Sound discrimination in embryos affects lifetime fitness. This project aims to investigate the role of prenatal sound discrimination on postnatal vocal learning and foraging breadth. The project expects to generate new knowledge in the area of neuroscience and psychology using an innovative approach to measure how embryos learn, and determine effects of prenatal vocal experience on the repertoire of postnatal behaviour. Expected outcomes include understanding biological mechanisms for effective ....Sound discrimination in embryos affects lifetime fitness. This project aims to investigate the role of prenatal sound discrimination on postnatal vocal learning and foraging breadth. The project expects to generate new knowledge in the area of neuroscience and psychology using an innovative approach to measure how embryos learn, and determine effects of prenatal vocal experience on the repertoire of postnatal behaviour. Expected outcomes include understanding biological mechanisms for effective learning across life stages that would be useful to develop novel approaches for non-invasive monitoring of embryonic cognition.Read moreRead less
Data analytics-based tools and methods to enhance self-regulated learning. This project aims to develop student self-regulated learning skills by harnessing the potential of Big Data analytics. The project expects to generate new knowledge at the intersection of learning analytics, educational technology, learning sciences and teaching practice resulting from novel data collection and analysis tools and methods. The outputs are expected to include insights into metacognitive, motivational, and t ....Data analytics-based tools and methods to enhance self-regulated learning. This project aims to develop student self-regulated learning skills by harnessing the potential of Big Data analytics. The project expects to generate new knowledge at the intersection of learning analytics, educational technology, learning sciences and teaching practice resulting from novel data collection and analysis tools and methods. The outputs are expected to include insights into metacognitive, motivational, and technical issues facing analytics-based personalised feedback. The outcomes are intended to offer benefits for developing pedagogical and the design of educational technology. The outcomes can result in improved student learning outcomes in higher education to ensure graduates are prepared for the digital economy.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
Teaching how to learn: promoting self-regulated learning in STEM classes. This project aims to investigate key factors that influence change in teacher practices and student achievement in Science, Technology, Engineering and Mathematics (STEM). It will involve the development and evaluation of interventions designed to help teachers create learning environments that promote student engagement and the development of the cognitive and metacognitive skills needed for success in STEM. The project w ....Teaching how to learn: promoting self-regulated learning in STEM classes. This project aims to investigate key factors that influence change in teacher practices and student achievement in Science, Technology, Engineering and Mathematics (STEM). It will involve the development and evaluation of interventions designed to help teachers create learning environments that promote student engagement and the development of the cognitive and metacognitive skills needed for success in STEM. The project will advance our understanding of how to increase the quality of teaching and learning in STEM subjects. Improving teacher capacity and student performance in STEM is a national priority with significant social and economic benefits to Australia.Read moreRead less
Developing a generative transformational theory of visual perception. This project will develop and test a generative, transformational computer model of visual perception, based on fractal encoding. This uses a powerful similarity metric to select transformations, that, when applied to image elements, generate a replica of the image. The model can detect and analyse structure in regular and semi-regular images, even when embedded in noise. This approach provides an explanation for several perce ....Developing a generative transformational theory of visual perception. This project will develop and test a generative, transformational computer model of visual perception, based on fractal encoding. This uses a powerful similarity metric to select transformations, that, when applied to image elements, generate a replica of the image. The model can detect and analyse structure in regular and semi-regular images, even when embedded in noise. This approach provides an explanation for several perceptual phenomena and illusions. It can reconcile opposed theories of perception and provide a unifying perspective on perception and cognition. Practical applications include the automatic recognition of objects in imagery and the detection of structure in complex data.Read moreRead less
Developing an integrative theoretical account of some basic mechanisms and limiting factors in human perception and cognition. The principal factors limiting cognitive performance are widely considered to be information processing speed, working memory capacity, and the effective control of cognitive processes. The proposed programme aims to develop and test a unifying theory relating these to two of the most basic achievements of the brain - discrimination and identification. This will help us ....Developing an integrative theoretical account of some basic mechanisms and limiting factors in human perception and cognition. The principal factors limiting cognitive performance are widely considered to be information processing speed, working memory capacity, and the effective control of cognitive processes. The proposed programme aims to develop and test a unifying theory relating these to two of the most basic achievements of the brain - discrimination and identification. This will help us to understand the underlying basis of differences and changes in cognitive performance. The outcomes have implications for the design, analysis and interpretation of studies of perception, judgement, memory and intelligence. The research also has applied relevance to neuropsychology, information handling and the design of system interfaces.Read moreRead less
Using large scale modelling to understand reading development and dyslexia. This project aims to construct a computational model of reading that makes quantitative predictions about reading behaviour and dyslexia. It will test theories of reading development and dyslexia based on what they predict in terms of reading performance, predictions which many theories of dyslexia do not make. The model will be in English, French and Italian, which offer rich and constraining data to test the model. The ....Using large scale modelling to understand reading development and dyslexia. This project aims to construct a computational model of reading that makes quantitative predictions about reading behaviour and dyslexia. It will test theories of reading development and dyslexia based on what they predict in terms of reading performance, predictions which many theories of dyslexia do not make. The model will be in English, French and Italian, which offer rich and constraining data to test the model. The project is expected to explain the link between reading performance and underlying influences and why dyslexia manifests differently in different languages.Read moreRead less
The dynamics of witness confidence effects on juror judgments. While psychologists and criminal justice professionals concur that eyewitness confidence is one of the major influences on juror judgments, previous researchers' treatment of confidence as an invariant testimonial characteristic means that we actually know little about the impact of witness confidence. This research tests social persuasion theories and reveals the dynamic effects on juror judgments and verdicts of the sort of confide ....The dynamics of witness confidence effects on juror judgments. While psychologists and criminal justice professionals concur that eyewitness confidence is one of the major influences on juror judgments, previous researchers' treatment of confidence as an invariant testimonial characteristic means that we actually know little about the impact of witness confidence. This research tests social persuasion theories and reveals the dynamic effects on juror judgments and verdicts of the sort of confidence fluctuations that characterise real witnesses. Knowing how such fluctuations will shape jurors' judgments is vital for judges (when instructing jurors) and for police and lawyers when they assess the likely impact of the witnesses they intend to call.Read moreRead less
Investigation of the component distributions of pause duration in spontaneous speech: Constraints for models of language production. We have discovered that the distribution of pause durations in spontaneous speech of individual speakers can be decomposed into at least two log-normal distributions. Our project will investigate this finding and provide a foundation for new research relevant to language production models. This will be achieved by determining the semantic, lexical, psycholinguistic ....Investigation of the component distributions of pause duration in spontaneous speech: Constraints for models of language production. We have discovered that the distribution of pause durations in spontaneous speech of individual speakers can be decomposed into at least two log-normal distributions. Our project will investigate this finding and provide a foundation for new research relevant to language production models. This will be achieved by determining the semantic, lexical, psycholinguistic, physiological, and acoustic concomitants of each component distribution and by investigating the impact of selected variables on the shape and location of each. The project has important implications for models of language production and applied problems involving automatic speech recognition, forensic speaker identification, and human communication disorders.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