The Effect of Information Type on Human Cognitive Augmentation
This work addresses the problem of optimizing human-AI collaboration for cognitive tasks, though it appears incremental as it focuses on a specific factor (information type) within an established paradigm.
The paper investigates how the type of information contributed by cognitive systems affects human cognitive augmentation in human/cog ensembles, finding that conceptual information leads to the greatest improvements in cognitive accuracy, precision, and power.
When performing a task alone, humans achieve a certain level of performance. When humans are assisted by a tool or automation to perform the same task, performance is enhanced (augmented). Recently developed cognitive systems are able to perform cognitive processing at or above the level of a human in some domains. When humans work collaboratively with such cogs in a human/cog ensemble, we expect augmentation of cognitive processing to be evident and measurable. This paper shows the degree of cognitive augmentation depends on the nature of the information the cog contributes to the ensemble. Results of an experiment are reported showing conceptual information is the most effective type of information resulting in increases in cognitive accuracy, cognitive precision, and cognitive power.