Ting-Ting Chang

Word problems are essential for math learning and education, bridging numerical knowledge with real‐world applications. Despite their importance, the neural mechanisms underlying word problem solving, especially in children, remain poorly understood. Here, we examine children's cognitive and brain response profiles for arithmetic word problems (AWPs), which involve one‐step mathematical operations, and compare them with nonarithmetic word problems (NWPs), structured as parallel narratives without numerical operations. Behavioural results suggested that AWP performance was associated with both reading comprehension and arithmetic fluency, whereas NWP performance correlated only with reading comprehension. Neuroimaging results revealed distinct neural substrates: AWP solving primarily activated the anterior insula, middle frontal gyrus and intraparietal sulcus, whereas NWP solving engaged in the inferior frontal gyrus, middle temporal gyrus and angular gyrus. Critically, we observed a developmental shift: Children showed heightened prefrontal activation during AWP solving, contrasting with increased posterior parietal engagement in adults. Moreover, although adults demonstrated brain–behaviour associations, with slower AWP solving linked to stronger parietal activation, this relationship was absent in children. Taken together, these findings suggest that AWP solving recruits specialized arithmetic brain circuits that undergo a frontal‐to‐parietal trajectory. Our study thus provides a neurological basis for AWP solving in children, emphasizing the crucial role of the fronto‐insular‐parietal network. These insights into brain‐based contributions to developmental differences may guide the development of targeted remediation strategies and educational interventions tailored to individual learning needs.

The development of electrodes capable of maintaining strong adhesion and conductivity in wet environments is crucial for a wide range of electronic applications. From underwater monitoring systems to biomedical devices, the limitations of conventional electrodes pose a significant challenge. Inspired by the adhesive and self-healing properties of marine mussels, this work introduces a self-healing conductive adhesive that not only adheres tenaciously to various surfaces in wet conditions but also demonstrates exceptional conductivity for reliable detection of electrophysiological signals. In this study, our biocompatible adhesive consists of a boronic ester-based polyvinyl alcohol hydrogel with polydopamine particles (PVA/Borax/PDAP). Borate ions enable rapid self-healing (within 10 seconds) by forming dynamic bonds with hydroxyl groups, while the π–π interactions of PDAPs enhance adhesion strength (121 kPa). The inclusion of saturated saline further improves both stretchability (up to ∼1000%) and conductivity (38.3 mS cm⁻¹). We successfully demonstrate the utility of this self-healing conductive adhesive in recording electrophysiological signals, including electroencephalography (EEG), electrodermal activity detection, and electrocardiograms, highlighting its potential for next-generation biomedical wearable devices.

The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task‐based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7–30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U‐shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN‐SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross‐validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth.Practitioner Points: This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task‐state frontoparietal and salience networks. Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks. Leveraging advanced machine learning methods, we accurately predicted individuals' ages based on these brain circuits, with a more mature estimated brain age correlating with better math skills.

Numerous empirical studies have reported that males and females perform equally well in mathematical achievement. However, still to date, very limited is understood about the brain response profiles that are particularly characteristic of males and females when solving mathematical problems. The present study aimed to tackle this issue by manipulating arithmetic problem size to investigate functional significance using functional magnetic resonance imaging (fMRI) in young adults. Participants were instructed to complete two runs of simple calculation tasks with either large or small problem sizes. Behavioural results suggested that the performance did not differ between females and males. Neuroimaging data revealed that sex/gender‐related patterns of problem size effect were found in the brain regions that are conventionally associated with arithmetic, including the left middle frontal gyrus (MFG), left intraparietal sulcus (IPS) and insula. Specifically, females demonstrated substantial brain responses of problem size effect in these regions, whereas males showed marginal effects. Moreover, the machine learning method implemented over the brain signal levels within these regions demonstrated that sex/gender is discriminable. These results showed sex/gender effects in the activating patterns varying as a function of the distinct math problem size, even in a simple calculation task. Accordingly, our findings suggested that females and males use two complementary brain resources to achieve equally successful performance levels and highlight the pivotal role of neuroimaging facilities in uncovering neural mechanisms that may not be behaviourally salient.

