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: 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
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