Bayesian reasoning implicated in some mental disorders
From within the dark confines of the skull, the brain builds its own version of reality. By weaving together expectations and information gleaned from the senses, the brain creates a story about the outside world. For most of us, the brain is a skilled storyteller, but to spin a sensible yarn, it has to fill in some details itself.
“The brain is a guessing machine, trying at each moment of time to guess what is out there,” says computational neuroscientist Peggy Seriès.
Guesses just slightly off — like mistaking a smile for a smirk — rarely cause harm. But guessing gone seriously awry may play a part in mental illnesses such as schizophrenia, autism and even anxiety disorders, Seriès and other neuroscientists suspect. They say that a mathematical expression known as Bayes’ theorem — which quantifies how prior expectations can be combined with current evidence — may provide novel insights into pernicious mental problems that have so far defied explanation.
Bayes’ theorem “offers a new vocabulary, new tools and a new way to look at things,” says Seriès, of the University of Edinburgh.
Experiments guided by Bayesian math reveal that the guessing process differs in people with some disorders. People with schizophrenia, for instance, can have trouble tying together their expectations with what their senses detect. And people with autism and high anxiety don’t flexibly update their expectations about the world, some lab experiments suggest. That missed step can muddy their decision-making abilities.
Given the complexity of mental disorders such as schizophrenia and autism, it is no surprise that many theories of how the brain works have fallen short, says psychiatrist and neuroscientist Rick Adams of University College London. Current explanations for the disorders are often vague and untestable. Against that frustrating backdrop, Adams sees great promise in a strong mathematical theory, one that can be used to make predictions and actually test them.
“It’s really a step up from the old-style cognitive psychology approach, where you had flowcharts with boxes and labels on them with things like ‘attention’ or ‘reading,’ but nobody having any idea about what was going on in [any] box,” Adams says.
Applying math to mental disorders “is a very young field,” he adds, pointing to Computational Psychiatry, which plans to publish its first issue this summer. “You know a field is young when it gets its first journal.”
A mind for math
Bayesian reasoning may be new to the mental illness scene, but the math itself has been around for centuries. First described by the Rev. Thomas Bayes in the 18th century, this computational approach truly embraces history: Evidence based on previous experience, known as a “prior,” is essential to arriving at a good answer, Bayes argued. He may have been surprised to see his math meticulously applied to people with mental illness, but the logic holds. To make a solid guess about what’s happening in the world, the brain must not rely just on current input from occasionally unreliable senses. The brain must also use its knowledge about what has happened before. Merging these two streams of information correctly is at the heart of perceiving the world as accurately as possible.
Bayes figured out a way to put numbers to this process. By combining probabilities that come from prior evidence and current observations, Bayes’ formula can be used to calculate an overall estimate of the likelihood that a given suspicion is true. A properly functioning brain seems to do this calculation intuitively, behaving in many cases like a skilled Bayesian statistician, some studies show.