Can robots as teammates make humans lazy?
Humans pay less attention to their work when they think robots have already checked it, according to a research.
London: Humans pay less attention to their work when they think robots have already checked it, according to a research.
Scientists at the Technical University of Berlin in Germany investigated whether humans are more likely to relax, letting their colleagues (robots) do the work instead, in a process called social loafing, when they work with robots.
“Working together can motivate people to perform well but it can also lead to a loss of motivation because the individual contribution is not as visible,” said first author Dietlind Helene Cymek, from the varsity.
“We were interested in whether we could also find such motivational effects when the team partner is a robot,” she added, in the paper published in the Frontiers in Robotics and AI.
The scientists tested their hypothesis using a simulated industrial defect-inspection task: looking at circuit boards for errors. The scientists provided images of circuit boards to 42 participants. The circuit boards were blurred, and the sharpened images could only be viewed by holding a mouse tool over them. This allowed the scientists to track participants’ inspection of the board.
Half of the participants were told that they were working on circuit boards that had been inspected by a robot called Panda. Although these participants did not work directly with Panda, they had seen the robot and could hear it while they worked.
After examining the boards for errors and marking them, all participants were asked to rate their own effort, how responsible for the task they felt, and how they performed.
At first sight, it looked as if the presence of Panda had made no difference — there was no statistically significant difference between the groups in terms of time spent inspecting the circuit boards and the area searched. Participants in both groups rated their feelings of responsibility for the task, effort expended, and performance similarly.
But when the scientists looked more closely at participants’ error rates, they realised that the participants working with Panda were catching fewer defects later in the task, when they’d already seen that Panda had successfully flagged many errors.
This could reflect a ‘looking but not seeing’ effect, where people get used to relying on something and engage with it less mentally.
Although the participants thought they were paying an equivalent amount of attention, subconsciously they assumed that Panda hadn’t missed any defects.
