Adaptive Prediction for Social Contexts: The Cerebellar Contribution to Typical and Atypical Social Behaviors.

Social interactions involve processes ranging from face recognition to understanding others' intentions. To guide appropriate behavior in a given context, social interactions rely on accurately predicting the outcomes of one's actions and the thoughts of others. Because social interactions are inherently dynamic, these predictions must be continuously adapted. The neural correlates of social processing have largely focused on emotion, mentalizing, and reward networks, without integration of systems involved in prediction. The cerebellum forms predictive models to calibrate movements and adapt them to changing situations, and cerebellar predictive modeling is thought to extend to nonmotor behaviors. Primary cerebellar dysfunction can produce social deficits, and atypical cerebellar structure and function are reported in autism, which is characterized by social communication challenges and atypical predictive processing. We examine the evidence that cerebellar-mediated predictions and adaptation play important roles in social processes and argue that disruptions in these processes contribute to autism.

Sharing massive biomedical data at magnitudes lower bandwidth using implicit neural function.

Efficient storage and sharing of massive biomedical data would open up their wide accessibility to different institutions and disciplines. However, compressors tailored for natural photos/videos are rapidly limited for biomedical data, while emerging deep learning-based methods demand huge training data and are difficult to generalize. Here, we propose to conduct Biomedical data compRession with Implicit nEural Function (BRIEF) by representing the target data with compact neural networks, which are data specific and thus have no generalization issues. Benefiting from the strong representation capability of implicit neural function, BRIEF achieves 2[Formula: see text]3 orders of magnitude compression on diverse biomedical data at significantly higher fidelity than existing techniques. Besides, BRIEF is of consistent performance across the whole data volume, and supports customized spatially varying fidelity. BRIEF's multifold advantageous features also serve reliable downstream tasks at low bandwidth. Our approach will facilitate low-bandwidth data sharing and promote collaboration and progress in the biomedical field.

On the different regimes of stochastic gradient descent.

Modern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch size [Formula: see text], and the step size or learning rate [Formula: see text]. For small [Formula: see text] and large [Formula: see text], SGD corresponds to a stochastic evolution of the parameters, whose noise amplitude is governed by the "temperature" [Formula: see text]. Yet this description is observed to break down for sufficiently large batches [Formula: see text], or simplifies to gradient descent (GD) when the temperature is sufficiently small. Understanding where these cross-overs take place remains a central challenge. Here, we resolve these questions for a teacher-student perceptron classification model and show empirically that our key predictions still apply to deep networks. Specifically, we obtain a phase diagram in the [Formula: see text]-[Formula: see text] plane that separates three dynamical phases: i) a noise-dominated SGD governed by temperature, ii) a large-first-step-dominated SGD and iii) GD. These different phases also correspond to different regimes of generalization error. Remarkably, our analysis reveals that the batch size [Formula: see text] separating regimes (i) and (ii) scale with the size [Formula: see text] of the training set, with an exponent that characterizes the hardness of the classification problem.

Predicting implicit attitudes with natural language data.

Large-scale language datasets and advances in natural language processing offer opportunities for studying people's cognitions and behaviors. We show how representations derived from language can be combined with laboratory-based word norms to predict implicit attitudes for diverse concepts. Our approach achieves substantially higher correlations than existing methods. We also show that our approach is more predictive of implicit attitudes than are explicit attitudes, and that it captures variance in implicit attitudes that is largely unexplained by explicit attitudes. Overall, our results shed light on how implicit attitudes can be measured by combining standard psychological data with large-scale language data. In doing so, we pave the way for highly accurate computational modeling of what people think and feel about the world around them.

All human social groups are human, but some are more human than others: A comprehensive investigation of the implicit association of "Human" to US racial/ethnic groups.

