How Science, Religion Square Off in the Brain
Inside the brain, there's a tug-of-war going on between faith and reason.
Science versus faith. It's a heavyweight match-up between two often entirely opposed different worldviews that has been playing out for centuries.
Whatever tension exists between the ideas of those who lean more toward faith versus others more inclined toward reason, the roots of the conflict could trace back to the structure of our brains, according to a new study in the journal PLOS ONE.
Previous research using functional magnetic resonance imaging (fMRI) suggests the brain has an analytical neural network that allows for critical thinking as well as a social network that supports empathy. This idea of tension existing between the two networks in the brain is known as the opposing domains hypothesis.
The two networks play a game of tug-of-war when faced with a problem. When critical thinking is required, the empathetic part of the brain is suppressed. When moral reasoning is needed, the analytical network is subdued.
Belief in a higher power tends to engage the empathetic network of the brain, tuning out the more analytical area. Thinking analytically about the physical world has the opposite effect, according to the authors.
For their study, researchers from Case Western Reserve University and Babson College conducted a series of eight experiments, involving between 159 to 527 adults. Individuals who were more likely to be religious also tended to show a greater moral concern, though the researchers couldn't demonstrate cause and effect.
Similarly, strong analytic thinking tended to discourage any kind of spiritual or religious belief. Again, the relationship was correlational rather than causal.
"When there's a question of faith, from the analytic point of view, it may seem absurd," said lead author Tony Jack of Case Western Reserve University. "But, from what we understand about the brain, the leap of faith to belief in the supernatural amounts to pushing aside the critical/analytical way of thinking to help us achieve greater social and emotional insight."
The role that these neural networks play in the brain in affecting belief helps to explain certain trends seen in religiosity across different groups. Women, for example, tend to be more empathetic than men, the study's authors point out, and past research has demonstrated that women also tend to be more religious or spiritual.
Religious individuals aren't necessarily less intelligent than their agnostic or atheist counterparts. As the researchers note, citing a book that documented the religious affiliations of Nobel Prize winners, nearly 90 percent of the laureates adhered to some sort of faith.
Are science and religion really in conflict? Nearly three-in-five Americans believe they are, according to a survey published last year by the Pew Research Center.
Interestingly, both individuals who are highly religious as well as those more scientific-minded are less likely to see such a conflict, previous surveys have found. A Rice University survey published in 2015 found that nearly 70 percent of evangelicals saw no conflict between science and religion and close to half of them saw the two as complimentary.
Nearly 76 percent of the scientists surveyed reported belonging to one religion or another. An earlier study published by Rice University in 2011 also found that just 15 percent of scientists surveyed from the nation's major universities saw constant conflict between science and faith.
Whatever conflict may arise between religion and reason, the study's authors offer potential boundaries for the two. "Religion has no place telling us about the physical structure of the world; that's the business of science," Jack said in a statement. "Science should inform our ethical reasoning, but it cannot determine what is ethical or tell us how we should construct meaning and purpose in our lives."
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.
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 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.
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.
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.
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.
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.
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.
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.