If the original input is ambiguous, such as the coffee shop, then the network activates multiple locations in the lower layer—for example, all the locations where a coffee shop exists. This is what happens if you touch the rim of a coffee cup with one finger. Many objects have a rim, so you can’t at first be certain what object you are touching. When you move, the lower layer changes all the possible locations, which then make multiple predictions in the upper layer. The next input will eliminate any locations that don’t match. We simulated this two-layer circuit in software using realistic
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