Psychological User Profiling in the Telecommunications Industry. Recording user behaviour allows businesses to learn about their customers. This is particularly important in telecommunications, since the core business involves a large number of users who vary considerably from one another. This project combines psychological insights with modern statistical methods to develop a psychologically plausible user profiling framework, accounting for the idiosyncratic usage patterns of customers, and t ....Psychological User Profiling in the Telecommunications Industry. Recording user behaviour allows businesses to learn about their customers. This is particularly important in telecommunications, since the core business involves a large number of users who vary considerably from one another. This project combines psychological insights with modern statistical methods to develop a psychologically plausible user profiling framework, accounting for the idiosyncratic usage patterns of customers, and the way in which they change over time. The profiles will be tied to marketing prospects through interviews and surveys. Applied benefits include the ability to predict, understand and act upon user behaviour. The project also adds substantially to theories of memory, individual differences and decision-making.Read moreRead less
Hierarchical Bayesian Models for Human Conceptual Learning. This project seeks to understand the nature of human conceptual learning. With the shift to an information-based economy, it becomes important to understand what assumptions a real-world learning system should make. Even given the impressive growth of machine learning and artificial intelligence, the human mind remains the most successful example of such a system. In this light, the scientific study of human conceptual structure present ....Hierarchical Bayesian Models for Human Conceptual Learning. This project seeks to understand the nature of human conceptual learning. With the shift to an information-based economy, it becomes important to understand what assumptions a real-world learning system should make. Even given the impressive growth of machine learning and artificial intelligence, the human mind remains the most successful example of such a system. In this light, the scientific study of human conceptual structure presents the opportunity to discover how an intelligent thinking system should operate. In addition, many important problems facing an information economy involve being able to understand how people behave. An understanding of the concepts people use is central to this endeavour.Read moreRead less
Evaluating models of category learning that use general feature-based representations. Three competing models of human category learning will be evaluated by comparing their behaviour to human performance on an experimental task where each model makes qualitatively different predictions. A series of theoretical and algorithmic advances will be undertaken to ensure each of the category learning models uses the same feature-based representation. Because the three models propose very different lear ....Evaluating models of category learning that use general feature-based representations. Three competing models of human category learning will be evaluated by comparing their behaviour to human performance on an experimental task where each model makes qualitatively different predictions. A series of theoretical and algorithmic advances will be undertaken to ensure each of the category learning models uses the same feature-based representation. Because the three models propose very different learning processes, their comparison will give insight into the basic cognitive process of categorisation. The algorithms for generating feature representations and modelling human category learning will also have potential for application in data visualisation and information handling systems.Read moreRead less