Learning and choosing in a complex world. How do people make choices in a complex world? Making good choices requires expertise, but people must often forego rewards in order to acquire this knowledge. This is the essence of an "explore-exploit dilemma": to maximise rewards across a long time frame, people must take the time to explore and learn now. Empirically, this project aims to unify much of the existing psychological literature and extend it to cover richer, more complex problems. Theoret ....Learning and choosing in a complex world. How do people make choices in a complex world? Making good choices requires expertise, but people must often forego rewards in order to acquire this knowledge. This is the essence of an "explore-exploit dilemma": to maximise rewards across a long time frame, people must take the time to explore and learn now. Empirically, this project aims to unify much of the existing psychological literature and extend it to cover richer, more complex problems. Theoretically, the project aims to use tools from machine learning to compare human decision making to optimal planning models.Read moreRead less
Expecting the unexpected: how people prioritise predictability. This project aims to investigate how people represent and use information about unpredictability in their environment. Seeing too much predictability is problematic, but seeing too little can also be a problem, for example inappropriate "learned helplessness" can occur, whereby people feel disempowered because the world is seen as random. Recent findings demonstrated a bias in fundamental learning that may maintain these inappropria ....Expecting the unexpected: how people prioritise predictability. This project aims to investigate how people represent and use information about unpredictability in their environment. Seeing too much predictability is problematic, but seeing too little can also be a problem, for example inappropriate "learned helplessness" can occur, whereby people feel disempowered because the world is seen as random. Recent findings demonstrated a bias in fundamental learning that may maintain these inappropriate beliefs about unpredictability. This bias is not anticipated by formal theories of learning. The project will investigate how this bias is brought about by first formalising a novel theory of fundamental learning and then systematically testing its assumptions.Read moreRead less
Uncovering the processes underlying human reasoning: A state-trace approach. This project aims to answer the most important unresolved question in the psychology of reasoning; how many distinct cognitive processes underlie human reasoning? To answer this question, this project aims to conduct an extensive experimental investigation of the factors that selectively impact inductive and deductive inferences and the application of high-dimensional state-trace analysis; a powerful new method for diag ....Uncovering the processes underlying human reasoning: A state-trace approach. This project aims to answer the most important unresolved question in the psychology of reasoning; how many distinct cognitive processes underlie human reasoning? To answer this question, this project aims to conduct an extensive experimental investigation of the factors that selectively impact inductive and deductive inferences and the application of high-dimensional state-trace analysis; a powerful new method for diagnosing underlying processes from behavioural data. The project is expected also to develop a new computational model that accounts for both inductive and deductive forms of reasoning.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
Improving the diagnosticity of eyewitness memory choices. Eyewitness identification error is common and costly. This project aims to improve the quality of information provided by eyewitnesses, and the ability of police officers and triers of fact (e.g., juries, judges) to evaluate this information. Laboratory investigations will determine how best to test memory and confidence to achieve this aim. A new class of cognitive models will provide a unified account of response accuracy, response time ....Improving the diagnosticity of eyewitness memory choices. Eyewitness identification error is common and costly. This project aims to improve the quality of information provided by eyewitnesses, and the ability of police officers and triers of fact (e.g., juries, judges) to evaluate this information. Laboratory investigations will determine how best to test memory and confidence to achieve this aim. A new class of cognitive models will provide a unified account of response accuracy, response time, and confidence, suitable for application to computerized testing scenarios. The models and testing methods validated in the laboratory will be refined for application in eyewitness memory settings, facilitating better evaluation of identification evidence, and potentially reducing wrongful convictions.Read moreRead less
Towards an integrated model of reasoning and reasoning development. This project aims to identify the core cognitive processes that underlie different forms of reasoning and how they develop. The project intends to use a signal detection framework to derive detailed computational models of reasoning which can then be tested through Bayesian computational modelling as well as the first systematic investigation of developmental change in reasoning processes. Expected outcomes include a more princi ....Towards an integrated model of reasoning and reasoning development. This project aims to identify the core cognitive processes that underlie different forms of reasoning and how they develop. The project intends to use a signal detection framework to derive detailed computational models of reasoning which can then be tested through Bayesian computational modelling as well as the first systematic investigation of developmental change in reasoning processes. Expected outcomes include a more principled and comprehensive computational model of reasoning in both adults and children. The project should provide significant benefits by helping to resolve long-standing debates about how humans reason complex arguments relevant to everyday lives and guide development of more effective methods for teaching reasoning.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE150100667
Funder
Australian Research Council
Funding Amount
$328,000.00
Summary
How “known unknowns” become known: How do people encode unpredictability? As Donald Rumsfeld noted, there are 'known unknowns’. That is to say, people are seemingly capable of learning that some things cannot be reliably predicted. This learning underpins decisions from the trivial (whether to pack a jacket) to the life-defining (whom to marry). An aberrant form of this learning may also underlie mental health disorders. Yet the mechanisms of such learning have been largely overlooked by cogniti ....How “known unknowns” become known: How do people encode unpredictability? As Donald Rumsfeld noted, there are 'known unknowns’. That is to say, people are seemingly capable of learning that some things cannot be reliably predicted. This learning underpins decisions from the trivial (whether to pack a jacket) to the life-defining (whom to marry). An aberrant form of this learning may also underlie mental health disorders. Yet the mechanisms of such learning have been largely overlooked by cognitive scientists and thus are poorly understood. The project, which is based on significant pilot data, aims to examine when and how people learn about unpredictability, and what the cognitive, memorial, neural and affective consequences of this learning are.Read moreRead less
Developing group-based elicitation methods to improve decision making. This project aims to develop an elicitation methodology enabling multiple members of a team to contribute to the same technical problem - enabling expertise to be accurately combined while avoiding group and individual sources of bias. Good elicitation methods minimise bias in estimates and forecasts - which otherwise erode value and lead to sub-optimal decision making. Existing methods, however, ignore group structures; that ....Developing group-based elicitation methods to improve decision making. This project aims to develop an elicitation methodology enabling multiple members of a team to contribute to the same technical problem - enabling expertise to be accurately combined while avoiding group and individual sources of bias. Good elicitation methods minimise bias in estimates and forecasts - which otherwise erode value and lead to sub-optimal decision making. Existing methods, however, ignore group structures; that is that decisions made by, or on, the advice of teams have different characteristics than individual decisions and often preclude the use of methods designed to limit individuals' biases. By encoding the method into a computerised tool the project will assist public and private sector enterprises to improve group decision making.Read moreRead less
Choice foundations: Diagnostic tools for individual-level model development. This project aims to improve policy making in areas such as transport, environment and health, by better representation of how people make decisions. An interdisciplinary team of economists and psychologists will build on new mathematical and statistical tools to test for adherence to choice axioms that underlie observed choice behaviour. The project will produce a set of computerized statistical tools to implement the ....Choice foundations: Diagnostic tools for individual-level model development. This project aims to improve policy making in areas such as transport, environment and health, by better representation of how people make decisions. An interdisciplinary team of economists and psychologists will build on new mathematical and statistical tools to test for adherence to choice axioms that underlie observed choice behaviour. The project will produce a set of computerized statistical tools to implement the testing of choice axioms using Bayesian methods with the capacity to improve a wide array of applied economics work at the national and international levels.Read moreRead less