Running together influences where you look.

To read this article, you have to constantly direct your gaze at the words on the page. If you go for a run instead, your gaze will be less constrained, so many factors could influence where you look. We show that you are likely to spend less time looking at the path just in front of you when running alone than when running with someone else, presumably because the presence of the other runner makes foot placement more critical.

Spatial contextual cues that help predict how a target will accelerate can be used to guide interception.

Objects in one's environment do not always move at a constant velocity but often accelerate or decelerate. People are very poor at visually judging acceleration and normally make systematic errors when trying to intercept accelerating objects. If the acceleration is perpendicular to the direction of motion, it gives rise to a curved path. Can spatial contextual cues help one predict such accelerations and thereby help interception? To answer this question, we asked participants to hit a target that moved as if it were attached to a rolling disk, like a valve (target) on a bicycle wheel (disk) moves when cycling: constantly accelerating toward the wheel's center. On half the trials, the disk was visible such that participants could use the spatial relations between the target and the rolling disk to guide their interception. On the other half, the disk was not visible, so participants had no help in predicting the target's complicated pattern of accelerations and decelerations. Importantly, the target's path was the same in both cases. Participants hit more targets when the disk was visible than when it was invisible, even when using a strategy that can compensate for neglecting acceleration. We conclude that spatial contextual cues that help predict the target's accelerations can help intercept it.

Implicit reward-based motor learning.

Binary feedback, providing information solely about task success or failure, can be sufficient to drive motor learning. While binary feedback can induce explicit adjustments in movement strategy, it remains unclear if this type of feedback also induces implicit learning. We examined this question in a center-out reaching task by gradually moving an invisible reward zone away from a visual target to a final rotation of 7.5° or 25° in a between-group design. Participants received binary feedback, indicating if the movement intersected the reward zone. By the end of the training, both groups modified their reach angle by about 95% of the rotation. We quantified implicit learning by measuring performance in a subsequent no-feedback aftereffect phase, in which participants were told to forgo any adopted movement strategies and reach directly to the visual target. The results showed a small, but robust (2-3°) aftereffect in both groups, highlighting that binary feedback elicits implicit learning. Notably, for both groups, reaches to two flanking generalization targets were biased in the same direction as the aftereffect. This pattern is at odds with the hypothesis that implicit learning is a form of use-dependent learning. Rather, the results suggest that binary feedback can be sufficient to recalibrate a sensorimotor map.

Implicit reward-based motor learning.

Binary feedback, providing information solely about task success or failure, can be sufficient to drive motor learning. While binary feedback can induce explicit adjustments in movement strategy, it remains unclear if this type of feedback also induce implicit learning. We examined this question in a center-out reaching task by gradually moving an invisible reward zone away from a visual target to a final rotation of 7.5° or 25° in a between-group design. Participants received binary feedback, indicating if the movement intersected the reward zone. By the end of the training, both groups modified their reach angle by about 95% of the rotation. We quantified implicit learning by measuring performance in a subsequent no-feedback aftereffect phase, in which participants were told to forgo any adopted movement strategies and reach directly to the visual target. The results showed a small, but robust (2-3°) aftereffect in both groups, highlighting that binary feedback elicits implicit learning. Notably, for both groups, reaches to two flanking generalization targets were biased in the same direction as the aftereffect. This pattern is at odds with the hypothesis that implicit learning is a form of use-dependent learning. Rather, the results suggest that binary feedback can be sufficient to recalibrate a sensorimotor map.

Continuous use of visual information about the position of the moving hand.

People generally look at a target when they want to reach for it. Doing so presumably helps them continuously update their judgments about the target's position and motion. But not looking at their hand does not prevent people from updating judgments about its position on the basis of visual information, because people do respond to experimental perturbations of visual information about the position of their hand. Here, we study such responses by adding jitter to the movement of a cursor that follows participants' fingers. We analyse the response to the jitter in a way that reveals how the vigour of the response depends on the moment during the movement at which the change in cursor position occurs. We compare the change in vigour to that for equivalent jitter in the position of the target. We find that participants respond to jitter in the position of a cursor in much the same way as they respond to jitter in the target's position. The responses are more vigorous late in the movement, when adjustments need to be made within less time, but similarly so for the cursor as for the target. The responses are weaker for the cursor, presumably because of the jitter-free kinaesthetic information about the position of the finger.

Online updating of obstacle positions when intercepting a virtual target.