All human groups are equally human, but are they automatically represented as such? Harnessing data from 61,377 participants across 13 experiments (six primary and seven supplemental), a sharp dissociation between implicit and explicit measures emerged. Despite explicitly affirming the equal humanity of all racial/ethnic groups, White participants consistently associated Human (relative to Animal) more with White than Black, Hispanic, and Asian groups on Implicit Association Tests (IATs; experiments 1-4). This effect emerged across diverse representations of Animal that varied in valence (pets, farm animals, wild animals, and vermin; experiments 1-2). Non-White participants showed no such Human=Own Group bias (e.g., Black participants on a White-Black/Human-Animal IAT). However, when the test included two outgroups (e.g., Asian participants on a White-Black/Human-Animal IAT), non-White participants displayed Human=White associations. The overall effect was largely invariant across demographic variations in age, religion, and education but did vary by political ideology and gender, with self-identified conservatives and men displaying stronger Human=White associations (experiment 3). Using a variance decomposition method, experiment 4 showed that the Human=White effect cannot be attributed to valence alone; the semantic meaning of Human and Animal accounted for a unique proportion of variance. Similarly, the effect persisted even when Human was contrasted with positive attributes (e.g., God, Gods, and Dessert; experiment 5a). Experiments 5a-b clarified the primacy of Human=White rather than Animal=Black associations. Together, these experiments document a factually erroneous but robust Human=Own Group implicit stereotype among US White participants (and globally), with suggestive evidence of its presence in other socially dominant groups.

Repetition learning is neither a continuous nor an implicit process.

Learning advances through repetition. A classic paradigm for studying this process is the Hebb repetition effect: Immediate serial recall performance improves for lists presented repeatedly as compared to nonrepeated lists. Learning in the Hebb paradigm has been described as a slow but continuous accumulation of long-term memory traces over repetitions [e.g., Page & Norris, Phil. Trans. R. Soc. B 364, 3737-3753 (2009)]. Furthermore, it has been argued that Hebb repetition learning requires no awareness of the repetition, thereby being an instance of implicit learning [e.g., Guérard et al., Mem. Cogn. 39, 1012-1022 (2011); McKelvie,  J. Gen. Psychol. 114, 75-88 (1987)]. While these assumptions match the data from a group-level perspective, another picture emerges when analyzing data on the individual level. We used a Bayesian hierarchical mixture modeling approach to describe individual learning curves. In two preregistered experiments, using a visual and a verbal Hebb repetition task, we demonstrate that 1) individual learning curves show an abrupt onset followed by rapid growth, with a variable time for the onset of learning across individuals, and that 2) learning onset was preceded by, or coincided with, participants becoming aware of the repetition. These results imply that repetition learning is not implicit and that the appearance of a slow and gradual accumulation of knowledge is an artifact of averaging over individual learning curves.

Neural correlates of musical familiarity: a functional magnetic resonance study.

Existing neuroimaging studies on neural correlates of musical familiarity often employ a familiar vs. unfamiliar contrast analysis. This singular analytical approach reveals associations between explicit musical memory and musical familiarity. However, is the neural activity associated with musical familiarity solely related to explicit musical memory, or could it also be related to implicit musical memory? To address this, we presented 130 song excerpts of varying familiarity to 21 participants. While acquiring their brain activity using functional magnetic resonance imaging (fMRI), we asked the participants to rate the familiarity of each song on a five-point scale. To comprehensively analyze the neural correlates of musical familiarity, we examined it from four perspectives: the intensity of local neural activity, patterns of local neural activity, global neural activity patterns, and functional connectivity. The results from these four approaches were consistent and revealed that musical familiarity is related to the activity of both explicit and implicit musical memory networks. Our findings suggest that: (1) musical familiarity is also associated with implicit musical memory, and (2) there is a cooperative and competitive interaction between the two types of musical memory in the perception of music.

Patient activation reduces effects of implicit bias on doctor-patient interactions.

Disparities between Black and White Americans persist in medical treatment and health outcomes. One reason is that physicians sometimes hold implicit racial biases that favor White (over Black) patients. Thus, disrupting the effects of physicians' implicit bias is one route to promoting equitable health outcomes. In the present research, we tested a potential mechanism to short-circuit the effects of doctors' implicit bias: patient activation, i.e., having patients ask questions and advocate for themselves. Specifically, we trained Black and White standardized patients (SPs) to be "activated" or "typical" during appointments with unsuspecting oncologists and primary care physicians in which SPs claimed to have stage IV lung cancer. Supporting the idea that patient activation can promote equitable doctor-patient interactions, results showed that physicians' implicit racial bias (as measured by an implicit association test) predicted racially biased interpersonal treatment among typical SPs (but not among activated SPs) across SP ratings of interaction quality and ratings from independent coders who read the interaction transcripts. This research supports prior work showing that implicit attitudes can undermine interpersonal treatment in medical settings and provides a strategy for ensuring equitable doctor-patient interactions.

Selective engram coreactivation in idling brain inspires implicit learning.

