Discrimination learning in humans: Associative and attentional mechanisms. This project offers three major benefits: (1) Australian researchers excel in cognitive neuroscience, learning and psychopharmacology, areas based largely on animal models of human cognition. This project contributes to these areas by specifying the relationship between animal learning and human cognition; (2) the project enhances Australia's international reputation in these areas via its collaboration with a scientist ....Discrimination learning in humans: Associative and attentional mechanisms. This project offers three major benefits: (1) Australian researchers excel in cognitive neuroscience, learning and psychopharmacology, areas based largely on animal models of human cognition. This project contributes to these areas by specifying the relationship between animal learning and human cognition; (2) the project enhances Australia's international reputation in these areas via its collaboration with a scientist of Geoff Hall's stature; it also offers students outstanding research training and international exposure; (3) given Chris Mitchell's industry experience and the relevance of this work to advertising/marketing, this project will generate knowledge relevant to, and possible future collaborations with, Australian industries.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
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
Solving the inert knowledge problem. A central goal of education is for students to transfer what they learn to new contexts or problems. Indeed, expert reasoning is often characterised by seeing the deep structural commonalities across seemingly disparate situations. However, the knowledge students acquire is notoriously inert, tied to the specifics of the learning examples. This project aims to move towards solving 'the inert knowledge problem' by investigating how humans learn concepts define ....Solving the inert knowledge problem. A central goal of education is for students to transfer what they learn to new contexts or problems. Indeed, expert reasoning is often characterised by seeing the deep structural commonalities across seemingly disparate situations. However, the knowledge students acquire is notoriously inert, tied to the specifics of the learning examples. This project aims to move towards solving 'the inert knowledge problem' by investigating how humans learn concepts defined by abstract relational structure, and by designing educational applications that enhance the use of relational learning mechanisms in students with a wide range of cognitive abilities.Read moreRead less
Stochastic Construction of Error Correcting Codes with Application to Digital Communications. Modern society would be unrecognisable without error correcting codes; mobile telephones, storage devices such as DVD's and high speed data communications simply would not exist. Yet most theoretical results on error correcting codes are asymptotic in nature and ignore computational complexity issues, that is, they are not representative of many real life situations. By building on recent breakthrough ....Stochastic Construction of Error Correcting Codes with Application to Digital Communications. Modern society would be unrecognisable without error correcting codes; mobile telephones, storage devices such as DVD's and high speed data communications simply would not exist. Yet most theoretical results on error correcting codes are asymptotic in nature and ignore computational complexity issues, that is, they are not representative of many real life situations. By building on recent breakthroughs in statistics and stochastic optimisation, this project will develop algorithms for designing optimised error correcting codes subject to realistic finite data length and computational complexity constraints. Successful outcomes will lead to enhanced data communications and storage, greatly benefiting industry and consumers alike.
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The role of memory and reasoning processes in associative learning. The project will investigate how people learn to detect cues that predict or cause significant events in their environment (associative learning). The research builds on recent empirical and theoretical work by the investigators supporting the role of deductive reasoning processes in associative learning. Novel experimental strategies will be used to identify the separate and interacting roles of lower-level memory processes a ....The role of memory and reasoning processes in associative learning. The project will investigate how people learn to detect cues that predict or cause significant events in their environment (associative learning). The research builds on recent empirical and theoretical work by the investigators supporting the role of deductive reasoning processes in associative learning. Novel experimental strategies will be used to identify the separate and interacting roles of lower-level memory processes and higher-level reasoning processes. Existing competing models and novel cooperative models will be tested. The research will lead to a better understanding of associative learning in humans, and will also inform the construction of intelligent artificial systems.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
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.
A Generic Framework for Verifying Machine Learning Algorithms. This project aims to discover new ways to verify whether decisions made by Artificial Intelligence and Machine Learning algorithms are as per the specifications set by their designers and/or regulatory bodies. The project also provides new methods to align algorithm decisions when they are found to be non-abiding. The outcomes will include new machine learning theories and frameworks for algorithmic assurance. The significance of the ....A Generic Framework for Verifying Machine Learning Algorithms. This project aims to discover new ways to verify whether decisions made by Artificial Intelligence and Machine Learning algorithms are as per the specifications set by their designers and/or regulatory bodies. The project also provides new methods to align algorithm decisions when they are found to be non-abiding. The outcomes will include new machine learning theories and frameworks for algorithmic assurance. The significance of the project is that it will offer a crucial platform for certifying algorithms and thus benefit society and businesses in deciding the right Artificial Intelligence algorithms. Read moreRead less
Using shape change for object perception: human and artificial vision. This project aims to examine the steps taken by the visual system to code the shape of objects, including those that change shape over time. The project seeks to employ experiments assessing human vision and machine learning techniques to examine these codes and, in particular, focus on the advantages of a system that exaggerates shape change over time. Expected outcomes include an improved shape code based on superior human ....Using shape change for object perception: human and artificial vision. This project aims to examine the steps taken by the visual system to code the shape of objects, including those that change shape over time. The project seeks to employ experiments assessing human vision and machine learning techniques to examine these codes and, in particular, focus on the advantages of a system that exaggerates shape change over time. Expected outcomes include an improved shape code based on superior human performance that can have many applications in automated visual systems. This project can directly benefit the animation industries where the creation of realistic movement of humans and animals remains a computationally intensive challenge.Read moreRead less