People rely upon sensory information in the environment to guide their actions. Ongoing goal-directed arm movements are constantly adjusted to the latest estimate of both the target and hand's positions. Does the continuous guidance of ongoing arm movements also consider the latest visual information of the position of obstacles in the surrounding? To find out, we asked participants to slide their finger across a screen to intercept a laterally moving virtual target while moving through a gap that was created by two virtual circular obstacles. At a fixed time during each trial, the target suddenly jumped slightly laterally while still continuing to move. In half the trials, the size of the gap changed at the same moment as the target jumped. As expected, participants adjusted their movements in response to the target jump. Importantly, the magnitude of this response depended on the new size of the gap. If participants were told that the circles were irrelevant, changing the gap between them had no effect on the responses. This shows that obstacles' instantaneous positions can be considered when visually guiding goal-directed movements.

The cost of aiming for the best answers: Inconsistent perception.

The laws of physics and mathematics describe the world we live in as internally consistent. As these rules provide a very effective description, and our interaction with the world is also very effective, it seems self-evident that our perception follows these laws. As a result, when trying to explain imperfections in perception, we tend to impose consistency and introduce concepts such as deformations of visual space. In this review, we provide numerous examples that show that in many situations we perceive related attributes to have inconsistent values. We discuss how our tendency to assume consistency leads to erroneous conclusions on how we process sensory information. We propose that perception is not about creating a consistent internal representation of the outside world, but about answering specific questions about the outside world. As the information used to answer a question is specific for that question, this naturally leads to inconsistencies in perception and to an apparent dissociation between some perceptual judgments and related actions.

How the timing of visual feedback influences goal-directed arm movements: delays and presentation rates.

Visual feedback normally helps guide movements to their goal. When moving one's hand, such guidance has to deal with a sensorimotor delay of about 100 ms. When moving a cursor, it also has to deal with a delay of tens of milliseconds that arises between the hand moving the mouse and the cursor moving on the screen. Moreover, the cursor is presented at a certain rate, so only positions corresponding with the position of the mouse at certain moments are presented. How does the additional delay and the rate at which cursor positions are updated influence how well the cursor can be guided to the goal? We asked participants to move a cursor to consecutive targets as quickly as they could. They did so for various additional delays and presentation rates. It took longer for the mouse to reach the target when the additional delay was longer. It also took longer when a lower presentation rate was achieved by not presenting the cursor all the time. The fraction of the time during which the cursor was present was more important than the rate at which the cursor's position was updated. We conclude that the way human arm movements are guided benefits from continuous access to recent visual feedback.

How prism adaptation reveals the distinct use of size and positions in grasping.

The size of an object equals the distance between the positions of its opposite edges. However, human sensory processing for perceiving positions differs from that for perceiving size. Which of these two information sources is used to control grip aperture? In this paper, we answer this question by prism adaptation of single-digit movements of the index finger and thumb. We previously showed that it is possible to adapt the index finger and thumb in opposite directions and that this adaptation induces an aftereffect in grip aperture in grasping. This finding suggests that grasping is based on the perceived positions of the contact points. However, it might be compatible with grasping being controlled based on size provided that the opposing prism adaptation leads to changes in visually perceived size or proprioception of hand opening. In that case, one would predict a similar aftereffect in manually indicating the perceived size. In contrast, if grasping is controlled based on information about the positions of the edges, the aftereffect in grasping is due to altered position information, so one would predict no aftereffect in manually indicating the perceived size. Our present experiment shows that there was no aftereffect in manually indicating perceived size. We conclude that grip aperture during grasping is based on perceived positions rather than on perceived size.

Tapping on a target: dealing with uncertainty about its position and motion.

Reaching movements are guided by estimates of the target object's location. Since the precision of instantaneous estimates is limited, one might accumulate visual information over time. However, if the object is not stationary, accumulating information can bias the estimate. How do people deal with this trade-off between improving precision and reducing the bias? To find out, we asked participants to tap on targets. The targets were stationary or moving, with jitter added to their positions. By analysing the response to the jitter, we show that people continuously use the latest available information about the target's position. When the target is moving, they combine this instantaneous target position with an extrapolation based on the target's average velocity during the last several hundred milliseconds. This strategy leads to a bias if the target's velocity changes systematically. Having people tap on accelerating targets showed that the bias that results from ignoring systematic changes in velocity is removed by compensating for endpoint errors if such errors are consistent across trials. We conclude that combining simple continuous updating of visual information with the low-pass filter characteristics of muscles, and adjusting movements to compensate for errors made in previous trials, leads to the precise and accurate human goal-directed movements.

Pursuing a target with one's eyes helps judge its velocity.

When intercepting moving targets, people perform slightly better if they follow their natural tendency to pursue the target with their eyes. Is this because the velocity is judged more precisely when pursuing the target? To find out, we compared how well people could determine which of two sequentially presented moving bars was moving faster. There was always also a static bar on the screen. People judged the moving bar's velocity about 10% more precisely when pursuing it than when fixating the static bar.

How similar are responses to background motion and target displacements?

