Quantifying the Effects of Multi-Tasking on Processing Efficiency

The type and amount of task demands that humans must simultaneously process and respond to influences how efficient they are in completing the tasks. Capturing how and to what degree human efficiency changes in different task environments is crucial to inform an appropriate system design. An individual-based analytic approach is necessary to accurately capture performance changes and lend practical suggestions. We can provide designers with the amount and type of task demands that we expect a person to sustain adequate performance given their unique underlying cognitive properties. We develop a metric, multi-tasking throughput (MT), that provides the extent to which a person processes tasks more efficiently, the same, or less efficiently when required to complete several different types of tasks at once. This is a cognitive-based, standardized metric; meaning it yields the relative degree of change from a baseline model that is created to accommodate to unique individual differences, numbers of tasks, and task characteristics. We quantify MT by using transformations of RTs to predict the extent that external demands of multi-tasking exceeds what the cognitive system can accommodate to thereby hindering performance. We use a real world dual-task application to highlight the apparent differences in strategy and ability across individuals and alternative task environments.