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.
Build competency aware and assuring machine learning systems. Recent development in machine learning (ML) has seen ML models with extremely high prediction accuracy. However, to support human-machine partnership in decision-making in complex environments, beyond accuracy, it is essential for ML systems to be competency aware and reliable, and at the same time be exploratory. This project aims to develop novel techniques to equip a ML system with the ability to identify own competency, to justify ....Build competency aware and assuring machine learning systems. Recent development in machine learning (ML) has seen ML models with extremely high prediction accuracy. However, to support human-machine partnership in decision-making in complex environments, beyond accuracy, it is essential for ML systems to be competency aware and reliable, and at the same time be exploratory. This project aims to develop novel techniques to equip a ML system with the ability to identify own competency, to justify its competency and decisions, to explore unknown situations and fully utilise existing expertise to deal with unknowns. The expected outcomes of the project will enable ML systems to become truely intelligent and reliable machine partners for human decision makers in a wide range of applications.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
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
Discovery Early Career Researcher Award - Grant ID: DE140101749
Funder
Australian Research Council
Funding Amount
$379,480.00
Summary
A computational network model of the mental lexicon. Understanding a word's meaning is a challenge when learning a language and a capacity that is seriously affected in various disorders such as Alzheimer's disease, however little is known about how meaning is organised in the mental lexicon and evolves from childhood into old age. This project aims to build a detailed computational model integrating information available through the senses and structure in the language environment to derive a l ....A computational network model of the mental lexicon. Understanding a word's meaning is a challenge when learning a language and a capacity that is seriously affected in various disorders such as Alzheimer's disease, however little is known about how meaning is organised in the mental lexicon and evolves from childhood into old age. This project aims to build a detailed computational model integrating information available through the senses and structure in the language environment to derive a lexicon that covers most words people know. By distinguishing qualitative different types of meaning relations, this project will allow the prediction of the kind of information and processes required to understand words and an understanding of how this lexicon grows in childhood and declines in old age.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
Making Meta-learning Generalised . This project aims to develop novel machine learning techniques, termed generalised meta-learning, to make machines better utilise past experience to solve new tasks with few data. It expects to reduce the undesirable dependence of current machine learning on labelled data and significantly expand its application scope. Expected outcomes of the project consist of new theoretical results on meta-learning and a set of innovative algorithms that can support the bui ....Making Meta-learning Generalised . This project aims to develop novel machine learning techniques, termed generalised meta-learning, to make machines better utilise past experience to solve new tasks with few data. It expects to reduce the undesirable dependence of current machine learning on labelled data and significantly expand its application scope. Expected outcomes of the project consist of new theoretical results on meta-learning and a set of innovative algorithms that can support the building of next generation of computer vision systems to work in open and dynamic environments. This should be able to produce solid benefits to the science, society, and economy of Australian via the application of these advanced intelligent systems.Read moreRead less
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
Learning how people read: Models, brains, big data and maths. Aims: This project aims to understand how people read. We will use novel mathematical methods, experimentation, brain imaging and computational modelling to adjudicate between model predictions.
Significance: This project expects to develop methods to understand and test important aspects of reading.
Expected outcomes: Expected outcomes are the development of novel methods for understanding complex models and the collection of data t ....Learning how people read: Models, brains, big data and maths. Aims: This project aims to understand how people read. We will use novel mathematical methods, experimentation, brain imaging and computational modelling to adjudicate between model predictions.
Significance: This project expects to develop methods to understand and test important aspects of reading.
Expected outcomes: Expected outcomes are the development of novel methods for understanding complex models and the collection of data that can extend and falsify current models of reading.
Benefits: These developments will significantly increase our understanding of how people read and what causes dyslexia. This work will also provide new ways to evaluate complex computational psychological models.Read moreRead less