When making a goal-directed movement towards a target, our hand follows abrupt background motion. This response resembles that of a shift in the target's position. Does background motion simply change the position towards which the movement is guided? If so, the response to background motion should resemble the response to a target displacement. To find out whether this is the case, we ran two exploratory studies where we asked participants to hit a moving target at a specified moment. At various times during the hand's movement, the background could move briefly at one of several speeds, and for various durations. The response to abrupt background motion was larger when the background moved later in the movement and when the background moved faster, in line with known responses to target displacements. The response to a second epoch of background motion was smaller than it would have been if there had been no first epoch, in contrast to responses to multiple target displacements. If the background was already moving before the target appeared, the hand even moved in the opposite direction. Thus, the response to background motion and that to a target displacement are clearly not identical, but they do share several features.

Having several options does not increase the time it takes to make a movement to an adequate end point.

Throughout the day, people constantly make choices such as where to direct their gaze or place their foot. When making such movement choices, there are usually multiple acceptable options, although some are more advantageous than others. How much time does it take to make such choices and to what extent is the most advantageous option chosen from the available alternatives? To find out, we asked participants to collect points by tapping on any of several targets with their index finger. It did not take participants more time to direct their movements to an advantageous target when there were more options. Participants chose targets that were advantageous because they were easier to reach. Targets could be easier to reach because the finger was already moving in their direction when Amsterdam they appeared, or because they were larger or oriented along the movement direction so that the finger could move faster towards them without missing them. When the target's colour indicated that it was worth more points they chose it slightly less fast, presumably because it generally takes longer to respond to colour than to respond to attributes such as size. They also chose it less often than they probably should have, presumably because the advantage of choosing it was established arbitrarily. We conclude that having many options does not increase the time it takes to move to an adequate target.

Familiarity with an Object's Size Influences the Perceived Size of Its Image.

It is known that judgments about objects' distances are influenced by familiar size: a soccer ball looks farther away than a tennis ball if their images are equally large on the retina. We here investigate whether familiar size also influences judgments about the size of images of objects that are presented side-by-side on a computer screen. Sixty-three participants indicated which of two images appeared larger on the screen in a 2-alternative forced-choice discrimination task. The objects were either two different types of balls, two different types of coins, or a ball and a grey disk. We found that the type of ball biased the comparison between their image sizes: the size of the image of the soccer ball was over-estimated by about 5% (assimilation). The bias in the comparison between the two balls was equal to the sum of the biases in the comparisons with the grey disk. The bias for the coins was smaller and in the opposite direction (contrast). The average precision of the size comparison was 3.5%, irrespective of the type of object. We conclude that knowing a depicted object's real size can influence the perceived size of its image, but the perceived size is not always attracted towards the familiar size.

Hand movements respond to any motion near the endpoint.

Hand movements are pulled in the direction of motion near their planned endpoints. Is this an automatic response to motion signals near those positions, or do we consider what is moving? To find out, we asked participants to hit a target that moved rightward across a patterned surface when it reached an interception zone that was indicated by a circle. The circle was initially at the center of a square. The square was either filled, occluding the patterned surface (tile), or open, such that the patterned surface was not occluded (frame). The square briefly moved leftward or rightward shortly after the target appeared. Thus, participants were either aiming to hit the target on the surface that moved (the tile) or to hit the target on the patterned surface that did not move. Moving the two types of squares produced very similar local motion signals, but for the tile this could be interpreted as motion of an extended surface, while for the frame it could not. Motion onset of the two types of squares yielded very similar responses. Increasing the size of the square, and thus the eccentricity of the local motion signal, reduced the magnitude of the response. Since this reduction was seen for both types of squares, the surface on which the interception zone was presented was clearly not considered. We conclude that the response is driven by local motion signals near the endpoint of the action without considering whether the local surface is moving.

Sprint Performance in Arms-Only Front Crawl Swimming Is Strongly Associated With the Power-To-Drag Ratio.

To date, optimal propulsion in swimming has been studied predominantly using physical or computational models of the arm and seldom during real-life swimming. In the present study we examined the contributions of selected power, technique and anthropometric measures on sprint performance during arms-only front crawl swimming. To this end, 25 male adult competitive swimmers, equipped with markers on their arms and hands, performed four 25-m sprint trials, which were recorded on video. For the fastest trial of each swimmer, we determined the average swim speed as well as two technique variables: the average stroke width and average horizontal acceleration. Each participant also swam 10-12 trials over a custom-made system for measuring active drag, the MAD system. Since the propelling efficiency is 100% while swimming over the MAD system, the power output of the swimmer is fully used to overcome the drag acting on the body. The resulting speed thus represents the ratio between power output and drag. We included this power-to-drag ratio, the power output and the drag coefficient of the fastest trial on the MAD system in the analysis. Finally, the body height and hand surface area of each swimmer were determined as anthropometric variables. A model selection procedure was conducted to predict the swim speed from the two technique variables, three power variables and the two anthropometric variables. The ratio between power output and the drag was the only significant predictor of the maximal swimming speed (v = 0.86·power/drag). The variations in this ratio explained 65% of the variance in swimming performance. This indicates that sprint performance in arms-only front crawl swimming is strongly associated with the power-to-drag ratio and not with the isolated power variables and the anthropometric and technique variables selected in the present study.

