The Role of the Cerebellum in Advanced Cognitive Processes in Children.

Over the last several years, a growing body of evidence from anatomical, physiological, and functional neuroimaging studies has increasingly indicated that the cerebellum is actively involved in managing higher order cognitive functions and regulating emotional responses. It has become clear that when children experience congenital or acquired cerebellar lesions, these injuries can lead to a variety of cognitive and emotional disorders, manifesting in different combinations. This underscores the cerebellum's essential role not only throughout developmental stages but particularly in facilitating learning processes, highlighting its critical importance beyond its traditional association with motor control. Furthermore, the intricate neural circuits within the cerebellum are believed to contribute to the fine-tuning of motor actions and coordination but are also increasingly recognized for their involvement in cognitive processes such as attention, language, and problem solving. Recent research has highlighted the importance of cerebellar health and integrity for optimal functioning across various domains of the human experience.

Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation.

Spontaneous reactivation of brain activity from learning to a subsequent off-line period has been implicated as a neural mechanism underlying memory consolidation. However, similarities in brain activity may also emerge as a result of individual, trait-like characteristics. Here, we introduced a novel approach for analyzing continuous electroencephalography (EEG) data to investigate learning-induced changes as well as trait-like characteristics in brain activity underlying memory consolidation. Thirty-one healthy young adults performed a learning task, and their performance was retested after a short (∼1 h) delay. Consolidation of two distinct types of information (serial-order and probability) embedded in the task were tested to reveal similarities in functional networks that uniquely predict the changes in the respective memory performance. EEG was recorded during learning and pre- and post-learning rest periods. To investigate brain activity associated with consolidation, we quantified similarities in EEG functional connectivity between learning and pre-learning rest (baseline similarity) and learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes from baseline to post-learning similarity could indicate learning-induced changes, possibly spontaneous reactivation. Higher learning-induced changes in alpha frequency connectivity (8.5-9.5 Hz) were associated with better consolidation of serial-order information, particularly for long-range connections across central and parietal sites. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5-3 Hz) specifically for more local, short-range connections. Furthermore, there was a substantial overlap between the baseline and post-learning similarities and their associations with consolidation performance, suggesting robust (trait-like) differences in functional connectivity networks underlying memory processes.

Perceptual error based on Bayesian cue combination drives implicit motor adaptation.

The sensorimotor system can recalibrate itself without our conscious awareness, a type of procedural learning whose computational mechanism remains undefined. Recent findings on implicit motor adaptation, such as over-learning from small perturbations and fast saturation for increasing perturbation size, challenge existing theories based on sensory errors. We argue that perceptual error, arising from the optimal combination of movement-related cues, is the primary driver of implicit adaptation. Central to our theory is the increasing sensory uncertainty of visual cues with increasing perturbations, which was validated through perceptual psychophysics (Experiment 1). Our theory predicts the learning dynamics of implicit adaptation across a spectrum of perturbation sizes on a trial-by-trial basis (Experiment 2). It explains proprioception changes and their relation to visual perturbation (Experiment 3). By modulating visual uncertainty in perturbation, we induced unique adaptation responses in line with our model predictions (Experiment 4). Overall, our perceptual error framework outperforms existing models based on sensory errors, suggesting that perceptual error in locating one's effector, supported by Bayesian cue integration, underpins the sensorimotor system's implicit adaptation.

Sense of Agency and Skills Learning in Virtual-Mediated Environment: A Systematic Review.

Agency is central to remote actions, and it may enhance skills learning due to a partial overlap between brain structures and networks, the promotion of confidence towards a telemanipulator, and the feeling of congruence of the motor choice to the motor plan. We systematically reviewed studies aiming to verify the role of agency in improving learning. Fifteen studies were selected from MEDLINE and Scopus®. When a mismatch is introduced between observed and performed actions, the decrease in agency and learning is proportional to the intensity of the mismatch, which is due to greater interference with the motor programming. Thanks to multisensory integration, agency and learning benefit both from sensory and performance feedback and from the timing of feedback based on control at the goal level or the perceptual-motor level. This work constitutes a bedrock for professional teleoperation settings (e.g., robotic surgery), with particular reference to the role of agency in performing complex tasks with remote control.

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.

Plasticity-stability dynamics during post-training processing of learning.

Learning continues beyond the end of training. Post-training learning is supported by changes in plasticity and stability in the brain during both wakefulness and sleep. However, the lack of a unified measure for assessing plasticity and stability dynamics during training and post-training periods has limited our understanding of how these dynamics shape learning. Focusing primarily on procedural learning, we integrate work using behavioral paradigms and a recently developed measure, the excitatory-to-inhibitory (E/I) ratio, to explore the delicate balance between plasticity and stability and its relationship to post-training learning. This reveals plasticity-stability cycles during both wakefulness and sleep that enhance learning and protect it from new learning during post-training processing.

Autistic Adults Show Intact Learning on a Visuospatial Serial Reaction Time Task.

