Investigation of Smart Responses of Human Serum Albumin in Fever Condition: An In Vitro Approach

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

Prediction and final temporal errors are used for trial-to-trial motor corrections

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

How does our motor system determine its learning rate?

Motor learning is driven by movement errors. The speed of learning can be quantified by the learning rate, which is the proportion of an error that is corrected for in the planning of the next movement. Previous studies have shown that the learning rate depends on the reliability of the error signal and on the uncertainty of the motor system’s own state. These dependences are in agreement with the predictions of the Kalman filter, which is a state estimator that can be used to determine the optimal learning rate for each movement such that the expected movement error is minimized. Here we test whether not only the average behaviour is optimal, as the previous studies showed, but if the learning rate is chosen optimally in every individual movement. Subjects made repeated movements to visual targets with their unseen hand. They received visual feedback about their endpoint error immediately after each movement. The reliability of these error-signals was varied across three conditions. The results are inconsistent with the predictions of the Kalman filter because correction for large errors in the beginning of a series of movements to a fixed target was not as fast as predicted and the learning rates for the extent and the direction of the movements did not differ in the way predicted by the Kalman filter. Instead, a simpler model that uses the same learning rate for all movements with the same error-signal reliability can explain the data. We conclude that our brain does not apply state estimation to determine the optimal planning correction for every individual movement, but it employs a simpler strategy of using a fixed learning rate for all movements with the same level of error-signal reliability

Random walk of motor planning in task-irrelevant dimensions

The movements that we make are variable. It is well established that at least a part of this variability is caused by noise in central motor planning. Here, we studied how the random effects of planning noise translate into changes in motor planning. Are the random effects independently added to a constant mean end point, or do they accumulate over movements? To distinguish between these possibilities, we examined repeated, discrete movements in various tasks in which the motor output could be decomposed into a task-relevant and a task-irrelevant component. We found in all tasks that the task-irrelevant component had a positive lag 1 autocorrelation, suggesting that the random effects of planning noise accumulate over movements. In contrast, the task-relevant component always had a lag 1 autocorrelation close to zero, which can be explained by effective trial-by-trial correction of motor planning on the basis of observed motor errors. Accumulation of the effects of planning noise is consistent with current insights into the stochastic nature of synaptic plasticity. It leads to motor exploration, which may subserve motor learning and performance optimization

Motor learning is optimally tuned to the properties of motor noise

SummaryIn motor learning, our brain uses movement errors to adjust planning of future movements. This process has traditionally been studied by examining how motor planning is adjusted in response to visuomotor or dynamic perturbations. Here, I show that the learning strategy can be better identified from the statistics of movements made in the absence of perturbations. The strategy identified this way differs from the learning mechanism assumed in mainstream models for motor learning. Crucial for this strategy is that motor noise arises partly centrally, in movement planning, and partly peripherally, in movement execution. Corrections are made by modification of central planning signals from the previous movement, which include the effects of planning but not execution noise. The size of the corrections is such that the movement variability is minimized. This physiologically plausible strategy is optimally tuned to the properties of motor noise, and likely underlies learning in many motor tasks

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

What autocorrelation tells us about motor variability: Insights from dart throwing

In sports such as golf and darts it is important that one can produce ballistic movements of an object towards a goal location with as little variability as possible. A factor that influences this variability is the extent to which motor planning is updated from movement to movement based on observed errors. Previous work has shown that for reaching movements, our motor system uses the learning rate (the proportion of an error that is corrected for in the planning of the next movement) that is optimal for minimizing the endpoint variability. Here we examined whether the learning rate is hard-wired and therefore automatically optimal, or whether it is optimized through experience. We compared the performance of experienced dart players and beginners in a dart task. A hallmark of the optimal learning rate is that the lag-1 autocorrelation of movement endpoints is zero. We found that the lag-1 autocorrelation of experienced dart players was near zero, implying a near-optimal learning rate, whereas it was negative for beginners, suggesting a larger than optimal learning rate. We conclude that learning rates for trial-by-trial motor learning are optimized through experience. This study also highlights the usefulness of the lag-1 autocorrelation as an index of performance in studying motor-skill learning

SDSSJ103913.70+533029.7: A Super Star Cluster in the Outskirts of a Galaxy Merger

We describe the serendipitous discovery in the spectroscopic data of the Sloan Digital Sky Survey of a star-like object, SDSSJ103913.70+533029.7, at a heliocentric radial velocity of +1012 km/s. Its proximity in position and velocity to the spiral galaxy NGC 3310 suggests an association with the galaxy. At this distance, SDSSJ103913.70+533029.7 has the luminosity of a super star cluster and a projected distance of 17 kpc from NGC 3310. Its spectroscopic and photometric properties imply a mass of > 10^6 solar masses and an age close to that of the tidal shells seen around NGC 3310, suggesting that it formed in the event which formed the shells.Comment: Accepted by AJ: 4 figures (1 color

Saccadic Eye Movements Minimize the Consequences of Motor Noise

The durations and trajectories of our saccadic eye movements are remarkably stereotyped. We have no voluntary control over these properties but they are determined by the movement amplitude and, to a smaller extent, also by the movement direction and initial eye orientation. Here we show that the stereotyped durations and trajectories are optimal for minimizing the variability in saccade endpoints that is caused by motor noise. The optimal duration can be understood from the nature of the motor noise, which is a combination of signal-dependent noise favoring long durations, and constant noise, which prefers short durations. The different durations of horizontal vs. vertical and of centripetal vs. centrifugal saccades, and the somewhat surprising properties of saccades in oblique directions are also accurately predicted by the principle of minimizing movement variability. The simple and sensible principle of minimizing the consequences of motor noise thus explains the full stereotypy of saccadic eye movements. This suggests that saccades are so stereotyped because that is the best strategy to minimize movement errors for an open-loop motor system

How the required precision influences the way we intercept a moving object

Do people perform a given motor task differently if it is easy than if it is difficult? To find out, we asked subjects to intercept moving virtual targets by tapping on them with their fingers. We examined how their behaviour depended on the required precision. Everything about the task was the same on all trials except the extent to which the fingertip and target had to overlap for the target to be considered hit. The target disappeared with a sound if it was hit and deflected away from the fingertip if it was missed. In separate sessions, the required precision was varied from being quite lenient about the required overlap to being very demanding. Requiring a higher precision obviously decreased the number of targets that were hit, but it did not reduce the variability in where the subjects tapped with respect to the target. Requiring a higher precision reduced the systematic deviations from landing at the target centre and the lag-one autocorrelation in such deviations, presumably because subjects received information about smaller deviations from hitting the target centre. We found no evidence for lasting effects of training with a certain required precision. All the results can be reproduced with a model in which the precision of individual movements is independent of the required precision, and in which feedback associated with missing the target is used to reduce systematic errors. We conclude that people do not approach this motor task differently when it is easy than when it is difficult. © 2013 Springer-Verlag Berlin Heidelberg