Sensorimotor Learning Research Articles

Whole-brain modular dynamics at rest predict sensorimotor learning performance

Abstract Neural measures that predict cognitive performance are informative about the mechanisms underlying cognitive phenomena, with diagnostic potential for neuropathologies with cognitive symptoms. Among such markers, the modularity (subnetwork composition) of whole-brain functional networks is especially promising, due to its longstanding theoretical foundations and recent success in predicting clinical outcomes. We used functional magnetic resonance imaging to identify whole-brain modules at rest, calculating metrics of their spatio-temporal dynamics before and after a sensorimotor learning task on which fast learning is widely believed to be supported by a cognitive strategy. We found that participants’ learning performance was predicted by the degree of coordination of modular reconfiguration, and the strength of recruitment and integration of networks derived during the task itself. Our findings identify these whole-brain metrics as promising network-based markers of cognition, with relevance to basic neuroscience and the potential for clinical application.

De novo sensorimotor learning through reuse of movement components.

From tying one's shoelaces to driving a car, complex skills involving the coordination of multiple muscles are common in everyday life; yet relatively little is known about how these skills are learned. Recent studies have shown that new sensorimotor skills involving re-mapping familiar body movements to unfamiliar outputs cannot be learned by adjusting pre-existing controllers, and that new task-specific controllers must instead be learned "de novo". To date, however, few studies have investigated de novo learning in scenarios requiring continuous and coordinated control of relatively unpractised body movements. In this study, we used a myoelectric interface to investigate how a novel controller is learned when the task involves an unpractised combination of relatively untrained continuous muscle contractions. Over five sessions on five consecutive days, participants learned to trace a series of trajectories using a computer cursor controlled by the activation of two muscles. The timing of the generated cursor trajectory and its shape relative to the target improved for conditions trained with post-trial visual feedback. Improvements in timing transferred to all untrained conditions, but improvements in shape transferred less robustly to untrained conditions requiring the trained order of muscle activation. All muscle outputs in the final session could already be generated during the first session, suggesting that participants learned the new task by improving the selection of existing motor commands. These results suggest that the novel controllers acquired during de novo learning can, in some circumstances, be constructed from components of existing controllers.

The cerebellum acts as the analog to the medial temporal lobe for sensorimotor memory

The cerebellum is critical for sensorimotor learning. The specific contribution that it makes, however, remains unclear. Inspired by the classic finding that for declarative memories, medial temporal lobe (MTL) structures provide a gateway to the formation of long-term memory but are not required for short-term memory, we hypothesized that for sensorimotor memories, the cerebellum may play an analogous role. Here, we studied the sensorimotor learning of individuals with severe ataxia from cerebellar degeneration. We dissected the memories they formed during sensorimotor learning into a short-term temporally-volatile component, that decays rapidly with a time constant of just 15 to 20 s and thus cannot lead to long-term retention, and a longer-term temporally-persistent component that is stable for 60 s or more and leads to long-term retention. Remarkably, we find that these individuals display dramatically reduced levels of temporally-persistent sensorimotor memory, despite spared and even elevated levels of temporally-volatile sensorimotor memory. In particular, we find both impairment that systematically worsens with memory window duration over shorter memory windows (<12 s) and near-complete impairment of memory maintenance over longer memory windows (>25 s). This dissociation uncovers a unique role for the cerebellum as a gateway for the formation of long-term but not short-term sensorimotor memories, mirroring the role of the MTL for declarative memories. It thus reveals the existence of distinct neural substrates for short-term and long-term sensorimotor memory, and it explains both the trial-to-trial differences identified in this study and long-standing study-to-study differences in the effects of cerebellar damage on sensorimotor learning ability.

Sensorimotor adaptation to a nonuniform formant perturbation generalizes to untrained vowels.