Passive priming of prior knowledge to assimilate ongoing experiences underlies advanced cognitive processing. However, the necessary neural dynamics of memory assimilation remains elusive. Uninstructed brain could also show boosted creativity, particularly after idling states, yet it remains unclear whether the idling brain can spontaneously spark relevant knowledge assimilations. We established a paradigm that links/separates context-dependent memories according to geometrical similarities. Mice exploring one of four contexts 1 d before undergoing contextual fear conditioning in a square context showed a gradual fear transfer to preexposed geometrically relevant contexts the next day, but not after 15 min. Anterior cingulate cortex neurons representing relevant, rather than distinct, memories were significantly coreactivated during postconditioning sleep only, before their selective integration the next day during testing. Disrupting sleep coreactivations prevented assimilation while preserving recent memory consolidation. Thus, assimilating pertinent memories during sleep through coreactivation of their respective engrams represents the neural underpinnings of sleep-triggered implicit cortical learning.

The contributions of positive outgroup and negative ingroup evaluation to implicit bias favoring outgroups.

People sometimes prefer groups to which they do not belong (outgroups) over their own groups (ingroups). Many long-standing theoretical perspectives assume that this outgroup favorability bias primarily reflects negative ingroup evaluations rather than positive outgroup evaluations. To examine the contributions of negative ingroup versus positive outgroup evaluations to outgroup bias, we examined participants' data (total n > 879,000) from Implicit Association Tests [A. G. Greenwald, D. E. McGhee, J. L. K. Schwartz, J. Pers. Soc. Psychol. 74, 1464-1480 (1998)] measuring intergroup attitudes across four social domains in exploratory and preregistered confirmatory analyses. Process modeling [F. R. Conrey, J. W. Sherman, B. Gawronski, K. Hugenberg, C. J. Groom, J. Pers. Soc. Psychol. 89, 469-487 (2005)] was applied to the responses of participants who demonstrated implicit outgroup bias to separately estimate the contributions of negative ingroup and positive outgroup evaluations. The outgroup biases of lower-status group members (i.e., Asian, Black, gay and lesbian, and older people) consistently reflected greater contributions of positive outgroup evaluations than negative ingroup evaluations. In contrast, the outgroup biases of higher-status group members (i.e., White, straight, and younger people) reflected a more varied pattern of evaluations. We replicated this pattern of results using explicitly measured intergroup evaluations. Taking these data together, the present research demonstrates a positive-negative asymmetry effect of outgroup bias, primarily among members of lower-status groups.

Unlocking adults' implicit statistical learning by cognitive depletion.

Human learning is supported by multiple neural mechanisms that maturate at different rates and interact in mostly cooperative but also sometimes competitive ways. We tested the hypothesis that mature cognitive mechanisms constrain implicit statistical learning mechanisms that contribute to early language acquisition. Specifically, we tested the prediction that depleting cognitive control mechanisms in adults enhances their implicit, auditory word-segmentation abilities. Young adults were exposed to continuous streams of syllables that repeated into hidden novel words while watching a silent film. Afterward, learning was measured in a forced-choice test that contrasted hidden words with nonwords. The participants also had to indicate whether they explicitly recalled the word or not in order to dissociate explicit versus implicit knowledge. We additionally measured electroencephalography during exposure to measure neural entrainment to the repeating words. Engagement of the cognitive mechanisms was manipulated by using two methods. In experiment 1 (n = 36), inhibitory theta-burst stimulation (TBS) was applied to the left dorsolateral prefrontal cortex or to a control region. In experiment 2 (n = 60), participants performed a dual working-memory task that induced high or low levels of cognitive fatigue. In both experiments, cognitive depletion enhanced word recognition, especially when participants reported low confidence in remembering the words (i.e., when their knowledge was implicit). TBS additionally modulated neural entrainment to the words and syllables. These findings suggest that cognitive depletion improves the acquisition of linguistic knowledge in adults by unlocking implicit statistical learning mechanisms and support the hypothesis that adult language learning is antagonized by higher cognitive mechanisms.

Mutation protocols share with sexual reproduction the physiological role of producing genetic variation within 'constraints that deconstrain'.

