Mental Fatigue Reduces the Benefits of Self-Controlled Feedback on Learning a Force Production Task

This study investigated the impact of mental fatigue and self-controlled versus yoked feedback on learning a force production task. We randomly assigned 44 non-athlete male students (Mage = 21.4, SD = 1.4 years) to four groups; (a) MF&SCF = mental fatigue & self-controlled feedback, (b) MF&Y = mental fatigue & yoked, (c) NMF&SCF = no mental fatigue & self-controlled feedback, and (d) NMF&Y = no mental fatigue & yoked). SCF group participants were provided feedback whenever they requested it, while YK group participants received feedback according to a schedule created by their SCF counterparts. To induce mental fatigue, participants performed a Stroop color-word task for one hour. During the acquisition (practice) phase, participants were asked to produce a given percentage of their maximum force (20%) in 12 blocks of six trials. We recorded the participants’ absolute error at the end of the acquisition phase, the immediate retention test, the first transfer test, and the second transfer test (after 24 hours and without any further mental fatigue). The acquisition phase data were analyzed in a 2 (feedback) × 2 (mental fatigue) × 12 (block) ANOVA with repeated measures on the last factor, while the retention and transfer data were analyzed in 2 (feedback) × 2 (mental fatigue) ANOVAs. We found that all four groups made significant progress during practice ( p < .001), but there were no significant group differences during this phase ( p>.05). There was a significant interaction effect of self-controlled feedback and mental fatigue at retention ( p = .018) and transfer testing ( p < .001). In the mental fatigue condition, participants in the self-controlled group had poorer learning compared to participants in the yoked group; but when not mentally fatigued, participants in the self-controlled group had better learning than those in the yoked group. These findings suggest that mental fatigue reduces typical advantages of self-controlled feedback in motor learning.

Unveiling the neuromechanical mechanisms underlying the synergistic interactions in human sensorimotor system

Motor synergies are neural organizations of a set of redundant motor effectors that interact with one another to compensate for each other’s error and ensure the stabilization of a performance variable. Recent studies have demonstrated that central nervous system synergistically coordinates its numerous motor effectors through Bayesian multi-sensory integration. Deficiency in sensory synergy weakens the synergistic interaction between the motor effectors. Here, we scrutinize the neuromechanical mechanism underlying this phenomenon through spectral analysis and modeling. We validate our model-generated results using experimental data reported in the literature collected from participants performing a finger force production task with and without tactile feedback (manipulated through injection of anesthetic in fingers). Spectral analysis reveals that the error compensation feature of synergies occurs only at low frequencies. Modeling suggests that the neurophysiological structures involving short-latency back-coupling loops similar to the well-known Renshaw cells explain the deterioration of synergy due to sensory deprivation.

A cryptography-based approach for movement decoding

Brain decoders use neural recordings to infer a user’s activity or intent. To train a decoder, we generally need infer the variables of interest (covariates) using simultaneously measured neural activity. However, there are many cases where this approach is not possible. Here we overcome this problem by introducing a fundamentally new approach for decoding called distribution alignment decoding (DAD). We use the statistics of movement, much like cryptographers use the statistics of language, to find a mapping between neural activity and motor variables. DAD learns a linear decoder which aligns the distribution of its output with the typical distribution of motor outputs by minimizing their KL-divergence. We apply our approach to a two datasets collected from the motor cortex of non-human primates (NHPs): a reaching task and an isometric force production task. We study the performance of DAD and find regimes where DAD provides comparable and in some cases, better performance than a typical supervised decoder. As DAD does not rely on the ability to record motor-related outputs, it promises to broaden the set of potential applications of brain decoding.

Motor control hierarchy in joint action that involves bimanual force production

The concept of hierarchical motor control has been viewed as a means of progressively decreasing the number of variables manipulated by each higher control level. We tested the hypothesis that turning an individual bimanual force-production task into a joint (two-participant) force-production task would lead to positive correlation between forces produced by the two hands of the individual participant (symmetric strategy) to enable negative correlation between forces produced by two participants (complementary strategy). The present study consisted of individual and joint tasks that involved both unimanual and bimanual conditions. In the joint task, 10 pairs of participants produced periodic isometric forces, such that the sum of forces that they produced matched a target force cycling between 5% and 10% of maximum voluntary contraction at 1 Hz. In the individual task, individuals attempted to match the same target force. In the joint bimanual condition, the two hands of each participant adopted a symmetric strategy of force, whereas the two participants adopted a complementary strategy of force, highlighting that the bimanual action behaved as a low level of a hierarchy, whereas the joint action behaved as an upper level. The complementary force production was greater interpersonally than intrapersonally. However, whereas the coherence was highest at 1 Hz in all conditions, the frequency synchrony was stronger intrapersonally than interpersonally. Moreover, whereas the bimanual action exhibited a smaller error and variability of force than the unimanual action, the joint action exhibited a less-variable interval and force than the individual action.