Evaluating recurrence as a measure of change in interpersonal dynamics. This project aims to develop an automated conversation analysis system to quantify how communication changes over extended periods of time. It is innovative in proposing to extend the theory and methods of recurrence analysis (a dynamical systems technique) to interacting modalities combining text, audio and video, and to longitudinal analyses. The project is significant in being the first to aim to measure communication dyn ....Evaluating recurrence as a measure of change in interpersonal dynamics. This project aims to develop an automated conversation analysis system to quantify how communication changes over extended periods of time. It is innovative in proposing to extend the theory and methods of recurrence analysis (a dynamical systems technique) to interacting modalities combining text, audio and video, and to longitudinal analyses. The project is significant in being the first to aim to measure communication dynamics over time in the fields of education, health, public discourse and science. It is expected to result in new theories and methods for recurrence analysis validated using real-world data; and to enable new technologies for evaluating professional communication training and communication changes resulting from education or disease progression.Read moreRead less
The role of relational information in the guidance of visual attention. The project aims to develop a new theory of attention that describes more accurately which items in the visual field can pop out and grab attention. The potential practical gains of the project are high, as it can lead to significant advancements in robotic vision, transport safety, and provide insights into clinical disorders such as ADHD.
Protein structure prediction by deep long-range learning. This project aims to address the challenging problem of protein structure prediction by developing deep long-range learning methods. The project expects to advance protein structure prediction by capturing the long-range interactions through whole sequence learning, rather than short window-based learning. Expected outcomes include next-generation machine-learning techniques for predicting one, two and three-dimensional protein structures ....Protein structure prediction by deep long-range learning. This project aims to address the challenging problem of protein structure prediction by developing deep long-range learning methods. The project expects to advance protein structure prediction by capturing the long-range interactions through whole sequence learning, rather than short window-based learning. Expected outcomes include next-generation machine-learning techniques for predicting one, two and three-dimensional protein structures from their sequences. The expected outcomes should provide significant benefits by computationally determining protein structures beyond homologous sequences, and enabling structure-based drug discovery to disease-causing protein targets previously inaccessible to biotech and pharmaceutical companies.Read moreRead less
Testing a relational account for visual working memory. This project aims to test whether Becker's relational theory of attention can explain visual working memory performance, the ability to remember visual items over brief time periods. According to the relational account, elementary features such as colours are encoded relative to other features in the context (e.g. as redder, larger, darker). Our ability to detect a change in a feature would depend on the features in the context, and on whet ....Testing a relational account for visual working memory. This project aims to test whether Becker's relational theory of attention can explain visual working memory performance, the ability to remember visual items over brief time periods. According to the relational account, elementary features such as colours are encoded relative to other features in the context (e.g. as redder, larger, darker). Our ability to detect a change in a feature would depend on the features in the context, and on whether the context remains constant. This project expects to provide insights into how features are represented in memory, and to predict which items will be remembered. This could help to develop interactions and therapies for the ageing population and in clinical disorders.Read moreRead less
Can the relational account of attention explain search in natural environments and inattentional blindness? This project aims to further extend the relational theory of attention to account for visual search and inattentional blindness in natural environments. In addition, the neuronal correlates for inattentional blindness will be investigated with the use of Functional Magnetic Resonance Imaging (fMRI). The research has fundamental implications for theories of visual attention and awareness, a ....Can the relational account of attention explain search in natural environments and inattentional blindness? This project aims to further extend the relational theory of attention to account for visual search and inattentional blindness in natural environments. In addition, the neuronal correlates for inattentional blindness will be investigated with the use of Functional Magnetic Resonance Imaging (fMRI). The research has fundamental implications for theories of visual attention and awareness, and will advance understandings of how and why we frequently fail to notice potentially important objects and events in the environment.Read moreRead less
Mechanisms of learning at the interface between perception and action. Using the latest in brain imaging and simulator technology, this project will advance understanding of how experience shapes the visual centres of our brain. It will also support partnerships with construction, mining and health services by developing real and virtual machine interfaces and tools to enhance the outcome of simulator-based training.
Faster, cheaper, safer: how to accelerate rail driver training and avert the looming skills shortage. The Australian rail industry is growing rapidly and needs to double the number of drivers trained in order to meet demand. This project will bring together Australia's leading hi-tech simulator company and Australia's leading rail human factors research team to 'reinvent' driver training technologies and techniques for the 21st century.
Discovery Early Career Researcher Award - Grant ID: DE140100772
Funder
Australian Research Council
Funding Amount
$393,414.00
Summary
Response Time Constraints on Category Learning. Theories of associative learning and decision-making are among the most mathematically well developed in psychology. However, theories of learning do not account for the time course of decision-making, and theories of decision-making do not account for how decision-relevant information is learned. This project will develop an integrated theoretical framework linking core principles of associative learning theories with sequential sampling models of ....Response Time Constraints on Category Learning. Theories of associative learning and decision-making are among the most mathematically well developed in psychology. However, theories of learning do not account for the time course of decision-making, and theories of decision-making do not account for how decision-relevant information is learned. This project will develop an integrated theoretical framework linking core principles of associative learning theories with sequential sampling models of the time course of decision-making. The new theory will provide a quantitative account of how incremental associative learning processes drive changes in cognitive representations that, in turn, account for known changes in the time course of decision-making.Read moreRead less