When choosing between options, we must solve an important binding problem.
The values of the options must be associated with other information, including
the action needed to select them. We hypothesized that the brain solves this
binding problem through use of distinct population subspaces. We examined
responses of single neurons in five value-sensitive regions in rhesus macaques
performing a risky choice task. In all areas, neurons encoded the values of
both possible options, but used semi-orthogonal coding subspaces associated
with left and right options, which served to link options to their positions in
space. We also observed a covariation between subspace orthogonalization and
behavior: trials with less orthogonalized subspaces were associated with
greater likelihood of choosing the less valued option. These semi-orthogonal
subspaces arose from a combination of linear and non-linear mixed selective
neurons. By decomposing the neural geometry, we show this combination of
selectivity achieves a code that balances binding/separation and
generalization. These results support the hypothesis that binding operations
serve to convert high-dimensional codes to multiple low-dimensional neural
subspaces to flexibly solve decision problems.Comment: 45 pages, 4 figure