Electrophysiological markers of poor versus superior math abilities in healthy individuals

Detecting cognitive traits and occupational proficiency using EEG and statistical inference

Machine learning (ML) is widely used in classification tasks aimed at detecting various cognitive states or neurological diseases using noninvasive electroencephalogram (EEG) time series. However, successfully detecting specific cognitive skills in a healthy population, independent of subject, remains challenging. This study compared the subject-independent classification performance of three different pipelines: supervised and Riemann projections with logistic regression and handcrafted power spectral features with light gradient boosting machine (LightGBM). 128-channel EEGs were recorded from 26 healthy volunteers while they solved arithmetic, logical, and verbal tasks. The participants were divided into two groups based on their higher education and occupation: specialists in mathematics and humanities. The balanced accuracy of the education type was significantly above chance for all pipelines: 0.84-0.89, 0.85-0.88, and 0.86-0.88 for each type of task, respectively. All three pipelines allowed us to distinguish mathematical proficiency based on learning experience with different trade-offs between performance and explainability. Our results suggest that ML approaches could also be effective for recognizing individual cognitive traits using EEG.

Early neural markers for individual difference in mathematical achievement determined from rational number processing

The neural markers for individual differences in mathematical achievement have been studied extensively using magnetic resonance imaging; however, high temporal resolution electrophysiological evidence for individual differences in mathematical achievement require further elucidation. This study evaluated the event-related potential (ERP) when 48 college students with high or low mathematical achievement (HA vs. LA) matched non-symbolic and symbolic rational numbers. Behavioral results indicated that HA students had better performance in the discretized non-symbolic matching, although the two groups showed similar performances in the continuous matching. ERP data revealed that even before non-symbolic stimulus presentation, HA students had greater Bereitschaftspotential (BP) amplitudes over posterior central electrodes. After the presentation of non-symbolic numbers, HA students had larger N1 amplitudes at 160 ms post-stimulus, over left-lateralized parieto-occipital electrodes. After the presentation of symbolic numbers, HA students displayed more profound P1 amplitudes at 100 ms post-stimulus, over left parietal electrodes. Furthermore, larger BP and N1 amplitudes were associated with the shorter reaction times, and larger P1 amplitudes corresponded to lower error rates. The BP effect could indicate preparation processing, and early left-lateralized N1 and P1 effects could reflect the non-symbolic and symbolic number processing along the dorsal neural pathways. These results suggest that the left-lateralized P1 and N1 components elicited by matching non-symbolic and symbolic rational numbers can be considered as neurocognitive markers for individual differences in mathematical achievement.

Effect of Obesity on Arithmetic Processing in Preteens With High and Low Math Skills: An Event-Related Potentials Study

Preadolescence is an important period for the consolidation of certain arithmetic facts, and the development of problem-solving strategies. Obese subjects seem to have poorer academic performance in math than their normal-weight peers, suggesting a negative effect of obesity on math skills in critical developmental periods. To test this hypothesis, event-related potentials (ERPs) were collected during a delayed-verification math task using simple addition and subtraction problems in obese [above 95th body mass index (BMI) percentile] and non-obese (between 5th and 90th BMI percentile) preteens with different levels of math skill; thirty-one with low math skills (14 obese, mean BMI = 26.40, 9.79 years old; 17 non-obese, BMI = 17.45, 9.76 years old) and thirty-one with high math skills (15 obese, BMI = 26.90, 9.60 years old; 16 non-obese, BMI = 17.13, 9.63 years old). No significant differences between weight groups were observed in task accuracy regardless of their mathematical skill level. For ERPs, electrophysiological differences were found only in the subtraction condition; participants with obesity showed an electrophysiologic pattern associated with a reduced ability to allocate attention resources regardless of their math skill level, these differences were characterized by longer P300 latency than their normal-weight peers. Moreover, the participants with obesity with high math skills displayed hypoactivity in left superior parietal lobule compared with their normal-weight peers. Additionally, obese preteens with low math skills displayed smaller arithmetic N400 amplitude than non-obese participants, reflecting difficulties in retrieving visual, semantic, and lexical information about numbers. We conclude that participants with obesity are less able than their normal-weight peers to deploy their attention regardless of their behavioral performance, which seems to have a greater effect on obese participants with low math skills because they also show problems in the retrieval of solutions from working memory, resulting in a delay in the development of mathematical skills.

Alpha and theta oscillations in mental addition for high and low performers

While many contemporary studies aim to explore the sources of notable individual differences in arithmetic skills, this study specifically aims to highlight cognitive differences between high and low math performers. Thirty-six undergraduate female students who were identified as either high or low math performers, according to an arithmetic fluency test, were recruited. In the main experiment, EEG recordings were taken, while the participants performed a mental addition task. The mental addition problems were classified as either easy or difficult, and were presented to participants in several forms. The results indicated that problem difficulty increases the gap in accuracy attainment between high and low math performers. Additionally, high performers displayed larger alpha power during mental arithmetic in P7, corresponding to the left parietal lobe. This indicated that combining behavioral and neural data can improve our understanding of the differences between high and low math performers. Interpretations and implications are discussed.

Finger-counting observation interferes with number processing

Aim of this study was to investigate the association between finger and number representation in a task in which students had to perform arithmetic calculations and decide whether the provided solution was correct or incorrect, while a pair of task-irrelevant hands gesturally expressed the same or a different number. In particular we aimed at investigating whether irrelevant finger-counting might interfere with arithmetic computing, thus showing the existence of a strict neural association between the two processes. 20 volunteers took part to the investigation and EEG/ERPs were recorded from 128 scalp sites. P300 amplitude was greater to correct than incorrect solutions. Accuracy was higher when there was no conflict between the two sets of information A numerical error-related negativity (nERN) was elicited by incorrect solutions, and also by correct solutions when the finger-counting was incongruent. Source analysis applied to the incongruent minus congruent difference showed that when finger-counting was incorrect nERN mostly derived from medial and superior prefrontal cortex activity (supporting action monitoring and suppression). Conversely, when finger-counting indicated the correct solution brain activation included occipital areas, somatosensory regions and visuomotor mirror areas, inferior and superior temporal cortex, reflecting attentional orienting toward the hands. In both cases, the left angular gyrus (BA39) was found active during conjoined digit/number processing, suggesting a strict neural association between finger and digit processing. The present findings help explaining why a lesion in the left parietal cortex may simultaneously lead to finger apraxia and acalculia (Gertsmann syndrome).