How the Uncanny Valley Works

Mapping the Uncanny Valley
C-3PO would be in the middle of the uncanny valley graph, since he's built like a person but not nearly mistakable for human. Daniel Boczarski/WireImage/Getty Images

Let's use some examples from real life and pop culture to map out the uncanny valley more clearly. At the low end of the chart lie industrial robots, which are not humanlike and don't inspire much affinity. An android like C-3PO from "Star Wars" would be in the middle: His build closely resembles a human's, and he speaks and acts like a human, but his metal exterior and robotic face clearly show he's not a human. Yet, we feel some affinity for him.

Further along the uncanny valley graph are computer-generated humans from Disney animated films such as "Frozen" and "Moana." While these characters obviously portray humans, animators intentionally exaggerate their features so they don't appear too realistic. Based on the success of these films, audiences feel a high level of affinity for them. And then there are simulations like the computer-generated version of Tom Hanks in the 2004 animated movie "The Polar Express." The film's creators attempted to make a perfectly lifelike character but fell short, resulting in many critics describing the film as creepy or nightmare-inducing instead of charming [source: Zacharek]. That eerie Tom Hanks? Right near the bottom of the uncanny valley. And according to Mori, the intensity of the uncanny valley effect heightens when simulations move rather than remain static.

So, when features that characterize humans — like voice, proportion and texture — are inconsistent in replicas, it throws us off. Mori's theory that slightly flawed human replicas are reminiscent of corpses and death may be partly valid, but doesn't encompass the complexity of the uncanny valley. It's likely the phenomenon is the result of several different reactions. Here are some reasons humans might be freaked out by almost-perfect human simulations [sources: Hsu; Looser and Wheatley]:

  • Humans tend to identify potential threats in our surroundings. A shrub that is clearly a shrub is not a threat, so we feel at ease. A lion that is clearly a lion is a threat and we react appropriately. A shrub that looks like a lion creates a sense of unease, since we aren't sure how to react. This pattern could hold true for realistic robots that make us unsure whether they are humans or androids. (This is similar to pareidolia, our tendency to notice familiar patterns where there are none — like when we see a face in a cloud.)
  • Human perception is attuned to human faces, a vital skill in recognizing friends and family members and noticing outsiders who might pose a threat. This close attention to faces suggests the uncanny valley effect would be stronger for artificial human faces versus hands or legs.
  • We recognize the slight differences in a not-quite-human android as deformities, which we instinctively associate with disease, causing revulsion.

Researchers have been hard at work studying how and why the uncanny valley occurs. Let's take a look at some recent studies that have tested the uncanny valley effect and uncovered data about its potential causes.

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