Spatial Learning And Memory In Huntington's Disease
Funder
National Health and Medical Research Council
Funding Amount
$475,969.00
Summary
This project will develop a spatial learning and memory test battery sensitive to dementia in Huntington’s disease, relate the task to atrophy in key brain regions, and then apply the test in a clinical trial aimed at developing a regeneration of damaged brain regions in Huntington’s disease. The overarching goal is to develop a cognitive test that is closely aligned to brain pathology in dementia as a tool for more precise, mechanism-based investigations in the dementia clinical trial setting.
Disruption Of The Ability To Simulate One’s Personal Future: Insights From Epilepsy And Implications For Neurosurgical Planning And Presurgical Counselling
Funder
National Health and Medical Research Council
Funding Amount
$353,711.00
Summary
The human memory system supports not only recollecting the past but also imagining the future (prospection). This is an important skill, enabling us to envision the consequences of alternative courses of action. Patients with temporal lobe epilepsy (TLE) frequently experience memory problems, suggesting that they will show parallel difficulties with prospection. We will study prospection in TLE patients before and after temporal lobe surgery, and the clinical implications thereof.
Discovery Early Career Researcher Award - Grant ID: DE170100915
Funder
Australian Research Council
Funding Amount
$345,000.00
Summary
Uncovering the dynamics of object selection from movement trajectories. This project aims to establish the dynamic properties of selection for perception and action, and develop a computational model of object selection across perception and action. Everyday actions depend on isolating the relevant object (perceptual selection) and appropriate grasp (action selection). It was long thought that distinct and sequential stages of processing carried out perceptual and action selection, but recent fi ....Uncovering the dynamics of object selection from movement trajectories. This project aims to establish the dynamic properties of selection for perception and action, and develop a computational model of object selection across perception and action. Everyday actions depend on isolating the relevant object (perceptual selection) and appropriate grasp (action selection). It was long thought that distinct and sequential stages of processing carried out perceptual and action selection, but recent findings suggested that a single mechanism may subserve both. Through a two-pronged approach including rigorous empirical work and computational modelling, this project aims to study this fundamental aspect of human cognition.Read moreRead less
Decoding the neural representation of objects in the human brain. Humans can effortlessly recognise thousands of objects in a fraction of a second. This essential capacity is an integral part of our daily lives that allows us to recognise our keys, our car, our friends and family. This project will elucidate how humans recognise objects by investigating the neural representation of objects in the brain.
Cortical regulation of attentional capture. The proposed experiments examine how brain mechanisms interact to determine whether a stimulus will capture our attention, distracting us from the task at hand. The experiments test competing theories of attentional control and have implications for clinical populations (for example, stroke) that have difficulty avoiding distraction.
In search of relevant things: A novel approach for image analysis. This project aims to investigate how experts’ cognitive processes may be transferred to computers for the automatic recognition of visual features. By merging computer and brain sciences, the project will characterise the way the brains of experts understand what is seen, in order to translate such a process in a new computer vision tool. This should provide significant benefits, such as automatic detection of threats or diseases ....In search of relevant things: A novel approach for image analysis. This project aims to investigate how experts’ cognitive processes may be transferred to computers for the automatic recognition of visual features. By merging computer and brain sciences, the project will characterise the way the brains of experts understand what is seen, in order to translate such a process in a new computer vision tool. This should provide significant benefits, such as automatic detection of threats or diseases in satellite and diagnostic imaging, respectively, among other applications. For the first time, the combination of how a computer analyses an image and how an expert interprets it will be used as a common language to enable machines to process visual information in a manner that mimics the way human brains do.Read moreRead less
Attention please! Selective attention and human associative learning. Selective attention allows us to pick useful pieces of information out of the mass of stimulation that we're faced with every moment. This project investigates how what we've previously learnt about the significance of events influences whether we'll pick them out as useful in future, and how this might be impaired by old age or mental disorder.
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.