Over the long run, STEM fields had been perceived as dominant by males, despite that numerous studies have shown that female students do not underperform their male classmates in mathematics and science. In this review, we discuss whether and how sex/gender shows specificity in arithmetic processing using a cognitive neuroscience approach not only to capture contemporary differences in brain and behavior but also to provide exclusive brain bases knowledge that is unseen in behavioral outcomes alone. We begin by summarizing studies that had examined sex differences/similarities in behavioral performance of mathematical learning, with a specific focus on large‐scale meta‐analytical data. We then discuss how the magnetic resonance imaging (MRI) approach can contribute to understanding neural mechanisms underlying sex‐specific effects of mathematical learning by reviewing structural and functional data. Finally, we close this review by proposing potential research issues for further exploration of the sex effect using neuroimaging technology. Through the lens of advancement in the neuroimaging technique, we seek to provide insights into uncovering sex‐specific neural mechanisms of learning to inform and achieve genuine gender equality in education.

Introduction: Math anxiety severely impacts individuals’ learning and future success. However, limited is understood about the profile in East Asian cultures where students genuinely show high‐level math anxiety, despite that they outperform their Western counterparts. Here, we investigate the relation between math anxiety and math achievement in children as young as first and second graders in Taiwan. Further, we evaluate whether intensive exposure to digital game‐based learning in mathematics could ameliorate math anxiety.Methods: The study first evaluated a group of 159 first and second graders’ math anxiety and its correlation with math performance. Subsequently, a quasi‐experimental design was adopted: 77 of the children continued and participated in multi‐component digital game training targeting enumeration, speeded calculation, and working memory. Post‐assessment was administered afterward for further evaluation of training‐associated effects.Results: Results confirmed that math anxiety was negatively associated with school math achievement, which assessed numerical knowledge and arithmetic calculation. Furthermore, children's math anxiety was remarkably reduced via digital training in mathematics after 6‐week intensive remediation. Crucially, this math anxiety relief was more prominent in those with high‐level math anxiety. Although the children who underwent the training showed training‐induced math achievement and working memory enhancement, this cognitive improvement appeared to be independent of the math anxiety relief.Conclusion: Our findings demonstrate that students can show highly negative emotions and perceptions toward learning even in high‐achieving countries. Auspiciously, the feeling of distress toward learning has the feasibility to be relieved from short‐term intensive training. Our study suggests a new approach of early treatments to emotional disturbance that can lead to permanent consequences in individuals.

The practice of mathematical word problem is ubiquitous and thought to impact academic achievement. However, the underlying neural mechanisms are still poorly understood. In this study, we investigate how lexical consistency of word problem description is modulated in adults' brain responses during word problem solution. Using functional magnetic resonance imaging methods, we examined compare word problems that included relational statements, such as “A dumpling costs 9 dollars. A wonton is 2 dollars less than a dumpling. How much does a wonton cost?” and manipulated lexical consistency (consistent: the relational term consistent with the operation to be performed, e.g., more—addition/inconsistent: e.g., less—addition) and problem operation (addition/subtraction). We found a consistency by operation interaction in the widespread fronto-insular-parietal activations, including the anterior insula, dorsoanterior cingulate cortex, middle frontal gyrus, and intraparietal sulcus, such that inconsistent problems engaged stronger activations than consistent problems for addition, whereas the consistency effect was inverse for subtraction. Critically, these results were more salient in the less successful problem solvers than their more successful peers. Our study is the first to demonstrate that lexical consistency effects on arithmetic neural networks are modulated during reading word problem that required distinct arithmetic operations. More broadly, our study has strong potentials to add linkage between neuroscience and education by remediating deficits and enhance instruction design in the school curriculum.

Various types of arm-and-hand movements co-occurring with speech give rise to diverse cross-modal semantic relations. This study investigated how the brain processes self-adaptors, emblems, and iconic gestures with the same speech by using fMRI. Gestures with speech evoked bilateral fusiform gyrus and left supramarginal gyrus in visual and multisensory processing. These regions were involved in processing conventional forms and meanings in emblems with speech. Iconic gestures uniquely activated right supramarginal gyrus associated with spatial processing of unconventional configurations of event knowledge of lexical concepts. Self-adaptors without meaning particularly involved the left superior parietal lobule in directing spatial attention to hand movements and processing spatio-motoric attributes with motor representations. Without gestures, bilateral superior temporal gyrus and calcarine sulcus were engaged in processing sounds and meanings of audiovisual speech. The overall results demonstrate the brain’s sensitivity to gesture-speech semantic variation, and reveal the nature of knowledge in the involved neural regions.