Neuroscientists
identify a brain circuit that helps break decisions down into smaller pieces.
By Anne Trafton, MIT News Office
By Anne Trafton, MIT News Office
May 16, 2019 -- 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.
Daeyeol
Lee, a professor of neuroscience, psychology, and psychiatry at Yale School of
Medicine, says the study overcomes what has been a major obstacle in studying
this kind of decision-making, namely, a lack of animal models to study the
dynamics of brain activity at single-neuron resolution.
“Sarafyazd
and Jazayeri have developed an elegant decision-making task that required
animals to evaluate multiple types of evidence, and identified how the two
separate regions in the medial frontal cortex are critically involved in
handling different sources of errors in decision making,” says Lee, who was not
involved in the research. “This study is a tour de force in both rigor and
creativity, and peels off another layer of mystery about the prefrontal
cortex.”
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