When speakers learn to change the way they produce a speech sound, how much does that learning generalize to other speech sounds? Past studies of speech sensorimotor learning have typically tested the generalization of a single transformation learned in a single context. Here, we investigate the ability of the speech motor system to generalize learning when multiple opposing sensorimotor transformations are learned in separate regions of the vowel space. We find that speakers adapt to a nonuniform "centralization" perturbation, learning to produce vowels with greater acoustic contrast, and that this adaptation generalizes to untrained vowels, which pattern like neighboring trained vowels and show increased contrast of a similar magnitude.NEW & NOTEWORTHY We show that sensorimotor adaptation of vowels at the edges of the articulatory working space generalizes to intermediate vowels through local transfer of learning from adjacent vowels. These results extend findings on the locality of sensorimotor learning from upper limb control to speech, a complex task with an opaque and nonlinear transformation between motor actions and sensory consequences. Our results also suggest that our paradigm has potential to drive behaviorally relevant changes that improve communication effectiveness.

Implicit sensorimotor learning in ballistic movement for transporting an object to a target

To enhance and sustain movement accuracy, humans make corrections in subsequent trials based on previous errors. Trial-by-trial learning occurs unconsciously and has mostly been studied using reaching movements. Goal-directed projection movements, such as archery, have an inherent delay between releasing an object and observing an outcome (e.g. the arrival position of the object), and this delay may prevent trial-by-trial implicit learning. We aimed to investigate the learning in the projection movement and the impacts of the inherent delay. During the experiment, a joystick was flicked once to transport a cursor from the starting location to a target. To manipulate the length of the delay between the cursor release and outcome observation, the speed of the cursor movement was varied: a fast speed can lead to a short delay. We found trial-by-trial implicit learning under all speed conditions, and the error sensitivity was not significantly different across speed conditions. Furthermore, the error sensitivity depended on the target location, that is, the movement direction. The results indicate that trial-by-trial implicit learning occurred in goal-directed projection movement, despite the length of the inherent delay. Additionally, the degree of this learning was affected by the movement direction.

Shifts in neural tuning systematically alter sensorimotor learning ability.

Sensorimotor learning can change the tuning of neurons in motor-related brain areas and rotate their preferred directions (PDs). These PD rotations are commonly interpreted as reflecting motor command changes; however, cortical neurons that display PD rotations also contribute to sensorimotor learning. Sensorimotor learning should, therefore, alter not only motor commands but also the tuning of neurons responsible for this learning, and thus impact subsequent learning ability. Here, we investigate this possibility with computational modeling and by directly measuring adaptive responses during sensorimotor learning in humans. Modeling shows that the PD rotations induced by sensorimotor learning, predict specific anisotropic changes in PD distributions that in turn predict a specific spatial pattern of changes in learning ability. Remarkably, experiments in humans then reveal large, systematic changes in learning ability in a spatial pattern that precisely reflects these model-predicted changes. We find that this pattern defies conventional wisdom and implements Newton's method, a learning rule where the step size is inversely proportional rather than proportional to the learning gradient's amplitude, limiting overshooting in the adaptive response. Our findings indicate that PD rotation provides a mechanism whereby the motor system can simultaneously learn how to move and learn how to learn.

Attention defines the context for implicit sensorimotor adaptation.

Movement errors are used to continuously recalibrate the sensorimotor map, a process known as sensorimotor adaptation. Here we examined how attention influences this automatic and obligatory learning process. Focusing first on spatial attention, we compared conditions in which the visual feedback that provided information about the movement outcome was either attended or unattended. Surprisingly, this manipulation had no effect on the rate of adaptation. We next used a dual-task methodology to examine the influence of attentional resources on adaptation. Here, again, we found no effect of attention, with the rate of adaptation similar under focused or divided attention conditions. Interestingly, we found that attention modulates adaptation in an indirect manner: Attended stimuli serve as cues that define the context for learning. The rate of adaptation was significantly attenuated when the attended stimulus changed from the end of one trial to the start of the next trial. In contrast, similar changes to unattended stimuli had no impact on adaptation. Together, these results suggest that visual attention defines the cues that establish the context for sensorimotor learning.

