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
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
How are beliefs altered by data? Robust Bayesian models for human inductive learning. This project applies state of the art mathematical models to study how people think and reason, and how we can make good guesses about the future. The goal is to understand how the human mind can operate so effectively in real environments, when even the most powerful computers struggle to handle the complexities of everyday learning problems.
Uncovering the processes underlying human reasoning: A state-trace approach. This project aims to answer the most important unresolved question in the psychology of reasoning; how many distinct cognitive processes underlie human reasoning? To answer this question, this project aims to conduct an extensive experimental investigation of the factors that selectively impact inductive and deductive inferences and the application of high-dimensional state-trace analysis; a powerful new method for diag ....Uncovering the processes underlying human reasoning: A state-trace approach. This project aims to answer the most important unresolved question in the psychology of reasoning; how many distinct cognitive processes underlie human reasoning? To answer this question, this project aims to conduct an extensive experimental investigation of the factors that selectively impact inductive and deductive inferences and the application of high-dimensional state-trace analysis; a powerful new method for diagnosing underlying processes from behavioural data. The project is expected also to develop a new computational model that accounts for both inductive and deductive forms of reasoning.Read moreRead less
Where do inductive biases come from? A Bayesian investigation. This project aims to investigate the origin of our thinking and learning biases using state-of-the-art mathematical models and sophisticated experimental designs. Expected outcomes include bridging the gap between human and machine learning by pairing mathematical modelling with experimental work, forming a necessary step toward the development of machine systems that can reason like people do. This will provide significant benefits ....Where do inductive biases come from? A Bayesian investigation. This project aims to investigate the origin of our thinking and learning biases using state-of-the-art mathematical models and sophisticated experimental designs. Expected outcomes include bridging the gap between human and machine learning by pairing mathematical modelling with experimental work, forming a necessary step toward the development of machine systems that can reason like people do. This will provide significant benefits such as understanding how people operate so effectively in real environments, when even the most powerful computers struggle to handle the complexities of everyday learning problems.Read moreRead less
Learning from others: Inductive reasoning based on human-generated data. Most of the data we see every day, from politics to gossip, comes from other people. Making inferences about such data is difficult because the people who provided it may have biases or limitations in their knowledge that we do not know about and must figure out. This project uses a series of experiments tied to normative computational models of social reasoning to explore how people solve this problem. This work has the po ....Learning from others: Inductive reasoning based on human-generated data. Most of the data we see every day, from politics to gossip, comes from other people. Making inferences about such data is difficult because the people who provided it may have biases or limitations in their knowledge that we do not know about and must figure out. This project uses a series of experiments tied to normative computational models of social reasoning to explore how people solve this problem. This work has the potential to make a major impact in understanding how information is understood and shared, especially when it is about topics that people lack firsthand knowledge about, like climate change. The computational models also have applications to the development of expert systems upon which our information economy relies.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
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
Eyewitness identification: Metacognitive influences on choosing behaviour. There is major national and community interest in the successful conduct of criminal investigations. This research addresses the two most significant problems associated with the conduct of eyewitness identification tests: mistaken identifications of innocent suspects and failure to identify guilty suspects when they are present in the lineup. Progress on the latter problem - which results in offenders avoiding detection ....Eyewitness identification: Metacognitive influences on choosing behaviour. There is major national and community interest in the successful conduct of criminal investigations. This research addresses the two most significant problems associated with the conduct of eyewitness identification tests: mistaken identifications of innocent suspects and failure to identify guilty suspects when they are present in the lineup. Progress on the latter problem - which results in offenders avoiding detection - would be a major contribution with national impact. As well as the obvious implications for the conduct of lineups, 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