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.
Expectations
People have assumptions about the world, which are either inborn or learned early in life. For example:
- Light comes from above.
- Noses stick out.
- Objects move slowly.
- Background images are uniformly colored.
- Other people’s gazes are directed at us.
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 (
SN: 10/8/11, p. 18).
Story continues after graphic
Where there's smoke
The example below shows how Bayesian reasoning is applied to a question of the probability of a dangerous fire.
This reckoning requires the brain to give the right
amount of weight to prior expectations and current information.
Depending on the circumstances, those weights change. When the senses
falter, for instance, the brain should lean more heavily on prior
expectations. Say the mail carrier comes each day at 4 p.m. On a stormy
afternoon when visual cues are bad, we rely less on sight and more on
prior knowledge to guess that the late-afternoon noise on the front
porch is probably the mail carrier delivering letters. In certain mental
illnesses, this flexible balancing act may falter.
People with schizophrenia often suffer from
hallucinations and delusions, debilitating symptoms that arise when
lines between reality and imagination blur. That confusion can lead to
hearing voices that aren’t there and believing things that can’t
possibly be true. These departures from reality could arise from
differences in how people integrate new evidence with previous beliefs.
There’s
evidence for such distorted calculations. People with schizophrenia
don’t fall for certain visual illusions that trick most people, for
instance. When shown a picture of the inside of a hollowed-out face
mask, most people’s brains mistakenly convert the image to a face that
pops outward off the page. People with schizophrenia, however, are more
likely to see the face as it actually is — a concave mask. In that
instance, people with schizophrenia give more weight to information
that’s coming from their eyes than to their expectation that noses
protrude from the rest of the face.
To complicate matters, the opposite can be true, too,
says neuropsychologist Chris Frith of the Wellcome Trust Centre for
Neuroimaging at University College London. “In this case, their prior is
too weak, but in other cases, their prior is too strong,” he says.
In a recent study, healthy people and those who recently began experiencing psychosis, a symptom of schizophrenia,
were shown confusing shadowy black-and-white images.
Participants then saw color versions of the images that were easier to
interpret. When shown the black-and-white images again, people with
early psychosis were better at identifying the images, suggesting that
they used their prior knowledge — the color pictures — to truly “see”
the images. For people without psychosis, the color images weren’t as
much help. That difference suggests that the way people with
schizophrenia balance past knowledge and present observations is
distinct from the behavior of people without the disorder. Sometimes the
balance tips too far — in either direction.
In a talk at the annual Computational and Systems
Neuroscience meeting in February in Salt Lake City, Seriès described the
results of a different visual test: A small group of people with
schizophrenia had to describe which way a series of dots were moving on a
screen. The dots moved in some directions more frequently than others —
a statistical feature that let the scientists see how well people could
learn to predict the dots’ directions. The 11 people with schizophrenia
seemed just as good at learning which way the dots were likely to move
as the 10 people without, Seriès said. In this situation, people with
schizophrenia seemed able to learn priors just fine.
But when another trick was added, a split between the
two groups emerged. Sometimes, the dots were almost impossible to see,
and sometimes, there were no dots at all. People with schizophrenia were
less likely to claim that they saw dots when the screen was blank.
Perhaps they didn’t hallucinate dots because of the medication they were
on, Seriès says. In fact, very early results from unmedicated people
with schizophrenia suggest that they actually see dots that aren’t there
more than healthy volunteers.
Preliminary
results so far on schizophrenia are sparse and occasionally
conflicting, Seriès admits. “It’s the beginning,” she says. “We don’t
understand much.”
The research is so early that no straightforward
story exists yet. But that’s not unexpected. “If 100 years of
schizophrenia research have taught us anything, it’s that there’s not
going to be a nice, simple explanation,” Adams says. But using math to
describe how people perceive the world may lead to new hunches about how
that process goes wrong in mental illnesses, he argues.
“You can instill expectations in subjects in many
different ways, and you can control what evidence they see,” Adams says.
Bayesian theory “tells you what they should conclude from those prior
beliefs and that evidence.” If their conclusions diverge from
predictions, scientists can take the next step. Brain scans, for
instance, may reveal how the wrong answers arise. With a clear
description of these differences, he says, “we might be able to measure
people’s cognition in a new way, and diagnose their disorders in a new
way.”
Now vs. then
The way the brain combines incoming sensory information with
existing knowledge may also be different in autism,
some researchers argue. In some cases, people with autism might put
excess weight on what their senses take in about the world and rely less
on their expectations. Old observations fit with this idea. In the
1960s, psychologists had discovered that children with autism were just
as good at remembering nonsense sentences (“By is go tree stroke lets”)
as meaningful ones (“The fish swims in the pond”). Children without
autism struggled to remember the non sequiturs. But the children with
autism weren’t thrown by the random string of words, suggesting that
their expectations of sentence meaning weren’t as strong as their
ability to home in on each word in the series.
