Sensorimotor integration on a rapid time scale

Sensing is fundamental to the control of movement: From grasping objects to speech production, sensing guides action. So far, most of our knowledge about sensorimotor integration comes from visually guided reaching and oculomotor integration, in which the time course and trajectories of movements can be measured at a high temporal resolution. By contrast, production of vocalizations by humans and animals involves complex and variable actions, and each syllable often lasts a few hundreds of milliseconds, making it difficult to infer underlying neural processes. Here, we measured and modeled the transfer of sensory information into motor commands for vocal amplitude control in response to background noise, also known as the Lombard effect. We exploited the brief vocalizations of echolocating bats to trace the time course of the Lombard effect on a millisecond time scale. Empirical studies revealed that the Lombard effect features a response latency of a mere 30 ms and provided the foundation for the quantitative audiomotor model of the Lombard effect. We show that the Lombard effect operates by continuously integrating the sound pressure level of background noise through temporal summation to guide the extremely rapid vocal-motor adjustments. These findings can now be extended to models and measures of audiomotor integration in other animals, including humans.