Distributed brain networks for anticipatory modulation of human response times

Abstract Simple sensory-motor behaviors that engage the central nervous system can provide a window onto key building blocks of cognition, such as via response times (RTs) that are sensitive to learned, task-dependent expectations. However, our understanding of the mechanisms that support these cognitive effects on behavior, particularly in the human brain, remains limited, in part because of a major disconnect between the simple algorithmic models often used to describe behavioral patterns and the complex neural dynamics associated with sensory-motor and cognitive information processing in the brain. Here we used quantitative behavioral analyses and intracranial recordings from patients with medically refractory epilepsy to identify specific, computationally defined components of expectation-dependent RT variability in the brain. Behaviorally derived models based on abstracted motor-preparatory processes provided useful, low-dimensional descriptions of the underlying neural computations. These descriptions did not have clear local neural representations, including in motor-preparatory regions, but instead were evident in certain emergent network-wide neural response properties that reflected task-relevant neural activity patterns with diverse timing, form, and anatomical locations. Our results illustrate the complex mapping between algorithmic and implementation-level descriptions of flexible neural information processing and identify specific, distributed networks the brain uses to combine expectations with sensory input to guide behavior.