Speaker
Description
Behavior is visible, but the internal processes that generate it are not. Inferring these hidden computations and states is a central challenge in systems and computational neuroscience. The same action can arise from different strategies, while other ongoing processes may not be expressed in the current choice. We addressed this challenge in a mouse foraging task with multiple computational solutions. By combining behavioral modeling, large-scale neural recordings, optogenetic perturbations, and facial videography, we tracked latent variables across behavior, neural activity, and movement. We found that mice used distinct computations over reward and failure history to decide when to leave a foraging site. Yet population activity in the secondary motor cortex (M2) simultaneously represented variables supporting both current and unused alternative strategies. The same variables could be decoded from subtle and reproducible facial movements, and partial M2 inactivation reduced the accuracy and delayed the emergence of this facial readout. Together, these findings show that otherwise hidden internal processes can be exposed by linking formal models to high-dimensional neural and behavioral signals. More broadly, they expand what can count as a readout of internal processes: what is hidden from choice may leave structured traces in neural activity and movement that we are only beginning to read.