Some theories have proposed that autistic individuals have difficulty learning predictive relationships. We tested this hypothesis using a serial reaction time task in which participants learned to predict the locations of a repeating sequence of target locations. We conducted a large-sample online study with 61 autistic and 71 neurotypical adults. The autistic group had slower overall reaction times, but demonstrated sequence-specific learning equivalent to the neurotypical group, consistent with other findings of typical procedural memory in autism. The neurotypical group, however, made significantly more prediction-related errors early in the experiment when the stimuli changed from repeated sequences to random locations, suggesting certain limited behavioural differences in the learning or utilization of predictive relationships for autistic adults.

Alternative covariance structures in mixed-effects models: Addressing intra- and inter-individual heterogeneity.

Mixed-effects models for repeated measures and longitudinal data include random coefficients that are unique to the individual, and thus permit subject-specific growth trajectories, as well as direct study of how the coefficients of a growth function vary as a function of covariates. Although applications of these models often assume homogeneity of the within-subject residual variance that characterizes within-person variation after accounting for systematic change and the variances of the random coefficients of a growth model that quantify individual differences in aspects of change, alternative covariance structures can be considered. These include allowing for serial correlations between the within-subject residuals to account for dependencies in data that remain after fitting a particular growth model or specifying the within-subject residual variance to be a function of covariates or a random subject effect to address between-subject heterogeneity due to unmeasured influences. Further, the variances of the random coefficients can be functions of covariates to relax the assumption that these variances are constant across subjects and to allow for the study of determinants of these sources of variation. In this paper, we consider combinations of these structures that permit flexibility in how mixed-effects models are specified to understand within- and between-subject variation in repeated measures and longitudinal data. Data from three learning studies are analyzed using these different specifications of mixed-effects models.

A Dyadic Cooking-Based Intervention for Improving Subjective Health and Well-Being of Older Adults with Subjective Cognitive Decline and Their Caregivers: A Randomized Controlled Trial.

OBJECTIVES: Evidence on the effectiveness of cooking activities as a well-being promotion intervention for older adults with subjective cognitive decline (SCD) and their caregivers is scarce. In view of this, a randomized controlled trial was conducted to examine whether a dyadic cooking-based intervention can improve the subjective health and well-being of older adults with SCD and their caregivers, as well as the cooking competence of the former group. DESIGN: Randomized controlled trial. SETTING: Community. PARTICIPANTS: Sixty pairs of community-dwelling older adults aged 60 years or above with SCD (mean age = 78.4 years) and their caregivers (mean age = 65.3 years) were randomly assigned to the intervention group (N = 30 pairs) and the wait-list control group (N = 30 pairs). INTERVENTION: The intervention was an innovative 5-week (two hours per week) dyadic cooking-based intervention employing procedural learning methods specifically adapted for older adults with SCD. MEASUREMENTS: The outcome measures included 1) a well-being index composed by four indicators: life satisfaction, feeling of happiness, sense of purpose and meaning in life, and perceived health, and 2) cooking competence. RESULTS: For both older adults with SCD and their caregivers, the increases in the well-being index were significantly greater in the intervention group than in the control group (ß = 0.508, 95% CI [0.036, 0.980]). For older adults with SCD, the increases in the cooking competence score were significantly greater in the intervention group than in the control group (ß = 1.629, 95% CI [0.165, 3.071]). CONCLUSION: The dyadic cooking-based intervention resulted in improvements in the cooking competence and well-being of older adults with SCD, as well as the well-being of caregivers.

Procedural auditory category learning is selectively disrupted in developmental language disorder.

Speech communication depends on accurate perception and identification of speech sounds, which vary across talkers and word or sentence contexts. The ability to map this variable input onto discrete speech sound representations relies on categorization. Recent research and theoretical models implicate the procedural learning system in the ability to learn novel speech and non-speech categories. This connection is particularly intriguing because several language disorders that demonstrate linguistic impairments are proposed to stem from procedural learning and memory dysfunction. One such disorder, Developmental Language Disorder (DLD), affects 7.5% of children and persists into adulthood. While DLD is associated with general linguistic impairments, it is not yet clear how fundamental perceptual and cognitive processes supporting language are impacted, such as the ability to learn novel auditory categories. We examined auditory category learning in children with DLD and typically developed (TD) children using two well-matched nonspeech auditory category learning challenges to draw upon presumed procedural (information-integration) versus declarative (rule-based) learning systems. We observed impaired information-integration category learning and intact rule-based category learning in the DLD group. Quantitative model-based analyses revealed reduced use of, and slower shifting to, optimal procedural-based strategies in DLD and slower shifting to but similarly efficient use of optimal hypothesis-testing strategies. The dissociation is consistent with the Procedural Deficit Hypothesis of language disorders and supports the theoretical distinction of multiple category learning systems. These findings demonstrate that highly controlled experimental tasks assessing perceptual and cognitive abilities can relate to real-world challenges facing individuals with DLD in forming stable linguistic representations.