Mechanisms within the Parietal Cortex Correlate with the Benefits of Random Practice in Motor Adaptation

The motor learning literature shows an increased retest or transfer performance after practicing under unstable (random) conditions. This random practice effect (also known as contextual interference effect) is frequently investigated on the behavioral level and discussed in the context of mechanisms of the dorsolateral prefrontal cortex and increased cognitive efforts during movement planning. However, there is a lack of studies examining the random practice effect in motor adaptation tasks and, in general, the underlying neural processes of the random practice effect are not fully understood. We tested 24 right-handed human subjects performing a reaching task using a robotic manipulandum. Subjects learned to adapt either to a blocked or a random schedule of different force field perturbations while subjects’ electroencephalography (EEG) was recorded. The behavioral results showed a distinct random practice effect in terms of a more stabilized retest performance of the random compared to the blocked practicing group. Further analyses showed that this effect correlates with changes in the alpha band power in electrodes over parietal areas. We conclude that the random practice effect in this study is facilitated by mechanisms within the parietal cortex during movement execution which might reflect online feedback mechanisms

Understanding the behavioral and neurocognitive relation between mind wandering and learning

In the last decade, tremendous advances have been made in the effort to understand mind wandering, yet many questions remain unanswered. Chief among them is how mind wandering relates to learning. Insofar as mind wandering has been linked to poor learning, finding ways to reduce the propensity to mind wander could potentially improve learning. Two experiments were conducted to examine this. The first experiment evaluated how difficulty of the to-be-learned materials affected one’s tendency to mind wander and revealed that people mind wandered when there was a mismatch between their level of expertise and the difficulty of materials studied. The second experiment compared whether participants were more likely to mind wander in blocked or interleaved conditions and showed that participants were more likely to mind wander when materials were presented in a blocked fashion. Together, these results indicate that techniques such as studying materials specific to one’s own level of mastery or changing the way in which one studies might reduce mind wandering and improve learning. Of equal importance is the question of what happens on in the brain when a person mind wanders. While the effect of mind wandering on early sensory processing is known, the impact it has on learning-related processing is not. In two event-related potential (ERP) experiments, participants were asked to report whether they were mind wandering or not while studying materials they were later tested on. Analyses revealed that elaborative semantic processing – indexed by a late, sustained slow wave that was maximal at posterior parietal electrode sites – was attenuated when participants mind wandered. Crucially, the pattern when people were on task rather than mind wandering was similar to the subsequent memory effect previously reported by other memory researchers, suggesting that mind wandering disrupts the deep level of processing required for learning

The influence of attentional focus on neuroplasticity following a seven-day balance training intervention

It is well established that focusing on the external effect of one’s movement (an external focus of attention) results in enhanced motor learning and produces superior motor performance compared to focusing inward on the body’s own physical execution of the motor movement (an internal focus). While the benefits of an external focus in motor learning, and the detriments of an inward or ‘internal’ focus have been highly replicated, there is still little mechanistic understanding pertaining to the brain-related changes that may result from these two different foci of attention during motor training. Since the brain is highly malleable and has been shown to adapt in response to motor training (i.e., neuroplasticity), it is postulated that attentional focus may change the brain’s structure and function. However, no direct examination exploring the influence of attentional focus on neuroplasticity (structural or functional) exists. The primary objective of this study was to determine the effects of balance training with different attentional foci on brain-related neuroplasticity in a young healthy population. Participants (n = 33) were randomly assigned to a control, internal focus, or external focus condition. Functional and structural brain connectivity analyses was conducted using neuroimaging data collected through functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) prior to (baseline) and following a seven-day balance training intervention (retention). Between baseline and retention data collection, participants in the internal and external focus training groups practiced a dynamic balance task for one hour per day, each day for seven consecutive days (acquisition). For the internal focus trials, participants were asked to, ‘focus on keeping their feet level;’ whereas, for the external focus trials participants were asked to, ‘focus on keeping the board level.’ The control group did not complete any balance training, but completed baseline and retention balance measurements. An inertial measurement unit was attached to the center of the balance board to assess the performance and learning of the balance task. Resting-state brain connectivity analyses were performed on the fMRI data to contrast connectivity differences for each group at retention relative to baseline, and, for the diffusion data (DTI), fractional anisotropy analyses (a metric to quantify water diffusion within a voxel of white-matter) was performed to quantify the relationship between changes in balance and water diffusivity within white-matter tracts. Classical attentional focus effects were observed for acquisition, with those in the external focus condition producing significantly less mean and standard deviation velocity compared to the internal focus group (both p .05). These results suggest that a seven-day balance training program with attentional focus in a young healthy population influences brain function (specifically correlated activity at rest), but longer training programs or more rest may be needed to influence brain structure (as measured by fractional anisotropy). These findings have important implications for a variety of clinical populations who show altered resting-sate connectivity and deteriorations in balance control (e.g., Alzheimer’s disease, stroke survivors). Seven days of balance training with an external focus may be useful in improving balance control and may influence correlated brain activity at rest, but longer training programs or more rest may be needed to influence brain structure. We discuss these findings in the context of the constrained-action hypothesis and OPTIMAL theory

