In the field of robotics, it has been observed that the more similar to a real human a robot is, the more empathy it generates in people. However, there comes a point where, if the robot's appearance and behavior are too close to the human's, but still somewhat different, empathy is replaced by a strong rejection. This phenomenon, initially observed in 1970 and called Uncanny Valley, has been widely studied by the scientific community. However, the reason for this effect is still unknown, and it is not understood if this is something generalized in the population, or highly dependent on each individual. Additionally, there are questions about how this phenomenon could change over the years, as human-looking robots become more and more common in our lives. The test that we propose will allow you to know if this effect occurs in your case, as it measures the nonconscious preferences that you feel in the appearance of robots (Human-like Robots vs. Robot-like Robots). You can also compare your results with those obtained by the rest of the participants at an aggregate level.
Implicit Association Tests (IAT) allows us to measure the implicit associations towards two concepts that are compared (in this case robots with a human appearance vs. robots with a robot appearance) and the strength of these associations. These tools have been widely used for years by social psychology researchers to analyze unconscious prejudices, biases, and preferences toward race, gender, or religion. Today, companies and other organizations use these techniques to measure consumers' automatic preferences when comparing two brands, two logos, or two celebrities, thereby improving their marketing and communication strategies.
You can find more information about these techniques on our blog.
The objective of this page is exclusively to show how an IAT is carried out, offering the possibility of experiencing a test and understanding how the results are obtained.
Bitbrain does not guarantee the scientific validity of the results of this specific test, as they are not properly segmented or processed, and the information that appears on this page could bias the participants. Bitbrain is not responsible for external interpretations that may be derived from the results presented here.
Do not continue if you are not prepared to see results that may be perceived as controversial.
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