Dynamics of directional motor tuning in the primate premotor and primary motor cortices during sensorimotor learning

Sensorimotor learning requires reorganization of neuronal activity in the premotor cortex (PM) and primary motor cortex (M1). To reveal PM- and M1-specific reorganization in a primate, we conducted calcium imaging in common marmosets while they learned a two-target reaching (pull/push) task after mastering a one-target reaching (pull) task. Throughout learning of the two-target reaching task, the dorsorostral PM (PMdr) showed peak activity earlier than the dorsocaudal PM (PMdc) and M1. During learning, the reaction time in pull trials increased and correlated strongly with the peak timing of PMdr activity. PMdr showed decreasing representation of newly introduced (push) movement, whereas PMdc and M1 maintained high representation of pull and push movements. Many task-related neurons in PMdc and M1 exhibited a strong preference to either movement direction. PMdc neurons dynamically switched their preferred direction depending on their performance in push trials in the early learning stage, whereas M1 neurons stably retained their preferred direction and high similarity of preferred direction between neighbors. These results suggest that in primate sensorimotor learning, dynamic directional motor tuning in PMdc converts the sensorimotor association formed in PMdr to the stable and specific motor representation of M1.

Primary cilia signaling in astrocytes mediates development and regional-specific functional specification.

Astrocyte diversity is greatly influenced by local environmental modulation. Here we report that the majority of astrocytes across the mouse brain possess a singular primary cilium localized to the cell soma. Comparative single-cell transcriptomics reveals that primary cilia mediate canonical SHH signaling to modulate astrocyte subtype-specific core features in synaptic regulation, intracellular transport, energy and metabolism. Independent of canonical SHH signaling, primary cilia are important regulators of astrocyte morphology and intracellular signaling balance. Dendritic spine analysis and transcriptomics reveal that perturbation of astrocytic cilia leads to disruption of neuronal development and global intercellular connectomes in the brain. Mice with primary ciliary-deficient astrocytes show behavioral deficits in sensorimotor function, sociability, learning and memory. Our results uncover a critical role for primary cilia in transmitting local cues that drive the region-specific diversification of astrocytes within the developing brain.

Coupled Conditional Neural Movement Primitives

Learning sensorimotor trajectories through flexible neural representations is fundamental for robots as it facilitates the building of motor skills as well as equipping them with the ability to represent the world as predictable temporal events. Recent advances in deep learning led to the development of powerful learning from demonstration (LfD) systems such as Conditional Neural Movement Primitives (CNMPs). CNMPs can robustly represent skills as movement distributions and allow them to be ‘recalled’ by conditioning the movement on a few observation points. In this study, we focus on improving CNMPs to achieve a higher resource economy by adopting a divide-and-conquer approach. We propose a novel neural architecture called Coupled CNMP (C-CNMP), that couples the latent spaces of a pair of CNMPs that splits a given sensorimotor trajectory into segments whose learning is undertaken by smaller sub-networks. Therefore, each sub-network needs to deal with a less complex trajectory making the learning less resource-hungry. With systematic simulations on a controlled trajectory data set, we show that the overhead brought by the coupling introduced in our model is well offset by the resource and performance gain obtained. To be specific, with CNMP model as the baseline, it is shown that the proposed model is able to learn to generate trajectories in the data set with a lower trajectory error measured as the mean absolute difference between the generated trajectory and the ground truth. Importantly, our model can perform well with relatively limited resources, i.e., with less number of neural network parameters compared to the baseline. To show that the findings from the controlled data set well-transfer to robot data, we use robot joint data in an LfD setting and compare the learning performance of the proposed model with the baseline model at equal complexity levels. The simulation experiments show that with also the robot joint data, the proposed model, C-CNMP, learns to generate the joint trajectories with significantly less error than the baseline model. Overall, our study improves the state of the art in sensorimotor trajectory learning and exemplifies how divide-and-conquer approaches can benefit deep learning architectures for resource economy.

Fundamental processes in sensorimotor learning: Reasoning, refinement, and retrieval.