Another study supports the notion that sensory
information takes priority in people with autism. People with and
without autism were asked to judge whether a sight and a sound happened
at the same time. They saw a white ring on a screen, and a tone played
before, after or at the same time. Adults without autism were influenced
by previous trials in which the ring and tone were slightly off. But
adults with autism were not swayed by earlier trials, researchers reported in February in
Scientific Reports.
This literal perception might get in the way of
speech perception, Marco Turi of the University of Pisa in Italy and
colleagues suggest. Comprehending speech requires a listener to mentally
stitch together sights and sounds that may not arrive at the eyes and
ears at the same time. Losing that flexibility could make speech harder
to understand.
A different study found that children with autism perceive moving dots more clearly than children without autism (
SN Online: 5/5/15).
The brains of people with autism seem to prioritize incoming sensory
information over expectations about how things ought to work. Elizabeth
Pellicano of University College London and David Burr of the University
of Western Australia in Perth described the concept in 2012 in an
opinion paper in
Trends in Cognitive Sciences. Intensely attuned to information streaming in from the senses,
people with autism experience the world as “too real,” Pellicano and Perth wrote.
New data, however, caution against a too-simple
explanation. In an experiment presented in New York City in April at the
annual meeting of the Cognitive Neuroscience Society, 20 adults with
and without autism had to quickly hit a certain key on a keyboard when
they saw its associated target on a screen. Their job was made easier
because the targets came in a certain sequence. All of the participants
improved as they learned which keys to expect. But when the sequence
changed to a new one, people with autism faltered. This result suggests
that they learned prior expectations just fine, but had trouble updating
them as conditions changed, said cognitive neuroscientist Owen Parsons
of the University of Cambridge.
Story continues after graphic
Memory test
In a 1967 study, children with autism were just as
good at remembering nonsense strings of words as they were at
remembering sentences. The results suggest that they had weaker
expectations about meaningful sentences.
Distorted calculations — and the altered versions of
the world they create — may also play a role in depression and anxiety,
some researchers think. While suffering from depression, people may hold
on to distorted priors — believing that good things are out of reach,
for instance. And people with high anxiety can have trouble making good
choices in a volatile environment, neuroscientist Sonia Bishop of the
University of California, Berkeley and colleagues reported in 2015 in
Nature Neuroscience.
In their experiment, people had to choose a shape,
which sometimes came with a shock. People with low anxiety quickly
learned to avoid the shock, even when the relationship between shape and
shock changed. But
people with high anxiety performed worse
when those relationships changed, the researchers found. “High-anxious
individuals didn’t seem able to adjust their learning to handle how
volatile or how stable the environment was,” Bishop says.
Stress shutdown
People with higher levels of anxiety (light blue)
learned less from shocks (measured by changes in pupil size) when the
environment changed than people with low anxiety (dark blue).
Source: Michael Browning
et al/
Nature Neuroscience 2015.
Scientists can’t yet say what causes this difficulty
adjusting to a new environment in anxious people and in people with
autism. It could be that once some rule is learned (a sequence of
computer keys, or the link between a shape and a shock), these two
groups struggle to update that prior with newer information.
This rigidity might actually contribute to anxiety in
the first place, Bishop speculates. “When something unexpected happens
that is bad, you wouldn’t know how to respond,” and that floundering “is
likely to be a huge source of anxiety and stress.”
Recalculating
“There’s been a lot of frustration with a failure to
make progress” on psychiatric disorders, Bishop says. Fitting
mathematical theories to the brain may be a way to move forward.
Researchers “are very excited about computational psychiatry in
general,” she says.
Computational psychiatrist Quentin Huys of the
University of Zurich is one of those people. Math can help clarify
mental illnesses in a way that existing approaches can’t, he says. In
the March issue of
Nature Neuroscience, Huys and colleagues argued that math can demystify psychiatric disorders, and that
thinking of the brain as a Bayesian number cruncher
might lead to a more rigorous understanding of mental illness. Huys
says that a computational approach is essential. “We can’t get away
without it.” If people with high anxiety perform differently on a
perceptual test, then that test could be used to both diagnose people
and monitor how well a treatment works, for instance.
Scientists hope that a deeper description of mental
illnesses may lead to clearer ways to identify a disorder, chart how
well treatments work and even improve therapies. Bishop raises the
possibility of developing apps to help people with high anxiety evaluate
situations — outsourcing the decision making for people who have
trouble. Frith points out that cognitive behavioral therapy could help
depressed people recalculate their experiences by putting less weight on
negative experiences and perhaps breaking out of cycles of despondence.
Beyond these potential interventions, simply
explaining to people how their brains are working might ease distress,
Adams says. “If you can give people an explanation that makes sense of
some of the experiences they’ve had, that can be a profoundly helpful
thing,” he says. “It destigmatizes the experience.”
This article
appears in the May 28, 2016, Science News with the headline, "Misguided
math: Faulty Bayesian reasoning may explain some mental disorders."
