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
The Dreamscape Project: Phenomenology and neurophysiology of dreams. The Dreamscape Project aims to discover the neural basis of dreaming. Building on the world’s largest database of sleep electroencephalograms (EEG) and associated dream reports, the project applies cutting-edge analyses of neural activity to resolve why each night, healthy adults alternate between unconscious sleep and vivid dreams. The results promise to shed light on the mystery of dreaming and help locate consciousness in th ....The Dreamscape Project: Phenomenology and neurophysiology of dreams. The Dreamscape Project aims to discover the neural basis of dreaming. Building on the world’s largest database of sleep electroencephalograms (EEG) and associated dream reports, the project applies cutting-edge analyses of neural activity to resolve why each night, healthy adults alternate between unconscious sleep and vivid dreams. The results promise to shed light on the mystery of dreaming and help locate consciousness in the physical world. Expected outcomes include best-practice guidelines for dream research and a model of open data-sharing for consciousness science. Anticipated benefits include deeper understanding of how and why everyone dreams, the role of dreams in waking life, and their impact on sleep quality and well-being.Read moreRead less
Bridging the meaning gap: A computational approach to semantic variation. This project aims to create and validate a new class of large language models that capture and partially explain semantic variation between people. We will (1) measure nuanced differences in word meaning and linguistic experience across individuals; (2) develop computational models that incorporate this variation; and (3) evaluate the extent to which the models capture behavioural and cognitive differences related to polit ....Bridging the meaning gap: A computational approach to semantic variation. This project aims to create and validate a new class of large language models that capture and partially explain semantic variation between people. We will (1) measure nuanced differences in word meaning and linguistic experience across individuals; (2) develop computational models that incorporate this variation; and (3) evaluate the extent to which the models capture behavioural and cognitive differences related to political affiliation, gender, and culture. This will advance our understanding of the nature and origin of individual differences as well as improve the calibration of AI systems for under-represented groups. These advances will support eventual applied outcomes in health, domestic security, and resilience to misinformation. Read moreRead less