Brain wave signals may tell how you feel toward your encounter with a robot in the room.
The increasing presence of robots in society calls for a deeper understanding into what attitudes humans have toward robots. People may treat robots as mechanical artifacts, or may consider them to be intentional agents.
This might result in explaining robots’ behavior as stemming from operations of the mind (intentional interpretation), or as a result of mechanical design (mechanical interpretation). A quick look at the robotics industry market size research shows that robots including industrial, commercial, social, and humanoid robots are here to stay. The sooner humans get used to this fact and adapt to co-living and co-working with humanoid robots the better.
The Global Commercial Robotics Market Size is projected to reach $24,760 million by 2026, from $8,310.7 million in 2020, at a CAGR of 20.0 percent During 2021-2026, according to Industry Research Biz.
There are different types of humanoid robots as well. Androids are humanoid robots designed and built to resemble a male human, whereas Gynoids are built to resemble human females.
Based on this, a human’s attitude toward the robot in the room will be different if it is a robot that vacuums a floor or a humanoid robot –Android or Gynoid– who serves in a restaurant or household.
The goal of the humanoid robotics is to create machines that behave as if they were humans. The ultimate goal is to engineer and embed humanoid robots with human-like-intelligence, reaching the next-level within the types of Artificial Intelligence, Artificial General Intelligence (AGI). These are humanoids expected to live, work, and co-exist with human beings triggering an array of different individual reactions an attitudes toward the humanoid in the room, humanoids that are about as capable as humans.
And the question arises, can we accurately learn from human general attitudes toward robots, including social acceptance and social acceptability? Researchers found the answer in brain wave signals.
By the end of the study, the researchers concluded that, indeed, it is possible to predict attitudes that humans have with respect to artificial agents, humanoid robots specifically, from EEG data already in the baseline default mode of the resting state.
According to the paper, this casts a light on how a given individual might approach humanoid robots that are increasingly occupying our social environments around the world. Decoding such a high-level cognitive phenomenon from the neural activity is quite marked and can be highly informative with respect to the mechanisms underlying attitudes that people adopt.
It might be that the intentional/mechanistic bias in attitudes toward robots is a similar mechanism to other biases such as racial and gender biases. The researchers anticipate that future studies might address the question of whether the neural correlates of biases in attitudes toward robots generalize to other types of biases as well.
“The present study, however, does not address the issue of whether the observed differential effect across participants is related to a particular context in which they observe the robot, particular robot appearance, or a general attitude that a given individual has toward robots.”
Future research should address the question of whether the neural correlates of the biases/attitudes observed in this study are signatures of a general individual trait or are rather related to a given state or context. In either case, the research shows that there are detectable neural characteristics underlying the likelihood of treating robots as intentional agents or, rather, as mechanistic artifacts.