Inferring Visuomotor Priors for Sensorimotor Learning

Sensorimotor learning has been shown to depend on both prior expectations and sensory evidence in a way that is consistent with Bayesian integration. Thus, prior beliefs play a key role during the learning process, especially when only ambiguous sensory information is available. Here we develop a novel technique to estimate the covariance structure of the prior over visuomotor transformations – the mapping between actual and visual location of the hand – during a learning task. Subjects performed reaching movements under multiple visuomotor transformations in which they received visual feedback of their hand position only at the end of the movement. After experiencing a particular transformation for one reach, subjects have insufficient information to determine the exact transformation, and so their second reach reflects a combination of their prior over visuomotor transformations and the sensory evidence from the first reach. We developed a Bayesian observer model in order to infer the covariance structure of the subjects' prior, which was found to give high probability to parameter settings consistent with visuomotor rotations. Therefore, although the set of visuomotor transformations experienced had little structure, the subjects had a strong tendency to interpret ambiguous sensory evidence as arising from rotation-like transformations. We then exposed the same subjects to a highly-structured set of visuomotor transformations, designed to be very different from the set of visuomotor rotations. During this exposure the prior was found to have changed significantly to have a covariance structure that no longer favored rotation-like transformations. In summary, we have developed a technique which can estimate the full covariance structure of a prior in a sensorimotor task and have shown that the prior over visuomotor transformations favor a rotation-like structure. Moreover, through experience of a novel task structure, participants can appropriately alter the covariance structure of their prior

Sensorimotor priors in non-stationary environments

In the course of its interaction with the world, the human nervous system must constantly estimate various variables in the surrounding environment. Past research indicates that environmental variables may be represented as probabilistic distributions of a priori information (priors). Priors for environmental variables that do not change much over time have been widely studied. Little is known however, about how priors develop in environments with non-stationary statistics. We examine whether humans change their reliance on the prior based on recent changes in environmental variance. Through experimentation, we obtain an online estimate of the human sensorimotor prior (prediction) and then compare it to similar online predictions made by various non-adaptive and adaptive models. Simulations show that models that rapidly adapt to non-stationary components in the environments predict the stimuli better than models that do not take the changing statistics of the environment into consideration. We found that adaptive models best predict participants' responses in most cases. However, we find no support for the idea that this is a consequence of increased reliance on recent experience just after the occurrence of a systematic change in the environment

Atypical biological kinematics are represented during observational practice

The present study investigated the effect of stimulus-response compatibility on the representation of atypical biological kinematics during observational practice. A compatible group observed an atypical model that moved rightward, whereas an incompatible group observed an atypical model that moved leftward. Both groups were instructed to observe the model with the intention to later reproduce the movement trajectory. This was examined in a posttest where participants were asked to move rightward with a kinematic profile that matched the atypical kinematics. Compared to a control group that did not engage in practice, and irrespective of whether the stimulus was observed in a spatially compatible or incompatible orientation, participants from both experimental groups reproduced velocity profiles that were comparable and similar to the atypical biological kinematics. Bayesian analysis indicated equality between the 2 experimental groups, thus suggesting comparable sensorimotor processing. Therefore, by rotating the incompatible stimulus by 180 degrees during observational practice, the current study has isolated the processing and representation of atypical biological kinematics to the underlying sensorimotor processes, rather than spatial encoding of peak velocity via processes associated with stimulus-response compatibility. (PsycINFO Database Recor

Exploring Disturbance as a Force for Good in Motor Learning

Disturbance forces facilitate motor learning, but theoretical explanations for this counterintuitive phenomenon are lacking. Smooth arm movements require predictions (inference) about the force-field associated with a workspace. The Free Energy Principle (FEP) suggests that such ‘active inference’ is driven by ‘surprise’. We used these insights to create a formal model that explains why disturbance might help learning. In two experiments, participants undertook a continuous tracking task where they learned how to move their arm in different directions through a novel 3D force field. We compared baseline performance before and after exposure to the novel field to quantify learning. In Experiment 1, the exposure phases (but not the baseline measures) were delivered under three different conditions: (i) robot haptic assistance; (ii) no guidance; (iii) robot haptic disturbance. The disturbance group showed the best learning as our model predicted. Experiment 2 further tested our FEP inspired model. Assistive and/or disturbance forces were applied as a function of performance (low surprise), and compared to a random error manipulation (high surprise). The random group showed the most improvement as predicted by the model. Thus, motor learning can be conceptualised as a process of entropy reduction. Short term motor strategies (e.g. global impedance) can mitigate unexpected perturbations, but continuous movements require active inference about external force-fields in order to create accurate internal models of the external world (motor learning). Our findings reconcile research on the relationship between noise, variability, and motor learning, and show that information is the currency of motor learning

