When choosing between options, we must associate their values with the action
needed to select them. We hypothesize that the brain solves this binding
problem through neural population subspaces. To test this hypothesis, we
examined neuronal responses in five reward-sensitive regions in macaques
performing a risky choice task with sequential offers. Surprisingly, in all
areas, the neural population encoded the values of offers presented on the left
and right in distinct subspaces. We show that the encoding we observe is
sufficient to bind the values of the offers to their respective positions in
space while preserving abstract value information, which may be important for
rapid learning and generalization to novel contexts. Moreover, after both
offers have been presented, all areas encode the value of the first and second
offers in orthogonal subspaces. In this case as well, the orthogonalization
provides binding. Our binding-by-subspace hypothesis makes two novel
predictions borne out by the data. First, behavioral errors should correlate
with putative spatial (but not temporal) misbinding in the neural
representation. Second, the specific representational geometry that we observe
across animals also indicates that behavioral errors should increase when
offers have low or high values, compared to when they have medium values, even
when controlling for value difference. Together, these results support the idea
that the brain makes use of semi-orthogonal subspaces to bind features
together.Comment: arXiv admin note: substantial text overlap with arXiv:2205.0676