Linking Neural Representations To Adaptive Behavior With Cognitive Modeling

Humans efficiently navigate complex learning and decision-making by forming representations of task-relevant information, a process facilitated by selective attention. Although the lateral prefrontal cortex (LPFC) and orbitofrontal cortex (OFC) are linked to attention and value-based decision-making, the neurophysiological processes that coordinate these regions in the maintenance of task-relevant representations remain unclear. To investigate this, we combined intracranial electrophysiology (iEEG) from OFC and LPFC of neurosurgical epilepsy patients with cognitive modeling of behavior, providing the spatiotemporal resolution to test local and circuit-level hypotheses about neural representations. Our findings reveal how shared computational strategies across brain regions and individuals enable the brain to maintain representations critical for adaptive decision-making. This approach offers a novel framework for measuring representational alignment at both neural and subject levels, uncovering the neurocomputational principles that drive real-world behavior. By integrating iEEG and cognitive modeling, we present an approach for studying representational alignment, revealing how it emerges both across brain regions, reflected in shared spectral and temporal features of neural state representations, and across individuals who adopt similar computational strategies to solve real-world decision-making tasks.