Preferred auditory temporal processing regimes and auditory-motor synchronization

Decoding the rich temporal dynamics of complex sounds such as speech is constrained by the underlying neuronal-processing mechanisms. Oscillatory theories suggest the existence of one optimal perceptual performance regime at auditory stimulation rates in the delta to theta range (< 10 Hz), but reduced performance in the alpha range (10–14 Hz) is controversial. Additionally, the widely discussed motor system contribution to timing remains unclear. We measured rate discrimination thresholds between 4 and 15 Hz, and auditory-motor coupling strength was estimated through a behavioral auditory-motor synchronization task. In a Bayesian model comparison, high auditory-motor synchronizers showed a larger range of constant optimal temporal judgments than low synchronizers, with performance decreasing in the alpha range. This evidence for optimal processing in the theta range is consistent with preferred oscillatory regimes in auditory cortex that compartmentalize stimulus encoding and processing. The findings suggest, remarkably, that increased auditory-motor synchronization might extend such an optimal range towards faster rates

Speech-to-Speech synchronization protocol to classify human participants as high or low auditory-motor synchronizers

The ability to synchronize a motor action to a rhythmic auditory stimulus is often considered an innate human skill. However, some individuals lack the ability to synchronize speech to a perceived syllabic rate. Here, we describe a simple and fast protocol to classify a single native English speaker as being or not being a speech synchronizer. This protocol consists of four parts: the pretest instructions and volume adjustment, the training procedure, the execution of the main task, and data analysis

Prosthetic Avian Vocal Organ Controlled by a Freely Behaving Bird Based on a Low Dimensional Model of the Biomechanical Periphery

Because of the parallels found with human language production and acquisition, birdsong is an ideal animal model to study general mechanisms underlying complex, learned motor behavior. The rich and diverse vocalizations of songbirds emerge as a result of the interaction between a pattern generator in the brain and a highly nontrivial nonlinear periphery. Much of the complexity of this vocal behavior has been understood by studying the physics of the avian vocal organ, particularly the syrinx. A mathematical model describing the complex periphery as a nonlinear dynamical system leads to the conclusion that nontrivial behavior emerges even when the organ is commanded by simple motor instructions: smooth paths in a low dimensional parameter space. An analysis of the model provides insight into which parameters are responsible for generating a rich variety of diverse vocalizations, and what the physiological meaning of these parameters is. By recording the physiological motor instructions elicited by a spontaneously singing muted bird and computing the model on a Digital Signal Processor in real-time, we produce realistic synthetic vocalizations that replace the bird's own auditory feedback. In this way, we build a bio-prosthetic avian vocal organ driven by a freely behaving bird via its physiologically coded motor commands. Since it is based on a low-dimensional nonlinear mathematical model of the peripheral effector, the emulation of the motor behavior requires light computation, in such a way that our bio-prosthetic device can be implemented on a portable platform