Predicting the conscious experience of other people

There has been considerable interest recently in using multivariate decoding
techniques applied to functional MRI signals in order to decode the
contents of a person’s consciousness. The use of such signals has inherent
disadvantages due to the delay of the hemodynamic response. Moreover to
date it has not been shown possible to generalize the decoding of brain signals
from one individual to another. This limits the potential utility of such
approaches. Here we used a different approach that circumvented these
difficulties by using magnetoencephalographic (MEG) signals to decode the
contents of consciousness, and to test whether such correlates generalized
reliably across individuals. We used a 274 channel MEG system to record
signals from 8 healthy participants while they viewed an intermittently presented
binocular rivalry stimulus consisting of a face and a grating. Using
a leave-one-out cross-validation procedure, we trained support vector
machines on the MEG signals to decode the rivalry percept. Decoding was
significantly better than chance in all participants. We then tested whether a
support vector machine trained on MEG signals from one participant could
successfully decode the rivalry percept of another. Again, decoding accuracy
was significantly better than chance. These findings demonstrate that
it is possible to decode perception independently of physical stimulation
using MEG signals in near real time in a way that generalizes across individuals.
Our findings indicate that there may be universal neural correlates
of consciousness, and mark a potentially important step in the design of
devices for decoding the contents of consciousness in individuals unable to
report their experience behaviorally.