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Using Best’s (1995) perceptual assimilation model (PAM), we investigated auditory–visual (AV), auditory-only (AO), and visual-only (VO) perception of Thai tones. Mandarin and Cantonese (tone-language) speakers were asked to categorize Thai tones according to their own native tone categories, and Australian English (non-tone-language) speakers to categorize Thai tones into their native intonation categories—for instance, question or statement. As comparisons, Thai participants completed a straightforward identification task, and another Australian English group identified the Thai tones using simple symbols. All of the groups also completed an AX discrimination task. Both the Mandarin and Cantonese groups categorized AO and AV Thai falling tones as their native level tones, and Thai rising tones as their native rising tones, although the Mandarin participants found it easier to categorize Thai level tones than did the Cantonese participants. VO information led to very poor categorization for all groups, and AO and AV information also led to very poor categorizations for the English intonation categorization group. PAM’s predictions regarding tone discriminability based on these category assimilation patterns were borne out for the Mandarin group’s AO and AV discriminations, providing support for the applicability of the PAM to lexical tones. For the Cantonese group, however, PAM was unable to account for one specific discrimination pattern—namely, their relatively good performance on the Thai high–rising contrast in the auditory conditions—and no predictions could be derived for the English groups. A full account of tone assimilation will likely need to incorporate considerations of phonetic, and even acoustic, similarity and overlap between nonnative and native tone categories.

In this paper, a new method of blotch detection for digitised film sequences is proposed. Due to the aging of film stocks, their poor storage and/or repeated viewing, it is estimated that approximately 50% of all films produced prior to 1950 have either been destroyed or rendered unwatchable [1,2]. To prevent their complete destruction, original film reels must be scanned into digital format; however, any defects such as blotches will be retained. By combining a variation of a linear time, contour tracing technique with a simple temporal nearest neighbour algorithm, a preliminary detection system has been created. Using component labelling of dirt and sparkle the overall performance of the completed system, in terms of time and accuracy, will compare favourably to traditional motion compensated detection methods. This small study (based on 13 film sequences) represents a significant first step towards automatic blotch detection.

This study investigates the differences in movement patterns followed by users navigating within a virtual environment. The analysis has been carried out between two groups of users, identified on the basis of their performance on a search task. Results indicate significant differences between efficient and inefficient navigators’ trajectories. They are related to rotational, translational and localised-landmarks behaviour. These findings are discussed in the light of theoretical outcomes provided by environmental psychology.

In this paper we present a novel system which automatically converts text documents into XML by extracting information from previously tagged XML documents. The system uses the Self-Organizing Map (SOM) learning algorithm to arrange tagged documents on a two-dimensional map such that nearby locations contain similar documents. It then employs the inductive learning algorithm C5.0 to automatically extract and apply auto-tagging rules from the nearest SOM neighbours of an untagged document. The system is designed to be adaptive, so that once a document is tagged in XML, it learns from its errors in order to improve accuracy. The automatically tagged documents can be categorized on the SOM, further improving the map’s resolution. Various experiments were carried out on our system, using documents from a number of different domains. The results show that our approach performs well with impressive accuracy.

This study proposes a modular system for clustering on-line motion trajectories obtained while users navigate within a virtual environment. It presents a neural network simulation that gives a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organizing map algorithm was tested and improved to above 85% by using learning vector quantization. The benefits of this approach and the possibility of extending the methodology to the study of navigation in Human Computer Interaction are discussed.

In this paper we present a novel system that can automatically mark up text documents into XML. The system uses the Self-Organizing Map (SOM) algorithm to organize marked documents on a map so that similar documents are placed on nearby locations. Then by using the inductive learning algorithm C5, it automatically generates and applies the markup rules from the nearest SOM neighbours of an unmarked document. The system is adaptive in nature and learns from errors in the automatically marked-up document to improve accuracy. The automatically marked-up documents are again arranged on the SOM.

The goal of this chapter is to propose a candidate for consideration as a building block of cortical computation. The candidate in question is the collaborative cell assembly (CCA), and I will make the case in the rest of this chapter that the concept provides a unifying framework for accounting for the mechanisms of perceptual “binding” in the sensory areas of the brain, as well as providing a mechanism for the incremental construction of cognitive and language systems from simpler components.

As the demand for high levels of interaction in computer systems increases, so too does the need for real-time, interactive animation. Detecting collisions between geometrically modelled objects remains a major bottleneck in areas such as Virtual Reality (VR). In order to maintain a constant frame-rate, a trade-off between speed and accuracy is necessary. This is possible if, at each frame, potential collisions are graded by their importance to the viewer’s perception. An appropriate Level Of Detail (LOD) at which to test each object may then be chosen, based on the importance of the collision in which it is involved. We adopt some ideas from an emerging area of research, Interactive Vision, and propose a scheme which uses an eye-tracking device to locate the position of the user’s gaze. This, along with other perceptual criteria, may be used to choose an appropriate LOD for each colliding object at each frame, allowing the application to degrade detection accuracy where it is least likely to affect the user’s perception of the collision.

This paper is an exploration of the relationship between language and vision from the perspective of language evolution on the one hand, and metaphor on the other. Recent research has suggested that the origins of human language capacity can be traced to the evolution of a region in the brain that permits the interaction of information from sensory and motor cortices. In light of this, it is hypothesised that the computational mechanisms of language are derived from those of the sensory-motor domain, and that the pervasiveness of metaphor is one manifestation of language's computational antecedants. A variety of cognitive and computational implications are drawn from these hypotheses.