Motor learning during reaching movements: model acquisition and recalibration

This thesis marks a departure from the traditional task-based distinction between sensorimotor adaptation and skill learning by focusing on the mechanisms that underlie adaptation and skill learning. I argue that adaptation is a recalibration of an existing control policy, whereas skill learning is the acquisition and subsequent automatization of a new control policy. A behavioral criterion to distinguish the two mechanisms is offered. The first empirical chapter contrasts learning in visuomotor rotations of 40° with learning left-right reversals during reaching movements. During left-right reversals, speed-accuracy trade-offs increased and offline gains emerged, whereas during visual rotations, speed-accuracy trade-offs remained constant and instead of offline gains, there was offline forgetting. I argue that these dissociations reflect differences in the underlying learning mechanisms: acquisition and recalibration. The second empirical chapter tests whether the dissociation based on time-accuracy trade-offs reveals a general property of recalibration or whether instead the interpretation is limited to the specific contrast between left-right reversals and visuomotor rotations. When the size of the prediction error– the difference between intended and perceived movement – was gradually increased participants switched from recalibration to control policy acquisition. This switching point can be derived by considering the role of internal models in recalibration: If the internal model that learns from errors and the environment are too dissimilar – e.g. in left-right reversal and large rotations– recalibration would cause the system to learn from errors in the wrong way, such that prediction errors would increase further. To address this problem the final empirical chapter explores if the way the system learns from errors can be reversed. In conclusion, the results provide behavioral criteria to differentiate between adaptation and skill learning. By exploring the boundaries of recalibration this thesis contributes to a more principled understanding of the mechanisms involved in adaptation and skill learning

Inferring visuomotor priors for sensorimotor learning.

Sensorimotor learning has been shown to depend on both prior expectations and sensory evidence in a way that is consistent with Bayesian integration. Thus, prior beliefs play a key role during the learning process, especially when only ambiguous sensory information is available. Here we develop a novel technique to estimate the covariance structure of the prior over visuomotor transformations--the mapping between actual and visual location of the hand--during a learning task. Subjects performed reaching movements under multiple visuomotor transformations in which they received visual feedback of their hand position only at the end of the movement. After experiencing a particular transformation for one reach, subjects have insufficient information to determine the exact transformation, and so their second reach reflects a combination of their prior over visuomotor transformations and the sensory evidence from the first reach. We developed a Bayesian observer model in order to infer the covariance structure of the subjects' prior, which was found to give high probability to parameter settings consistent with visuomotor rotations. Therefore, although the set of visuomotor transformations experienced had little structure, the subjects had a strong tendency to interpret ambiguous sensory evidence as arising from rotation-like transformations. We then exposed the same subjects to a highly-structured set of visuomotor transformations, designed to be very different from the set of visuomotor rotations. During this exposure the prior was found to have changed significantly to have a covariance structure that no longer favored rotation-like transformations. In summary, we have developed a technique which can estimate the full covariance structure of a prior in a sensorimotor task and have shown that the prior over visuomotor transformations favor a rotation-like structure. Moreover, through experience of a novel task structure, participants can appropriately alter the covariance structure of their prior

Sensorimotor learning and control in autism spectrum disorders: The role of sensorimotor integration.