Size, weight, and expectations.

The size-weight illusion is well-known: if two equally heavy objects differ in size, the large one feels lighter than the small one. Most explanations for this illusion assume that because the information about the relevant attribute (weight itself) is unreliable, information about an irrelevant but correlated attribute (size) is used as well. If such reasoning is correct, one would expect that the illusion can be inverted: if size information is unreliable, weight information will be used to judge size. We explored whether such a weight-size illusion exists by asking participants to lift Styrofoam balls that were coated with glow in the dark paint. The balls (2 sizes, 3 weights) were lifted using a pulley system in complete darkness at 2 distances. Participants reported the size using free magnitude estimation. The visual size information was indeed unreliable: balls that were presented at a 20% larger distance were judged 15% smaller. Nevertheless, the judgments of size were not systematically affected by the 20% weight change (differences < 0.5%). We conclude that because the weight-size illusion does not exist, the mechanism behind the size-weight illusion is specific for judging heaviness.

The influences of target size and recent experience on the vigour of adjustments to ongoing movements.

People adjust their on-going movements to changes in the environment. It takes about 100 ms to respond to an abrupt change in a target's position. Does the vigour of such responses depend on the extent to which responding is beneficial? We asked participants to tap on targets that jumped laterally once their finger started to move. In separate blocks of trials the target either remained at the new position so that it was beneficial to respond to the jump, or jumped back almost immediately so that it was disadvantageous to do so. We also varied the target's size, because a smaller, less vigorous adjustment is enough to place the finger within a larger target. There was a systematic relationship between the vigour of the response and the remaining time until the tap: the shorter the remaining time the more vigorous the response. This relationship did not depend on the target's size or whether or not the target jumped back. It was already known that the vigour of responses to target jumps depends on the magnitude of the jump and on the time available for adjusting the movement to that jump. We show that the vigour of the response is precisely tuned to the time available for making the required adjustment irrespective of whether responding in this manner is beneficial.

The response to background motion: Characteristics of a movement stabilization mechanism.

When making goal-directed movements toward a target, our hand deviates from its path in the direction of sudden background motion. We propose that this manual following response arises because ongoing movements are constantly guided toward the planned movement endpoint. Such guidance is needed to compensate for modest, unexpected self-motion. Our proposal is that the compensation for such self-motion does not involve a sophisticated analysis of the global optic flow. Instead, we propose that any motion in the vicinity of the planned endpoint is attributed to the endpoint's egocentric position having shifted in the direction of the motion. The ongoing movement is then stabilized relative to the shifted endpoint. In six experiments, we investigate what aspects of motion determine this shift of planned endpoint. We asked participants to intercept a moving target when it reached a certain area. During the target's motion, background structures briefly moved either leftward or rightward. Participants' hands responded to background motion even when each background structure was only briefly visible or when the vast majority of background structures remained static. The response was not restricted to motion along the target's path but was most sensitive to motion close to where the target was to be hit, both in the visual field and in depth. In this way, a movement stabilization mechanism provides a comprehensive explanation of many aspects of the manual following response.

Pitfalls in quantifying exploration in reward-based motor learning and how to avoid them.

When learning a movement based on binary success information, one is more variable following failure than following success. Theoretically, the additional variability post-failure might reflect exploration of possibilities to obtain success. When average behavior is changing (as in learning), variability can be estimated from differences between subsequent movements. Can one estimate exploration reliably from such trial-to-trial changes when studying reward-based motor learning? To answer this question, we tried to reconstruct the exploration underlying learning as described by four existing reward-based motor learning models. We simulated learning for various learner and task characteristics. If we simply determined the additional change post-failure, estimates of exploration were sensitive to learner and task characteristics. We identified two pitfalls in quantifying exploration based on trial-to-trial changes. Firstly, performance-dependent feedback can cause correlated samples of motor noise and exploration on successful trials, which biases exploration estimates. Secondly, the trial relative to which trial-to-trial change is calculated may also contain exploration, which causes underestimation. As a solution, we developed the additional trial-to-trial change (ATTC) method. By moving the reference trial one trial back and subtracting trial-to-trial changes following specific sequences of trial outcomes, exploration can be estimated reliably for the three models that explore based on the outcome of only the previous trial. Since ATTC estimates are based on a selection of trial sequences, this method requires many trials. In conclusion, if exploration is a binary function of previous trial outcome, the ATTC method allows for a model-free quantification of exploration.