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Contains fulltext : 307382.pdf (Publisher’s version ) (Closed access), Alzheimer's disease (AD) is characterized by an initial decline in declarative memory, while nondeclarative memory processing remains relatively intact. Error-based motor adaptation is traditionally seen as a form of nondeclarative memory, but recent findings suggest that it involves both fast, declarative, and slow, nondeclarative adaptive processes. If the declarative memory system shares resources with the fast process in motor adaptation, it can be hypothesized that the fast, but not the slow, process is disturbed in AD patients. To test this, we studied 20 early-stage AD patients and 21 age-matched controls of both sexes using a reach adaptation paradigm that relies on spontaneous recovery after sequential exposure to opposing force fields. Adaptation was measured using error clamps and expressed as an adaptation index (AI). Although patients with AD showed slightly lower adaptation to the force field than the controls, both groups demonstrated effects of spontaneous recovery. The time course of the AI was fitted by a hierarchical Bayesian two-state model in which each dynamic state is characterized by a retention and learning rate. Compared to controls, the retention rate of the fast process was the only parameter that was significantly different (lower) in the AD patients, confirming that the memory of the declarative, fast process is disturbed by AD. The slow adaptive process was virtually unaffected. Since the slow process learns only weakly from an error, our results provide neurocomputational evidence for the clinical practice of errorless learning of everyday tasks in people with dementia., 13 p.

Radboud University, 23 juni 2023, Promotor : Medendorp, W.P. Co-promotores : Oostwoud Wijdenes, L., Beers, R.J. van, Contains fulltext : 292969.pdf (Publisher’s version ) (Closed access), A simple movement, like pushing the door open, requires complex control. Movement control entails a cascade of processes from movement planning to motor learning, and dealing with noise arising at different levels. This thesis sought to unravel how variability and motor learning shape reaching movements in health and disease. To this aim we studied whether there is a tradeoff between movement planning time and movement precision. We observed a sizeable reduction of initial movement variability with increasing movement preparation time. Furthermore, we tested the hypothesis that repeating a well-practiced movement leads to a reduction of movement variability. Our findings show that, with repetition, movements are not only initiated earlier but their variability is reduced across the entire movement trajectory. Finally, we investigated how an impairment of declarative memory affects the dynamics of motor learning. We measured Alzheimer’s disease patients and healthy age-matched controls in a reaching task with force-field learning. The time course of the motor learning was modeled by fitting a hierarchical Bayesian two-state model. Our results demonstrate that impairment of declarative memory leads to lower overall motor learning and pinpoints that this is due to faster forgetting of the fast motor learning process. The results of the present thesis contribute to the understanding of reach planning, control, and adaptation in health and disease. There are numerous follow-up questions that require further study. These studies should not only address the neurocomputational correlates, but should also come up with paradigms that can translate the findings to rehabilitation practice and other applications.

Contains fulltext : 296667.pdf (Publisher’s version ) (Closed access)

Item does not contain fulltext, Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Although it seems straightforward, this simple representation belies the complex link between an activation in a somatotopic map and the associated touch location on the body. Any isolated activation is spatially ambiguous without a neural decoder that can read its position within the entire map, but how this is computed by neural networks is unknown. We propose that the somatosensory system implements multilateration, a common computation used by surveying and global positioning systems to localize objects. Specifically, to decode touch location on the body, multilateration estimates the relative distance between the afferent input and the boundaries of a body part (e.g., the joints of a limb). We show that a simple feedforward neural network, which captures several fundamental receptive field properties of cortical somatosensory neurons, can implement a Bayes-optimal multilateral computation. Simulations demonstrated that this decoder produced a pattern of localization variability between two boundaries that was unique to multilateration. Finally, we identify this computational signature of multilateration in actual psychophysical experiments, suggesting that it is a candidate computational mechanism underlying tactile localization.

Item does not contain fulltext, Professional golf players spend years practicing, but will still perform one or two practice swings without a ball before executing the actual swing. Why do they do this? In this study we tested the hypothesis that repeating a well-practiced movement leads to a reduction of movement variability. To operationalize this hypothesis, participants were tested in a center-out reaching task with four different targets, on four different days. To probe the effect of repetition they performed random sequences from one to six movements to the same target. Our findings show that, with repetition, movements are not only initiated earlier but their variability is reduced across the entire movement trajectory. Furthermore, this effect is present within and across the four sessions. Together, our results suggest that movement repetition changes the tradeoff between movement initiation and movement precision.