Motor Preparatory Activity in Posterior Parietal Cortex is Modulated by Subjective Absolute Value

For optimal response selection, the consequences associated with behavioral success or failure must be appraised. To determine how monetary consequences influence the neural representations of motor preparation, human brain activity was scanned with fMRI while subjects performed a complex spatial visuomotor task. At the beginning of each trial, reward context cues indicated the potential gain and loss imposed for correct or incorrect trial completion. FMRI-activity in canonical reward structures reflected the expected value related to the context. In contrast, motor preparatory activity in posterior parietal and premotor cortex peaked in high “absolute value” (high gain or loss) conditions: being highest for large gains in subjects who believed they performed well while being highest for large losses in those who believed they performed poorly. These results suggest that the neural activity preceding goal-directed actions incorporates the absolute value of that action, predicated upon subjective, rather than objective, estimates of one's performance

Assessing the detailed time course of perceptual sensitivity change in perceptual learning.

The learning curve in perceptual learning is typically sampled in blocks of trials, which could result in imprecise and possibly biased estimates, especially when learning is rapid. Recently, Zhao, Lesmes, and Lu (2017, 2019) developed a Bayesian adaptive quick Change Detection (qCD) method to accurately, precisely, and efficiently assess the time course of perceptual sensitivity change. In this study, we implemented and tested the qCD method in assessing the learning curve in a four-alternative forced-choice global motion direction identification task in both simulations and a psychophysical experiment. The stimulus intensity in each trial was determined by the qCD, staircase or random stimulus selection (RSS) methods. Simulations showed that the accuracy (bias) and precision (standard deviation or confidence bounds) of the estimated learning curves from the qCD were much better than those obtained by the staircase and RSS method; this is true for both trial-by-trial and post hoc segment-by-segment qCD analyses. In the psychophysical experiment, the average half widths of the 68.2% credible interval of the estimated thresholds from the trial-by-trial and post hoc segment-by-segment qCD analyses were both quite small. Additionally, the overall estimates from the qCD and staircase methods matched extremely well in this task where the behavioral rate of learning is relatively slow. Our results suggest that the qCD method can precisely and accurately assess the trial-by-trial time course of perceptual learning

Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering

Perivascular Spaces (PVS) are a recently recognised feature of Small Vessel Disease (SVD), also indicating neuroinflammation, and are an important part of the brain's circulation and glymphatic drainage system. Quantitative analysis of PVS on Magnetic Resonance Images (MRI) is important for understanding their relationship with neurological diseases. In this work, we propose a segmentation technique based on the 3D Frangi filtering for extraction of PVS from MRI. Based on prior knowledge from neuroradiological ratings of PVS, we used ordered logit models to optimise Frangi filter parameters in response to the variability in the scanner's parameters and study protocols. We optimized and validated our proposed models on two independent cohorts, a dementia sample (N=20) and patients who previously had mild to moderate stroke (N=48). Results demonstrate the robustness and generalisability of our segmentation method. Segmentation-based PVS burden estimates correlated with neuroradiological assessments (Spearman's $\rho$ = 0.74, p $<$ 0.001), suggesting the great potential of our proposed metho

A Wizard Hat for the Brain: Predicting Long-Term Memory Retention Using Electroencephalography

Learning is a ubiquitous process that transforms novel information and events into stored memory representations that can later be accessed. As a learner acquires new information, any feature of a memory that is shared with other memories may produce some level of retrieval- competition, making accurate recall more difficult. One of the most effective ways to reduce this competition and create distinct representations for potentially confusable memories is to practice retrieving all of the information through self-testing with feedback. As a person tests themself, competition between easily-confusable memories (e.g. memories that share similar visual or semantic features) decreases and memory representations for unique items are made more distinct. Using a portable, consumer-grade electroencephalography (EEG) device, I attempted to harness competition levels in the brain by training a machine learning classifier to predict long- term retention of novel associations. Specifically, I compare the accuracy of two logistic regression classifiers: one trained using existing category-word pairings (as has been done previously in the literature), and one trained using new episodic image-name associations developed to more closely model memory competition. I predicted that the newly developed classifier would be able to more accurately predict long-term retention. Further refinements to the predictive model and its applications are discussed

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings