Further, we show that these geometric measures are associated with trial-to-trial and average choice behavior. In particular, some suboptimal choices look like spatial (mixing up which value was on the left or right) misbinding errors! (10/n)

Finally, we show that a similar process unfolds over time: The current offer is represented in a distinct subspace from the remembered past offer – this time, the subspaces are actually orthogonal! (9/n)

Then, we show that the representational geometry in all but one of the regions we recorded from supports reliable binding; at the same time, the geometry can also support reliable generalization in every region. (8/n)

We develop a mathematical theory that captures the tradeoff between the reliability of binding and generalization as a function of the representational geometry – which we then relate back to subspace correlation. (7/n)

That they are not fully orthogonal means that the representation of value may be abstract: A decoder trained to decode the value of left offers could generalize to decode the value of right offers. This is important for learning and generalization to novel situations. (6/n)

That they are not the same means that this representation *binds* offer value to position, and a decoder can figure out which value corresponds to which offer. This wouldn’t be the case if both values were encoded in the same subspace! (5/n)

How does the monkey keep the different offer values straight? We show that the value of offers presented on the left is encoded in one subspace of population activity, while the value of offers presented on the right is in a distinct subspace. (3/n)

In this setup, monkeys chose between two sequentially presented offers based on their expected reward value. While they did this, neural activity was recorded from neurons in five different value-sensitive regions. (2/n)