Explaining the high working memory capacity of gifted children: Contributions of processing skills and executive control

Do gifted children without specific learning disabilities read more efficiently than typically developing children?

Introduction

There are no published data on the written language skills of gifted children (GC). The objective of the present study was to evaluate reading abilities of GC vs. normative data from typically developing French children (TDC). Like English, French is considered to be an opaque language.

Method

GC completed the Wechsler Intelligence Scales and a battery of language tests. Only children with a score two standard deviations (SD) above the norm were included. GC with current or past academic difficulties or specific learning disorders were excluded. The GC's scores were compared with TDC's normative scores for language tests in a chi-square-test and corrected for multiple comparisons.

Results

Forty-five GC were included. The highest GC's mean scores were for the WISC's Verbal Comprehension Index (VCI) and the lowest for the Processing Speed Index (from more than two SDs to one SD higher above the TDC's normative scores). GC were between 1.3 and 4.7 times more likely than TDC to achieve a high score. After correction, the distributions of the GC's and TDC's scores differed significantly with regard to spoonerism, phoneme deletion, and rapid automatic naming (p < 0.001), word and sentence repetition (p ≤ 0.007), and the reading of meaningful text (p = 0.03). GC and TDC did not differ significantly for reading meaningless texts and spelling accuracy.

Discussion

As described in the literature, the GC in the present study had heterogeneous scores on the Wechsler Intelligence Scales. The GC performed better than TDC in assessments of the underlying skills of reading and when reading of meaningful texts. This advantage was lost in the absence of context, as shown by the lack of significant GC vs. TDC differences for reading meaningless texts and for spelling accuracy. Hence, GC presented a heterogeneous profile with regard to the underlying skills of reading and reading abilities. The present data should help to improve our understanding of GC's reading skills. In particular, it is now essential to determine which written language tests and which score thresholds are appropriate for identifying specific learning disorders in GC.

The emergence of enhanced intelligence in a brain-inspired cognitive architecture

The Causal Cognitive Architecture is a brain-inspired cognitive architecture developed from the hypothesis that the navigation circuits in the ancestors of mammals duplicated to eventually form the neocortex. Thus, millions of neocortical minicolumns are functionally modeled in the architecture as millions of "navigation maps." An investigation of a cognitive architecture based on these navigation maps has previously shown that modest changes in the architecture allow the ready emergence of human cognitive abilities such as grounded, full causal decision-making, full analogical reasoning, and near-full compositional language abilities. In this study, additional biologically plausible modest changes to the architecture are considered and show the emergence of super-human planning abilities. The architecture should be considered as a viable alternative pathway toward the development of more advanced artificial intelligence, as well as to give insight into the emergence of natural human intelligence.

A Study on the Effect of Executive Control Network Functional Connection on the Therapeutic Efficacy of Repetitive Transcranial Magnetic Stimulation in Alzheimer's Disease

Background

Alzheimer's disease (AD) is a neurodegenerative disease characterized by brain network dysfunction. Few studies have investigated whether the functional connections between executive control networks (ECN) and other brain regions can predict the therapeutic effect of repetitive transcranial magnetic stimulation (rTMS).

Objective

The purpose of this study is to examine the relationship between the functional connectivity (FC) within ECN networks and the efficacy of rTMS.

Methods

We recruited AD patients for rTMS treatment. We established an ECN using baseline period fMRI data and conducted an analysis of the ECN's FC throughout the brain. Concurrently, the support vector regression (SVR) method was employed to project post-rTMS cognitive scores, utilizing the connectional attributes of the ECN as predictive markers.

Results

The average age of the patients was 66.86±8.44 years, with 8 males and 13 females. Significant improvement on most cognitive measures. We use ECN connectivity and brain region functions in baseline patients as features for SVR model training and fitting. The SVR model could demonstrate significant predictability for changes in Montreal Cognitive Assessment scores among AD patients after rTMS treatment. The brain regions that contributed most to the prediction of the model (the top 10% of weights) were located in the medial temporal lobe, middle temporal gyrus, frontal lobe, parietal lobe and occipital lobe.

Conclusions

The stronger the antagonism between ECN and parieto-occipital lobe function, the better the prediction of cognitive improvement; the stronger the synergy between ECN and fronto-temporal lobe function, the better the prediction of cognitive improvement.

