Finger Posture and Finger Load are Perceived Independently

Proprioceptive sensitivity to imposed finger deflections

Hand proprioception, the sense of the posture and movements of the wrist and digits, is critical to dexterous manual behavior and to stereognosis, the ability to sense the three-dimensional structure of objects held in the hand. To better understand this sensory modality and its role in hand function, we sought to characterize the acuity with which the postures and movements of finger joints are sensed. To this end, we measured the ability of human subjects to discriminate changes in posture and speed around the three joints of the index finger. In these experiments, we isolated the sensory component by imposing the postures on an otherwise still hand, to complement other studies in which subjects made judgments on actively achieved postures. We found that subjects could reliably sense 12-16% changes in joint angle and 18-32% changes in joint speed. Furthermore, the acuity for posture and speed was comparable across the three joints of the finger. Finally, task performance was unaffected by the presence of a vibratory stimulus, calling into question the role of cutaneous cues in hand proprioception.NEW & NOTEWORTHY Manual dexterity and stereognosis are supported by two exquisite sensory systems, namely touch and proprioception. Here, we measure the sensitivity of hand proprioception and show that humans can sense the posture and movements of the fingers with great accuracy. We also show that application of a skin vibration does not impair sensitivity, suggesting that proprioceptive acuity relies primarily on receptors in the muscles (and possibly tendons) rather than the skin.

Evolution of touch and proprioception of the limbs: Insights from fish and humans

The function of the hands is inextricably linked to cutaneous mechanosensation, both in touch and in how hand movement and posture (proprioception) are controlled. The structure and behavior of hands and distal forelimbs of other vertebrates have been evolutionarily shaped by these mechanosensory functions. The distal forelimb of tetrapod vertebrates is homologous to the pectoral fin rays and membrane of fishes. Fish fins demonstrate similar mechanosensory abilities to hands and other distal tetrapod forelimbs in touch and proprioception. These results indicate that vertebrates were using the core mechanosensory inputs, such as fast adapting and slow adapting nerve responses, to inform fin and limb function and behavior before their diversification in fish and tetrapod lineages.

Visual and somatosensory feedback mechanisms of precision manual motor control in autism spectrum disorder

BACKGROUND

Individuals with autism spectrum disorder (ASD) show deficits processing sensory feedback to reactively adjust ongoing motor behaviors. Atypical reliance on visual and somatosensory feedback each have been reported during motor behaviors in ASD suggesting that impairments are not specific to one sensory domain but may instead reflect a deficit in multisensory processing, resulting in reliance on unimodal feedback. The present study tested this hypothesis by examining motor behavior across different visual and somatosensory feedback conditions during a visually guided precision grip force test.

METHODS

Participants with ASD (N = 43) and age-matched typically developing (TD) controls (N = 23), ages 10-20 years, completed a test of precision gripping. They pressed on force transducers with their index finger and thumb while receiving visual feedback on a computer screen in the form of a horizontal bar that moved upwards with increased force. They were instructed to press so that the bar reached the level of a static target bar and then to hold their grip force as steadily as possible. Visual feedback was manipulated by changing the gain of the force bar. Somatosensory feedback was manipulated by applying 80 Hz tendon vibration at the wrist to disrupt the somatosensory percept. Force variability (standard deviation) and irregularity (sample entropy) were examined using multilevel linear models.

RESULTS

While TD controls showed increased force variability with the tendon vibration on compared to off, individuals with ASD showed similar levels of force variability across tendon vibration conditions. Individuals with ASD showed stronger age-associated reductions in force variability relative to controls across conditions. The ASD group also showed greater age-associated increases in force irregularity relative to controls, especially at higher gain levels and when the tendon vibrator was turned on.

CONCLUSIONS

Our findings that disrupting somatosensory feedback did not contribute to changes in force variability or regularity among individuals with ASD suggests a reduced ability to integrate somatosensory feedback information to guide ongoing precision manual motor behavior. We also document stronger age-associated gains in force control in ASD relative to TD suggesting delayed development of multisensory feedback control of motor behavior.

A Neural Network Model for Learning 3D Object Representations Through Haptic Exploration

Humans initially learn about objects through the sense of touch, in a process called “haptic exploration.” In this paper, we present a neural network model of this learning process. The model implements two key assumptions. The first is that haptic exploration can be thought of as a type of navigation, where the exploring hand plays the role of an autonomous agent, and the explored object is this agent's “local environment.” In this scheme, the agent's movements are registered in the coordinate system of the hand, through slip sensors on the palm and fingers. Our second assumption is that the learning process rests heavily on a simple model of sequence learning, where frequently-encountered sequences of hand movements are encoded declaratively, as “chunks.” The geometry of the object being explored places constraints on possible movement sequences: our proposal is that representations of possible, or frequently-attested sequences implicitly encode the shape of the explored object, along with its haptic affordances. We evaluate our model in two ways. We assess how much information about the hand's actual location is conveyed by its internal representations of movement sequences. We also assess how effective the model's representations are in a reinforcement learning task, where the agent must learn how to reach a given location on an explored object. Both metrics validate the basic claims of the model. We also show that the model learns better if objects are asymmetrical, or contain tactile landmarks, or if the navigating hand is articulated, which further constrains the movement sequences supported by the explored object.