To make sense of our unpredictable world, humans use sensory information streaming
through billions of peripheral neurons. Uncertainty and ambiguity plague each sensory
stream, yet remarkably our perception of the world is seamless, robust and often
optimal in the sense of minimising perceptual variability. Moreover, humans have
a remarkable capacity for dexterous manipulation. Initiation of precise motor actions
under uncertainty requires awareness of not only the statistics of our environment but
also the reliability of our sensory and motor apparatus.
What happens when our sensory and motor systems are disrupted? Upper-limb amputees
tted with a state-of-the-art prostheses must learn to both control and make
sense of their robotic replacement limb. Tactile feedback is not a standard feature of
these open-loop limbs, fundamentally limiting the degree of rehabilitation. This thesis
introduces a modular closed-loop upper-limb prosthesis, a modified Touch Bionics ilimb
hand with a custom-built linear vibrotactile feedback array. To understand the utility of
the feedback system in the presence of multisensory and sensorimotor influences, three
fundamental open questions were addressed: (i) What are the mechanisms by which
subjects compute sensory uncertainty? (ii) Do subjects integrate an artificial modality
with visual feedback as a function of sensory uncertainty? (iii) What are the influences
of open-loop and closed-loop uncertainty on prosthesis control?
To optimally handle uncertainty in the environment people must acquire estimates of
the mean and uncertainty of sensory cues over time. A novel visual tracking experiment
was developed in order to explore the processes by which people acquire these statistical
estimators. Subjects were required to simultaneously report their evolving estimate of
the mean and uncertainty of visual stimuli over time. This revealed that subjects could
accumulate noisy evidence over the course of a trial to form an optimal continuous estimate
of the mean, hindered only by natural kinematic constraints. Although subjects
had explicit access to a measure of their continuous objective uncertainty, acquired from
sensory information available within a trial, this was limited by a conservative margin
for error.
In the Bayesian framework, sensory evidence (from multiple sensory cues) and prior
beliefs (knowledge of the statistics of sensory cues) are combined to form a posterior
estimate of the state of the world. Multiple studies have revealed that humans behave as
optimal Bayesian observers when making binary decisions in forced-choice tasks. In this
thesis these results were extended to a continuous spatial localisation task. Subjects
could rapidly accumulate evidence presented via vibrotactile feedback (an artificial
modality ), and integrate it with visual feedback. The weight attributed to each sensory
modality was chosen so as to minimise the overall objective uncertainty.
Since subjects were able to combine multiple sources of sensory information with respect
to their sensory uncertainties, it was hypothesised that vibrotactile feedback would benefit prosthesis wearers in the presence of either sensory or motor uncertainty. The
closed-loop prosthesis served as a novel manipulandum to examine the role of feed-forward
and feed-back mechanisms for prosthesis control, known to be required for
successful object manipulation in healthy humans. Subjects formed economical grasps
in idealised (noise-free) conditions and this was maintained even when visual, tactile
and both sources of feedback were removed. However, when uncertainty was introduced
into the hand controller, performance degraded significantly in the absence of visual or
tactile feedback. These results reveal the complementary nature of feed-forward and
feed-back processes in simulated prosthesis wearers, and highlight the importance of
tactile feedback for control of a prosthesis
