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
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
Improving eyewitness identification accuracy using free-report lineups. There is major national and community interest in the successful conduct of criminal investigations. This research addresses the accuracy of eyewitness identification tests. Specifically, we investigate whether developing lineup procedures that separate out witnesses who are unsure of their response will prevent erroneous identifications. Progress on this issue makes an important contribution to decisions about the ideal lin ....Improving eyewitness identification accuracy using free-report lineups. There is major national and community interest in the successful conduct of criminal investigations. This research addresses the accuracy of eyewitness identification tests. Specifically, we investigate whether developing lineup procedures that separate out witnesses who are unsure of their response will prevent erroneous identifications. Progress on this issue makes an important contribution to decisions about the ideal lineup procedure, thereby preventing innocent people from being prosecuted and perpetrators being free to re-offend. Additionally the international collaboration on the project will increase the visibility of Australian social science research and provide crucial development opportunities for young Australian scientists.Read moreRead less
Identifying the bad guy with deadlined confidence judgments. There is major interest in the successful conduct of criminal investigations. Identity tests are commonly used in such investigations, but eyewitness decision accuracy is still unacceptably low. While eyewitness memory research has already contributed significantly to the development of procedures that improve the diagnosticity of identification decisions, our proposal offers radical new alternatives that can significantly improve diag ....Identifying the bad guy with deadlined confidence judgments. There is major interest in the successful conduct of criminal investigations. Identity tests are commonly used in such investigations, but eyewitness decision accuracy is still unacceptably low. While eyewitness memory research has already contributed significantly to the development of procedures that improve the diagnosticity of identification decisions, our proposal offers radical new alternatives that can significantly improve diagnosticity. In refining and evaluating these alternatives we will boost the profile of Australian science research and provide rich international training environments for young Australian and overseas scientists.Read moreRead less
Interviewing eyewitnesses: Enhancing output quantity and diagnosing accuracy. Although there has been general international agreement that open-ended police interviews (e.g., the Cognitive Interview) enhance output quantity and accuracy, it is also well documented that police investigators often depart from these procedures in order to probe for additional information. An approach to eyewitness interviewing that allows police to elicit greater detail while able to assess likely accuracy not only ....Interviewing eyewitnesses: Enhancing output quantity and diagnosing accuracy. Although there has been general international agreement that open-ended police interviews (e.g., the Cognitive Interview) enhance output quantity and accuracy, it is also well documented that police investigators often depart from these procedures in order to probe for additional information. An approach to eyewitness interviewing that allows police to elicit greater detail while able to assess likely accuracy not only has the potential to be widely adopted but would also provide a major breakthrough in the investigation of crimes and other incidents where interview data are so critical. This in turn would further enhance the profile of Australian (and UK) forensic science.Read moreRead less
Data analytics-based tools and methods to enhance self-regulated learning. This project aims to develop student self-regulated learning skills by harnessing the potential of Big Data analytics. The project expects to generate new knowledge at the intersection of learning analytics, educational technology, learning sciences and teaching practice resulting from novel data collection and analysis tools and methods. The outputs are expected to include insights into metacognitive, motivational, and t ....Data analytics-based tools and methods to enhance self-regulated learning. This project aims to develop student self-regulated learning skills by harnessing the potential of Big Data analytics. The project expects to generate new knowledge at the intersection of learning analytics, educational technology, learning sciences and teaching practice resulting from novel data collection and analysis tools and methods. The outputs are expected to include insights into metacognitive, motivational, and technical issues facing analytics-based personalised feedback. The outcomes are intended to offer benefits for developing pedagogical and the design of educational technology. The outcomes can result in improved student learning outcomes in higher education to ensure graduates are prepared for the digital economy.Read moreRead less
Uncovering the processes underlying human category learning. There is a pervasive belief that complex tasks can somehow be learned via a 'smart' implicit or procedural learning mechanism, which operates independently of memory and attention. This idea has important implications for our understanding of cognition. If true, there seems little point in providing explicit instruction in such tasks, and efforts to do so are, at best, wasted time and, at worst, detrimental to the learning process. Th ....Uncovering the processes underlying human category learning. There is a pervasive belief that complex tasks can somehow be learned via a 'smart' implicit or procedural learning mechanism, which operates independently of memory and attention. This idea has important implications for our understanding of cognition. If true, there seems little point in providing explicit instruction in such tasks, and efforts to do so are, at best, wasted time and, at worst, detrimental to the learning process. This project will provide much-needed scrutiny of this idea and will help not only to re-orient our understanding of how we deal with complex information, but will also highlight issues about data interpretation that are fundamental for the research and wider communities.Read moreRead less
Learning to Reason in Reinforcement Learning. Deep Reinforcement Learning (RL) uses deep neural networks to represent and learn optimal decision-making policies for intelligent agents in complex environments. However, most RL approaches require millions of episodes to converge to good policies, making it difficult for RL to be applied in real-world scenarios taking significant resources. This project aims to equip RL with capabilities such as counterfactual reasoning and outcome anticipation to ....Learning to Reason in Reinforcement Learning. Deep Reinforcement Learning (RL) uses deep neural networks to represent and learn optimal decision-making policies for intelligent agents in complex environments. However, most RL approaches require millions of episodes to converge to good policies, making it difficult for RL to be applied in real-world scenarios taking significant resources. This project aims to equip RL with capabilities such as counterfactual reasoning and outcome anticipation to significantly reduce the number of interactions required, improve generalisation, and provide the agent with the capability to consider the cause-effects. These improvements would narrow the gap between AI and human capabilities and broaden the adoption of RL in real-world applications.Read moreRead less
Teaching how to learn: promoting self-regulated learning in STEM classes. This project aims to investigate key factors that influence change in teacher practices and student achievement in Science, Technology, Engineering and Mathematics (STEM). It will involve the development and evaluation of interventions designed to help teachers create learning environments that promote student engagement and the development of the cognitive and metacognitive skills needed for success in STEM. The project w ....Teaching how to learn: promoting self-regulated learning in STEM classes. This project aims to investigate key factors that influence change in teacher practices and student achievement in Science, Technology, Engineering and Mathematics (STEM). It will involve the development and evaluation of interventions designed to help teachers create learning environments that promote student engagement and the development of the cognitive and metacognitive skills needed for success in STEM. The project will advance our understanding of how to increase the quality of teaching and learning in STEM subjects. Improving teacher capacity and student performance in STEM is a national priority with significant social and economic benefits to Australia.Read moreRead less