Human neuronal excitation/inhibition balance explains and predicts neurostimulation induced learning benefits.

Previous research has highlighted the role of the excitation/inhibition (E/I) ratio for typical and atypical development, mental health, cognition, and learning. Other research has highlighted the benefits of high-frequency transcranial random noise stimulation (tRNS)-an excitatory form of neurostimulation-on learning. We examined the E/I as a potential mechanism and studied whether tRNS effect on learning depends on E/I as measured by the aperiodic exponent as its putative marker. In addition to manipulating E/I using tRNS, we also manipulated the level of learning (learning/overlearning) that has been shown to influence E/I. Participants (n = 102) received either sham stimulation or 20-minute tRNS over the dorsolateral prefrontal cortex (DLPFC) during a mathematical learning task. We showed that tRNS increased E/I, as reflected by the aperiodic exponent, and that lower E/I predicted greater benefit from tRNS specifically for the learning task. In contrast to previous magnetic resonance spectroscopy (MRS)-based E/I studies, we found no effect of the level of learning on E/I. A further analysis using a different data set suggest that both measures of E/I (EEG versus MRS) may reflect, at least partly, different biological mechanisms. Our results highlight the role of E/I as a marker for neurostimulation efficacy and learning. This mechanistic understanding provides better opportunities for augmented learning and personalized interventions.

Towards an Integrative Model of Math Cognition: Interactions between Working Memory and Emotions in Explaining Children's Math Performance.

Individual variation in mathematical skills can be ascribed to differences in cognitive ability, but also to students' emotional experiences of mathematics, such as enjoyment and anxiety. The current study investigated how the interplay of working memory with math anxiety and enjoyment explains mathematical performance in primary school students. We also explored whether these relations differed with the type of math test and students' age. Using mixed effect models, we reanalyzed data from 4471 Dutch primary school students (grades 2-6) who had completed two computerized working memory tasks, had filled out a questionnaire on math emotions, and had completed two math tests: story problems and speeded arithmetic. Findings showed that working memory, anxiety, and enjoyment were linear (but not curvilinear) predictors of performance on both tests, while some relations were stronger for the math (story)-problem-solving test. Higher math anxiety negatively impacted performance more strongly for students with stronger working memory skills, but only on the arithmetic test. No interaction between working memory and enjoyment was found. The relation between math anxiety and math performance increased with grade level, but no other age-related changes were found. Interpretations and recommendations focus on situated views on learning and emotion.

Predicting Math Ability Using Working Memory, Number Sense, and Neurophysiology in Children and Adults.

Previous work has shown relations between domain-general processes, domain-specific processes, and mathematical ability. However, the underlying neurophysiological effects of mathematical ability are less clear. Recent evidence highlighted the potential role of beta oscillations in mathematical ability. Here we investigate whether domain-general (working memory) and domain-specific (number sense) processes mediate the relation between resting-state beta oscillations and mathematical ability, and how this may differ as a function of development (children vs. adults). We compared a traditional analysis method normally used in EEG studies with a more recently developed parameterization method that separates periodic from aperiodic activity. Regardless of methods chosen, we found no support for mediation of working memory and number sense, neither for children nor for adults. However, we found subtle differences between the methods. Additionally, we showed that the traditional EEG analysis method conflates periodic activity with aperiodic activity; in addition, the latter is strongly related to mathematical ability and this relation differs between children and adults. At the cognitive level, our findings do not support previous suggestions of a mediation of working memory and number sense. At the neurophysiological level our findings suggest that aperiodic, rather than periodic, activity is linked to mathematical ability as a function of development.

Impact of the COVID-19 lockdown on gifted and non-gifted primary school students' well-being and motivation from a self-determination perspective.

This study examined the impact of the COVID-19-induced school lockdown on need satisfaction, well-being and motivation in both gifted and non-gifted primary school students in the Netherlands. A total of 312 parents (122 from gifted children) participated. The lockdown had mainly negative effects on students' need satisfaction, well-being and motivation. However, the impact of the lockdown was less negative for gifted students. There was also a levelling effect: Before the lockdown, gifted students had lower need satisfaction, well-being and motivation than their non-gifted peers, but these differences decreased during the lockdown due to (stronger) declines in the non-gifted. Changes in non-gifted students' well-being and motivation, because of the lockdown, were negatively mediated by autonomy and relatedness with classmates. Among the gifted, this was positively mediated by competence. Only before the lockdown, the effects of giftedness on well-being and motivation were mediated by autonomy and relatedness satisfaction.

Personalized brain stimulation for effective neurointervention across participants.

Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants-personalized Bayesian optimization (pBO)-that searches available parameter combinations to optimize an intervention as a function of an individual's ability. This novel technique was utilized to identify transcranial alternating current stimulation (tACS) frequency and current strength combinations most likely to improve arithmetic performance, based on a subject's baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, simulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.

The relevance of subtyping children with mathematical learning disabilities.

BACKGROUND: Profiles of mathematical learning disability (MLD) have been conceptualized in the literature, but empirical evidence to support them based on academic and cognitive characteristics is lacking. AIMS: We examined whether profiles of mathematics performance can empirically be identified and whether the identified profiles also differ in underlying cognitive skills. METHODS AND PROCEDURES: Latent profile analysis in 281 fourth-graders. Basic arithmetic and advanced mathematics were used to identify profiles. Cognitive skills were then described for each profile of mathematics performance. OUTCOMES AND RESULTS: Four profiles of mathematics performance were retrieved from the data, including one general low-achieving profile. Additional profiles of MLD were not found, possibly because individual variation was substantial. CONCLUSIONS AND IMPLICATIONS: It is highly important to understand children's mathematics performance from an individual perspective, rather than by averaging these children over subgroups. These new insights can be used to better tend to the specific needs of children with mathematical difficulties.

Early Executive Function at Age Two Predicts Emergent Mathematics and Literacy at Age Five.

Previous work has shown that individual differences in executive function (EF) are predictive of academic skills in preschoolers, kindergartners, and older children. Across studies, EF is a stronger predictor of emergent mathematics than literacy. However, research on EF in children below age three is scarce, and it is currently unknown whether EF, as assessed in toddlerhood, predicts emergent academic skills a few years later. This longitudinal study investigates whether early EF, assessed at two years, predicts (emergent) academic skills, at five years. It examines, furthermore, whether early EF is a significantly stronger predictor of emergent mathematics than of emergent literacy, as has been found in previous work on older children. A sample of 552 children was assessed on various EF and EF-precursor tasks at two years. At age five, these children performed several emergent mathematics and literacy tasks. Structural Equation Modeling was used to investigate the relationships between early EF and academic skills, modeled as latent factors. Results showed that early EF at age two was a significant and relatively strong predictor of both emergent mathematics and literacy at age five, after controlling for receptive vocabulary, parental education, and home language. Predictive relations were significantly stronger for mathematics than literacy, but only when a verbal short-term memory measure was left out as an indicator to the latent early EF construct. These findings show that individual differences in emergent academic skills just prior to entry into the formal education system can be traced back to individual differences in early EF in toddlerhood. In addition, these results highlight the importance of task selection when assessing early EF as a predictor of later outcomes, and call for further studies to elucidate the mechanisms through which individual differences in early EF and precursors to EF come about.

Self-concept mediates the relation between achievement and emotions in mathematics.

BACKGROUND: Mathematics achievement is related to positive and negative emotions. Pekrun's control-value theory of achievement emotions suggests that students' self-concept (i.e., self-appraisal of ability) may be an important mediator of the relation between mathematics achievement and emotions. AIMS: The aims were (1) to investigate the mediating role of mathematical self-concept in the relation between mathematics achievement and the achievement emotions of enjoyment and anxiety in a comprehensive model, and (2) to test possible differences in this mediating role between low-, average-, and high-achieving students. SAMPLE: Participants were ninth-grade students (n = 1,014) from eight secondary schools in the Netherlands. METHODS: Through an online survey including mathematical problems, students were asked to indicate their levels of mathematics enjoyment, anxiety, and self-concept. Structural equation modelling was used to test the mediating role of self-concept in the relation between mathematics achievement and emotions. Multigroup analyses were performed to compare these relations across the three achievement groups. RESULTS: Results confirmed full mediation of the relation between mathematics achievement and emotions by mathematical self-concept. Furthermore, we found higher self-concepts, more enjoyment and less math anxiety in high-achieving students compared to their average and low-achieving peers. No differences across these achievement groups were found in the relations in the mediational model. CONCLUSIONS: Mathematical self-concept plays a pivotal role in students' appraisal of mathematics. Mathematics achievement is only one factor explaining students' self-concept. Likely also classroom instruction and teachers' feedback strategies help to shape students' self-concept.

The structure of executive functions in children: a closer examination of inhibition, shifting, and updating.

An increasing number of studies has investigated the latent factor structure of executive functions. Some studies found a three-factor structure of inhibition, shifting, and updating, but others could not replicate this finding. We assumed that the task choices and scoring methods might be responsible for these contradictory findings. Therefore, we selected tasks in which input modality was varied, controlled for baseline speed, and used both speed and accuracy scores, in order to investigate whether a three factor model with inhibition, shifting, and updating could still be replicated. In a group of 211 children, who were tested at the beginning of grade 1, at approximately 6 years of age, and again after 18 months, the best fitting model was not the three-factor model, but instead consisted of an updating factor and a combined inhibition and shifting factor, besides two baseline speed factors (verbal and motor). We argue that these results might indicate that the structural organization of executive functions might be different in children than in adults, but that there might also be an alternative explanation: the distinction in executive functions might not accurately represent cognitive structures but instead be a methodological artefact.

