People get lazier when they work alongside robots, reveals a new study.
Researchers discovered that humans pay less attention to their work when they think robots have already checked it.
They found that humans who work with automatons have learned to see them as team-mates – but teamwork can have negative as well as positive effects on people’s performance.
People sometimes relax, letting their colleagues do the work instead, called ‘social loafing’ – and scientists say it’s common where people know their contribution won’t be noticed or they’ve acclimatized to another team member’s high performance.
Scientists at the Technical University of Berlin in Germany investigated whether humans “social loaf” when they work with robots.
Study first author Dietlind Helene Cymek said: “Teamwork is a mixed blessing.
“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.
“We were interested in whether we could also find such motivational effects when the team partner is a robot.”
The team tested their hypothesis using a simulated industrial defect-inspection task – looking at circuit boards for errors.
They 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.
That 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 the 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, the participants were asked to rate their own effort, how responsible for the task they felt, and how they performed.
The researchers said that, 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 research team looked more closely at participants’ error rates, they realized 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.
They said that 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.
Study senior author Dr. Linda Onnasch said: “It is easy to track where a person is looking, but much harder to tell whether that visual information is being sufficiently processed at a mental level.”
The team, whose findings were published in the journal Frontiers in Robotics and AI, warned that it could have safety implications.
Dr. Onnasch said: “In our experiment, the subjects worked on the task for about 90 minutes, and we already found that fewer quality errors were detected when they worked in a team.
“In longer shifts, when tasks are routine and the working environment offers little performance monitoring and feedback, the loss of motivation tends to be much greater.
“In manufacturing in general, but especially in safety-related areas where double checking is common, this can have a negative impact on work outcomes.”
The research team said social loafing is hard to simulate in the lab because participants know they are being watched.
Cymek added: “The main limitation is the laboratory setting.
“To find out how big the problem of loss of motivation is in human-robot interaction, we need to go into the field and test our assumptions in real work environments, with skilled workers who routinely do their work in teams with robots.”
Produced in association with SWNS Talker