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Network science provides a set of quantitative methods to investigate complex systems, including human cognition. Although cognitive theories in different domains are strongly based on a network perspective, the application of network science methodologies to quantitatively study cognition has so far been limited in scope. This review demonstrates how network science approaches have been applied to the study of human cognition and how network science can uniquely address and provide novel insight on important questions related to the complexity of cognitive systems and the processes that occur within those systems. Drawing on the literature in cognitive network science, with a focus on semantic and lexical networks, we argue three key points. (i) Network science provides a powerful quantitative approach to represent cognitive systems. (ii) The network science approach enables cognitive scientists to achieve a deeper understanding of human cognition by capturing how the structure, i.e., the underlying network, and processes operating on a network structure interact to produce behavioral phenomena. (iii) Network science provides a quantitative framework to model the dynamics of cognitive systems, operationalized as structural changes in cognitive systems on different timescales and resolutions. Finally, we highlight key milestones that the field of cognitive network science needs to achieve as it matures in order to provide continued insights into the nature of cognitive structures and processes.

In this work, we challenge the assumption that weak ties play uniquely important social support roles on social network sites, particularly regarding informational support. To overcome methodological limitations of earlier research, we present a mixed-methods study. Forty-one participants were interviewed and asked to identify five weak, medium, and strong ties each and to report on perceived and actually received social support (emotional, informational, instrumental, and appraisal) associated with each. Complicating traditional understandings of “the strength of weak ties,” the qualitative analyses of actual support events show that both emotional and informational support is provided by strong ties. In an additional quantitative between-subjects study design, 352 participants were asked about various aspects of a weak, medium, or strong tie. These results indicate that participants valued their strong ties more regarding every form of support. Although there were only weak correlations between perceived and recalled actually received support, people also report actual support events with strong ties to be more helpful—overall suggesting the strength of strong ties.

The human visual system is an intricate network of brain regions that enables us to recognize the world around us. Despite its abundant lateral and feedback connections, object processing is commonly viewed and studied as a feedforward process. Here, we measure and model the rapid representational dynamics across multiple stages of the human ventral stream using time-resolved brain imaging and deep learning. We observe substantial representational transformations during the first 300 ms of processing within and across ventral-stream regions. Categorical divisions emerge in sequence, cascading forward and in reverse across regions, and Granger causality analysis suggests bidirectional information flow between regions. Finally, recurrent deep neural network models clearly outperform parameter-matched feedforward models in terms of their ability to capture the multiregion cortical dynamics. Targeted virtual cooling experiments on the recurrent deep network models further substantiate the importance of their lateral and top-down connections. These results establish that recurrent models are required to understand information processing in the human ventral stream.

Although a large proportion of our lives are spent participating in social interactions, the investigation of the neural mechanisms supporting these interactions has largely been restricted to situations of social observation — that is, situations in which an individual observes a social stimulus without opportunity for interaction. In recent years, efforts have been made to develop a truly social, or ‘second-person’, neuroscientific approach to these investigations in which neural processes are examined within the context of a real-time reciprocal social interaction. These developments have helped to elucidate the behavioural and neural mechanisms of social interactions; however, further theoretical and methodological innovations are still needed. Findings to date suggest that the neural mechanisms supporting social interaction differ from those involved in social observation and highlight a role of the so-called ‘mentalizing network’ as important in this distinction. Taking social interaction seriously may also be particularly important for the advancement of the neuroscientific study of different psychiatric conditions.Studies that examine brain activity during real-time social interactions may advance our understanding of human social behaviour. Redcay and Schilbach describe progress in ‘second-person’ neuroscience and discuss the insights into the brain mechanisms of social behaviour that have been gained.

SignificanceMastering diverse cognitive tasks is crucial for humans. We study how the brain’s functional organization at rest is configured to support diverse cognitive phenotypes. Emphasizing the multilevel, hierarchical modular structure of brain’s functional connectivity to derive eigenmode-based measures, we demonstrate that the resting brain’s functional organization in healthy young adults is configured to maintain a balance between network segregation and integration. This functional balance is associated with better memory. Furthermore, brains tending toward stronger segregation versus integration foster different cognitive abilities. Thus, the segregation–integration balance empowers the brain to support diverse cognitive abilities. These findings yield high potential to understand the role of whole-brain resting state dynamics in human cognition and to develop neural biomarkers of atypical cognition. Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. However, it remains an open question how resting brains configure their functional organization to balance the demands on network segregation and integration to best serve cognition. Here we use an eigenmode-based approach to identify hierarchical modules in functional brain networks and quantify the functional balance between network segregation and integration. In a large sample of healthy young adults (n = 991), we combine the whole-brain resting state functional magnetic resonance imaging (fMRI) data with a mean-filed model on the structural network derived from diffusion tensor imaging and demonstrate that resting brain networks are on average close to a balanced state. This state allows for a balanced time dwelling at segregated and integrated configurations and highly flexible switching between them. Furthermore, we employ structural equation modeling to estimate general and domain-specific cognitive phenotypes from nine tasks and demonstrate that network segregation, integration, and their balance in resting brains predict individual differences in diverse cognitive phenotypes. More specifically, stronger integration is associated with better general cognitive ability, stronger segregation fosters crystallized intelligence and processing speed, and an individual’s tendency toward balance supports better memory. Our findings provide a comprehensive and deep understanding of the brain’s functioning principles in supporting diverse functional demands and cognitive abilities and advance modern network neuroscience theories of human cognition.

