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Awareness of task demands is often used during rehabilitation and sports training by providing instructions which appears to accelerate learning and improve performance through explicit motor learning. However, the effects of awareness of perturbations on the changes in estimates of hand position resulting from motor learning are not well understood. In this study, people adapted their reaches to a visuomotor rotation while either receiving instructions on the nature of the perturbation, experiencing a large rotation, or both to generate awareness of the perturbation and increase the contribution of explicit learning. We found that instructions and/or larger rotations allowed people to activate or deactivate part of the learned strategy at will and elicited explicit changes in open-loop reaches, while a small rotation without instructions did not. However, these differences in awareness, and even manipulations of awareness and perturbation size, did not appear to affect learning-induced changes in hand-localization estimates. This was true when estimates of the adapted hand location reflected changes in proprioception, produced when the hand was displaced by a robot, and also when hand location estimates were based on efferent-based predictions of self-generated hand movements. In other words, visuomotor adaptation led to significant shifts in predicted and perceived hand location that were not modulated by either instruction or perturbation size. Our results indicate that not all outcomes of motor learning benefit from an explicit awareness of the task. Particularly, proprioceptive recalibration and the updating of predicted sensory consequences appear to be largely implicit. (data: https://doi.org/10.17605/osf.io/mx5u2, preprint: https://doi.org/10.31234/osf.io/y53c2).

In motor learning, the slow development of implicit learning is traditionally taken for granted. While much is known about training performance during adaptation to a perturbation in reaches, saccades and locomotion, little is known about the time course of the underlying implicit processes during normal motor adaptation. Implicit learning is characterized by both changes in internal models and state estimates of limb position. Here, we measure both as reach aftereffects and shifts in hand localization in our participants, after every training trial. The observed implicit changes were near asymptote after only one to three perturbed training trials and were not predicted by a two-rate model’s slow process that is supposed to capture implicit learning. Hence, we show that implicit learning is much faster than conventionally believed, which has implications for rehabilitation and skills training.

Adapting reaches to altered visual feedback not only leads to motor changes, but also to shifts in perceived hand location; \"proprioceptive recalibration\". These changes are robust to many task variations and can occur quite rapidly. For instance, our previous study found both motor and sensory shifts arise in as few as 6 rotated-cursor training trials. The aim of this study is to investigate one of the training signals that contribute to these rapid sensory and motor changes. We do this by removing the visuomotor error signals associated with classic visuomotor rotation training; and provide only experience with a visual-proprioceptive discrepancy for training. While a force channel constrains reach direction 30o away from the target, the cursor representing the hand unerringly moves straight to the target. The resulting visual-proprioceptive discrepancy drives significant and rapid changes in no-cursor reaches and felt hand position, again within only 6 training trials. The extent of the sensory change is unexpectedly larger following the visual-proprioceptive discrepancy training. Not surprisingly the size of the reach aftereffects is substantially smaller than following classic visuomotor rotation training. However, the time course by which both changes emerge is similar in the two training types. These results suggest that even the mere exposure to a discrepancy between felt and seen hand location is a sufficient training signal to drive robust motor and sensory plasticity.

During motor adaptation the discrepancy between predicted and actually perceived sensory feedback is thought to be minimized, but it can be difficult to measure predictions of the sensory consequences of actions. Studies attempting to do so have found that self-directed, unseen hand position is mislocalized in the direction of altered visual feedback. However, our lab has shown that motor adaptation also leads to changes in perceptual estimates of hand position, even when the target hand is passively displaced. We attribute these changes to a recalibration of hand proprioception, since in the absence of a volitional movement, efferent or predictive signals are likely not involved. The goal here is to quantify the extent to which changes in hand localization reflect a change in the predicted sensory (visual) consequences or a change in the perceived (proprioceptive) consequences. We did this by comparing changes in localization produced when the hand movement was self-generated ('active localization') versus robot-generated ('passive localization') to the same locations following visuomotor adaptation to a rotated cursor. In this passive version, there should be no predicted consequences of these robot-generated hand movements. We found that although changes in localization were somewhat larger in active localization, the passive localization task also elicited substantial changes. Our results suggest that the change in hand localization following visuomotor adaptation may not be based entirely on updating predicted sensory consequences, but may largely reflect changes in our proprioceptive state estimate.

Sensorimotor learning is supported by at least two parallel systems: a strategic process that benefits from explicit knowledge and an implicit process that adapts subconsciously. How do these systems interact? Does one system’s contributions suppress the other, or do they operate independently? Here, we illustrate that during reaching, implicit and explicit systems both learn from visual target errors. This shared error leads to competition such that an increase in the explicit system’s response siphons away resources that are needed for implicit adaptation, thus reducing its learning. As a result, steady-state implicit learning can vary across experimental conditions, due to changes in strategy. Furthermore, strategies can mask changes in implicit learning properties, such as its error sensitivity. These ideas, however, become more complex in conditions where subjects adapt using multiple visual landmarks, a situation which introduces learning from sensory prediction errors in addition to target errors. These two types of implicit errors can oppose each other, leading to another type of competition. Thus, during sensorimotor adaptation, implicit and explicit learning systems compete for a common resource: error.