Item does not contain fulltext, We previously proposed a Bayesian model of multisensory integration in spatial orientation (1). Using a Gaussian prior, centered on an upright head orientation, this model could explain various perceptual observations in roll-tilted participants, such as the subjective visual vertical, the subjective body tilt (1), the rod-and-frame effect (2), as well as their clinical (3) and age-related deficits (4). Because it is generally assumed that the prior reflects an accumulated history of previous head orientations, and recent work on natural head motion suggests non-Gaussian statistics, we examined how the model would perform with a non-Gaussian prior. In the present study, we first experimentally generalized the previous observations in showing that also the natural statistics of head orientation are characterized by long tails, best quantified as a t-location-scale distribution. Next, we compared the performance of the Bayesian model and various model variants using such a t-distributed prior to the original model with the Gaussian prior on their accounts of previously published data of the subjective visual vertical and subjective body tilt tasks. All of these variants performed substantially worse than the original model, suggesting a special value of the Gaussian prior. We provide computational and neurophysiological reasons for the implementation of such a prior, in terms of its associated precision-accuracy trade-off in vertical perception across the tilt range.

Contains fulltext : 284042.pdf (Publisher’s version ) (Closed access), The brain's computations for active and passive self-motion estimation can be unified with a single model that optimally combines vestibular and visual signals with sensory predictions based on efference copies. It is unknown whether this theoretical framework also applies to the integration of artificial motor signals, like those that occur when driving a car, or whether self-motion estimation in this situation relies on sole feedback control. Here, we examined if training humans to control a self-motion platform leads to the construction of an accurate internal model of the mapping between the steering movement and the vestibular reafference. Participants (n=15) sat on a linear motion platform and actively controlled the platform's velocity using a steering wheel to translate their body to a memorized visual target (Motion condition). We compared their steering behavior to that of participants (n=15) who remained stationary and instead aligned a non-visible line with the target (Stationary condition). To probe learning, the gain between the steering wheel angle and the platform or line velocity changed abruptly twice during the experiment. These gain changes were virtually undetectable in the displacement error in the Motion condition, whereas clear deviations were observed in the Stationary condition, showing that participants in the Motion condition made within-trial changes to their steering behavior. We conclude that vestibular feedback allows not only the online control of steering, but also a rapid adaptation to the gain changes in order to update the brain's internal model of the mapping between the steering movement and the vestibular reafference.

Contains fulltext : 234095.pdf (Publisher’s version ) (Closed access), Faster movements are typically more variable - a speed-accuracy tradeoff known as Fitts' law. Are movements that are initiated faster also more variable? Neurophysiological work has associated larger neural variability during motor preparation with longer reaction time (RT) and larger movement variability, implying that movement variability decreases with increasing RT. Here, we recorded over 30000 reaching movements in eleven human participants who moved to visually-cued targets. Half of the visual cues was accompanied by a beep to evoke a wide RT range in each participant. Results show that initial reach variability decreases with increasing RT, for voluntarily produced RTs up to ~300 ms, while other kinematic aspects and endpoint accuracy remained unaffected. We conclude that movement preparation time determines initial movement variability. We suggest that the chosen movement preparation time reflects a trade-off between movement initiation and precision.

Contains fulltext : 236031.pdf (Publisher’s version ) (Closed access), It has been suggested that sensorimotor adaptation involves at least two processes (i.e., fast and slow) that differ in retention and error sensitivity. Previous work has shown that repeated exposure to an abrupt force field perturbation results in greater error sensitivity for both the fast and slow processes. While this implies that the faster relearning is associated with increased error sensitivity, it remains unclear what aspects of prior experience modulate error sensitivity. In the present study, we manipulated the initial training using different perturbation schedules, thought to differentially affect fast and slow learning processes based on error magnitude, and then observed what effect prior learning had on subsequent adaptation. During initial training of a visuomotor rotation task, we exposed three groups of participants to either an abrupt, a gradual, or a random perturbation schedule. During a testing session, all three groups were subsequently exposed to an abrupt perturbation schedule. Comparing the two sessions of the control group who experienced repetition of the same perturbation, we found an increased error sensitivity for both processes. We found that the error sensitivity was increased for both the fast and slow processes, with no reliable changes in the retention, for both the gradual and structural learning groups when compared to the first session of the control group. We discuss the findings in the context of how fast and slow learning processes respond to a history of errors.

