The neurobiology of curiosity. This project aims to define the neurobiology of curiosity by combining cutting-edge techniques in computational modelling, pharmacointervention and neuroimaging. It is expected to lead to a comprehensive neuroscientific framework of curiosity, which will characterise its evolution over the lifespan, and its dependency on key neurotransmitter systems. Expected outcomes include a legacy of open access stimulus & data sets; the development of a global collaborative ne ....The neurobiology of curiosity. This project aims to define the neurobiology of curiosity by combining cutting-edge techniques in computational modelling, pharmacointervention and neuroimaging. It is expected to lead to a comprehensive neuroscientific framework of curiosity, which will characterise its evolution over the lifespan, and its dependency on key neurotransmitter systems. Expected outcomes include a legacy of open access stimulus & data sets; the development of a global collaborative network; and an increase in our national capacity and profile in decision neuroscience. The benefits of this project include laying the foundations for future interventions to improve curiosity, with potential downstream effects on many aspects of education, social & public policy.Read moreRead less
Using cognitive models to understand memorability of real world images. This proposal aims to understand and make predictions about which real world images -- specifically living things, objects, and human faces -- that people will remember remember via an integration of cognitive models of memory and machine learning techniques. Computer vision models and similarity scaling techniques will be used to produce psychological representations of the images. These representations will then be integra ....Using cognitive models to understand memorability of real world images. This proposal aims to understand and make predictions about which real world images -- specifically living things, objects, and human faces -- that people will remember remember via an integration of cognitive models of memory and machine learning techniques. Computer vision models and similarity scaling techniques will be used to produce psychological representations of the images. These representations will then be integrated with cognitive models of memory, which predict that images are more likely to be recognized if they are similar to each of the representations in memory. Large scale memory and similarity rating datasets will be used to develop and test the model.Read moreRead less