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•Structural brain networks partly agree with functional brain networks.•Distinct nature of abilities is represented by unique conjunction of brain networks.•Abilities rely on node(s) as hub(s) to connect several different brain networks. Cognitive neuroscience assumes that different mental abilities correspond to at least partly separable brain subnetworks and strives to understand their relationships. However, single-task approaches typically revealed multiple brain subnetworks to be involved in performance. Here, we chose a bottom-up approach of investigating the association between structural and functional brain subnetworks, on the one hand, and domain-specific cognitive abilities, on the other. Structural network was identified using machine-learning graph neural network by clustering anatomical brain properties measured in 838 individuals enroled in the WU-Minn Young Adult Human Connectome Project. Functional network was adapted from seven Resting State Networks (7-RSN). We then analyzed the results of 15 cognitive tasks and estimated five latent abilities: fluid reasoning (Gf), crystallized intelligence (Gc), memory (Mem), executive functions (EF), and processing speed (Gs). In a final step we determined linear associations between these independently identified ability and brain entities. We found no one-to-one mapping between latent abilities and brain subnetworks. Analyses revealed that abilities are associated with properties of particular combinations of brain subnetworks. While some abilities are more strongly associated to within-subnetwork connections, others are related with connections between multiple subnetworks. Importantly, domain-specific abilities commonly rely on node(s) as hub(s) to connect with other subnetworks. To test the robustness of our findings, we ran the analyses through several defensible analytical decisions. Together, the present findings allow a novel perspective on the distinct nature of domain-specific cognitive abilities building upon unique combinations of associated brain subnetworks.

Although most people can identify facial expressions of emotions well, they still differ in this ability. According to embodied simulation theories understanding emotions of others is fostered by involuntarily mimicking the perceived expressions, causing a \"reactivation\" of the corresponding mental state. Some studies suggest automatic facial mimicry during expression viewing; however, findings on the relationship between mimicry and emotion perception abilities are equivocal. The present study investigated individual differences in emotion perception and its relationship to facial muscle responses - recorded with electromyogram (EMG)--in response to emotional facial expressions. N° = °269 participants completed multiple tasks measuring face and emotion perception. EMG recordings were taken from a subsample (N° = °110) in an independent emotion classification task of short videos displaying six emotions. Confirmatory factor analyses of the m. corrugator supercilii in response to angry, happy, sad, and neutral expressions showed that individual differences in corrugator activity can be separated into a general response to all faces and an emotion-related response. Structural equation modeling revealed a substantial relationship between the emotion-related response and emotion perception ability, providing evidence for the role of facial muscle activation in emotion perception from an individual differences perspective.

Reading is a complex task involving different brain areas. As a crystallized ability, reading is also known to have effects on brain structure and function development. However, there are still open questions about what are the elements of the reading networks and how structural and functional brain measures shape the reading ability. The present study used a data-driven approach to investigate whether reading-related brain structural measures of cortical thickness, myelination, sulcus depth and structural connectivity and functional connectivity from the whole brain can predict individual differences in reading skills. It used different brain measures and performance scores from the Oral Reading Recognition Test (ORRT) measuring reading ability from 998 participants. We revealed reading-related brain areas and connections, and evaluated how well area and connection measures predict reading performance. Interestingly, the combination of all brain measures obtained the best predictions. We further grouped reading-related areas into positive and negative networks, each with four different levels (Core Regions, Extended-Regions 1, 2, 3), representing different correlation levels with the reading scores, and the non-correlated Region irrelevant to reading ability. The Core Regions are composed of areas that are most strongly correlated with reading performance. Insular and frontal opercular cortex, lateral temporal cortex, and early auditory cortex occupy the positive Core Region, while inferior temporal and motor cortex occupy the negative Core Region. Aside from those areas, the present study also found more reading-related areas including visual and language-related areas. In addition, connections predicting reading scores are denser inside the reading-related networks than outside. Together, the present study reveals extended reading networks of the brain and provides an extended data-driven analytical framework to study interpretable brain-behavior relationships, which are transferable also to studying other abilities.

We investigated whether face-specific processes as indicated by the N170 in event-related brain potentials (ERPs) are modulated by emotional significance in facial expressions. Results yielded that emotional modulations over temporo-occipital electrodes typically used to measure the N170 were less pronounced when ERPs were referred to mastoids than when average reference was applied. This offers a potential explanation as to why the literature has so far yielded conflicting evidence regarding effects of emotional facial expressions on the N170. However, spatial distributions of the N170 and emotion effects across the scalp were distinguishable for the same time point, suggesting different neural sources for the N170 and emotion processing. We conclude that the N170 component itself is unaffected by emotional facial expressions, with overlapping activity from the emotion-sensitive early posterior negativity accounting for amplitude modulations over typical N170 electrodes. Our findings are consistent with traditional models of face processing assuming face and emotion encoding to be parallel and independent processes.

