A virtual robot has acquired a cognitive wrinkle common in people – further evidence that computers need bodies if they're ever going to think like us

Video: See a virtual robot mimic a human baby

One lorry: iCub’s virtual self can count, too (Image: David Paul Morris/Bloomberg via Getty Images)

Read more: “Squishybots: Soft, bendy and smarter than ever“

ONE of the many curious habits of the human brain is that we tend to associate small numbers with the left side of our body and large numbers with our right.

Now a virtual robot embedded in a synthetic world has acquired the quirk. This is helping untangle the puzzle of how even highly abstract concepts such as numbers might be rooted in our physical interactions with the world.


“Numbers are so abstract and pure, they seemed detached,” says Marek Rucinski of the University of Plymouth, UK, who presented the virtual robot at the Cognitive Science conference in Boston in July. “Yet every concept we have is somehow grounded in the real world.”

The so-called SNARC (spatial-numerical association of response codes) effect is well established: people respond faster to a number (by pressing a button, say) with their left hand when the number is small and with their right hand when the number is large. Similarly, people who have brain damage that causes them to ignore the left side of their body show a bias towards larger numbers when asked to report the middle of a numerical interval.

These number-space associations epitomise the broader, surprising insight that many other aspects of abstract thought are believed to have their roots in our sensory and motor interactions, says Martin Fischer, who specialises in what’s known as embodied cognition at the University of Potsdam in Germany. “The SNARC effect shows that even abstract knowledge goes directly back to the sensory and motor experiences that we had when we acquired the knowledge.”

“Even abstract knowledge leads directly back to the motor experiences we had as children”

The accepted explanation for the SNARC effect is that when we in the west learn to count, we are most likely to be taught in a left-to-right direction – on a blackboard perhaps or by a parent pointing to objects in a row. This way of learning sets up links between small numbers and the part of the brain that controls our left side, and vice versa, that persists into adulthood. There is also evidence from cultures that read from right to left that the association is reversed.

To better understand how these connections might form, Rucinski and his colleagues turned to a virtual version of a child – a digital simulation of the humanoid robot iCub – and exposed it to processes that might help these connections form in people.

iCub’s development began with a phase called “motor babbling”, randomly moving its virtual arms and gaze, which human babies are thought to use to become aware of their bodies. In a crude simulation of the architecture of our brains, the researchers set up three areas for spatial processing in the iCub’s brain: two corresponding to each arm and one to its gaze.

Next the researchers taught iCub to count by presenting it with a stream of digits from 1 to 15. To mimic the west’s cultural bias towards counting from left to right, low numbers were presented to its left, and high to the right. Learning software that mimics the way synapses form connections in the human brain then associated low numbers with the left area of the gaze map and the left arm – and vice versa for the right.

iCub was then ready to take the SNARC test. It was presented with a random series of odd and even numbers. In one instance iCub had to press a button with its left hand when the number was odd and right hand when it was even; in a second instance, the buttons were reversed. Like humans who have taken the same test, iCub was faster both when the number was small and it pressed the button with its left hand, and when the number was large and the button pressed was on the right.

In a second, modified version of SNARC, the robot was presented with numbers followed by an object either on the right or left. The time the robot took to notice the object was then recorded. It was faster both when the number was small and the object was on the left, and the number was large and the object was on the right.

Rucinski can explain what’s going on. In both cases, the connections laid down during motor babbling and learning to count meant that simply by appearing, each number was automatically activating the spatial brain areas associated with either the right or left side of iCub’s body. This made the robot faster at using that side of the body to complete the task – whether it was hitting a button in the classic SNARC test or noticing an object in the modified test.

“The model provides an embodied explanation for number-space interactions,” says Rucinski. The results show that these associations can emerge from a combination of learning to count from left to right and from the way the human body is built. That doesn’t mean that iCub’s thought processes mirror exactly what is going on in a human brain – but it does imply that the association can arise from just those two factors, both of which humans have, he says.

“This is a really good complementary approach to what we are doing in experimental psychology,” says Fischer, who adds that he looks forward to a day when work in cognitive robotics could make a prediction about cognition that is then tested in humans, instead of the other way around.

Why robot bodies need a makeover It is becoming clear that our physical bodies drive much of our thought. So building a machine that thinks like a human may mean building robots whose limbs enable them to interact with the physical world. “The body plays a crucial role – it is the means by which the brain can acquire information and learn about the environment,” says Rolf Pfeifer, a researcher in artificial intelligence at the University of Zurich, Switzerland. Robots’ bodies can be easily manipulated – adding or removing legs, arms or eyes, say – to systematically test the extent to which the shape of our bodies and their interaction with the environment influences cognition (see “Squishybots: soft, bendy and smarter than ever”).