The Long and Winding Road to Real-Life Experiments: Remote Assessment of Executive Functions with Computerized Games-Results from 8 Years of Naturalistic Interventions.

Mate Marote is an open-access cognitive training software aimed at children between 4 and 8 years old. It consists of a set of computerized games specifically tailored to train and evaluate Executive Functions (EF), a class of processes critical for purposeful, goal-directed behavior, including working memory, planning, flexibility, and inhibitory control. Since 2008, several studies were performed with this software at children's own schools in interventions supervised in-person by cognitive scientists. After 2015, we incorporated naturalistic, yet controlled, interventions with children's own teachers' help. The platform includes a battery of standardized tests, disguised as games, to assess children's EF. The main question that emerges is whether the results, obtained with these traditional tasks but conducted without the presence of researchers, are comparable to those widely reported in the literature, that were obtained in more supervised settings. In this study, we were able to replicate the expected difficulty and age effects in at least one of the analyzed dependent variables of each employed test. We also report important discrepancies between the expected and the observed response time patterns, specifically for time-constrained tasks. We hereby discuss the benefits and setbacks of a new possible strategy for this type of assessment in naturalistic settings. We conclude that this battery of established EF tasks adapted for its remote usage is appropriate to measure the expected mental processes in naturalistic settings, enriching opportunities to upscale cognitive training interventions at schools. These types of tools can constitute a concerted strategy to bring together educational neuroscience research and real-life practice.

A better alignment between chronotype and school timing is associated with lower grade retention in adolescents.

Schools start early in the morning all over the world, contrasting with adolescents' late chronotype. Interestingly, lower academic performance (i.e. grades or qualifications) was associated with later chronotypes. However, it is unclear whether it is a direct effect of chronotype or because students attend school too early to perform at their best. Moreover, little is known about how this affects students' academic success beyond their grades. To address this gap in knowledge, we studied how school timing and chronotype affect grade retention (i.e. repeat a year) in a unique sample of students randomly assigned to one of three different school timings (starting at 07:45, 12:40, or 17:20). Even when controlling for academic performance, we found that later chronotypes exhibit higher odds of grade retention only in the morning, but not in later school timings. Altogether, ensuring a better alignment between school timing and students' biological rhythms might enhance future opportunities of adolescents.

A Machine Learning Approach to Personalize Computerized Cognitive Training Interventions.

Executive functions are a class of cognitive processes critical for purposeful goal-directed behavior. Cognitive training is the adequate stimulation of executive functions and has been extensively studied and applied for more than 20 years. However, there is still a lack of solid consensus in the scientific community about its potential to elicit consistent improvements in untrained domains. Individual differences are considered one of the most important factors of inconsistent reports on cognitive training benefits, as differences in cognitive functioning are both genetic and context-dependent, and might be affected by age and socioeconomic status. We here present a proof of concept based on the hypothesis that baseline individual differences among subjects would provide valuable information to predict the individual effectiveness of a cognitive training intervention. With a dataset from an investigation in which 73 6-year-olds trained their executive functions using an online software with a fixed protocol, freely available at www.matemarote.org.ar, we trained a support vector classifier that successfully predicted (average accuracy = 0.67, AUC = 0.707) whether a child would improve, or not, after the cognitive stimulation, using baseline individual differences as features. We also performed a permutation feature importance analysis that suggested that all features contribute equally to the model's performance. In the long term, this results might allow us to design better training strategies for those players who are less likely to benefit from the current training protocols in order to maximize the stimulation for each child.

Interplay of chronotype and school timing predicts school performance.

Most adolescents exhibit very late chronotypes and attend school early in the morning, a misalignment that can affect their health and psychological well-being. Here we examine how the interaction between the chronotype and school timing of an individual influences academic performance, studying a unique sample of 753 Argentinian students who were randomly assigned to start school in the morning (07:45), afternoon (12:40) or evening (17:20). Although chronotypes tend to align partially with class time, this effect is insufficient to fully account for the differences with school start time. We show that (1) for morning-attending students, early chronotypes perform better than late chronotypes in all school subjects, an effect that is largest for maths; (2) this effect vanishes for students who attend school in the afternoon; and (3) late chronotypes benefit from evening classes. Together, these results demonstrate that academic performance is improved when school times are better aligned with the biological rhythms of adolescents.

Parsing heuristic and forward search in first-graders' game-play behavior.

Seventy-three children between 6 and 7 years of age were presented with a problem having ambiguous subgoal ordering. Performance in this task showed reliable fingerprints: (a) a non-monotonic dependence of performance as a function of the distance between the beginning and the end-states of the problem, (b) very high levels of performance when the first move was correct, and (c) states in which accuracy of the first move was significantly below chance. These features are consistent with a non-Markov planning agent, with an inherently inertial decision process, and that uses heuristics and partial problem knowledge to plan its actions. We applied a statistical framework to fit and test the quality of a proposed planning model (Monte Carlo Tree Search). Our framework allows us to parse out independent contributions to problem-solving based on the construction of the value function and on general mechanisms of the search process in the tree of solutions. We show that the latter are correlated with children's performance on an independent measure of planning, while the former is highly domain specific.

Neuroscience and education: prime time to build the bridge.

As neuroscience gains social traction and entices media attention, the notion that education has much to benefit from brain research becomes increasingly popular. However, it has been argued that the fundamental bridge toward education is cognitive psychology, not neuroscience. We discuss four specific cases in which neuroscience synergizes with other disciplines to serve education, ranging from very general physiological aspects of human learning such as nutrition, exercise and sleep, to brain architectures that shape the way we acquire language and reading, and neuroscience tools that increasingly allow the early detection of cognitive deficits, especially in preverbal infants. Neuroscience methods, tools and theoretical frameworks have broadened our understanding of the mind in a way that is highly relevant to educational practice. Although the bridge's cement is still fresh, we argue why it is prime time to march over it.

Effect of melatonin on the retinal glutamate/glutamine cycle in the golden hamster retina.

Glutamate is the main excitatory neurotransmitter in the retina, but it is neurotoxic when present in excessive amounts. The metabolic dependence of glutamatergic neurons upon glia via the glutamate/glutamine cycle to provide the precursor for neurotransmitter glutamate is well established. Since melatonin has been shown to be neuroprotective in several systems, in the present report, its effect on the glutamate/glutamine cycle activity was examined in the golden hamster retina. Melatonin (0.1-10 nM) significantly increased retinal glutamine synthetase activity but it did not affect L-glutamine release. A characterization of the hamster retinal L-glutamine uptake mechanism was performed. This mechanism was partly Na+-dependent, and it was significantly inhibited by 2-aminobicyclo (2, 2, 1) heptane 2-carboxylic acid (BCH, a selective antagonists for the L-type system) and by alpha-(methylamino)-isobutyric acid (MeAIB, substrate characteristic for the A -type transporter) suggesting the coexistence of these transport systems in the hamster retina. Melatonin (0.1-10 nM) significantly increased total glutamine uptake as well as the BCH and the MeAIB-insensitive transporters activity. On the other hand, melatonin significantly decreased retinal glutaminase activity. On the basis of these results, it might be presumed that hamster retinal glutamate/glutamine cycle activity is regulated by physiological concentrations of melatonin. Furthermore, these findings suggest that a treatment with melatonin could be considered as a new approach to handling glutamate-mediated neuronal degeneration.