Contains fulltext : 226706.pdf (publisher's version ) (Open Access), People have a good intuition of how to move a computer mouse to place a cursor at a desired position on a screen. This is surprising because the hand and the mouse are at different locations and they generally move in different directions and over different distances. But using a computer mouse is not always intuitive: try positioning a cursor after turning the mouse by 90° in your hand. To examine when using a computer mouse is intuitive, we asked participants to move a cursor to targets on a screen by moving a mouse along a surface. We varied the orientation of this surface in space and that of the mouse in the hand. Participants performed best when the mapping between hand and cursor motion was close to what we are accustomed to, either in space or relative to the forearm.

Item does not contain fulltext, Humans quickly and sophisticatedly correct their movements in response to changes in the world, such as when reaching to a target that abruptly changes its location. The vigor of these movement corrections is time-dependent, increasing if the time left to make the correction decreases, which can be explained by Optimal feedback control (OFC) theory as an increase of optimal feedback gains. It is unknown if corrections for changes in the world are as sophisticated under full-body motion. For successful visually-probed motor corrections during full-body motion not only the motion of the hand relative to the body needs to be taken into account, also the motion of the hand in the world should be considered, because their relative positions are changing. Here, in two experiments, we show that visuomotor feedback corrections in response to target jumps are more vigorous for faster passive full-body translational acceleration than for slower acceleration, suggesting that vestibular information modulates visuomotor feedback gains. Interestingly, these corrections do not demonstrate the time-dependent characteristics that body-stationary visuomotor feedback gains typically show, such that an Optimal feedback control model fell short to explain them. We further show that the vigor of corrections generally decreased over the course of trials within the experiment, suggesting that the sensorimotor system adjusted its gains when learning to integrate the vestibular input into hand motor control.

Contains fulltext : 213858.pdf (publisher's version ) (Open Access), Many daily life situations (e.g. dodging an approaching object or hitting a moving target) require people to correct planning of future movements based on previous temporal errors. However, the actual temporal error can be difficult to perceive: imagine a baseball batter that swings and misses a fastball. Here we show that in such situations people can use an internal error signal to make corrections in the next trial. This signal is based on the discrepancy between the actual and the planned action onset time: the prediction error. In this study, we used three interception tasks: reaching movements, saccadic eye movements and a button press that released a cursor moving ballistically for a fixed time. We found that action onset depended on the previous temporal error in the arm movement experiment only and not in the saccadic and button press experiments. However, this dependency was modulated by the movement time: faster arm movements depended less on the previous actual temporal error. An analysis using a Kalman filter confirmed that people used the prediction error rather than the previous temporal error for trial-by-trial corrections in fast arm movements, saccades and button press.

Radboud University, 18 oktober 2019, Promotor : Medendorp, W.P. Co-promotores : Selen, L.P.J., Beers, R.J. van, Contains fulltext : 208569.pdf (publisher's version ) (Open Access)

Contains fulltext : 195162.pdf (publisher's version ) (Open Access), Comparing models facilitates testing different hypotheses regarding the computational basis of perception and action. Effective model comparison requires stimuli for which models make different predictions. Typically, experiments use a predetermined set of stimuli or sample stimuli randomly. Both methods have limitations; a predetermined set may not contain stimuli that dissociate the models, whereas random sampling may be inefficient. To overcome these limitations, we expanded the psi-algorithm (Kontsevich & Tyler, 1999) from estimating the parameters of a psychometric curve to distinguishing models. To test our algorithm, we applied it to two distinct problems. First, we investigated dissociating sensory noise models. We simulated ideal observers with different noise models performing a two-alternative forced-choice task. Stimuli were selected randomly or using our algorithm. We found using our algorithm improved the accuracy of model comparison. We also validated the algorithm in subjects by inferring which noise model underlies speed perception. Our algorithm converged quickly to the model previously proposed (Stocker & Simoncelli, 2006), whereas if stimuli were selected randomly, model probabilities separated slower and sometimes supported alternative models. Second, we applied our algorithm to a different problem - comparing models of target selection under body acceleration. Previous work found target choice preference is modulated by whole body acceleration (Rincon-Gonzalez et al., 2016). However, the effect is subtle, making model comparison difficult. We show that selecting stimuli adaptively could have led to stronger conclusions in model comparison. We conclude that our technique is more efficient and more reliable than current methods of stimulus selection for dissociating models.