Motor learning is often viewed as a unitary process that operates outside of conscious awareness. This perspective has led to the development of sophisticated models designed to elucidate the mechanisms of implicit sensorimotor learning. In this review, we argue for a broader perspective, emphasizing the contribution of explicit strategies to sensorimotor learning tasks. Furthermore, we propose a theoretical framework for motor learning that consists of three fundamental processes: reasoning, the process of understanding action-outcome relationships; refinement, the process of optimizing sensorimotor and cognitive parameters to achieve motor goals; and retrieval, the process of inferring the context and recalling a control policy. We anticipate that this '3R' framework for understanding how complex movements are learned will open exciting avenues for future research at the intersection between cognition and action.

Когда задача кажется проще: влияние иллюзорного размера цели на точность попадания

&lt;p style="text-align: justify;"&gt;The article investigates the influence of subjective beliefs about one's efficiency on the results of sensorimotor activity through experimental studies using size illusions. Sensorimotor learning is a process of increasing the efficiency of activity as it is practised. It involves a two-way relationship between performance expectations and performance outcomes. Performance expectations are subjective beliefs about the likely success of a particular task. Visual illusions are often used to investigate this relationship. Illusorily larger targets are perceived as easier compared to illusorily smaller ones; as a consequence, subjects are more efficient at hitting targets that appear easier to them. However, results that do not fit the model are still observed. The paper proposes an explanation for the inconsistent results by a possible "failure" in a three-level mechanism involving replication of the size illusion, perception of targets as different in difficulty, and changing performance prediction according to difficulty. The paper analyses the design and results of 18 experimental studies conducted from 2012 to 2023 and suggests possible modifications of the experimental paradigms at each of the three levels of the mechanism of the effect of performance prediction on sensorimotor activity.&lt;/p&gt;

Age-dependent predictors of effective reinforcement motor learning across childhood.

Across development, children must learn motor skills such as eating with a spoon and drawing with a crayon. Reinforcement learning, driven by success and failure, is fundamental to such sensorimotor learning. It typically requires a child to explore movement options along a continuum (grip location on a crayon) and learn from probabilistic rewards (whether the crayon draws or breaks). Here, we studied the development of reinforcement motor learning using online motor tasks to engage children aged 3 to 17 and adults (cross-sectional sample, N=385). Participants moved a cartoon penguin across a scene and were rewarded (animated cartoon clip) based on their final movement position. Learning followed a clear developmental trajectory when participants could choose to move anywhere along a continuum and the reward probability depended on final movement position. Learning was incomplete or absent in 3 to 8-year-olds and gradually improved to adult-like levels by adolescence. A reinforcement learning model fit to each participant identified three age-dependent factors underlying improvement: amount of exploration after a failed movement, learning rate, and level of motor noise. We predicted, and confirmed, that switching to discrete targets and deterministic reward would improve 3 to 8-year-olds' learning to adult-like levels by increasing exploration after failed movements. Overall, we show a robust developmental trajectory of reinforcement motor learning abilities under ecologically relevant conditions i.e., continuous movement options mapped to probabilistic reward. This learning appears to be limited by immature spatial processing and probabilistic reasoning abilities in young children and can be rescued by reducing the demands in these domains.

Differential Aging Effects on Implicit and Explicit Sensorimotor Learning.

Deterioration in motor control is a hallmark of aging, significantly contributing to a decline in quality of life. More controversial is the question of whether and how aging impacts sensorimotor learning. We hypothesized that the inconsistent picture observed in the current literature can be attributed to at least two factors. First, aging studies tend to be underpowered. Second, the learning assays used in these experiments tend to reflect, to varying degrees, the operation of multiple learning processes, making it difficult to make inferences across studies. We took a two-pronged approach to address these issues. We first performed a meta-analysis of the sensorimotor adaptation literature focusing on outcome measures that provide estimates of explicit and implicit components of adaptation. We then conducted two well-powered experiments to re-examine the effect of aging on sensorimotor adaptation, using behavioral tasks designed to isolate explicit and implicit processes. Convergently, both approaches revealed a striking dissociation: Older individuals exhibited a marked impairment in their ability to discover an explicit strategy to counteract a visuomotor perturbation. However, they exhibited enhanced implicit recalibration. We hypothesize that the effect of aging on explicit learning reflects an age-related decline in reasoning and problem solving, and the effect of aging on implicit learning reflects age-related changes in multisensory integration. Taken together, these findings deepen our understanding of the impact of aging on sensorimotor learning.

