Associative learning abilities vary considerably among individuals, with attentional processes suggested to play a role in these variations. However, the relationship between attentional processes and individual differences in associative learning remains unclear, and whether these variations reflect in event-related potentials (ERPs) is unknown. This study aimed to investigate the relationship between attentional processes and associative learning by recording electrocortical activity of 38 young adults (18–32 years) during an associative learning task. Learning performance was assessed using the signal detection index d’. EEG topographic analyses and source localizations were applied to examine the neural correlates of attention and associative learning. Results revealed that better learning scores are associated with (1) topographic differences during early (126–148 ms) processing of the stimulus, coinciding with a P1 ERP component, which corresponded to a participation of the precuneus (BA 7), (2) topographic differences at 573–638 ms, overlapping with an increase of global field power at 530–600 ms, coinciding with a P3b ERP component and localized within the superior frontal gyrus (BA11) and (3) an increase of global field power at 322–507 ms, underlay by a stronger participation of the middle occipital gyrus (BA 19). These insights into the neural mechanisms underlying individual differences in associative learning suggest that better learners engage attentional processes more efficiently than weaker learners, making more resources available and displaying increased functional activity in areas involved in early attentional processes (BA7) and decision-making processes (BA11) during an associative learning task. This highlights the crucial role of attentional mechanisms in individual learning variability.

Coming from a disadvantaged background can have negative impact on an individual’s educational trajectory. Some people however seem unaffected and cope well with the demands and challenges posed by school education, despite growing up in adverse conditions, a phenomenon termed academic resilience. While it is uncertain which underlying factors make some people more likely to circumvent unfavorable odds than others, both socioeconomic status (SES) and cognitive ability have robustly been linked to school performance. The objective of the present work is to investigate if individual cognitive abilities and SES interact in their effect on grades. For this purpose, we analyzed SES, cognitive, and school performance data from 5001 participants from the Adolescent Brain Cognitive Development (ABCD) Study. Ordinal logistic regression models suggest similar patterns of associations between three SES measures (parental education, income-to-needs ratio, and neighborhood deprivation) and grades at two timepoints, with no evidence for interaction effects between SES and time. Parental education and income-to-needs ratio were associated with grades at both timepoints, irrespective of whether cognitive abilities were modeled or not. Neighborhood deprivation, in contrast, was only a statistically significant predictor of reported grades when cognitive abilities were not factored in. Cognitive abilities interacted with parental education level, meaning that they could be a safeguard against effects of SES on school performance.

This comment presents some general principles for multidisciplinary research to capitalize on the growing attention for inequality in education across academic disciplines. The variety of theoretical and methodological perspectives across disciplines results in different conceptual frameworks and empirical designs to study inequality in education. While each framework and design contributes to our shared understanding of the problem, combining these perspectives requires awareness of the various lenses through which educational inequalities are being studied. We identify three dimensions along which perspectives on inequality of education vary between disciplines. These dimensions pertain to (1) how the problem of inequality in education is framed, (2) how inequality in education is empirically evaluated, and (3) how the role of education in fostering (in)equality is conceptualized. In response, we propose three general principles that may help deal with this variation when building a multidisciplinary body of knowledge on inequality in education.

Exchanges of help between children are common and often have positive consequences. But not all help is equally beneficial, for example because some help does not provide an opportunity to practice and develop skills. Here I examine whether young children might perpetuate competence-based inequality by providing incompetent peers with less opportunity to practice and improve their skills compared to competent peers. Study 1 (N = 253, 6–9 years) shows that young children understand not all help is equally beneficial: Children think that peers who receive empowerment (hints) vs. non-empowerment (correct answers) help can learn more. Study 2 (N = 80) and 3 (N = 41) then assessed children’s (7–9 years) actual helping behavior in a lab-based experiment. Through a cover story, participants were introduced to two unknown, same-age children whom they later overheard were either good or not good at solving puzzles (Study 2) or math (Study 3). Subsequently, participants got to help both of them with a puzzle-quiz (Study 2) or a math-quiz (Study 3) by providing either empowerment or non-empowerment when they asked for help. Across both studies, children were more likely to provide empowerment help to competent peers, and non-empowerment help to incompetent peers. This work suggests that when young children perceive differences in competence (e.g., based on stereotypes), they contribute to maintaining the status quo by providing the most vulnerable students, that would profit the most from improving their skills, less opportunity to do so.

Can teachers’ inflated praise make children from low socioeconomic status (SES) backgrounds seem less smart? We conducted two preregistered experiments to address this question. We used hypothetical scenarios to ensure experimental control. An experiment with primary school teachers (N = 106, ages 21–63) showed that when a child from a low-SES (vs. high-SES) background succeeded in school, teachers attributed this success more to hard work and delivered more inflated praise (e.g., “You did incredibly well!”) but less modest praise (e.g., “You did well!”). An experiment with primary school children (N = 63, ages 10–13) showed that when children learned that another child received inflated praise (while an equally performing classmate received modest praise or no praise), they perceived this child as less smart but more hardworking. These studies provide converging evidence that teachers’ inflated praise, although well-intentioned, can make children from low-SES backgrounds seem less smart, thereby reinforcing negative stereotypes about these children’s academic abilities.

