Non-technical summary A single bout of resistance exercise stimulates the synthesis of new muscle proteins. Chronic performance of resistance exercise (i.e. weight training) is what makes your muscles grow bigger; a process known as hypertrophy. However, it is unknown if increasing the time that muscle is under tension will lead to greater increases in muscle protein synthesis. We report that leg extension exercise at 30% of the best effort (which is a load that is comparatively light), with a slow lifting movement (6 s up and 6 s down) performed to fatigue produces greater increases in rates of muscle protein synthesis than the same movement performed rapidly (1 s up and 1 s down). These results suggest that the time the muscle is under tension during exercise may be important in optimizing muscle growth; this understanding enables us to better prescribe exercise to those wishing to build bigger muscles and/or to prevent muscle loss that occurs with ageing or disease.Abstract We aimed to determine if the time that muscle is under loaded tension during low intensity resistance exercise affects the synthesis of specific muscle protein fractions or phosphorylation of anabolic signalling proteins. Eight men (24 ± 1 years (SEM), BMI = 26.5 ± 1.0 kg m −2 ) performed three sets of unilateral knee extension exercise at 30% of one-repetition maximum strength involving concentric and eccentric actions that were 6 s in duration to failure (SLOW) or a work-matched bout that consisted of concentric and eccentric actions that were 1 s in duration (CTL). Participants ingested 20 g of whey protein immediately after exercise and again at 24 h recovery. Needle biopsies (vastus lateralis) were obtained while fasted at rest and after 6, 24 and 30 h post-exercise in the fed-state following a primed, constant infusion of L-[ring -13 C 6 ]phenylalanine. Myofibrillar protein synthetic rate was higher in the SLOW condition versus CTL after 24-30 h recovery (P < 0.001) and correlated to p70S6K phosphorylation (r = 0.42, P = 0.02). Exercise-induced rates of mitochondrial and sarcoplasmic protein synthesis were elevated by 114% and 77%, respectively, above rest at 0-6 h post-exercise only in the SLOW condition (both P < 0.05). Mitochondrial protein synthesis rates were elevated above rest during 24-30 h recovery in the SLOW (175%) and CTL (126%) conditions (both P < 0.05). Lastly, muscle PGC-1α expression was increased at 6 h post-exercise compared to rest with no difference between conditions (main effect for time, P < 0.001). These data show that greater muscle time under tension increased the acute amplitude of mitochondrial and sarcoplasmic protein synthesis and also resulted in a robust, but delayed stimulation of myofibrillar protein synthesis 24-30 h after resistance exercise.

We aimed to determine if any mechanistic differences exist between a single set (1SET) and multiple sets (i.e. 3 sets; 3SET) of resistance exercise by utilizing a primed constant infusion of [ring-13 C 6 ]phenylalanine to determine myofibrillar protein synthesis (MPS) and Western blot analysis to examine anabolic signalling molecule phosphorylation following an acute bout of resistance exercise. Eight resistance-trained men (24 ± 5 years, BMI = 25 ± 4 kg m −2 ) were randomly assigned to perform unilateral leg extension exercise at 70% concentric one repetition maximum (1RM) until volitional fatigue for 1SET or 3SET. Biopsies from the vastus lateralis were taken in the fasted state (Fast) and fed state (Fed; 20 g of whey protein isolate) at rest, 5 h Fed, 24 h Fast and 29 h Fed post-exercise. Fed-state MPS was transiently elevated above rest at 5 h for 1SET (2.3-fold) and returned to resting levels by 29 h post-exercise. However, the exercise induced increase in MPS following 3SET was superior in amplitude and duration as compared to 1SET at both 5 h (3.1-fold above rest) and 29 h post-exercise (2.3-fold above rest). Phosphorylation of 70 kDa S6 protein kinase (p70S6K) demonstrated a coordinated increase with MPS at 5 h and 29 h post-exercise such that the extent of p70S6K phosphorylation was related to the MPS response (r = 0.338, P = 0.033). Phosphorylation of 90 kDa ribosomal S6 protein kinase (p90RSK) and ribosomal protein S6 (rps6) was similar for 1SET and 3SET at 24 h Fast and 29 h Fed, respectively. However, 3SET induced a greater activation of eukaryotic translation initiation factor 2Bε (eIF2Bε) and rpS6 at 5 h Fed. These data suggest that 3SET of resistance exercise is more anabolic than 1SET and may lead to greater increases in myofibrillar protein accretion over time.

Recent work suggests that the rate of learning in sensorimotor adaptation is likely not fixed, but rather can change based on previous experience. One example is savings, a commonly observed phenomenon whereby the relearning of a motor skill is faster than the initial learning. Sensorimotor adaptation is thought to be driven by sensory prediction errors, which are the result of a mismatch between predicted and actual sensory consequences. It has been proposed that during motor adaptation the generation of sensory prediction errors engages two processes (fast and slow) that differ in learning and retention rates. We tested the idea that a history of errors would influence both the fast and slow processes during savings. Participants were asked to perform the same force field adaptation task twice in succession. We found that adaptation to the force field a second time led to increases in estimated learning rates for both fast and slow processes. While it has been proposed that savings is explained by an increase in learning rate for the fast process, here we observed that the slow process also contributes to savings. Our work suggests that fast and slow adaptation processes are both responsive to a history of error and both contribute to savings. NEW & NOTEWORTHY We studied the underlying mechanisms of savings during motor adaptation. Using a two-state model to represent fast and slow processes that contribute to motor adaptation, we found that a history of error modulates performance in both processes. While previous research has attributed savings to only changes in the fast process, we demonstrated that an increase in both processes is needed to account for the measured behavioral data.

It has been proposed that the sensorimotor system uses a loss (cost) function to evaluate potential movements in the presence of random noise. Here we test this idea in the context of both error-based and reinforcement-based learning. In a reaching task, we laterally shifted a cursor relative to true hand position using a skewed probability distribution. This skewed probability distribution had its mean and mode separated, allowing us to dissociate the optimal predictions of an error-based loss function (corresponding to the mean of the lateral shifts) and a reinforcement-based loss function (corresponding to the mode). We then examined how the sensorimotor system uses error feedback and reinforcement feedback, in isolation and combination, when deciding where to aim the hand during a reach. We found that participants compensated differently to the same skewed lateral shift distribution depending on the form of feedback they received. When provided with error feedback, participants compensated based on the mean of the skewed noise. When provided with reinforcement feedback, participants compensated based on the mode. Participants receiving both error and reinforcement feedback continued to compensate based on the mean while repeatedly missing the target, despite receiving auditory, visual and monetary reinforcement feedback that rewarded hitting the target. Our work shows that reinforcement-based and error-based learning are separable and can occur independently. Further, when error and reinforcement feedback are in conflict, the sensorimotor system heavily weights error feedback over reinforcement feedback.

Consideration of previous successes and failures is essential to mastering a motor skill. Much of what we know about how humans and animals learn from such reinforcement feedback comes from experiments that involve sampling from a small number of discrete actions. Yet, it is less understood how we learn through reinforcement feedback when sampling from a continuous set of possible actions. Navigating a continuous set of possible actions likely requires using gradient information to maximize success. Here we addressed how humans adapt the aim of their hand when experiencing reinforcement feedback that was associated with a continuous set of possible actions. Specifically, we manipulated the change in the probability of reward given a change in motor action—the reinforcement gradient—to study its influence on learning. We found that participants learned faster when exposed to a steep gradient compared to a shallow gradient. Further, when initially positioned between a steep and a shallow gradient that rose in opposite directions, participants were more likely to ascend the steep gradient. We introduce a model that captures our results and several features of motor learning. Taken together, our work suggests that the sensorimotor system relies on temporally recent and spatially local gradient information to drive learning.