Our decisions are guided by the rewards we expect. These expectations are often based on incomplete knowledge and are thus subject to uncertainty. While the neurophysiology of expected rewards is well understood, less is known about the physiology of uncertainty. We hypothesize that uncertainty, or more specifically errors in judging uncertainty, are reflected in pupil dilation, a marker that has frequently been associated with decision making, but so far has remained largely elusive to quantitative models. To test this hypothesis, we measure pupil dilation while observers perform an auditory gambling task. This task dissociates two key decision variables - uncertainty and reward - and their errors from each other and from the act of the decision itself. We first demonstrate that the pupil does not signal expected reward or uncertainty per se, but instead signals surprise, that is, errors in judging uncertainty. While this general finding is independent of the precise quantification of these decision variables, we then analyze this effect with respect to a specific mathematical model of uncertainty and surprise, namely risk and risk prediction error. Using this quantification, we find that pupil dilation and risk prediction error are indeed highly correlated. Under the assumption of a tight link between noradrenaline (NA) and pupil size under constant illumination, our data may be interpreted as empirical evidence for the hypothesis that NA plays a similar role for uncertainty as dopamine does for reward, namely the encoding of error signals.

Being able to adapt our movements to changing circumstances allows people to maintain performance across a wide range of tasks throughout life, but it is unclear whether visuomotor learning abilities are fully developed in young children and, if so, whether they remain stable in the elderly. There is limited evidence of changes in motor adaptation ability throughout life, and the findings are inconsistent. Therefore, our goal was to compare visuomotor learning abilities throughout the lifespan. We used a shorter, gamified experimental task and collected data from participants in 5 age groups. Young children (M = 7 years), older children (M = 11 years), young adults (M = 20 years), adults (M = 40 years) and older adults (M = 67 years) adapted to a 45° visuomotor rotation in a centre-out reaching task. Across measures of rate of adaptation, extent of learning, rate of unlearning, generalization, and savings, we found that all groups performed similarly. That is, at least for short bouts of gamified learning, children and older adults perform just as well as young adults.

We investigate the subjective experience of space around the visual blind spot area, the cortical representation of which is missing feedforward connectivity from one eye. We performed these experiments as part of an adversarial collaboration to test contrasting theories of consciousness; Integrated Information Theory (IIT), Predictive Processing Active Inference (AI), and Predictive Processing Neurorepresentationalism (NREP) accounts. According to the Integrated Information Theory of consciousness, non-activatable retinotopic cortical regions, such as the blind spot region for the ipsilateral eye, create a different cause-effect structure and therefore should contribute differently to the perceived quality of space of activatable retinotopic regions. The two Predictive Processing accounts, in contrast, posit that internal models will accommodate structural deviations around the blind spot based on the available sensory evidence (particulars of this accommodation differ between the two accounts). We present a series of paradigms in which participants evaluate distances and areas that either include the blind spot or not (without stimulating it directly), as well as illusory motion that is either adjacent to the blind spot or not. We model psychometric functions relating perceived and objective space. These models vary in terms of bias and precision according to the experimental conditions (blind spot involved vs. not involved, ipsilateral vs. contralateral eye), making it possible to quantify the potential disruption of subjective spatial extendedness induced by the blind spot. We present simulated results for each experiment corresponding to the predictions of each account and conclude by discussing challenges and plans for dissemination.

An accurate estimate of limb position is necessary for movement planning, before and after motor learning. Where we localize our unseen hand after a reach depends on felt hand position, or proprioception, but in studies and theories on motor adaptation this is quite often neglected in favour of predicted sensory consequences based on efference copies of motor commands. Both sources of information should contribute, so here we set out to further investigate how much of hand localization depends on proprioception and how much on predicted sensory consequences. We use a training paradigm combining robot controlled hand movements with rotated visual feedback that eliminates the possibility to update predicted sensory consequences ('exposure training'), but still recalibrates proprioception, as well as a classic training paradigm with self-generated movements in another set of participants. After each kind of training we measure participants' hand location estimates based on both efference-based predictions and afferent proprioceptive signals with self-generated hand movements ('active localization') as well as based on proprioception only with robot-generated movements ('passive localization'). In the exposure training group, we find indistinguishable shifts in passive and active hand localization, but after classic training, active localization shifts more than passive, indicating a contribution from updated predicted sensory consequences. Both changes in open-loop reaches and hand localization are only slightly smaller after exposure training as compared to after classic training, confirming that proprioception plays a large role in estimating limb position and in planning movements, even after adaptation. (data: https://doi.org/10.17605/osf.io/zfdth, preprint: https://doi.org/10.1101/384941).

Training to reach with rotated visual feedback results in adaptation of hand movements, which persist when the perturbation is removed (reach aftereffects). Training also leads to changes in felt hand position, which we refer to as proprioceptive recalibration. The rate at which motor and proprioceptive changes develop throughout training is unknown. Here, we aim to determine the timescale of these changes in order to gain insight into the processes that may be involved in motor learning. Following six rotated reach training trials (30° rotation), at three radially located targets, we measured reach aftereffects and perceived hand position (proprioceptive guided reaches). Participants trained with opposing rotations one week apart to determine if the original training led to any retention or interference. Results suggest that both motor and proprioceptive recalibration occurred in as few as six rotated-cursor training trials (7.57° & 3.88° respectively), with no retention or interference present one week after training. Despite the rapid speed of both motor and sensory changes, these shifts do not saturate to the same degree. Thus, different processes may drive these changes and they may not constitute a single implicit process.