Neuroscientists identify a brain circuit that helps break decisions down
into smaller pieces
When making a complex decision, we often break the problem down into a
series of smaller decisions. For example, when deciding how to treat a patient,
a doctor may go through a hierarchy of steps — choosing a diagnostic test,
interpreting the results, and then prescribing a medication.
Making hierarchical decisions is straightforward when the sequence of
choices leads to the desired outcome. But when the result is unfavorable, it
can be tough to decipher what went wrong. For example, if a patient doesn’t
improve after treatment, there are many possible reasons why: Maybe the
diagnostic test is accurate only 75 percent of the time, or perhaps the
medication only works for 50 percent of the patients. To decide what do to
next, the doctor must take these probabilities into account.
In a new study, MIT neuroscientists explored how the brain reasons about
probable causes of failure after a hierarchy of decisions. They discovered that
the brain performs two computations using a distributed network of areas in the
frontal cortex. First, the brain computes confidence over the outcome of each
decision to figure out the most likely cause of a failure, and second, when it
is not easy to discern the cause, the brain makes additional attempts to gain
more confidence.
“Creating a hierarchy in one’s mind and navigating that hierarchy while
reasoning about outcomes is one of the exciting frontiers of cognitive
neuroscience,” says Mehrdad Jazayeri, the Robert A. Swanson Career Development
Professor of Life Sciences, a member of MIT’s McGovern Institute for Brain
Research, and the senior author of the study.
MIT graduate student Morteza Sarafyzad is the lead author of the paper,
which appears in Science on May 16.
Hierarchical reasoning
Previous studies of decision-making in animal models have focused on
relatively simple tasks. One line of research has focused on how the brain
makes rapid decisions by evaluating momentary evidence. For example, a large
body of work has characterized the neural substrates and mechanisms that allow
animals to categorize unreliable stimuli on a trial-by-trial basis. Other
research has focused on how the brain chooses among multiple options by relying
on previous outcomes across multiple trials.
“These have been very fruitful lines of work,” Jazayeri says. “However,
they really are the tip of the iceberg of what humans do when they make
decisions. As soon as you put yourself in any real decision-making situation,
be it choosing a partner, choosing a car, deciding whether to take this drug or
not, these become really complicated decisions. Oftentimes there are many
factors that influence the decision, and those factors can operate at different
timescales.”
The MIT team devised a behavioral task that allowed them to study how the
brain processes information at multiple timescales to make decisions. The basic
design was that animals would make one of two eye movements depending on
whether the time interval between two flashes of light was shorter or longer
than 850 milliseconds.
A twist required the animals to solve the task through hierarchical
reasoning: The rule that determined which of the two eye movements had to be
made switched covertly after 10 to 28 trials. Therefore, to receive reward, the
animals had to choose the correct rule, and then make the correct eye movement
depending on the rule and interval. However, because the animals were not
instructed about the rule switches, they could not straightforwardly determine
whether an error was caused because they chose the wrong rule or because they
misjudged the interval.
The researchers used this experimental design to probe the computational
principles and neural mechanisms that support hierarchical reasoning. Theory
and behavioral experiments in humans suggest that reasoning about the potential
causes of errors depends in large part on the brain’s ability to measure the
degree of confidence in each step of the process. “One of the things that is
thought to be critical for hierarchical reasoning is to have some level of
confidence about how likely it is that different nodes [of a hierarchy] could
have led to the negative outcome,” Jazayeri says.
The researchers were able to study the effect of confidence by adjusting
the difficulty of the task. In some trials, the interval between the two
flashes was much shorter or longer than 850 milliseconds. These trials were
relatively easy and afforded a high degree of confidence. In other trials, the
animals were less confident in their judgments because the interval was closer
to the boundary and difficult to discriminate.
As they had hypothesized, the researchers found that the animals’ behavior
was influenced by their confidence in their performance. When the interval was
easy to judge, the animals were much quicker to switch to the other rule when
they found out they were wrong. When the interval was harder to judge, the
animals were less confident in their performance and applied the same rule a
few more times before switching.
“They know that they’re not confident, and they know that if they’re not
confident, it’s not necessarily the case that the rule has changed. They know
they might have made a mistake [in their interval judgment],” Jazayeri says.
Decision-making circuit
By recording neural activity in the frontal cortex just after each trial
was finished, the researchers were able to identify two regions that are key to
hierarchical decision-making. They found that both of these regions, known as
the anterior cingulate cortex (ACC) and dorsomedial frontal cortex (DMFC),
became active after the animals were informed about an incorrect response. When
the researchers analyzed the neural activity in relation to the animals’
behavior, it became clear that neurons in both areas signaled the animals’
belief about a possible rule switch. Notably, the activity related to animals’
belief was “louder” when animals made a mistake after an easy trial, and after
consecutive mistakes.
The researchers also found that while these areas showed similar patterns
of activity, it was activity in the ACC in particular that predicted when the
animal would switch rules, suggesting that ACC plays a central role in
switching decision strategies. Indeed, the researchers found that direct
manipulation of neural activity in ACC was sufficient to interfere with the
animals’ rational behavior.
“There exists a distributed circuit in the frontal cortex involving these
two areas, and they seem to be hierarchically organized, just like the task
would demand,” Jazayeri says.
Journal article:
https://science.sciencemag.org/content/364/6441/eaav8911
https://science.sciencemag.org/content/364/6441/eaav8911
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