Category learning induces position invariance of pattern recognition across the visual field

Human object recognition is considered to be largely invariant to translation across the visual field. However, the origin of this invariance to positional changes has remained elusive, since numerous studies found that the ability to discriminate between visual patterns develops in a largely location-specific manner, with only a limited transfer to novel visual field positions. In order to reconcile these contradicting observations, we traced the acquisition of categories of unfamiliar grey-level patterns within an interleaved learning and testing paradigm that involved either the same or different retinal locations. Our results show that position invariance is an emergent property of category learning. Pattern categories acquired over several hours at a fixed location in either the peripheral or central visual field gradually become accessible at new locations without any position-specific feedback. Furthermore, categories of novel patterns presented in the left hemifield are distinctly faster learnt and better generalized to other locations than those learnt in the right hemifield. Our results suggest that during learning initially position-specific representations of categories based on spatial pattern structure become encoded in a relational, position-invariant format. Such representational shifts may provide a generic mechanism to achieve perceptual invariance in object recognition.

Elaboration and testing of new methodologies for automatic abstracting

The primary objective of this research was to understand what kinds of knowledge and skills people use in `extracting' relevant information from text and to assess the extent to which expert systems techniques could be applied to automate the process of abstracting. The approach adopted in this thesis is based on research in cognitive science, information science, psycholinguistics and textlinguistics. The study addressed the significance of domain knowledge and heuristic rules by developing an information extraction system, called INFORMEX. This system, which was implemented partly in SPITBOL, and partly in PROLOG, used a set of heuristic rules to analyse five scientific papers of expository type, to interpret the content in relation to the key abstract elements and to extract a set of sentences recognised as relevant for abstracting purposes. The analysis of these extracts revealed that an adequate abstract could be generated. Furthermore, INFORMEX showed that a rule based system was a suitable computational model to represent experts' knowledge and strategies. This computational technique provided the basis for a new approach to the modelling of cognition. It showed how experts tackle the task of abstracting by integrating formal knowledge as well as experiential learning. This thesis demonstrated that empirical and theoretical knowledge can be effectively combined in expert systems technology to provide a valuable starting approach to automatic abstracting.

Can computer-based testing achieve quality and efficiency in assessment?

This paper reports findings of a two year study concerning the development and implementation of a general-purpose computer-based assessment (CBA) system at a UK University. Data gathering took place over a period of nineteen months, involving a number of formative and summative assessments. Approximately 1,000 students, drawn from undergraduate courses, were involved in the exercise. The techniques used in gathering data included questionnaires, observation, interviews and an analysis of student scores in both conventional examinations and computer-based assessments. Comparisons with conventional assessment methods suggest that the use of CBA techniques may improve the overall performance of students. However it is clear that the technique must not be seen as a "quick fix" for problems such as rising student numbers. If one accepts that current systems test only a relatively narrow range of skills, then the hasty implementation of CBA systems will result in a distorted and inaccurate view of student performance. In turn, this may serve to reduce the overall quality of courses and - ultimately - detract from the student learning experience. On the other hand, if one adopts a considered and methodical approach to computer-based assessment, positive benefits might include increased efficiency and quality, leading to improved student learning.