Press Release 08-091
A Computer That Can 'Read'
Research team's work with brain scans and
computational modeling an important breakthrough in
understanding the brain and developing new computational
May 30, 2008
For centuries, the concept of mind readers was strictly the
domain of folklore and science fiction. But according to new
research published today in the journal Science,
scientists are closer to knowing how specific thoughts
activate our brains. The findings demonstrate the power of
computational modeling to improve our understanding of how the
brain processes information and thoughts.
The research was conducted by a computer scientist, Tom
Mitchell, and a cognitive neuroscientist, Marcel Just, both of
Carnegie Mellon University. Their previous research, supported
by the National Science Foundation (NSF) and the W.M. Keck
Foundation, had shown that functional magnetic resonance
imaging (fMRI) can detect and locate brain activity when a
person thinks about a specific word. Using this data, the
researchers developed a computational model that enabled a
computer to correctly determine what word a research subject
was thinking about by analyzing brain scan data.
In their most recent work, Just and Mitchell used fMRI data
to develop a more sophisticated computational model that can
predict the brain activation patterns associated with concrete
nouns, or things that we experience through our senses, even
if the computer did not already have the fMRI data for that
The researchers first built a model that took the fMRI
activation patterns for 60 concrete nouns broken down into 12
categories including animals, body parts, buildings, clothing,
insects, vehicles and vegetables. The model also analyzed a
text corpus, or a set of texts that contained more than a
trillion words, noting how each noun was used in relation to a
set of 25 verbs associated with sensory or motor functions.
Combining the brain scan information with the analysis of the
text corpus, the computer then predicted the brain activity
pattern of thousands of other concrete nouns.
In cases where the actual activation patterns were known,
the researchers found that the accuracy of the computer
model's predictions was significantly better than chance. The
computer can effectively predict what each participant's brain
activation patterns would look like when each thought about
these words, even without having seen the patterns associated
with those words in advance.
"We believe we have identified a number of the basic
building blocks that the brain uses to represent meaning,"
said Mitchell. "Coupled with computational methods that
capture the meaning of a word by how it is used in text files,
these building blocks can be assembled to predict neural
activation patterns for any concrete noun. And we have found
that these predictions are quite accurate for words where fMRI
data is available to test them."
Just said the computational model provides insight into the
nature of human thought. "We are fundamentally perceivers and
actors," he said. "So the brain represents the meaning of a
concrete noun in areas of the brain associated with how people
sense it or manipulate it. The meaning of an apple, for
instance, is represented in brain areas responsible for
tasting, for smelling, for chewing. An apple is what you do
with it. Our work is a small but important step in breaking
the brain's code."
In addition to representations in these sensory-motor areas
of the brain, the Carnegie Mellon researchers found
significant activation in other areas, including frontal areas
associated with planning functions and long-term memory. When
someone thinks of an apple, for instance, this might trigger
memories of the last time the person ate an apple, or initiate
thoughts about how to obtain an apple.
"This suggests a theory of meaning based on brain
function," Just added.
The work could eventually lead to the use of brain scans to
identify thoughts and could have applications in the study of
autism, disorders of thought such as paranoid schizophrenia,
and semantic dementias such as Pick's disease.
Officials at NSF say they are excited and intrigued by
these findings. "This has been an interesting project to
watch," said Kenneth Whang, a program officer at NSF who is
responsible for the grant to Mitchell and Just. "They started
with some fundamental ideas from machine learning about how to
get the most out of fMRI data, and now they've not only shown
the power of their computational approach, but also made
headway on one of the most important problems in the
understanding of language in the brain."
Whang believes that Mitchell and Just's research will
stimulate further research in the field of computational
neuroscience. "This opens up all sorts of new possibilities
for looking into the fine structure of how patterns of brain
activity relate to human thought processes."
Dana W. Cruikshank, NSF
(703) 292-8070 firstname.lastname@example.org
Spice, Carnegie Mellon University (412) 268-9068 email@example.com
Kenneth Whang, NSF
(703) 292-8930 firstname.lastname@example.org
More information about
the team's research.: /news/longurl.cfm?id=110
Mellon's Machine Learning Department: http://www.nsf.gov/cgi-bin/good-bye?http://www.ml.cmu.edu/
Mellon's Center for Cognative Brain Imaging: http://www.nsf.gov/cgi-bin/good-bye?http://coglab.psy.cmu.edu/
Division of Information & Intelligent Systems (IIS): http://www.nsf.gov/div/index.jsp?div=IIS
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