Frontopolar cortex represents complex features and decision value during choice between environments

Important decisions often involve choosing between complex environments
that define future item encounters. Despite its importance for adaptive
behaviour and distinct computational challenges, decision-making research
primarily focuses on item choice ignoring environment choice altogether.
Here we contrast previously studied item choice in ventromedial prefrontal
cortex with lateral frontopolar cortex (FPl) linked to environment choice.
Furthermore, we propose a mechanism for how FPl decomposes and represents
complex environments during decision-making. Specifically, we trained a
choice-optimized, brain-naive convolutional neural network (CNN) and
compared predicted CNN activation with actual FPl activity. We showed the
high dimensional FPl activity decomposes environment features to represent
the complexity of an environment to make such choice possible. Moreover,
FPl functionally connects with posterior cingulate cortex for guiding
environment choice. Further probing FPl’s computation revealed a parallel
processing mechanism in extracting multiple environment features.