The aim of the current thesis was to examine the role of sensorimotor integration during sensorimotor learning and control processes in autism spectrum disorders. Autistic participants were matched (IQ, age, gender) with control participants across three experimental chapters (chapters three-five) within the contexts of motor learning, imitation and observational practice. An additional control experiment (chapter two), which examined observational practice, was also completed in order to determine suitable data collection and analysis techniques. In Chapter Two it was confirmed that atypical biological kinematics properties are coded during observational practice via underlying sensorimotor processes, rather than spatial encoding of peak velocity via processes associated with stimulus- response compatibility. In Chapter Three it was observed that autistic participants can successfully form new internal action models, but their movements are characterised by increased variability in the spatial position of peak acceleration. In Chapter Four, it was shown that autism participants were able improve their imitation of atypical biological kinematics when presented in a fixed trial-order. Suggesting that in part imitation difficulties in autism may be related to differences in sensorimotor processing and integration. In Chapter Five it was observed that individuals with autism, like typically developed controls, can code atypical biological kinematics via observational practice. There are however potential differences in the processing of reafference when updating an existing internal action model. The findings of the current thesis will be summarised and critically evaluated with regards to the current literature. Theoretical implications will be considered, and potential future directions and research applications will be discussed

Sensorimotor Processes Underpinning Imitaiton Learning of Biological Motion

The aim of the present thesis was to examine the way in which biological motion is coded and imitated during imitation learning by improving upon methodologies currently used in the literature to examine imitation of underlying movement kinematics. Across four experiments, imitation of the kinematic structures of biological and non-biological motion models was examined to investigate the processes involved in imitation learning. The purpose of the first experimental chapter, Chapter Two, was to examine the way in which biological motion kinematics were coded during imitation learning by establishing whether imitation of biological motion kinematics was a function of lower-level visuomotor processing or top-down attentional modulation. Results showed that not only were imitations of typical and atypical biological motion different, but both models were imitated as accurately during spatially incompatible trials as compatible. Accurate imitation of spatially incompatible atypical biological motion confirmed biological motion coding is a function of lower-level visuomotor processing. Following results from Chapter Two, Chapters Three, Four and Five assumed lower-level visuomotor processing of biological motion and were designed to further examine whether this lower-level visuomotor processing of biological motion was modulated by top-down attentional factors (e.g. end-state-targets, visual attention, social primes). The first of these top-down modulations was included in Chapter Three, which examined the influence of end-state-targets on biological motion coding during imitation learning. Although kinematics was not modulated by end-state-targets, movement time was less accurate when end-state-targets were present, which suggests that lower-level and top-down processes operate together during the processing of visual information during imitation learning. In addition to end-state target modulation, imitation data further confirmed the coding of atypical biological motion by demonstrating differences in imitation of two relatively similar atypical biological motion models (atypical17 and atypical26). The top-down attentional factor examined In Chapter Four was visual attention, which was measured by recording eye movements during observation of the model stimuli. Analysis of eye movements demonstrated that visual attention was directed towards the model throughout the entirety of the observation phase during trials where end-state-targets were both present and absent. As goal-directed eye movements were not made during observation of the models, results suggest that the kinematic data contained within each of the models was observed and consequently featured in the representation formed for motor execution. Chapters Two, Three and Four provide a fundamental understanding of how biological motion is coded during imitation learning by using robust protocol that improves upon the validity of those used in the current literature and specific modulations that discredit significant top-down modulatory explanations for biological motion coding. The way in which biological motion coding occurs in neurotypicals (no neurologically atypical patterns of thought or behaviour) is important when trying to understand where deficiencies in those with intellectual disabilities occur. The intellectual disability most closely associated with the current thesis is autism, where deficiencies in imitation are suggested to be linked to social components. Therefore, to establish a foundational understanding of how social context influences neurotypical imitation, Chapter Five examined the influence of social primes on the coding of biological motion. Results showed that social primes modulated the accuracy of imitation, where peak velocity was more like those of the models following observation of an anti-social prime. In addition, observation of both the pro- and anti-social primes was shown to reduce the variability of imitation relative to observing no social prime at all. These findings demonstrate that social primes are being coded and incorporated into the motor output such that both the accuracy and consistency of imitation of biological motion are modulated. Together, the results presented in the current thesis demonstrate imitation of novel, atypical biological motion is a function of complimentary lower-level and top-down processes that facilitate the coding of both underlying kinematics and environmental context