Discovery Early Career Researcher Award - Grant ID: DE230100451
Funder
Australian Research Council
Funding Amount
$435,232.00
Summary
Quantifying thermal environmental impact on office productivity. This project aims to quantify thermal environmental impacts on office productivity. It expects to firmly dismiss the prevailing misbelief that an indoor temperature of 22 °C leads to maximum workplace productivity, and create a paradigm shift in building management practice in commercial buildings. Expected outcomes of this project include a novel productivity metric, a standard measurement protocol for assessing thermal environmen ....Quantifying thermal environmental impact on office productivity. This project aims to quantify thermal environmental impacts on office productivity. It expects to firmly dismiss the prevailing misbelief that an indoor temperature of 22 °C leads to maximum workplace productivity, and create a paradigm shift in building management practice in commercial buildings. Expected outcomes of this project include a novel productivity metric, a standard measurement protocol for assessing thermal environmental impacts on office productivity, and world first indoor thermal environmental control guidelines tailored to diverse cognitive activities in the workplaces of different industries. This should provide cost-effective solutions to reduce building energy use while maintaining optimum workforce productivity.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