Microgenetic patterns of children's multiplication learning: confirming the overlapping waves model by latent growth modeling.

Variability in strategy selection is an important characteristic of learning new skills such as mathematical skills. Strategies gradually come and go during this development. In 1996, Siegler described this phenomenon as "overlapping waves." In the current microgenetic study, we attempted to model these overlapping waves statistically. In addition, we investigated whether development in strategy selection is related to development in accuracy and to what degree working memory is related to both. We expected that children with poor working memory are limited in their possibilities to make the associations that are necessary to progress to more mature strategies. This limitation would explain the often-found relationship between working memory and mathematical abilities. To this aim, the strategy selection and accuracy of 98 children who were learning single-digit multiplication was assessed eight times on a weekly basis. Using latent growth modeling for categorical data, we confirmed Siegler's hypothesis of overlapping waves. Moreover, both the intercepts and the slopes of strategy selection and accuracy were strongly interrelated. Finally, working memory predicted both strategy selection and accuracy, confirming that working memory is related to mathematical problem solving in two ways because it influences both the maturity of strategy choice and the probability of making procedural mistakes.

The development of executive functions and early mathematics: a dynamic relationship.

BACKGROUND: The relationship between executive functions and mathematical skills has been studied extensively, but results are inconclusive, and how this relationship evolves longitudinally is largely unknown. AIM: The aim was to investigate the factor structure of executive functions in inhibition, shifting, and updating; the longitudinal development of executive functions and mathematics; and the relation between them. SAMPLE: A total of 211 children in grade 2 (7-8 years old) from 10 schools in the Netherlands. METHOD: Children were followed in grade 1 and 2 of primary education. Executive functions and mathematics were measured four times. The test battery contained multiple tasks for each executive function: Animal stroop, local global, and Simon task for inhibition; Animal Shifting, Trail Making Test in Colours, and Sorting Task for shifting; and Digit Span Backwards, Odd One Out, and Keep Track for updating. The factor structure of executive functions was assessed and relations with mathematics were investigated using growth modelling. RESULTS: Confirmatory factor analysis (CFA) showed that inhibition and shifting could not be distinguished from each other. Updating was a separate factor, and its development was strongly related to mathematical development while inhibition and shifting did not predict mathematics in the presence of the updating factor. CONCLUSIONS: The strong relationship between updating and mathematics suggest that updating skills play a key role in the maths learning process. This makes updating a promising target for future intervention studies.

Executive functions as predictors of math learning disabilities.

In the past years, an increasing number of studies have investigated executive functions as predictors of individual differences in mathematical abilities. The present longitudinal study was designed to investigate whether the executive functions shifting, inhibition, and working memory differ between low achieving and typically achieving children and whether these executive functions can be seen as precursors to math learning disabilities in children. Furthermore, the predictive value of working memory ability compared to preparatory mathematical abilities was examined. Two classifications were made based on (persistent) mathematical ability in first and second grade. Repeated measures analyses and discriminant analyses were used to investigate which functions predicted group membership best. Group differences in performance were found on one inhibition and three working memory tasks. The working memory tasks predicted math learning disabilities, even over and above the predictive value of preparatory mathematical abilities.

Voluntarily controlled bi-stable slant perception of real and photographed surfaces.

We have quantified voluntarily selected perceived slant of real trapezoidal surfaces (a 'reverse-perspective' scene) and their photographed counterparts (pictorial space). The surfaces were slanted about the vertical axis and observers estimated slant relative to the frontal plane. We were particularly interested in those cases in which binocular disparity and monocular perspective provided conflicting slant information. We varied the monocularly and binocularly specified surface slants independently across stimulus presentations. To eliminate texture and shading cues we used sand-blasted aluminium trapezoidal surfaces illuminated from all directions. When disparity-specified slant and perspective-specified slant were conflicting, observers were able to perceive the surfaces in two ways: they perceived either a trapezoid or a rectangle. Our main finding is twofold. First, when subjects chose to perceive the trapezoid, the slant estimates followed the disparity-predicted slant with only a slight underestimation, as if they selected a pure binocular representation of slant governed only by disparity. Second, when subjects chose to perceive the rectangle their estimates for real surfaces were similar to those for photographed surfaces, as if they selected a representation of slant governed by perspective foreshortening.