What a terrific idea.

Please do share your feedback and thoughts, and repost!

This is the hardest project I've worked on: extensive methods development, painstaking piloting, writing two grants, intensive data collection, and a LOT of thinking. It needed a huge dose of patience as we carried its burden over many years. A bit like Frodo carrying the ring.

Collectively, studying representations of two different task structures in the same subjects revealed generalizable principles by which lPFC tailors representations to different tasks.

The flat task showed local high-dim structure and orthogonality across clusters that were unrelated to the structure of the task. These may have been vestiges of an expressive, task-agnostic representation. Such a process has been observed in monkey lPFC www.biorxiv.org/content/10.1...

However, there were clues in the data suggesting lPFC may have started with a task-agnostic, high-dim representation with learning-driven dimensionality reduction helping reshape it to fit the task structure.

Therefore, at least in highly trained subjects, lPFC learned task-tailored representations that recapitulated the structure of the task, showing that lPFC representations are shaped by representation learning.

On the other hand, in the flat task, a global axis encoded the response-relevant, XOR categories abstractly. Category-specific local geometries were high-dimensional, retaining stimulus information that was not strictly required for readout.