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Status : Active
Field of Research : Cognition
Research Topic : Memory Structures
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP240102605

    Funder
    Australian Research Council
    Funding Amount
    $532,277.00
    Summary
    Towards a cognitive process model of how attention and choice interact. Before making any decision, we must gather information on what options are available. This process may influence the choices we make: if we do not notice an option, we will not choose it even if it would have been valuable. This project aims to examine how prior experience can produce attentional biases that influence decisions, and will develop a new computational model of this interaction of attention and choice as an outc .... Towards a cognitive process model of how attention and choice interact. Before making any decision, we must gather information on what options are available. This process may influence the choices we make: if we do not notice an option, we will not choose it even if it would have been valuable. This project aims to examine how prior experience can produce attentional biases that influence decisions, and will develop a new computational model of this interaction of attention and choice as an outcome. This new knowledge will enhance the world-class status of Australian cognitive psychology. Moreover, it should provide significant benefits through improving our ability to predict and shape behaviour, and shedding light on the role of biases in healthy cognition and in the context of compulsive behaviours.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP240101264

    Funder
    Australian Research Council
    Funding Amount
    $586,979.00
    Summary
    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.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP240101230

    Funder
    Australian Research Council
    Funding Amount
    $496,703.00
    Summary
    The Misinformation Future—Confronting Emerging Threats. Misinformation presents challenges to public health and democracy. Though psychological research has explored processing mechanisms and countermeasures, new threats are arising that need to be confronted. This project aims to help meet these threats by (a) investigating misinformation impacts on future-oriented cognition and behaviours, with a focus on global long-term issues and (b) addressing the unique challenges posed by visual and synt .... The Misinformation Future—Confronting Emerging Threats. Misinformation presents challenges to public health and democracy. Though psychological research has explored processing mechanisms and countermeasures, new threats are arising that need to be confronted. This project aims to help meet these threats by (a) investigating misinformation impacts on future-oriented cognition and behaviours, with a focus on global long-term issues and (b) addressing the unique challenges posed by visual and synthetic (AI-generated) misinformation. The expected outcome is new knowledge on the processing and impacts of emerging types of misinformation and translation into practical interventions. These promise to benefit consumers, educators and policymakers, contributing to a healthier information environment.
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