Getting back on track after the unexpected happens: decision making in predictable and unpredictable environments. This project intends to examine how the brain decides where to look next with our eyes, a decision made approximately three times every second. Understanding how the normal brain makes decisions will in turn help us to understand what happens when things go wrong in diseases like dementia and Parkinson's disease.
The Psychology of Misinformation—Towards A Theory-driven Understanding. The project aims to develop a psychological theory of misinformation effects. Misinformation influences people’s memory, reasoning and decision-making even after corrections – it thus poses a significant challenge for science and society. Through the combination of systematic experimentation with theory-driven computational modelling, the project will strive to concurrently consider individual-level cognition and the impact ....The Psychology of Misinformation—Towards A Theory-driven Understanding. The project aims to develop a psychological theory of misinformation effects. Misinformation influences people’s memory, reasoning and decision-making even after corrections – it thus poses a significant challenge for science and society. Through the combination of systematic experimentation with theory-driven computational modelling, the project will strive to concurrently consider individual-level cognition and the impact of sociocultural context. It is anticipated that this novel integrative approach will substantially expand our understanding of misinformation effects, and that this theoretical progress will result in the formulation of specific communication strategies to reduce the impact of misinformation on society.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE140100772
Funder
Australian Research Council
Funding Amount
$393,414.00
Summary
Response Time Constraints on Category Learning. Theories of associative learning and decision-making are among the most mathematically well developed in psychology. However, theories of learning do not account for the time course of decision-making, and theories of decision-making do not account for how decision-relevant information is learned. This project will develop an integrated theoretical framework linking core principles of associative learning theories with sequential sampling models of ....Response Time Constraints on Category Learning. Theories of associative learning and decision-making are among the most mathematically well developed in psychology. However, theories of learning do not account for the time course of decision-making, and theories of decision-making do not account for how decision-relevant information is learned. This project will develop an integrated theoretical framework linking core principles of associative learning theories with sequential sampling models of the time course of decision-making. The new theory will provide a quantitative account of how incremental associative learning processes drive changes in cognitive representations that, in turn, account for known changes in the time course of decision-making.Read moreRead less
Cognitive models of decision making in clinical populations. This cognitive science project aims to develop new methods for mathematical modelling of decision making, and to apply these methods to study decision making in people with problem drug use. Precise measures of the thought processes underlying decision making in drug users will help to direct efforts to prevent and treat drug problems.
Discovery Early Career Researcher Award - Grant ID: DE150101301
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Cognitive Models of Human Decision-making in Cybersecurity Settings. This project aims to study human decision-making by attackers, defenders and users, in a cyber-security setting. Cognitive modelling of these decisions will play a central role in understanding and optimising the safety of cyberspace. This project will involve three components: new behavioural experiments focusing on cybersecurity situations of prevention and detection; cognitive models to understand and predict how people make ....Cognitive Models of Human Decision-making in Cybersecurity Settings. This project aims to study human decision-making by attackers, defenders and users, in a cyber-security setting. Cognitive modelling of these decisions will play a central role in understanding and optimising the safety of cyberspace. This project will involve three components: new behavioural experiments focusing on cybersecurity situations of prevention and detection; cognitive models to understand and predict how people make decisions in such settings; and the evaluation of these models against behavioural data using Bayesian statistical methods. This will then be applied to operational problems that will involve, determining optimal security policies, automated behaviour in adversarial situations, and individualised training.Read moreRead less
The dog that didn't bark: a Bayesian account of reasoning from censored data. This project aims to develop and test a new computational theory of inductive reasoning. Inductive reasoning involves extending knowledge from known to novel instances, and is a central component of intelligent behaviour. This project will address the cognitive mechanisms that allow people to draw inferences based on both observed and censored evidence. The project intends to test the model through an extensive program ....The dog that didn't bark: a Bayesian account of reasoning from censored data. This project aims to develop and test a new computational theory of inductive reasoning. Inductive reasoning involves extending knowledge from known to novel instances, and is a central component of intelligent behaviour. This project will address the cognitive mechanisms that allow people to draw inferences based on both observed and censored evidence. The project intends to test the model through an extensive program of experimental investigation and computational modelling. The anticipated benefits include an enhanced understanding of human inference, especially in domains such as the evaluation of forensic or financial evidence, where data censoring is common.Read moreRead less
