Scientists teach computers to see optical illusions

Scientists teach computers to see optical illusions

Washington:

Scientists have taught computers to see optical illusions, an advance that may help artificial vision algorithms to take context into account and be more robust.

Understanding how human brains perceive optical illusions remains an active area of research, said scientists from the Brown University in the US.

For one class of optical illusions, called contextual phenomena, those perceptions are known to depend on context.

For example, the colour you think a central circle depends on is the colour of the surrounding ring.

Sometimes the outer colour makes the inner colour appear more similar, such as a neighbouring green ring making a blue ring appear turquoise.

“There’s growing consensus that optical illusions are not a bug but a feature,” said Thomas Serre, an associate professor at the Brown University.

“I think they are a feature. They may represent edge cases for our visual system, but our vision is so powerful in day-to-day life and in recognising objects,” Serre said.

The team started with a computational model constrained by anatomical and neurophysiological data of the visual cortex.

The model aimed to capture how neighbouring cortical neurons send messages to each other and adjust one another’s responses when presented with complex stimuli such as contextual optical illusions.

One innovation the team included in its model was a specific pattern of hypothesised feedback connections between neurons, said Serre.

These feedback connections are able to increase or decrease — excite or inhibit — the response of a central neuron, depending on the visual context.

These feedback connections are not present in most deep learning algorithms, researchers said.

Deep learning is a powerful kind of artificial intelligence that is able to learn complex patterns in data, such as recognising images and parsing normal speech, they said.

It depends on multiple layers of artificial neural networks working together.

However, most deep learning algorithmsonly include feed-forward connections between layers, not Serre’s innovative feedback connections between neurons within a layer.

Once the model was constructed, the team presented it a variety of context-dependent illusions.

The researchers “tuned” the strength of the feedback excitatory or inhibitory connections so that model neurons responded in a way consistent with neurophysiology data from the primate visual cortex.

Then they tested the model on a variety of contextual illusions and again found the model perceived the illusions like humans.

In order to test if they made the model needlessly complex, they lesioned the model — selectively removing some of the connections.

When the model was missing some of the connections, the data didn’t match the human perception data as accurately.

“Our model is the simplest model that is both necessary and sufficient to explain the behaviour of the visual cortex in regard to contextual illusions,” Serre said.

“This was really textbook computational neuroscience work — we started with a model to explain neurophysiology data and ended with predictions for human psychophysics data,” he said.

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