Limb-related sensory prediction errors and task-related performance errors facilitate human sensorimotor learning through separate mechanisms.

The unpredictable nature of our world can introduce a variety of errors in our actions, including sensory prediction errors (SPEs) and task performance errors (TPEs). SPEs arise when our existing internal models of limb-environment properties and interactions become miscalibrated due to changes in the environment, while TPEs occur when environmental perturbations hinder achievement of task goals. The precise mechanisms employed by the sensorimotor system to learn from such limb- and task-related errors and improve future performance are not comprehensively understood. To gain insight into these mechanisms, we performed a series of learning experiments wherein the location and size of a reach target were varied, the visual feedback of the motion was perturbed in different ways, and instructions were carefully manipulated. Our findings indicate that the mechanisms employed to compensate SPEs and TPEs are dissociable. Specifically, our results fail to support theories that suggest that TPEs trigger implicit refinement of reach plans or that their occurrence automatically modulates SPE-mediated learning. Rather, TPEs drive improved action selection, that is, the selection of verbally sensitive, volitional strategies that reduce future errors. Moreover, we find that exposure to SPEs is necessary and sufficient to trigger implicit recalibration. When SPE-mediated implicit learning and TPE-driven improved action selection combine, performance gains are larger. However, when actions are always successful and strategies are not employed, refinement in behavior is smaller. Flexibly weighting strategic action selection and implicit recalibration could thus be a way of controlling how much, and how quickly, we learn from errors.

Tutor auditory memory for guiding sensorimotor learning in birdsong.

Memory-guided motor shaping is necessary for sensorimotor learning. Vocal learning, such as speech development in human babies and song learning in bird juveniles, begins with the formation of an auditory template by hearing adult voices followed by vocally matching to the memorized template using auditory feedback. In zebra finches, the widely used songbird model system, only males develop individually unique stereotyped songs. The production of normal songs relies on auditory experience of tutor's songs (commonly their father's songs) during a critical period in development that consists of orchestrated auditory and sensorimotor phases. "Auditory templates" of tutor songs are thought to form in the brain to guide later vocal learning, while formation of "motor templates" of own song has been suggested to be necessary for the maintenance of stereotyped adult songs. Where these templates are formed in the brain and how they interact with other brain areas to guide song learning, presumably with template-matching error correction, remains to be clarified. Here, we review and discuss studies on auditory and motor templates in the avian brain. We suggest that distinct auditory and motor template systems exist that switch their functions during development.

Motor adaptation is reduced by symbolic compared to sensory feedback.

Motor adaptation - the process of reducing motor errors through feedback and practice - is an essential feature of human competence, allowing us to move accurately in dynamic and novel environments. Adaptation typically results from sensory feedback, with most learning driven by visual and proprioceptive feedback that arises with the movement. In humans, motor adaptation can also be driven by symbolic feedback. In the present study, we examine how implicit and explicit components of motor adaptation are modulated by symbolic feedback. We conducted three reaching experiments involving over 400 human participants to compare sensory and symbolic feedback using a task in which both types of learning processes could be operative (Experiment 1) or tasks in which learning was expected to be limited to only an explicit process (Experiments 2 and 3). Adaptation with symbolic feedback was dominated by explicit strategy use, with minimal evidence of implicit recalibration. Even when matched in terms of information content, adaptation to rotational and mirror reversal perturbations was slower in response to symbolic feedback compared to sensory feedback. Our results suggest that the abstract and indirect nature of symbolic feedback disrupts strategic reasoning and/or refinement, deepening our understanding of how feedback type influences the mechanisms of sensorimotor learning.