Because the universe of possible DNA sequences is inconceivably vast, organisms have evolved mechanisms for exploring DNA sequence space while substantially reducing the hazard that would otherwise accrue to any process of random, accidental mutation. One such mechanism is meiotic recombination. Although sexual reproduction imposes a seemingly paradoxical 50% cost to fitness, sex evidently prevails because this cost is outweighed by the advantage of equipping offspring with genetic variation to accommodate environmental vicissitudes. The potential adaptive utility of additional mechanisms for producing genetic variation has long been obscured by a presumption that the vast majority of mutations are deleterious. Perhaps surprisingly, the probability for adaptive variation can be increased by several mechanisms that generate mutations abundantly. Such mechanisms, here called 'mutation protocols', implement implicit 'constraints that deconstrain'. Like meiotic recombination, they produce genetic variation in forms that minimize potential for harm while providing a reasonably high probability for benefit. One example is replication slippage of simple sequence repeats (SSRs); this process yields abundant, reversible mutations, typically with small quantitative effect on phenotype. This enables SSRs to function as adjustable 'tuning knobs'. There exists a clear pathway for SSRs to be shaped through indirect selection favouring their implicit tuning-knob protocol. Several other molecular mechanisms comprise probable components of additional mutation protocols. Biologists might plausibly regard such mechanisms of mutation not primarily as sources of deleterious genetic mistakes but also as potentially adaptive processes for 'exploring' DNA sequence space.

Arm cooling selectively impacts sensorimotor control.

The benefits of cold have long been recognized in sport and medicine. However, it also brings costs, which have more rarely been investigated, notably in terms of sensorimotor control. We hypothesized that, in addition to peripheral effects, cold slows down the processing of proprioceptive cues, which has an impact on both feedback and feedforward control. We therefore compared the performances of participants whose right arm had been immersed in either cold water (arm temperature: 14°C) or lukewarm water (arm temperature: 34°C). In experiment 1, we administered a Fitts's pointing task and performed a kinematic analysis to determine whether sensorimotor control processes were affected by the cold. Results revealed 1) modifications in late kinematic parameters, suggesting changes in the use of proprioceptive feedback, and 2) modifications in early kinematic parameters, suggesting changes in action representations and/or feedforward processes. To explore our hypothesis further, we ran a second experiment in which no physical movement was involved, and thus no peripheral effects. Participants were administrated a hand laterality task, known to involve implicit motor imagery and assess the internal representation of the hand. They were shown left- and right-hand images randomly displayed in different orientations in the picture plane and had to identify as quickly and as accurately as possible whether each image was of the left hand or the right hand. Results revealed slower responses and more errors when participants had to mentally rotate the cooled hand in the extreme orientation of 160°, further suggesting the impact of cold on action representations.NEW & NOTEWORTHY We investigated how arm cooling modulates sensorimotor representations and sensorimotor control. Arm cooling induced changes in early kinematic parameters of pointing, suggesting an impact on feedforward processes or hand representation. Arm cooling induced changes in late kinematic parameters of pointing, suggesting an impact on feedback processes. Arm cooling also affected performance on a hand laterality task, suggesting that action representations were modified.

Reward actively engages both implicit and explicit components in dual force field adaptation.

Motor learning occurs through multiple mechanisms, including unsupervised, supervised (error based), and reinforcement (reward based) learning. Although studies have shown that reward leads to an overall better motor adaptation, the specific processes by which reward influences adaptation are still unclear. Here, we examine how the presence of reward affects dual adaptation to novel dynamics and distinguish its influence on implicit and explicit learning. Participants adapted to two opposing force fields in an adaptation/deadaptation/error-clamp paradigm, where five levels of reward (a score and a digital face) were provided as participants reduced their lateral error. Both reward and control (no reward provided) groups simultaneously adapted to both opposing force fields, exhibiting a similar final level of adaptation, which was primarily implicit. Triple-rate models fit to the adaptation process found higher learning rates in the fast and slow processes and a slightly increased fast retention rate for the reward group. Whereas differences in the slow learning rate were only driven by implicit learning, the large difference in the fast learning rate was mainly explicit. Overall, we confirm previous work showing that reward increases learning rates, extending this to dual-adaptation experiments and demonstrating that reward influences both implicit and explicit adaptation. Specifically, we show that reward acts primarily explicitly on the fast learning rate and implicitly on the slow learning rates.NEW & NOTEWORTHY Here we show that rewarding participants' performance during dual force field adaptation primarily affects the initial rate of learning and the early timescales of adaptation, with little effect on the final adaptation level. However, reward affects both explicit and implicit components of adaptation. Whereas the learning rate of the slow process is increased implicitly, the fast learning and retention rates are increased through both implicit components and the use of explicit strategies.