Influential theories emphasize the importance of predictions in learning: we learn from feedback to the extent that it is surprising, and thus conveys new information. Here, we explore the hypothesis that surprise depends not only on comparing current events to past experience, but also on online evaluation of performance via internal monitoring. Specifically, we propose that people leverage insights from response-based performance monitoring – outcome predictions and confidence – to control learning from feedback. In line with predictions from a Bayesian inference model, we find that people who are better at calibrating their confidence to the precision of their outcome predictions learn more quickly. Further in line with our proposal, EEG signatures of feedback processing are sensitive to the accuracy of, and confidence in, post-response outcome predictions. Taken together, our results suggest that online predictions and confidence serve to calibrate neural error signals to improve the efficiency of learning.

For emotional pictures with fear-, disgust-, or sex-related contents, stimulus size has been shown to increase emotion effects in attention-related event-related potentials (ERPs), presumably reflecting the enhanced biological impact of larger emotion-inducing pictures. If this is true, size should not enhance emotion effects for written words with symbolic and acquired meaning. Here, we investigated ERP effects of font size for emotional and neutral words. While P1 and N1 amplitudes were not affected by emotion, the early posterior negativity started earlier and lasted longer for large relative to small words. These results suggest that emotion-driven facilitation of attention is not necessarily based on biological relevance, but might generalize to stimuli with arbitrary perceptual features. This finding points to the high relevance of written language in today's society as an important source of emotional meaning.

Syntactic violations in sentences elicit a P600 component in the event-related potential, which is frequently interpreted as signaling reanalysis or repair of the sentence structure. However, P600 components have been reported also for semantic and combined semantic and syntactic violations, giving rise to still other interpretations. In many of these studies, the violation might be of special significance for the task of the participants; however there is a lack of studies directly targeting task effects on the P600. Here we repeated a previously published study but using a probe verification task, focusing on individual words rather than on sentence correctness and directly compared the results with the previous ones. Although a (somewhat smaller) N400 component occurred also in the present study, we did not observe a parietal P600 component. Instead, we found a late anterior negativity. Possibly, the parietal P600 observed in sentence acceptability paradigms relates to the target value of the violations or to late sentence structure-specific processes that are more task-sensitive than the N400 and which are or not initiated in the probe verification task. In any case the present findings show a strong dependency of P600-eliciting processes from attention to the sentences context whereas the N400 eliciting processes appear relatively robust.

Face cognition, the ability to perceive faces and interpret facial information, is a crucial skill in human social interactions. At the neurobiological level, several functionally specialized brain regions constitute a network of face processing. However, the evidence whether functional specialization within the face network is also reflected in the white matter structural connectivity patterns is yet limited. Based on imaging data from 1051 young healthy adult women and men, we investigated individual differences in the integrity of fibre tracts connecting face-processing regions relative to brain-general tract integrity. We analyzed individual tract-averaged fractional anisotropy (FA) values with structural equation modeling (SEM). Our results show that beyond the variance explained by a general factor indicating the quality of global tracts, the specificity of white matter integrity within the face network can be accounted for by additional factors. These factors correspond to the core and extended networks suggested in classic neuro-functional models of face processing. The right-hemisphere dominance, as commonly found in face cognition studies, is also reflected in this factorial structure. Overall, our results extend the structural brain substrate of the classic functional face processing system to the network of fibre tracts connecting these brain areas, and shed light on a structure-function correspondence from the perspective of individual differences. •White matter integrity in the face processing network is variable across individuals.•Face network variability is specific relative to fibre integrity of global brain tracts.•Face network links are separable into independent latent variables.•These links correspond to core and extended areas of neurofunctional face models.•Links to the extended system are more differentiated in the right hemisphere.

Background/Objectives: Change detection of social cues across individuals plays an important role in human interaction. Methods: Here we investigated the automatic change detection of facial and vocal attractiveness in 19 female participants by recording event-related potentials (ERPs). We adopted a ‘deviant-standard-reverse’ oddball paradigm where high- or low-attractive items were embedded as deviants in a sequence of opposite attractive standard stimuli. Results: Both high- and low-attractive faces and voices elicited mismatch negativities (MMNs). Furthermore, low-attractive versus high-attractive items induced larger mismatch negativities in the voice condition but larger P3 amplitudes in the face condition. Conclusions: These data indicate that attractiveness can be automatically detected but that differences exist between facial and vocal attractiveness processing. Generally, change detection seems to work better for unattractive than attractive information, possibly in line with a negativity bias.

At the group level, women consistently perform better in face memory tasks than men and also show earlier and larger N170 components of event-related brain potentials (ERP), considered to indicate perceptual structural encoding of faces. Here we investigated sex differences in the relationship between the N170 and face memory performance in 152 men and 141 women at group mean and individual differences levels. ERPs and performance were measured in separate tasks, avoiding statistical dependency between the two. We confirmed previous findings about superior face memory in women and a—sex-independent—negative relationship between N170 latency and face memory. However, whereas in men, better face memory was related to larger N170 components, face memory in women was unrelated with the amplitude or latency of the N170. These data provide solid evidence that individual differences in face memory within men are at least partially related to more intense structural face encoding.