Contains fulltext : 178698.pdf (publisher's version ) (Open Access), To move real objects, our hand needs to get in direct physical contact with the object. However, this is not necessarily the case when interacting with virtual objects, for example when displacing objects on tablets by swipe movements. Here, we performed two experiments to study the behavioral strategies of these movements, examining how visual information about the virtual object is mapped into a swipe that moves the object into a goal location. In the first experiment, we investigated how swiping behavior depends on whether objects were located within or outside the swiping workspace. Results show that participants do not start the swipe movement by placing their finger on the virtual object, as they do when reaching to real objects, but rather keep a systematic distance between the object location and the initial swipe location. This mismatch, which was experimentally imposed by placing the object outside the workspace, also occurred when the object was within the workspace. In the second experiment, we investigated which factors determine this mismatch by systematically manipulating the initial hand location, the location of the object, and the location of the goal. Dimensionality reduction of the data showed that three factors are taken into account when participants choose the initial swipe location: the expected total movement distance, the distance between their finger on the screen and the object, and a preference not to cover the object. The weight given to each factor differed among individuals. These results delineate, for the first time, the flexibility of visuomotor associations in the virtual world.

Item does not contain fulltext, Comparing models allows us to test different hypotheses regarding the computational basis of perception and action. One difficulty in model comparison is that it requires testing stimuli for which the models make different predictions. To date, experiments contain typically a predetermined set of stimuli or sample randomly over a large range of stimulus values. Both methods have limitations; a predetermined set may not contain the stimuli that dissociate the different models and random sampling may be inefficient. To overcome these limitations, we derived an algorithm to efficiently dissociate psychophysical models using adaptive stimuli selection. Formally, our method selects stimuli that minimize the expected entropy of the posterior distribution across models after the next stimulus or stimulus pair has been presented. To test our algorithm, we considered the problem of comparing sensory noise models. Many computational models assume a specific perceptual noise type, like constant noise, Weber noise or a combination. The appropriate noise model can be inferred using a 2-AFC task. We simulated ideal observers with different noise models performing such a task. Stimuli were selected randomly or using our adaptive algorithm. On average the number of trials required to converge to the correct model was lower for the adaptive algorithm compared to random sampling. We also verified our algorithm in human subjects by inferring which of the aforementioned models underlie speed perception. Subjects were presented with two Gabor patches moving at different speeds and indicated which was faster. On a trial the speeds were either chosen randomly or by the adaptive algorithm. The adaptive procedure converged to the model reported in earlier work (Stocker & Simoncelli, 2006), whereas the random sampling method was often inconclusive. We conclude that our technique is more efficient and more reliable than the methods that are used to date to dissociate psychophysical models. Me

Contains fulltext : 175174.pdf (publisher's version ) (Open Access), Many daily situations require us to track multiple objects and people. This ability has traditionally been investigated in observers tracking objects in a plane. This simplification of reality does not address how observers track objects when targets move in three dimensions. Here, we study how observers track multiple objects in 2D and 3D while manipulating the average speed of the objects and the average distance between them. We show that performance declines as speed increases and distance decreases and that overall tracking accuracy is always higher in 3D than in 2D. The effects of distance and dimensionality interact to produce a more than additive improvement in performance during tracking in 3D compared to 2D. We propose an ideal observer model that uses the object dynamics and noisy observations to track the objects. This model provides a good fit to the data and explains the key findings of our experiment as originating from improved inference of object identity by adding the depth dimension. Author summary: Many daily life situations require us to track objects that are in motion. In the laboratory, this multiple object tracking problem is classically studied with objects moving on a two-dimensional screen, but in the real world objects typically move in three dimensions. Here we show that, despite the complexity of seeing in depth, observers track multiple objects better when they move in 3D than 2D. A probabilistic inference model explains this by showing that the association of noisy visual signals to the objects that caused them is less ambiguous when depth cues are available. This highlights the role that depth cues play in our everyday ability to track objects.

