series of brief tones in a regular pattern can be disguised by interleaving identical distractors at irregular intervals. The disguised rhythm can be unmasked if the distractors are allocated to a separate stream from the rhythm by integration with temporally overlapping captors. Listeners identified which of 2 rhythms was presented, and the accuracy and rated clarity of their judgment was used to estimate the fusion of the distractors and captors. The extent of fusion depended primarily on onset asynchrony and degree of temporal overlap. Harmonic relations had some influence, but only an extreme difference in spatial location was effective (dichotic presentation). Both preattentive and attentionally driven processes governed performance. (PsycINFO Database Record (c) 2012 APA, all rights reserved)

Clustering algorithm based on minimal path loss ratio for vehicular communication

Emerging vehicular comfort applications pose a host of completely new set of requirements such as maintaining end-to-end connectivity, packet routing, and reliable communication for internet access while on the move. One of the biggest challenges is to provide good quality of service (QoS) such as low packet delay while coping with the fast topological changes. In this paper, we propose a clustering algorithm based on minimal path loss ratio (MPLR) which should help in spectrum efficiency and reduce data congestion in the network. The vehicular nodes which experience minimal path loss are selected as the cluster heads. The performance of the MPLR clustering algorithm is calculated by rate of change of cluster heads, average number of clusters and average cluster size. Vehicular traffic models derived from the Traffic Wales data are fed as input to the motorway simulator. A mathematical analysis for the rate of change of cluster head is derived which validates the MPLR algorithm and is compared with the simulated results. The mathematical and simulated results are in good agreement indicating the stability of the algorithm and the accuracy of the simulator. The MPLR system is also compared with V2R system with MPLR system performing better. © 2013 IEEE.