The cerebellum acts as the analog to the medial temporal lobe for sensorimotor memory.

The cerebellum is critical for sensorimotor learning. The specific contribution that it makes, however, remains unclear. Inspired by the classic finding that, for declarative memories, medial temporal lobe structures provide a gateway to the formation of long-term memory but are not required for short-term memory, we hypothesized that, for sensorimotor memories, the cerebellum may play an analogous role. Here we studied the sensorimotor learning of individuals with severe ataxia from cerebellar degeneration. We dissected the memories they formed during sensorimotor learning into a short-term temporally-volatile component, that decays rapidly with a time constant of just 15-20sec and thus cannot lead to long-term retention, and a longer-term temporally-persistent component that is stable for 60 sec or more and leads to long-term retention. Remarkably, we find that these individuals display dramatically reduced levels of temporally-persistent sensorimotor memory, despite spared and even elevated levels of temporally-volatile sensorimotor memory. In particular, we find both impairment that systematically increases with memory window duration over shorter memory windows (<12 sec) and near-complete impairment of memory maintenance over longer memory windows (>25 sec). This dissociation uncovers a new role for the cerebellum as a gateway for the formation of long-term but not short-term sensorimotor memories, mirroring the role of the medial temporal lobe for declarative memories. It thus reveals the existence of distinct neural substrates for short-term and long-term sensorimotor memory, and it explains both newly-identified trial-to-trial differences and long-standing study-to-study differences in the effects of cerebellar damage on sensorimotor learning ability. Significance Statement: A key discovery about the neural underpinnings of memory, made more than half a century ago, is that long-term, but not short-term, memory formation depends on neural structures in the brain's medial temporal lobe (MTL). However, this dichotomy holds only for declarative memories - memories for explicit facts such as names and dates - as long-term procedural memories - memories for implicit knowledge such as sensorimotor skills - are largely unaffected even with substantial MTL damage. Here we demonstrate that the formation of long-term, but not short-term, sensorimotor memory depends on a neural structure known as the cerebellum, and we show that this finding explains the variability previously reported in the extent to which cerebellar damage affects sensorimotor learning.

Hierarchical motor adaptations negotiate failures during force field learning.

Humans have the amazing ability to learn the dynamics of the body and environment to develop motor skills. Traditional motor studies using arm reaching paradigms have viewed this ability as the process of 'internal model adaptation'. However, the behaviors have not been fully explored in the case when reaches fail to attain the intended target. Here we examined human reaching under two force fields types; one that induces failures (i.e., target errors), and the other that does not. Our results show the presence of a distinct failure-driven adaptation process that enables quick task success after failures, and before completion of internal model adaptation, but that can result in persistent changes to the undisturbed trajectory. These behaviors can be explained by considering a hierarchical interaction between internal model adaptation and the failure-driven adaptation of reach direction. Our findings suggest that movement failure is negotiated using hierarchical motor adaptations by humans.

Reliance on Haptic Assistance Reflected in Haptic Cue Weighting.

When using an automated system, user trust in the automation is an important factor influencing performance. Prior studies have analyzed trust duringsupervisory control of automation, and how trust influences reliance: the behavioral correlate of trust. Here, we investigated how reliance on haptic assistance affects performance during shared control with an automated system. Subjects made reaches towards a hidden target using a visual cue and haptic cue (assistance from the automation). We sought to influence reliance by changing the variability of trial-by-trial random errors in the haptic assistance. Reliance was quantified in terms of the subject's position at the end of the reach relative to the two cues. Our results show that subjects aimed more towards the visual cue when the variability of the haptic cue errors increased, resembling cue weighting behavior. Similar behavior was observed both when subjects had explicit knowledge about the haptic cue error variability, as well as when they had only implicit knowledge (from experience). However, the group with explicit knowledge was able to more quickly adapt their reliance on the haptic assistance. The method we introduce here provides a quantitative way to study user reliance on the information provided by automated systems with shared control.

Trust in haptic assistance: weighting visual and haptic cues based on error history.