Coupling finite and boundary element methods to solve the Poisson-Boltzmann equation for electrostatics in molecular solvation.

The Poisson-Boltzmann equation is widely used to model electrostatics in molecular systems. Available software packages solve it using finite difference, finite element, and boundary element methods, where the latter is attractive due to the accurate representation of the molecular surface and partial charges, and exact enforcement of the boundary conditions at infinity. However, the boundary element method is limited to linear equations and piecewise constant variations of the material properties. In this work, we present a scheme that couples finite and boundary elements for the linearised Poisson-Boltzmann equation, where the finite element method is applied in a confined solute region and the boundary element method in the external solvent region. As a proof-of-concept exercise, we use the simplest methods available: Johnson-Nédélec coupling with mass matrix and diagonal preconditioning, implemented using the Bempp-cl and FEniCSx libraries via their Python interfaces. We showcase our implementation by computing the polar component of the solvation free energy of a set of molecules using a constant and a Gaussian-varying permittivity. As validation, we compare against well-established finite difference solvers for an extensive binding energy data set, and with the finite difference code APBS (to 0.5%) for Gaussian permittivities. We also show scaling results from protein G B1 (955 atoms) up to immunoglobulin G (20,148 atoms). For small problems, the coupled method was efficient, outperforming a purely boundary integral approach. For Gaussian-varying permittivities, which are beyond the applicability of boundary elements alone, we were able to run medium to large-sized problems on a single workstation. The development of better preconditioning techniques and the use of distributed memory parallelism for larger systems remains an area for future work. We hope this work will serve as inspiration for future developments that consider space-varying field parameters, and mixed linear-nonlinear schemes for molecular electrostatics with implicit solvent models.

Monovalent cation binding to model systems and the macrocyclic depsipeptide, emodepside.

This study focuses on the systematic exploration of the emodepside conformations bound to monovalent K+ ion using quantum mechanical density functional theory (DFT) calculations at the M06-2X/6-31+G(d,p) level of theory. Nine conformers of emodepside and their complexes with K+ ion were characterized as stationary points on the potential energy surface. The conformational isomers were examined for their 3D structures, bonding, energetics, and interactions with the cation. A cavitand-like structure (CC) is identified to be the energetically most stable arrangement. To arrive at a better understanding of the K+ ion binding, calculations were initially performed on complexes formed by the K+ and Na+ ions with model ligands (methyl ester and N,N-dimethyl acetamide). Both the natural bond orbital (NBO) method and the block-localized wavefunction (BLW) energy decomposition approach was employed to assess the bonding and energetic contributions stabilizing the ion-bound model complexes. Finally, the solvent effect was evaluated through complete geometry optimizations and energy minimizations for the model ion-ligand complexes and the emodepside-K+ bound complexes using an implicit solvent model mimicking water and DMSO.

Does transcranial direct current stimulation of the primary motor cortex improve implicit motor sequence learning in Parkinson's disease?

Implicit motor sequence learning (IMSL) is a cognitive function that is known to be associated with impaired motor function in Parkinson's disease (PD). We previously reported positive effects of transcranial direct current stimulation (tDCS) over the primary motor cortex (M1) on IMSL in 11 individuals with PD with mild cognitive impairments (MCI), with the largest effects occurring during reacquisition. In the present study, we included 35 individuals with PD, with (n = 15) and without MCI (n = 20), and 35 age- and sex-matched controls without PD, with (n = 13) and without MCI (n = 22). We used mixed-effects models to analyze anodal M1 tDCS effects on acquisition (during tDCS), short-term (five minutes post-tDCS) and long-term reacquisition (one-week post-tDCS) of general and sequence-specific learning skills, as measured by the serial reaction time task. At long-term reacquisition, anodal tDCS resulted in smaller general learning effects compared to sham, only in the PD group, p = .018, possibly due to floor effects. Anodal tDCS facilitated the acquisition of sequence-specific learning (M = 54.26 ms) compared to sham (M = 38.98 ms), p = .003, regardless of group (PD/controls). Further analyses revealed that this positive effect was the largest in the PD-MCI group (anodal: M = 69.07 ms; sham: M = 24.33 ms), p < .001. Although the observed effect did not exceed the stimulation period, this single-session tDCS study confirms the potential of tDCS to enhance IMSL, with the largest effects observed in patients with lower cognitive status. These findings add to the body of evidence that anodal tDCS can beneficially modulate the abnormal basal ganglia network activity that occurs in PD.