Evidence accumulation models have enabled strong advances in our understanding of decision-making, yet their application to examining learning has not been common. Using data from participants completing a dynamic random dot-motion direction discrimination task across four days, we characterized alterations in two components of perceptual decision-making (Drift Diffusion Model drift rate and response boundary). Continuous-time learning models were applied to characterize trajectories of performance change, with different models allowing for varying dynamics. The best-fitting model included drift rate changing as a continuous, exponential function of cumulative trial number. In contrast, response boundary changed within each daily session, but in an independent manner across daily sessions. Our results highlight two different processes underlying the pattern of behavior observed across the entire learning trajectory, one involving a continuous tuning of perceptual sensitivity, and another more variable process describing participants’ threshold of when enough evidence is present to act.

This study investigated behavioral and cortical mechanisms for short-term postural training with error amplification (EA) feedback in the elderly. Thirty-six elderly subjects (65.7 ± 2.2 years) were grouped (control and EA, n = 18) for training in stabilometer balance under visual guidance. During the training session (8 training rounds of 60 s in Day 2), the EA group received visual feedback that magnified errors to twice the real size, whereas the control group received visual feedback that displayed real errors. Scalp EEG and kinematic data of the stabilometer plate and ankle joint were recorded in the pre-test (Day 1) and post-test (Day 3). The EA group (−46.5 ± 4.7%) exhibited greater post-training error reduction than that of the control group (−27.1 ± 4.0%)(p = 0.020), together with a greater decline in kinematic coupling between the stabilometer plate and ankle joint (EA: −26.6 ± 4.8%, control: 2.3 ± 8.6%, p = 0.023). In contrast to the control group, the EA group manifested greater reductions in mean phase-lag index (PLI) connectivity in the theta (4–7 Hz)(p = 0.011) and alpha (8–12 Hz) (p = 0.027) bands. Only the EA group showed post-training declines in the mean PLI in the theta and alpha bands. Minimal spanning tree analysis revealed that EA-based training led to increases in the diameter (p = 0.002) and average eccentricity (p = 0.004) of the theta band for enhanced performance monitoring and reduction in the leaf fraction (p = 0.030) of the alpha band for postural response with enhanced automaticity. In conclusion, short-term EA training optimizes balance skill, favoring multi-segment coordination for the elderly, which is linked to more sophisticated error monitoring with less attentive control over the stabilometer stance.

Practice in real-world settings exhibits many idiosyncracies of scheduling and duration that can only be roughly approximated by laboratory research. Here we investigate 39,157 individuals’ performance on two cognitive games on the Lumosity platform over a span of 5 years. The large-scale nature of the data allows us to observe highly varied lengths of uncontrolled interruptions to practice and offers a unique view of learning in naturalistic settings. We enlist a suite of models that grow in the complexity of the mechanisms they postulate and conclude that long-term naturalistic learning is best described with a combination of long-term skill and task-set preparedness. We focus additionally on the nature and speed of relearning after breaks in practice and conclude that those components must operate interactively to produce the rapid relearning that is evident even at exceptionally long delays (over 2 years). Naturalistic learning over long time spans provides a strong test for the robustness of theoretical accounts of learning, and should be more broadly used in the learning sciences.

This research examines how the human genome and SES jointly and interactively shape verbal ability among youth in the U.S. The youth are aged 12–18 when the study starts. The research draws on findings from the latest GWAS as well as a rich set of longitudinal SES measures at individual, family and neighborhood levels from Add Health (N = 7194). Both SES and genome measures predict verbal ability well separately and jointly. More interestingly, the inclusion of both sets of predictors in the same model corrects for about 20% upward bias in the effect of the education PGS, and implies that about 20–30% of the effects of parental SES are not environmental, but parentally genomic. The three incremental R2s that measure the relative contributions of the two PGSs, the genomic component in parental SES, and the environmental component in parental SES are estimated to be about 1.5%, 1.5%, and 7.8%, respectively. The total environmental R2 and the total genomic R2 are, thus, 7.8% and 3%, respectively. These findings confirm the importance of SES environment and also pose challenges to traditional social-science research. Not only does an individual’s genome have an important direct influence on verbal ability, parental genomes also influence verbal ability through parental SES. The decades-long blueprint of including SES in a model and interpreting their effects as those of SES needs to be amended accordingly. A straightforward solution is to routinely collect DNA data for large social-science studies granted that the primary purpose is to understand social and environmental influences.

Enrichment in rodents affects brain structure, improves behavioral performance, and is neuroprotective. Similarly, in humans, according to the cognitive reserve concept, enriched experience is functionally protective against neuropathology. Despite this parallel, the ability to translate rodent studies to human clinical situations is limited. This limitation is likely due to the simple cognitive processes probed in rodent studies and the inability to control, with existing methods, the degree of rodent engagement with enrichment material. We overcome these two difficulties with behavioral tasks that probe, in a fine-grained manner, aspects of higher-order cognition associated with deterioration with aging and dementia, and a new enrichment protocol, the ‘Obstacle Course’ (OC), which enables controlled enrichment delivery, respectively. Together, these two advancements will enable better specification (and comparisons) of the nature of impairments in animal models of complex mental disorders and the potential for remediation from various types of intervention (e.g., enrichment, drugs). We found that two months of OC enrichment produced substantial and sustained enhancements in categorization memory, perceptual object invariance, and cross-modal sensory integration in mice. We also tested mice on behavioral tasks previously shown to benefit from traditional enrichment: spontaneous object recognition, object location memory, and pairwise visual discrimination. OC enrichment improved performance relative to standard housing on all six tasks and was in most cases superior to conventional home-cage enrichment and exercise track groups.