To effectively interpret and interact with the world, humans weight redundant estimates from different sensory cues to form one coherent, integrated estimate. Recent advancements in physical assistance systems, where guiding forces are computed by an intelligent agent, enable the presentation of augmented cues. It is unknown, however, if cue weighting can be extended to augmented cues. Previous research has shown that cue weighting is determined by the reliability (inversely related to uncertainty) of cues within a trial, yet augmented cues may also be affected by errors that vary over trials. In this study, we investigate whether people can learn to appropriately weight a haptic cue from an intelligent assistance system based on its error history. Subjects held a haptic device and reached to a hidden target using a visual (Gaussian distributed dots) and haptic (force channel) cue. The error of the augmented haptic cue varied from trial to trial based on a Gaussian distribution. Subjects learned to estimate the target location by weighting the visual and augmented haptic cues based on their perceptual uncertainty and experienced errors. With both cues available, subjects were able to find the target with an improved or equal performance compared to what was possible with one cue alone. Our results show that the brain can learn to reweight augmented cues from intelligent agents, akin to previous observations of the reweighting of naturally occurring cues. In addition, these results suggest that the weighting of a cue is not only affected by its within-trial reliability but also the history of errors.

Movement Strategy Discovery during Training via Haptic Guidance.

Haptic guidance has previously been investigated to facilitate motor skill training, whereupon a robotic device assists a trainee in executing the desired movement. However, many studies have reported a null or even detrimental effect of haptic guidance on training compared to unassisted practice. While prior studies have focused on using haptic guidance to refine a movement strategy, our study explores its effect on the discovery of a new strategy. Subjects learned to manipulate a virtual under-actuated system via a haptic device either with or without haptic guidance (and without haptic feedback of system dynamics). The guidance enabled subjects to experience a range of successful movements, rather than strictly enforcing one trajectory. Subjects who trained with guidance adopted a strategy that involved faster reaches, required greater control of the system's degrees of freedom, and increased the potential for faster task completion. However, overall improvement of task performance was limited with the new strategy.

Grip force control during virtual object interaction: effect of force feedback,accuracy demands, and training.

When grasping and manipulating objects, people are able to efficiently modulate their grip force according to the experienced load force. Effective grip force control involves providing enough grip force to prevent the object from slipping, while avoiding excessive force to avoid damage and fatigue. During indirect object manipulation via teleoperation systems or in virtual environments, users often receive limited somatosensory feedback about objects with which they interact. This study examines the effects of force feedback, accuracy demands, and training on grip force control during object interaction in a virtual environment. The task required subjects to grasp and move a virtual object while tracking a target. When force feedback was not provided, subjects failed to couple grip and load force, a capability fundamental to direct object interaction. Subjects also exerted larger grip force without force feedback and when accuracy demands of the tracking task were high. In addition, the presence or absence of force feedback during training affected subsequent performance, even when the feedback condition was switched. Subjects' grip force control remained reminiscent of their employed grip during the initial training. These results motivate the use of force feedback during telemanipulation and highlight the effect of force feedback during training.

Effect of age on stiffness modulation during postural maintenance of the arm.

The ability to modify the mechanical impedance of our limbs allows us to perform a variety of motor control tasks while interacting with the environment in a stable manner. Prior work has shown that young, healthy people are capable of modulating arm stiffness via selective muscle co-contraction to account for external disturbances in various directions. Increased age detrimentally affects control of movement and stability, although the neural mechanisms underlying these deficits are not entirely understood. In this study, younger and older subjects performed a static postural maintenance task with two types of directional force perturbations. Older individuals showed significantly less stiffness modification between the two perturbation conditions compared to the younger individuals, indicating less optimal modulation of arm impedance. This impairment should be considered during motor control evaluation in older populations, whether it be activities of daily living or skill assessment.

Cerebellar ataxia impairs modulation of arm stiffness during postural maintenance.

