Seen it, seen it, seen it, seen it, seen it. Most of us instinctively know whether objects are familiar or unfamiliar. Now we may know how we know. It turns out monkeys have a cluster of neurons in their brains that decides whether or not they have seen objects before.
The primary visual area, at the back of the brain, does most of the early work in perceiving an object, especially its physical attributes, such as what direction it is moving. However, the temporal lobes – the bits just above our ears – are also heavily involved.
In particular, a region of the temporal lobe called the perirhinal cortex has been linked with object recognition, memory and even helping primates recognise familiar faces. But researchers weren’t sure what aspects of an object were encoded by each visual area.
Monkeys know what’s what
To investigate, Yasushi Miyashita at the University of Tokyo, Japan, and his colleagues trained macaques to indicate whether an object was familiar. The monkeys saw some objects once a month, or once every 12,000 trials, and so categorised them as “new”. Meanwhile, other objects were shown in every trial, and so categorised as “old”.
Then the team began stimulating different parts of the perirhinal cortex during the recognition tests. They used optogenetics: modifying neurons so they fire when exposed to light.
When they stimulated the entire perirhinal cortex with light, the monkeys categorised all objects as old – regardless of whether they really were. This suggests that perirhinal neurons produce a “familiar” signal when they fire.
However, stimulating different parts of the perirhinal cortex in turn had varying effects: the front biased the monkeys to see everything as familiar, while the rear sometimes caused them to identify more objects as new.
Miyashita says perirhinal neurons help convert the perception of an object – what it is – into its meaning.
A better understanding of this conversion process could help improve machine learning, says David Sheinberg at Brown University in Providence, Rhode Island. “Any realistic robot would not only need to know what it’s looking at, but also need to know if it’s seen that thing before,” he says. “The computer vision world is still stuck looking at that first part.”
Journal reference: Science, DOI: 10.1126/science.aan4800
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