Predicting ncRNA-protein interactions based on dual graph convolutional network and pairwise learning.

Noncoding RNAs (ncRNAs) have recently attracted considerable attention due to their key roles in biology. The ncRNA-proteins interaction (NPI) is often explored to reveal some biological activities that ncRNA may affect, such as biological traits, diseases, etc. Traditional experimental methods can accomplish this work but are often labor-intensive and expensive. Machine learning and deep learning methods have achieved great success by exploiting sufficient sequence or structure information. Graph Neural Network (GNN)-based methods consider the topology in ncRNA-protein graphs and perform well on tasks like NPI prediction. Based on GNN, some pairwise constraint methods have been developed to apply on homogeneous networks, but not used for NPI prediction on heterogeneous networks. In this paper, we construct a pairwise constrained NPI predictor based on dual Graph Convolutional Network (GCN) called NPI-DGCN. To our knowledge, our method is the first to train a heterogeneous graph-based model using a pairwise learning strategy. Instead of binary classification, we use a rank layer to calculate the score of an ncRNA-protein pair. Moreover, our model is the first to predict NPIs on the ncRNA-protein bipartite graph rather than the homogeneous graph. We transform the original ncRNA-protein bipartite graph into two homogenous graphs on which to explore second-order implicit relationships. At the same time, we model direct interactions between two homogenous graphs to explore explicit relationships. Experimental results on the four standard datasets indicate that our method achieves competitive performance with other state-of-the-art methods. And the model is available at https://github.com/zhuoninnin1992/NPIPredict.

Self-supervised multicontrast super-resolution for diffusion-weighted prostate MRI.

PURPOSE: This study addresses the challenge of low resolution and signal-to-noise ratio (SNR) in diffusion-weighted images (DWI), which are pivotal for cancer detection. Traditional methods increase SNR at high b-values through multiple acquisitions, but this results in diminished image resolution due to motion-induced variations. Our research aims to enhance spatial resolution by exploiting the global structure within multicontrast DWI scans and millimetric motion between acquisitions. METHODS: We introduce a novel approach employing a "Perturbation Network" to learn subvoxel-size motions between scans, trained jointly with an implicit neural representation (INR) network. INR encodes the DWI as a continuous volumetric function, treating voxel intensities of low-resolution acquisitions as discrete samples. By evaluating this function with a finer grid, our model predicts higher-resolution signal intensities for intermediate voxel locations. The Perturbation Network's motion-correction efficacy was validated through experiments on biological phantoms and in vivo prostate scans. RESULTS: Quantitative analyses revealed significantly higher structural similarity measures of super-resolution images to ground truth high-resolution images compared to high-order interpolation (p < $$ < $$ 0.005). In blind qualitative experiments, 96 . 1 % $$ 96.1\% $$ of super-resolution images were assessed to have superior diagnostic quality compared to interpolated images. CONCLUSION: High-resolution details in DWI can be obtained without the need for high-resolution training data. One notable advantage of the proposed method is that it does not require a super-resolution training set. This is important in clinical practice because the proposed method can easily be adapted to images with different scanner settings or body parts, whereas the supervised methods do not offer such an option.

Transcranial electrical stimulation for procedural learning and rehabilitation.

Procedural learning is the acquisition of motor and non-motor skills through a gradual process that increases with practice. Impairments in procedural learning have been consistently demonstrated in neurodevelopmental, neurodegenerative, and neuropsychiatric disorders. Considering that noninvasive brain stimulation modulates brain activity and boosts neuroplastic mechanisms, we reviewed the effects of coupling transcranial direct current stimulation (tDCS) with training methods for motor and non-motor procedural learning to explore tDCS potential use as a tool for enhancing implicit learning in healthy and clinical populations. The review covers tDCS effects over i. motor procedural learning, from basic to complex activities; ii. non-motor procedural learning; iii. procedural rehabilitation in several clinical populations. We conclude that targeting the primary motor cortex and prefrontal areas seems the most promising for motor and non-motor procedural learning, respectively. For procedural rehabilitation, the use of tDCS is yet at an early stage but some effectiveness has been reported for implicit motor and memory learning. Still, systematic comparisons of stimulation parameters and target areas are recommended for maximising the effectiveness of tDCS and its robustness for procedural rehabilitation.