Contains fulltext : 173510.pdf (Publisher’s version ) (Open Access), In everyday life, we frequently have to decide which hand to use for a certain action. It has been suggested that for this decision the brain calculates expected costs based on action values, such as expected biomechanical costs, expected success rate, handedness and skillfulness. While these conclusions were based on experiments in stationary subjects, we often act while the body is in motion. We investigated how hand choice is affected by passive body motion, which directly affects the biomechanical costs of the arm movement due to its inertia. Using a linear motion platform, twelve right-handed subjects were sinusoidally translated (0.625Hz and 0.5Hz). At eight possible motion phases, they had to reach using either their left or right hand to a target presented at one of eleven possible locations. We predicted hand choice by calculating the expected biomechanical costs under different assumptions about the future acceleration involved in these computations, being the forthcoming acceleration during the reach, the instantaneous acceleration at target onset or zero acceleration as if the body is stationary. While hand choice was generally biased to using the dominant hand, it also modulated sinusoidally with the motion, with the amplitude of the bias depending on the motion's peak acceleration. The phase of hand choice modulation was consistent with the cost model that took the instantaneous acceleration signal at target onset. This suggest that the brain relies on the bottom-up acceleration signals, and not on predictions about future accelerations, when deciding on hand choice during passive whole-body motion.

Contains fulltext : 159620.pdf (publisher's version ) (Open Access), Previous studies suggest that the brain combines egocentric and allocentric cues to estimate the location of objects in the world. It remains unclear how the brain would combine these cues to immediately act upon objects in dynamic environments. For example, intercepting a moving object while we are moving requires us to predict the object's future location by compensating for our own displacement. In this situation, using allocentric information about the object location could improve this estimate as long as it carries reliable cues about the object's location. To test this hypothesis, we designed an interception task in virtual reality. While being moved using a vestibular motion platform and as soon as they received an auditory cue (response signal), participants had to intercept a virtual ball (target) moving in 3D with a virtual paddle that they controlled with a linear guide. The target was presented in isolation ("target only") or surrounded by two other balls (landmarks) moving along a similar trajectory. The target disappeared 250 ms before the landmarks, which were removed at the response signal. We manipulated the landmarks' reliability by varying the spatial variance of their trajectory. Both with and without self-motion, we found that increasing the landmarks' variability resulted in an increased reaching error and variability as compared to the "target only" condition, whereas the presence of "noiseless" landmarks reduced reaching error and variability compared to the "target only" condition. Our results show that while performing an interception task, the brain does integrate allocentric information with egocentric information in order to predict the object's position, even if it is at the cost of a noisier estimate. These results may be accounted for by a Bayesian model that combines predictions about the target location based on its last observation and the actual observation of the landmarks' dynamics. Meeting abstract presented at VSS 2016.

Contains fulltext : 160116.pdf (publisher's version ) (Open Access), Recent studies demonstrate that biases found in human behavior can be explained by rational agents that make incorrect generative-model assumptions. While predicting a sequence of uncorrelated events, humans are biased towards overestimating its serial correlation. We demonstrate how such biases may also be the consequence of considering noisy observations over limited timescales of previous observations. We use the Kalman filter (KF) to study the upper-bound on human prediction performance. We investigate how the brain could estimate the necessary parameters for the KF based on the only source of information available to it, previous observations. We develop a variant of the KF model (dual memory) that obtains estimates of the KF parameters and its state over limited timescales of previous observations. The dual memory model predicts that the serial correlation should be veridical in responses for observations that are correlated in time and should be overestimated for uncorrelated ones. Second, the extent of overestimated correlation in the responses should be robust to varying noise-levels. Third, the overestimated correlation should persist regardless of whether previous observations are shown or not, if the same world-model is used. To test these hypotheses we performed an experiment where human observers were asked to predict time series, each with varying autocorrelations and noise-levels. One group was provided brief feedback whereas another was provided the history of observations. We found that the behavior of the participants was consistent with all three predictions. Further, we found a strong agreement between predictions of the dual memory model and previous empirical reports of bias in human forecasts of time series. We conclude that a markovian state-estimation model that would otherwise be optimal in predicting time series, displays the same biases in its predictions as humans do if it obtains parametric information over limited timescales of noisy

Contains fulltext : 150720.pdf (publisher's version ) (Open Access), Even when provided with feedback after every movement, adaptation levels off before biases are completely removed. Incomplete adaptation has recently been attributed to forgetting: the adaptation is already partially forgotten by the time the next movement is made. Here we test whether this idea is correct. If so, the final level of adaptation is determined by a balance between learning and forgetting. Because we learn from perceived errors, scaling these errors by a magnification factor has the same effect as subjects increasing the amount by which they learn from each error. In contrast, there is no reason to expect scaling the errors to affect forgetting. The magnification factor should therefore influence the balance between learning and forgetting, and thereby the final level of adaptation. We found that adaptation was indeed more complete for larger magnification factors. This supports the idea that incomplete adaptation is caused by part of what has been learnt quickly being forgotten.