Assessing Ecologically Valid Methods of Auditory Feedback Measurement in Individuals With Typical Speech

Purpose: Auditory feedback is thought to contribute to the online control of speech production. Yet, the standard method of estimating auditory feedback control (i.e., reflexive responses to auditory–motor perturbations), although sound, requires specialized instrumentation, meticulous calibration, unnatural tasks, and specific acoustic environments. The purpose of this study was to explore more ecologically valid features of speech production to determine their relationships with auditory feedback mechanisms. Method: Two previously proposed measures of within-utterance variability (centering and baseline variability) were compared with reflexive response magnitudes in 30 adults with typical speech. These three measures were estimated for both the laryngeal and articulatory subsystems of speech. Results: Regardless of the speech subsystem, neither centering nor baseline variability was shown to be related to reflexive response magnitudes. Likewise, no relationships were found between centering and baseline variability. Conclusions: Despite previous suggestions that centering and baseline variability may be related to auditory feedback mechanisms, this study did not support these assertions. However, the detection of such relationships may have required a larger degree of variability in responses, relative to that found in those with typical speech. Future research on these relationships is warranted in populations with more heterogeneous responses, such as children or clinical populations. Supplemental Material https://doi.org/10.23641/asha.17330546

Integration of proprioceptive and visual feedback during online control of reaching

Visual and proprioceptive feedback both contribute to perceptual decisions, but it remains unknown how these feedback signals are integrated together or consider factors such as delays and variance during online control. We investigated this question by having participants reach to a target with randomly applied mechanical and/or visual disturbances. We observed that the presence of visual feedback during a mechanical disturbance did not increase the size of the muscle response significantly but did decrease variance, consistent with a dynamic Bayesian integration model. In a control experiment we verified that vision had a potent influence when mechanical and visual disturbances were both present but opposite in sign. These results highlight a complex process for multi-sensory integration, where visual feedback has a relatively modest influence when the limb is mechanically disturbed, but a substantial influence when visual feedback becomes misaligned with the limb.

Constrained stochastic optimal control with learned importance sampling: A path integral approach

Modern robotic systems are expected to operate robustly in partially unknown environments. This article proposes an algorithm capable of controlling a wide range of high-dimensional robotic systems in such challenging scenarios. Our method is based on the path integral formulation of stochastic optimal control, which we extend with constraint-handling capabilities. Under our control law, the optimal input is inferred from a set of stochastic rollouts of the system dynamics. These rollouts are simulated by a physics engine, placing minimal restrictions on the types of systems and environments that can be modeled. Although sampling-based algorithms are typically not suitable for online control, we demonstrate in this work how importance sampling and constraints can be used to effectively curb the sampling complexity and enable real-time control applications. Furthermore, the path integral framework provides a natural way of incorporating existing control architectures as ancillary controllers for shaping the sampling distribution. Our results reveal that even in cases where the ancillary controller would fail, our stochastic control algorithm provides an additional safety and robustness layer. Moreover, in the absence of an existing ancillary controller, our method can be used to train a parametrized importance sampling policy using data from the stochastic rollouts. The algorithm may thereby bootstrap itself by learning an importance sampling policy offline and then refining it to unseen environments during online control. We validate our results on three robotic systems, including hardware experiments on a quadrupedal robot.

Measuring fingerpad deformation during active object manipulation

During active object manipulation, the finger-object interactions give rise to complex fingertip skin deformations. These deformations are in turn encoded by the local tactile afferents and provide rich and behaviorally relevant information to the central nervous system. Most of the work studying the mechanical response of the finger to dynamic loading has been performed under a passive setup, thereby precisely controlling the kinematics or the dynamics of the loading. However, to identify aspects of the deformations that are relevant to online control during object manipulation, it is desirable to measure the skin response in an active setup. To that end, we developed a device that allows us to monitor finger forces, skin deformations, and kinematics during fine manipulation. We describe the device in detail and test it to precisely describe how the fingertip skin in contact with the object deforms during a simple vertical oscillation task. We show that the level of grip force directly influences the fingerpad skin strains and that the strain rates are substantial during active manipulation (norm up to 100%/s). The developed setup will enable us to causally relate sensory information, i.e. skin deformation, to online control, i.e. grip force adjustment, in future studies.