Non-Cognitive Specificities of Intellectually Gifted Children and Adolescents: A Systematic Review of the Literature

For several years, there was a growing interest in intellectual giftedness and in particular in the non-cognitive specificities of gifted individuals. This topic attracted much public attention and sometimes led to contradictions with the scientific literature. The current review synthesizes a broad set of results related to non-cognitive specificities of intellectual gifted in children and adolescents. This synthesis of scientific research on giftedness and its associated non-cognitive features does not support the conclusion that there is a stable profile across gifted individuals that would consistently separate them from non-gifted individuals. A few specificities in some areas are noted, but they are not necessarily being systematic. These specificities often turn out to be in favor of gifted youth, contrary to the view sometimes defended in the general public that gifted individuals suffer from major everyday difficulties. Finally, methodological issues are listed regarding the designs of existing studies, with recommendations for future research in the field.

The Relationship between Executive Functions, Working Memory, and Intelligence in Kindergarten Children

Executive functions (EF), working memory (WM), and intelligence are closely associated, but distinct constructs. What underlies the associations between these constructs, especially in childhood, is not well understood. In this pre-registered study, along with the traditional aggregate accuracy and RT-based measures of EF, we investigated post-error slowing (PES) in EF as a manifestation of metacognitive processes (i.e., monitoring and cognitive control) in relation to WM and intelligence. Thereby, we aimed to elucidate whether these metacognitive processes may be one underlying component to explain the associations between these constructs. We tested kindergarten children (M_age = 6.4 years, SD_age = 0.3) in an EF, WM (verbal and visuospatial), and fluid (non-verbal) intelligence task. We found significant associations of mainly the inhibition component of EF with fluid intelligence and verbal WM, and between verbal WM and intelligence. No significant associations emerged between the PES in EF and intelligence or WM. These results suggest that in the kindergarten age, inhibition rather than monitoring and cognitive control might be the underlying component that explains the associations between EF, WM, and intelligence.

Is variability in working memory capacity related to differences in the reactivation of memory traces? A test based on the time-based resource sharing (TBRS) model

Working memory performance depends on reactivating memory traces, by rapidly switching between refreshing item representations and performing concurrent cognitive processing (time-based resource sharing (TBRS) account). Prior research has suggested that variation in the effectiveness of this process could be a plausible source of developmental changes in working memory capacity. This could conceivably extend to adults, potentially bridging the barrier between developmental and adult experimental research and providing a possible functional role for attention control and processing speed in working memory tasks. The present work was designed to replicate the finding of developmental differences in reactivation in children, and to test whether the same process could be related to individual differences in adults. Experiment 1 confirmed the finding of more effective reactivation for 14-year-old children than for 8-year-old children. Experiment 2 using the same task in adults manipulated the feasibility of reactivation within an experimental-correlational approach, and failed to find more effective reactivation for individuals with high working memory capacity, contrary to our predictions. Overall, our results support the role of reactivation as defined by the TBRS model as an important process in working memory tasks, and as a possible source of developmental increase in working memory capacity; however, they rule out the possibility that adult individual differences in the effectiveness of this process are a major cause of variability in working memory capacity, suggesting that differences between adults are of a different nature.

Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis

Gifted children and normal controls can be distinguished by analyzing the structural connectivity (SC) extracted from MRI data. Previous studies have improved classification accuracy by extracting several features of the brain regions. However, the limited size of the database may lead to degradation when training deep neural networks as classification models. To this end, we propose to use a data augmentation method by adding artificial samples generated using graph empirical mode decomposition (GEMD). We decompose the training samples by GEMD to obtain the intrinsic mode functions (IMFs). Then, the IMFs are randomly recombined to generate the new artificial samples. After that, we use the original training samples and the new artificial samples to enlarge the training set. To evaluate the proposed method, we use a deep neural network architecture called BrainNetCNN to classify the SCs of MRI data with and without data augmentation. The results show that the data augmentation with GEMD can improve the average classification performance from 55.7 to 78%, while we get a state-of-the-art classification accuracy of 93.3% by using GEMD in some cases. Our results demonstrate that the proposed GEMD augmentation method can effectively increase the limited number of samples in the gifted children dataset, improving the classification accuracy. We also found that the classification accuracy is improved when specific features extracted from brain regions are used, achieving 93.1% for some feature selection methods.