Remapping tactile events from skin to external space is an essential process for human behaviour. It allows us to refer tactile sensations to their actual externally based location, by combining anatomically based somatosensory information with proprioceptive information about the current body posture. We examined the time course of tactile remapping by recording speeded saccadic responses to somatosensory stimuli delivered to the hands. We conducted two experiments in which arm posture varied (crossed or uncrossed), so that anatomical and external frames of reference were either put in spatial conflict or were aligned. The data showed that saccade onset latencies in the crossed hands conditions were slower than in the uncrossed hands condition, suggesting that, in the crossed hands condition, remapping had to be completed before a correct saccade could be executed. Saccades to tactile stimuli when the hands were crossed were sometimes initiated to the wrong direction and then corrected in-flight, resulting in a turn-around saccade. These turn-around saccades were more likely to occur in short-latency responses, compared to onset latencies of saccades that went straight to target. The latter suggests that participants were postponing their saccade until the time the tactile event was represented according to the current body posture. We propose that the difference between saccade onset latencies of crossed and uncrossed hand postures, and between the onset of a turn-around saccade and a straight saccade in the crossed hand posture, reveal the timing of tactile spatial remapping.

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We propose a model that distinguishes between parallel and serial search in haptics. To test this model, participants performed three haptic search experiments in which a target and distractors were presented to their fingertips. The participants indicated a target's presence by lifting the corresponding finger, or its absence by lifting all fingers. In one experiment, the target was a cross and the distractors were circles. In another, the target was a vertical line and the distractors were horizontal lines. In both cases, we found a serial search pattern. In a final experiment, the target was a horizontal line and the distractors were surfaces without any contours. In this case, we found a parallel search pattern. We conclude that the model can describe our data very well.

The brain rapidly adapts reaching movements to changing circumstances by using visual feedback about errors. Providing reward in addition to error feedback facilitates the adaptation but the underlying mechanism is unknown. Here, we investigate whether the proportion of trials rewarded (the ‘reward abundance’) influences how much participants adapt to their errors. We used a 3D multi-target pointing task in which reward alone is insufficient for motor adaptation. Participants (N = 423) performed the pointing task with feedback based on a shifted hand-position. On a proportion of trials we gave them rewarding feedback that their hand hit the target. Half of the participants only received this reward feedback. The other half also received feedback about endpoint errors. In different groups, we varied the proportion of trials that was rewarded. As expected, participants who received feedback about their errors did adapt, but participants who only received reward-feedback did not. Critically, participants who received abundant rewards adapted less to their errors than participants who received less reward. Thus, reward abundance negatively influences how much participants learn from their errors. Probably participants used a mechanism that relied more on the reward feedback when the reward was abundant. Because participants could not adapt to the reward, this interfered with adaptation to errors.

To investigate whether the relative positions of the fingers influence tactile localization, participants were asked to localize tactile stimuli applied to their fingertips. We measured the location and rate of errors for three finger configurations: fingers stretched out and together so that they are touching each other, fingers stretched out and spread apart maximally and fingers stretched out with the two hands on top of each other so that the fingers are interwoven. When the fingers contact each other, it is likely that the error rate to the adjacent fingers will be higher than when the fingers are spread apart. In particular, we reasoned that localization would probably improve when the fingers are spread. We aimed at assessing whether such adjacency was measured in external coordinates (taking proprioception into account) or on the body (in skin coordinates). The results confirmed that the error rate was lower when the fingers were spread. However, there was no decrease in error rate to neighbouring fingertips in the fingers spread condition in comparison with the fingers together condition. In an additional experiment, we showed that the lower error rate when the fingers were spread was not related to the continuous tactile input from the neighbouring fingers when the fingers were together. The current results suggest that information from proprioception is taken into account in perceiving the location of a stimulus on one of the fingertips.

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Could a pat on the back affect motor adaptation? Recent studies indeed suggest that rewards can boost motor adaptation. However, the rewards used were typically reward gradients that carried quite detailed information about performance. We investigated whether simple binary rewards affected how participants learned to correct for a visual rotation of performance feedback in a 3D pointing task. To do so, we asked participants to align their unseen hand with virtual target cubes in alternating blocks with and without spatial performance feedback. Forty participants were assigned to one of two groups: a ‘spatial only’ group, in which the feedback consisted of showing the (perturbed) endpoint of the hand, or to a ‘spatial & reward’ group, in which a reward could be received in addition to the spatial feedback. In addition, six participants were tested in a ‘reward only’ group. Binary reward was given when the participants’ hand landed in a virtual ‘hit area’ that was adapted to individual performance to reward about half the trials. The results show a typical pattern of adaptation in both the ‘spatial only’ and the ‘spatial & reward’ groups, whereas the ‘reward only’ group was unable to adapt. The rewards did not affect the overall pattern of adaptation in the ‘spatial & reward’ group. However, on a trial-by-trial basis, the rewards reduced adaptive changes to spatial errors.Electronic supplementary materialThe online version of this article (doi:10.1007/s00221-015-4540-1) contains supplementary material, which is available to authorized users.

We may be motivated to engage in a certain motor activity because it is instrumental to obtaining reward (e.g., money) or because we enjoy the activity, making it intrinsically rewarding. Enjoyment is related to intrinsic motivation which is considered to be a durable form of motivation. Therefore, many rehabilitation programs aim to increase task enjoyment by adding game elements (“gamification”). Here we ask how the influence of game elements on motivation develops over time and additionally explore whether enjoyment influences motor performance. We describe two different studies that varied game elements in different exercises. Experiment 1 compared the durability of enjoyment for a gamified and a conventional balance exercise in elderly. Experiment 2 addressed the question whether adding game elements to a gait adaptability exercise enhances the durability of enjoyment and additionally tested whether the game elements influenced movement vigor and accuracy (motor performance). The results show that the game elements enhanced enjoyment. Enjoyment faded over time, but this decrease tended to be less pronounced in gamified exercises. There was no evidence that the game elements affected movement vigor or accuracy.