Developing a Unified Theory of Episodic Memory. This project aims to develop a model of episodic memory and to apply the model to both adult and child development data. Unlike current approaches, the model is expected to address multiple memory tasks including item recognition, associative recognition, source recognition and cued recall, and also aims to address reaction time data, allowing different sources of interference causing forgetting in adults to be identified. By addressing both encodi ....Developing a Unified Theory of Episodic Memory. This project aims to develop a model of episodic memory and to apply the model to both adult and child development data. Unlike current approaches, the model is expected to address multiple memory tasks including item recognition, associative recognition, source recognition and cued recall, and also aims to address reaction time data, allowing different sources of interference causing forgetting in adults to be identified. By addressing both encoding and retrieval processes, the model can assess how changes in different sources of interference modulate performance through the trajectory of early development. Hierarchical Bayesian estimation aims to enable a simultaneous account of multiple tasks and support future deployment in applied contexts.Read moreRead less
A more intelligent knowledge-based system apprentice. Our previous techniques already had an impact on Australian industry, with five Australian companies marketing such technology, and for three of these it is a central technology. We expect an early uptake of the enhancements we propose by these companies, greatly increasing their international competitiveness against other rule technologies. Three of these companies are very recent, so we would expect other company uptake of the new enhance ....A more intelligent knowledge-based system apprentice. Our previous techniques already had an impact on Australian industry, with five Australian companies marketing such technology, and for three of these it is a central technology. We expect an early uptake of the enhancements we propose by these companies, greatly increasing their international competitiveness against other rule technologies. Three of these companies are very recent, so we would expect other company uptake of the new enhanced technology. In turn Australian companies using the technology will improve their competitiveness in an increasingly knowledge-based economy by being able to more rapidly and easily deploy knowledge-based systems. Our previous techniques have already had a significant impact in medical practice.Read moreRead less
Improving the diagnosticity of eyewitness memory choices. Eyewitness identification error is common and costly. This project aims to improve the quality of information provided by eyewitnesses, and the ability of police officers and triers of fact (e.g., juries, judges) to evaluate this information. Laboratory investigations will determine how best to test memory and confidence to achieve this aim. A new class of cognitive models will provide a unified account of response accuracy, response time ....Improving the diagnosticity of eyewitness memory choices. Eyewitness identification error is common and costly. This project aims to improve the quality of information provided by eyewitnesses, and the ability of police officers and triers of fact (e.g., juries, judges) to evaluate this information. Laboratory investigations will determine how best to test memory and confidence to achieve this aim. A new class of cognitive models will provide a unified account of response accuracy, response time, and confidence, suitable for application to computerized testing scenarios. The models and testing methods validated in the laboratory will be refined for application in eyewitness memory settings, facilitating better evaluation of identification evidence, and potentially reducing wrongful convictions.Read moreRead less
Choice models for learning and memory. Life is filled with familiar choices that often require quick decisions about objects in the environment and the contents of memory. This project examines how we learn to make rapid and accurate choices and how we quickly asses the level of confidence we have in recognition decisions based on our memories.
Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire ....Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire risk and spread, trained and validated on a two-decade satellite fire record. The predictive ability of the model will be tested on the 2019/20 fire season to determine if novel drivers of fire can be identified, and the model itself will be operationalised into a novel short-to-mid term fire risk prediction tool. Read moreRead less