Body composition was measured in a group of 35 healthy men and 37 healthy women aged 60-83 y. Body mass index (BMI) in men was 25.0 +/- 2.2 kg/m2 (means +/- SD) and in women, 25.9 +/- 3.2 kg/m2. BMI was low in relation to body fat percentage as determined by skinfold-thickness measurements or densitometry in comparison with the relation found in younger adults. Mean body fat percentage of the male subjects (aged 70.4 +/- 5.2 y) as determined by densitometry was 31.0 +/- 4.5%, whereas in women (aged 68.0 +/- 5.2 y) it was 43.9 +/- 4.3%. Body impedance correlated with fat-free mass (FFM). The best prediction formulas for the FFM from body impedance and anthropometric variables were 1) FFM (kg) = (0.671 x 10(4) x H2/R) + 3.1S + 3.9 where H is body height (m), R is resistance (omega), and S is gender (females, 0; males, 1) (r = 0.94; SEE = 3.1 kg) and 2) FFM (kg) = (0.360 x 10(4) x H2/R) + 0.359BW + 4.5S - 20T + 7.0 where BW is body weight (kg) and T is thigh circumference (m) (r = 0.96; SEE = 2.5 kg). The prediction equations from the literature, generally determined in younger populations, overestimated FFM in elderly subjects by approximately 6 kg and are not applicable to elderly subjects.

Does the nervous system continuously realign the senses so that objects are seen and felt in the same place? Conflicting answers to this question have been given. Research imposing a sensory mismatch has provided evidence that the nervous system realigns the senses to reduce the mismatch. Other studies have shown that when subjects point with the unseen hand to visual targets, their end points show visual-proprioceptive biases that do not disappear after episodes of visual feedback. These biases are indicative of intersensory mismatches that the nervous system does not align for. Here, we directly compare how the nervous system deals with natural and imposed mismatches. Subjects moved a hand-held cube to virtual cubes appearing at pseudorandom locations in three-dimensional space. We alternated blocks in which subjects moved without visual feedback of the hand with feedback blocks in which we rendered a cube representing the hand-held cube. In feedback blocks, we rotated the visual feedback by 5° relative to the subject's head, creating an imposed mismatch between vision and proprioception on top of any natural mismatches. Realignment occurred quickly but was incomplete. We found more realignment to imposed mismatches than to natural mismatches. We propose that this difference is related to the way in which the visual information changed when subjects entered the experiment: the imposed mismatches were different from the mismatch in daily life, so alignment started from scratch, whereas the natural mismatches were not imposed by the experimenter, so subjects are likely to have entered the experiment partly aligned.

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.

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.

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.

Exploration in reward-based motor learning is observable in experimental data as increased variability. In order to quantify exploration, we compare three methods for estimating other sources of variability: sensorimotor noise. We use a task in which participants could receive stochastic binary reward feedback following a target-directed weight shift. Participants first performed six baseline blocks without feedback, and next twenty blocks alternating with and without feedback. Variability was assessed based on trial-to-trial changes in movement endpoint. We estimated sensorimotor noise by the median squared trial-to-trial change in movement endpoint for trials in which no exploration is expected. We identified three types of such trials: trials in baseline blocks, trials in the blocks without feedback, and rewarded trials in the blocks with feedback. We estimated exploration by the median squared trial-to-trial change following non-rewarded trials minus sensorimotor noise. As expected, variability was larger following non-rewarded trials than following rewarded trials. This indicates that our reward-based weight-shifting task successfully induced exploration. Most importantly, our three estimates of sensorimotor noise differed: the estimate based on rewarded trials was significantly lower than the estimates based on the two types of trials without feedback. Consequently, the estimates of exploration also differed. We conclude that the quantification of exploration depends critically on the type of trials used to estimate sensorimotor noise. We recommend the use of variability following rewarded trials.

Recently it has been shown that rewarded variability can be used to adapt visuomotor behavior. However, its relevance seems limited because adaptation to binary rewards has been demonstrated only when the same movement is repeated throughout the experiment. We therefore investigated whether the adaptation is action-specific and whether the amount of exploration depends on spatial complexity. Participants pointed to 3-D visual targets without seeing their hand and could use only binary reward feedback to adapt their movements. We varied the number of target positions and the number of dimensions the feedback was based on. Because the feedback was based on a 5-cm rightward shifted hand position, adaptation was needed for good performance. The participants started naïve to the perturbation. If actions were made toward a single target position and the feedback was based on the lateral component of their response only, participants adapted completely within 200 trials. Having more than 1 target position or more than 1 dimension of performance resulted in considerably less adaptation but did not affect the exploration. Thus, reward-based adaptation can generalize across actions but is reduced by spatial complexity, whereas exploration is not affected by spatial complexity. (PsycINFO Database Record

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.