Associative white matter tracts selectively predict sensorimotor learning

Human learning varies greatly among individuals and is related to the microstructure of major white matter tracts in several learning domains, yet the impact of the existing microstructure of white matter tracts on future learning outcomes remains unclear. We employed a machine-learning model selection framework to evaluate whether existing microstructure might predict individual differences in learning a sensorimotor task, and further, if the mapping between tract microstructure and learning was selective for learning outcomes. We used diffusion tractography to measure the mean fractional anisotropy (FA) of white matter tracts in 60 adult participants who then practiced drawing a set of 40 unfamiliar symbols repeatedly using a digital writing tablet. We measured drawing learning as the slope of draw duration over the practice session and measured visual recognition learning for the symbols using an old/new 2-AFC task. Results demonstrated that tract microstructure selectively predicted learning outcomes, with left hemisphere pArc and SLF3 tracts predicting drawing learning and the left hemisphere MDLFspl predicting visual recognition learning. These results were replicated using repeat, held-out data and supported with complementary analyses. Results suggest that individual differences in the microstructure of human white matter tracts may be selectively related to future learning outcomes.

Refinement of efficient encodings of movement in the dorsolateral striatum throughout learning.

The striatum is required for normal action selection, movement, and sensorimotor learning. Although action-specific striatal ensembles have been well documented, it is not well understood how these ensembles are formed and how their dynamics may evolve throughout motor learning. Here we used longitudinal 2-photon Ca 2+ imaging of dorsal striatal neurons in head-fixed mice as they learned to self-generate locomotion. We observed a significant activation of both direct- and indirect-pathway spiny projection neurons (dSPNs and iSPNs, respectively) during early locomotion bouts and sessions that gradually decreased over time. For dSPNs, onset- and offset-ensembles were gradually refined from active motion-nonspecific cells. iSPN ensembles emerged from neurons initially active during opponent actions before becoming onset- or offset-specific. Our results show that as striatal ensembles are progressively refined, the number of active nonspecific striatal neurons decrease and the overall efficiency of the striatum information encoding for learned actions increases.

Divergent NMJ Development Supports Female Vocal Suppression in the Zebra Finch Songbird (Taeniopygia guttata)

Songbirds have long been central to the study of vocal learning and production, and in most species, males and females sing. However, in the best-studied species, the zebra finch ( Taeniopygia guttata), only males sing. Most theories of this sexual dimorphism had suggested that male zebra finches have specializations, e.g. to promote song, that females do not. We recently found the opposite. At the synapses that connect motoneurons to muscle fibers (neuromuscular junctions; NMJs) of the avian voicebox (syrinx, analogous to larynx), we found female synaptic specializations that support song suppression: Female syrinx, and only female syrinx (not tongue, latissimus dorsi, or any male muscle measured) has specialized NMJs that could work to suppress singing ability in this species. Specifically, the female syrinx shows a high proportion of putatively weaker en grappe NMJs, biased towards smaller and weaker muscle fibers, which fail to form a motor endplate band (MEB). However, these results raise an unanswered question: are male and female syringes born differently, or do they have divergent developmental programs? We analyzed males and females (n=3) across critical ages - the robust onset of sensory song learning (post-hatch day P30), song-like vocalizations (P50), and the end of sensorimotor learning (P70). We find that NMJs in male and female syrinx muscles are similar early in development and diverge around the age that vocal behavior becomes sexually dimorphic, approaching their different adult phenotypes by the end of this learning phase. We find that outside the MEB, en grappe NMJs are pruned away in males, while en plaque NMJs are lost here in females. Additionally, these NMJ features are highly labile in development, as there are many examples of NMJs intermediate between the vertebrate-typical en plaque and the less well-described en grappe, implying some transitory nature of NMJ class. Preliminary work using developmental hormonal manipulations, which induces sex-reversal of vocal behavior (i.e., promoting female song, or suppressing male song), indicates the en grappe NMJs specifically outside the MEB are most directly tied to vocal ability. Interestingly, the songbird syrinx lacks the tonic muscle fibers on which en grappe NMJs are typically found in mammals, opening the question of the functional attributes of these NMJs. Future work aims to characterize the physiological properties of these putatively weaker synapses, as well as their functional consequences to vocal sexual dimorphism. NSF-GRFP awarded to JVG; Neuronex awarded to NBK. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.