Impedance control enables humans to effectively interact with their environment during postural and movement tasks, adjusting the mechanical behavior of their limbs to account for instability. Previous work has shown that people are able to selectively modulate the end-point stiffness of their arms, adjusting for varying directions of environmental disturbances. Behavioral studies also suggest that separate controllers are used for impedance modulation versus joint torque coordination. Here we tested whether people with cerebellar damage have deficits in impedance control. It is known that these individuals have poor motor coordination, which has typically been attributed to deficits in joint torque control. Subjects performed a static postural maintenance task with two different types of directional force perturbations. On average, patients with cerebellar ataxia modified stiffness differentially for the two perturbation conditions, although significantly less than age-matched control subjects. Thus cerebellar damage may impair the ability to modulate arm impedance. Surprisingly, the patients' intact ability to generally alter their limb stiffness during the postural task (albeit less than age-matched control subjects) improved their movement performance in a subsequent tracing task. The transfer of stiffness control from the static to the movement task may be a strategy that can be used by patients to compensate for their motor deficits.

Cerebellar motor learning: are environment dynamics more important than error size?

Cerebellar damage impairs the control of complex dynamics during reaching movements. It also impairs learning of predictable dynamic perturbations through an error-based process. Prior work suggests that there are distinct neural mechanisms involved in error-based learning that depend on the size of error experienced. This is based, in part, on the observation that people with cerebellar degeneration may have an intact ability to learn from small errors. Here we studied the relative effect of specific dynamic perturbations and error size on motor learning of a reaching movement in patients with cerebellar damage. We also studied generalization of learning within different coordinate systems (hand vs. joint space). Contrary to our expectation, we found that error size did not alter cerebellar patients' ability to learn the force field. Instead, the direction of the force field affected patients' ability to learn, regardless of whether the force perturbations were introduced gradually (small error) or abruptly (large error). Patients performed best in fields that helped them compensate for movement dynamics associated with reaching. However, they showed much more limited generalization patterns than control subjects, indicating that patients rely on a different learning mechanism. We suggest that patients typically use a compensatory strategy to counteract movement dynamics. They may learn to relax this compensatory strategy when the external perturbation is favorable to counteracting their movement dynamics, and improve reaching performance. Altogether, these findings show that dynamics affect learning in cerebellar patients more than error size.

Gradual anisometric-isometric transition for human-machine interfaces.

Human-machine interfaces (HMIs) are widely used in biomedical applications, from teleoperated surgical systems to rehabilitation devices. This paper investigates a method of control that allows an HMI to transition from anisometric to isometric mode, shifting the control input from position to force as the user's movement is gradually reduced. Two different approaches for achieving this transition are discussed: one is based on the natural system dynamics, whereas the other involves selecting and controlling dynamics. The two approaches were implemented on a custom haptic device in a targeting task. Anisometric to isometric transitioning can potentially be used for training purposes, enabling transfer of what was learned in one mode to the other, as well as novel studies of the human sensorimotor system.

Dark-rearing-induced reduction of GABA and GAD and prevention of the effect by BDNF in the mouse retina.

Gamma-aminobutyric acid (GABA) is an important retinal neurotransmitter. We studied the expression of GABA, glutamate decarboxylase 65 (GAD65) and GAD67 by immunocytochemistry and Western blot, in the retinas of control and dark-reared C57BL/6J black mice. This study asked three questions. First, is visual input necessary for the normal expression of GABA, GAD65 and GAD67? Second, can the retina recover from the effects of dark-rearing if returned to a normal light-dark cycle? Third, does BDNF prevent the influence of dark-rearing on the expression of GABA and GAD? At postnatal day 10 (P10), before eye opening, GABA immunoreactivity was present in the ganglion cell layer (GCL), in the innermost rows of the inner nuclear layer (INL) and throughout the inner plexiform layer (IPL) of control and dark-reared retinas. In P30 control retinas, GABA immunoreactivity showed similar patterns to those at P10. However, in P30 dark-reared retinas, the density of GABA-immunoreactive cells was lower in both the INL and GCL than in control retinas. In addition, visual deprivation retarded GABA immunoreactivity in the IPL. Western blot analysis showed corresponding differences in the levels of GAD65 but not of GAD67 expression between control and dark-rearing conditions. In our study, dark-rearing effects were reversed when the mice were put in normal cyclic light-dark conditions for 2 weeks. Moreover, dark-reared retinas treated with BDNF showed normal expression of both GABA and GAD65. Our data indicate that normal expression of GABA and GAD65 is dependent on visual input. Furthermore, the data suggest that BDNF controls this dependence.