A study conducted at Carnegie Mellon University
investigated the brain’s neural activity during
learned behavior and found that the brain makes
mistakes because it applies incorrect inner beliefs:
its neural signals are consistent with its inner
beliefs, but not with what is happening in the real
world.
“Our brains are constantly trying to predict how the
world works. We do this by building internal models
through experience and learning when we interact
with the world,” says Steven Chase, an assistant
professor in the Department of Biomedical
Engineering and the Center for the Neural Basis of
Cognition. “However, it has not yet been possible to
track how these internal models affect
instant-by-instant behavioral decisions.”
The researchers conducted an experiment using a
brain-machine interface, a device that allows the
brain to control a computer cursor using thought
alone. By studying the brain’s activity, the
researchers could see how the brain thinks an action
should be performed. The researchers report that the
majority of errors made were caused by a mismatch
between the subjects’ internal models and reality.
In addition, they found that internal models
realigned to better match reality during the course
of learning. “To our knowledge, this is the most
detailed representation of a brain’s inner beliefs
that has been identified to date,” says Byron Yu, an
associate professor in the Department of Electrical
and Computer Engineering and the Department of
Biomedical Engineering.
The results from this study have wide-reaching
applications. Notably, the results have the
potential to improve the performance and reliability
of current brain-machine interfaces that assist
paralyzed patients and amputees.
On a more fundamental level, the results can inform
our understanding of how the brain learns: for
example, how we acquire knowledge or develop new
skills.
Because the study allows for a better understanding
of why the brain makes mistakes, the results also
can be a powerful tool to improve how we learn to
perform new tasks. “For example, a doctor may be
trying to learn how to use a new robotic surgical
device,” explains Matthew Golub, postdoctoral fellow
in the Department of Electrical and Computer
Engineering. “If you can take a snapshot of how the
doctor thinks the device works, you can identify
mismatch in his or her internal model and more
efficiently train the doctor to use the device.”
The study, which was published on December 8, 2015
in eLife, was conducted as part of Carnegie Mellon’s
BrainHub, a university initiative that focuses on
how the structure and activity of the brain give
rise to complex behaviors. The team included Golub,
Yu, and Chase. Research funding was provided by The
National Institute of Child Health and Human
Development, the PA Department of Health Research,
and the National Science Foundation Integrative
Graduate Education and Research Traineeship (IGERT)
program.
See also
Researchers pinpoint epicenter of brain’s predictive
ability (2015-11-08)
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For more information
eLIFE
Internal models for interpreting neural population
activity during sensorimotor control
Matthew D Golub, Byron M Yu, Steven M
Chase
Link...
Department of Electrical and Computer Engineering at
Carnegie Mellon University
Link...
MDN |