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Image Caption: Computers interpreted the above images to be (from left) an electric guitar, an African grey parrot, a strawberry, and a peacock.
To test this, Firestone and lead author Zhenglong Zhou, a Johns Hopkins senior majoring in cognitive science, essentially asked people to "think like a machine." Machines have only a relatively small vocabulary for naming images. So Firestone and Zhou showed people dozens of fooling images that had already tricked computers, and gave people the same kinds of labeling options that the machine had. In particular, they asked people which of two options the computer decided the object was—one being the computer's real conclusion and the other a random answer. Was the blob pictured a bagel or a pinwheel? It turns out, people strongly agreed with the conclusions of the computers.
People chose the same answer as computers 75 percent of the time. Perhaps even more remarkably, 98 percent of people tended to answer like the computers did.
Next researchers upped the ante by giving people a choice between the computer's favorite answer and its next-best guess&mash;for example was the blob pictured a bagel or a pretzel? People again validated the computer's choices, with 91 percent of those tested agreeing with the machine's first choice.
Even when the researchers had people guess between 48 choices for what the object was, and even when the pictures resembled television static, an overwhelming proportion of the subjects chose what the machine chose well above the rates for random chance. A total of 1,800 subjects were tested throughout the various experiments.
"The neural network model we worked with is one that can mimic what humans do at a large scale, but the phenomenon we were investigating is considered to be a critical flaw of the model," says Zhou, a cognitive science and mathematics major. "Our study was able to provide evidence that the flaw might not be as bad as people thought. It provides a new perspective, along with a new experimental paradigm that can be explored."
Zhou, who plans to pursue a career in cognitive neuroscience, began developing the study alongside Firestone early last year. Together, they designed the research, refined their methods, and analyzed their results for the paper.
"Research opportunities for undergraduate students are abundant at Johns Hopkins, but the experience can vary from lab to lab and depends on the particular mentor," he says. "My particular experience was invaluable. By working one-on-one with Dr. Firestone, I learned so much—not just about designing an experiment, but also about the publication process and what it takes to conduct research from beginning to end in an academic setting."
