Can the Relational Account predict search in multiple-element displays? . This project provides evidence of a novel mechanism that guides visual attention. Our results confirm the existence of a mechanism that can rapidly and automatically assess the dominant feature(s) in a visual scene and radically change how attention is tuned to a target object. Moreover, this attention-guiding target template can change systematically as observers search through different items in visual search, possibly d ....Can the Relational Account predict search in multiple-element displays? . This project provides evidence of a novel mechanism that guides visual attention. Our results confirm the existence of a mechanism that can rapidly and automatically assess the dominant feature(s) in a visual scene and radically change how attention is tuned to a target object. Moreover, this attention-guiding target template can change systematically as observers search through different items in visual search, possibly due to a re-shaping and narrowing of the target template. These are both ground-breaking discoveries that have not been described before. Work on this project promises to lead to important theoretical breakthroughs, resolve current discrepancies in the literature and advance methods of Cognitive Psychology and Neuroscience.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100171
Funder
Australian Research Council
Funding Amount
$438,560.00
Summary
Integrated models of learning and decision making in complex tasks. How do people learn to make decisions in complex work systems when assisted by automation? This project will develop computational models of human learning and decision making that explain and predict complex decisions relevant to industries such as aviation and defence. It will examine how humans learn to use automated advice, how learning affects remembering to perform planned (deferred) actions, and factors that pose a risk t ....Integrated models of learning and decision making in complex tasks. How do people learn to make decisions in complex work systems when assisted by automation? This project will develop computational models of human learning and decision making that explain and predict complex decisions relevant to industries such as aviation and defence. It will examine how humans learn to use automated advice, how learning affects remembering to perform planned (deferred) actions, and factors that pose a risk to learning and adaptation. The expected outcome is a significant theoretical advance in human factors and cognitive psychology, and a tool for informing work design (e.g., computer interface, task allocation) and training, with the potential to reduce human error in safety-critical workplaces.Read moreRead less