Where do inductive biases come from? A Bayesian investigation. This project aims to investigate the origin of our thinking and learning biases using state-of-the-art mathematical models and sophisticated experimental designs. Expected outcomes include bridging the gap between human and machine learning by pairing mathematical modelling with experimental work, forming a necessary step toward the development of machine systems that can reason like people do. This will provide significant benefits ....Where do inductive biases come from? A Bayesian investigation. This project aims to investigate the origin of our thinking and learning biases using state-of-the-art mathematical models and sophisticated experimental designs. Expected outcomes include bridging the gap between human and machine learning by pairing mathematical modelling with experimental work, forming a necessary step toward the development of machine systems that can reason like people do. This will provide significant benefits such as understanding how people operate so effectively in real environments, when even the most powerful computers struggle to handle the complexities of everyday learning problems.Read moreRead less
Decoding change of mind decisions and errors from brain activity in humans. This project intends to provide new insights into how the brain changes a decision to achieve better outcomes. Decision-making is rarely optimal, and in a dynamic world people must often change their initial decisions in order to avoid consequential errors. This project aims to investigate the neural mechanisms underlying such change-of-mind decisions and decision errors in humans. To this end, it plans to use novel deco ....Decoding change of mind decisions and errors from brain activity in humans. This project intends to provide new insights into how the brain changes a decision to achieve better outcomes. Decision-making is rarely optimal, and in a dynamic world people must often change their initial decisions in order to avoid consequential errors. This project aims to investigate the neural mechanisms underlying such change-of-mind decisions and decision errors in humans. To this end, it plans to use novel decoding techniques to predict the evolution of change-of-mind decisions from brain activity while decisions unfold. This approach would clarify how quality of information, effort, and reward are integrated at a neural level to bias people towards changing their decisions. The expected results would provide an improved understanding of the neural dynamics of errors and how the brain corrects decisions online to achieve better outcomes.Read moreRead less
Modelling trajectories of cognitive control in adolescents and young adults. This project aims to develop an innovative framework that models behaviour, brain function and brain structure to characterise developmental trajectories of cognitive control in typically-developing young people, and to test the model’s ability to predict psychosocial outcomes. Cognitive control processes are supported by complex frontal brain networks that develop well into adulthood. Poor cognitive control is linked t ....Modelling trajectories of cognitive control in adolescents and young adults. This project aims to develop an innovative framework that models behaviour, brain function and brain structure to characterise developmental trajectories of cognitive control in typically-developing young people, and to test the model’s ability to predict psychosocial outcomes. Cognitive control processes are supported by complex frontal brain networks that develop well into adulthood. Poor cognitive control is linked to negative psychosocial outcomes (e.g. substance use, high-risk behaviours). This work is expected to inform evidence-based programmes that identify young people at risk and develop targeted training strategies to improve psychosocial outcomes.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE140100350
Funder
Australian Research Council
Funding Amount
$375,042.00
Summary
Decoding unstable decision preferences from brain activity. We often have to make decisions despite lacking clear preferences. This leaves us susceptible to biases from stimuli and information in our environment. This project investigates how simple, perceptual decisions and financial decisions are influenced by contextual information. The project will combine state-of-the-art neuroimaging technology with machine learning methods to develop a novel decision-decoding toolbox that directly predict ....Decoding unstable decision preferences from brain activity. We often have to make decisions despite lacking clear preferences. This leaves us susceptible to biases from stimuli and information in our environment. This project investigates how simple, perceptual decisions and financial decisions are influenced by contextual information. The project will combine state-of-the-art neuroimaging technology with machine learning methods to develop a novel decision-decoding toolbox that directly predicts decision outcomes from brain activity. This will allow investigation of how decision encoding in the brain changes under the influence of contextual information, and will provide the basis for developing an advanced model for human decision-making in real-life situations.Read moreRead less