Crime and Punishment in the Brain

Justice may be hard-wired into our brains, but researchers have found a way to short circuit it.

The punishment should fit the crime. This universal concept underlies any reasonable justice system.

But the foundations of our sense of justice might be more precarious than we might imagine, given that scientists have figured out how to tinker with the part of the brain that deals with punishment.

According to a study published in the journal Neuron, two different regions of the brain separately deal with judgement of guilt or innocence and assessment of punishment. By stimulating the latter, researchers at Vanderbilt University and Harvard University figured out a way to influence penalty decisions.

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In experiments involving 66 male and female volunteers, researchers asked study participants to assess a series of scenarios in which a suspect committed a crime, the resulting damage of which ranged from property loss to severe injury and even death.

The part of the brain that deals with punishment decisions is the dorsolateral prefrontal cortex. In half of the study participants, the scientists used repetitive transcranial magnetic stimulation (rTMS), a painless process involving an electromagnet placed on the scalp to temporarily affect cognitive activity, on that region of the brain. The other half of the study participants received a placebo.

What researchers found is that rTMS manipulation changed the way participants assessed penalties for various crimes. While the study's volunteers universally factored in guilt and the level of harm in a crime in their punishment assessments, those who received rTMS opted for significantly lower punishments for guilty criminals than participants who received the placebo.

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"This research gives us deeper insights into how people make decisions relevant to law, and particularly how different parts of the brain contribute to decisions about crime and punishment," co-author Owen Jones, professor at Vanderbilt and director of the MacArthur Foundation Research Network on Law and Neuroscience, said in a statement.

This study builds on previous work identifying the pathways and cognitive processes of judgement and justice in the brain. Humans are undoubtedly a cooperative species, but our ability to assess guilt and hand out justice to those who don't go along with social norms is also important to our success as in forming large-scale societies.

A study published in 2011 in the journal PLoS Biology looked at neurological activity during a game involving economic decision-making. One player is asked how to share a fixed sum of money with another player. The second player can either accept the proposal and split the money accordingly, or reject it, and both of the players take home nothing.

If the offer is to share the money equally, the second player always accepts. Any time one player offers an unfair suggestion to his or her counterpart, however, with the split favoring the first player, the second player rejects the offer half of the time, even though that means the second player lost money, too. This inequitable offer triggers an automatic response in the amygdala of the brain, which deals with fear and anger and helps to explain the reaction of the second player to a sense of unfairness.

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The PLoS Biology study is an example of second-party punishment, in which the victim directly punishes the accuser. The results suggest that emotion plays a role when the party directly experiences the outcome. But third-party punishment, a uniquely human trait, is largely guided by reason, found a study published last year in the Journal of Neuroscience.

In an exercise where study participants were subjected to functional magnetic resonance imaging (fMRI) brain-scanning while evaluating situations meant to invoke a sense of justice, researchers found that people with high "justice sensitivity," a measure determined by responses to a questionnaire, showed higher-than-average activity in parts of the brain linked higher-order cognition. Areas of the brain tied to emotion, on the other hand, were unaffected.

Taken together, these studies show that justice is hard-wired into our brains, and with the latest research, scientists may have found a way to short circuit it.

It's easy to mistake this photo for some kind of surreal landscape painting, but this image in fact shows off the imagination of Google's advanced image detection software. Similar to an artist with a blank canvas, Google's software constructed this image out of nothing, or essentially nothing, anyway. This photo began as random noise before software engineers coaxed this pattern out of their machines. How is it possible for software to demonstrate what appears to be an artistic sensibility? It all begins with what is basically an artificial brain.

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Artificial neural networks are systems consisting of between 10 and 30 stacked layers of synthetic neurons. In order to train the network, "each image is fed into the input layer, which then talks to the next layer, until eventually the 'output' layer is reached,"

the engineers wrote in a blog post detailing their findings

. The layers work together to identify an image. The first layer detects the most basic information, such as the outline of the image. The next layers hone in on details about the shapes. The final output layer provides the "answer," or identification of the subject of an image. Shown is Google's image software before and after processing an image of two ibis grazing to detect their outlines.

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Searching for shapes in clouds isn't just a human pastime anymore. Google engineers trained the software to identify patterns by feeding millions of images to the artificial neural network. Give the software constraints, and it will scout out patterns to recognize objects even in photos where the search targets are not present. In this photo, for example, Google's software, like a daydreamer staring at the clouds, finds all kinds of different animals in the sky. This pattern emerged because the neural network was trained primarily on images of animals.

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How the machine is trained will determine its bias in terms of recognizing certain objects within an otherwise unfamiliar image. In this photo, a horizon becomes a pagoda; a tree is morphed into building; and a leaf is identified as a bird after image processing. The objects may have similar outlines to their counterparts, but all of the entries in the "before" images aren't a part of the software's image vocabulary, so the system improvises.

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When the software acknowledges an object, it modifies a photo to exaggerate the presence of that known pattern. Even if the software is able to correctly recognize the animals it has been trained to spot, image detection may be a little overzealous in identifying familiar shapes, particularly after the engineers send the photo back, telling the software to find more of the same, and thereby creating a feedback loop. In this photo of a knight, the software appears to recognize the horse, but also renders the faces of other animals on the knight's helmet, globe and saddle, among other places.

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Taken a step further, using the same image over several cycles in which the output is fed through over and over again, the artificial neural network will restructure an image into the shapes and patterns it has been trained to recognize. Again borrowing from an image library heavy on animals, this landscape scene is transformed into a psychedelic dream scene where clouds are apparently made of dogs.

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At its most extreme, the neural network can transform an image that started as random noise into a recognizable but still somewhat abstract kaleidoscopic expression of objects with which the software is most familiar. Here, the software has detected a seemingly limitless number of arches in what was a random collection of pixels with no coherence whatsoever.

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This landscape was created with a series of buildings. Google is developing this technology in order to boost its image recognition software. Future photo services might recognize an object, a location or a face in a photo. The engineers also suggest that the software could one day be a tool for artists that unlocks a new form of creative expression and may even shed light on the creative process more broadly.

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