Cognitive regulation of emotions is a fundamental prerequisite for intact social functioning which impacts on both well being and psychopathology. The neural underpinnings of this process have been studied intensively in recent years, without, however, a general consensus. We here quantitatively summarize the published literature on cognitive emotion regulation using activation likelihood estimation in fMRI and PET (23 studies/479 subjects). In addition, we assessed the particular functional contribution of identified regions and their interactions using quantitative functional inference and meta-analytic connectivity modeling, respectively. In doing so, we developed a model for the core brain network involved in emotion regulation of emotional reactivity. According to this, the superior temporal gyrus, angular gyrus and (pre) supplementary motor area should be involved in execution of regulation initiated by frontal areas. The dorsolateral prefrontal cortex may be related to regulation of cognitive processes such as attention, while the ventrolateral prefrontal cortex may not necessarily reflect the regulatory process per se, but signals salience and therefore the need to regulate. We also identified a cluster in the anterior middle cingulate cortex as a region, which is anatomically and functionally in an ideal position to influence behavior and subcortical structures related to affect generation. Hence this area may play a central, integrative role in emotion regulation. By focusing on regions commonly active across multiple studies, this proposed model should provide important a priori information for the assessment of dysregulated emotion regulation in psychiatric disorders. •We quantitatively summarize the literature on emotion regulation (ER) using ALE.•Using MACM and quantitative functional inference we develop a neural model of ER.•DLPFC is related to higher order “cold” regulatory processes.•VLPFC evaluates salience and indicates need to regulate.•STG, angular gyrus and SMA are associated to execution of regulation.

•A big dataset reveals age-related alterations in EEG biomarkers and cognition.•Prominent decline of individual alpha peak frequency primarily in temporal lobes.•A positive association between individual alpha peak frequency and working memory.•Absence of age-related alpha power decline when controlling for 1/f decay of the PSD.•Alpha power is negatively associated with the speed of processing in elderly sample. While many structural and biochemical changes in the brain have previously been associated with older age, findings concerning functional properties of neuronal networks, as reflected in their electrophysiological signatures, remain rather controversial. These discrepancies might arise due to several reasons, including diverse factors determining general spectral slowing in the alpha frequency range as well as amplitude mixing between the rhythmic and non-rhythmic parameters. We used a large dataset (N = 1703, mean age 70) to comprehensively investigate age-related alterations in multiple EEG biomarkers taking into account rhythmic and non-rhythmic activity and their individual contributions to cognitive performance. While we found strong evidence for an individual alpha peak frequency (IAF) decline in older age, we did not observe a significant relationship between theta power and age while controlling for IAF. Not only did IAF decline with age, but it was also positively associated with interference resolution in a working memory task primarily in the right and left temporal lobes suggesting its functional role in information sampling. Critically, we did not detect a significant relationship between alpha power and age when controlling for the 1/f spectral slope, while the latter one showed age-related alterations. These findings thus suggest that the entanglement of IAF slowing and power in the theta frequency range, as well as 1/f slope and alpha power measures, might explain inconsistencies reported previously in the literature. Finally, despite the absence of age-related alterations, alpha power was negatively associated with the speed of processing in the right frontal lobe while 1/f slope showed no consistent relationship to cognitive performance. Our results thus demonstrate that multiple electrophysiological features, as well as their interplay, should be considered for the comprehensive assessment of association between age, neuronal activity, and cognitive performance.

Young people today are expected to navigate their precarious careers in an entrepreneurial way. Self-employment is gaining ground on wage labour as one attractive strategy for winning the battle with precariousness. From Granovetter's studies to the present day, one of the most prolific lines of research on the factors influencing the strategies of job insertion emphasises the key importance of personal networks. Based on social capital theory, this article tests (1) whether the composition of young people's personal networks is associated with their desire to move towards independent careers; and (2) whether, among the mechanisms associated with this orientation, there is the ability to mobilise contacts' resources, for example, avoiding conflict and exploiting different forms of social support. Analysing data on the personal networks of a sample of 7827 young people in Switzerland, our results show that the orientation towards self-employment is more likely for those who access contacts with an unfavourable position in the labour market, such as people with lower educational levels and a foreign background. Although receiving social support plays a role, our results show that, for young people wishing to become self-employed, an even more important predictor is the presence of conflicts in their networks. In the context of the precarization of young people's labour pathways, these results suggest that self-employment can serve as a coping strategy for the most vulnerable, as well as an escape from difficult relationships.

Background The objectives of the study were to generate normative data for the Oslo Social Support Scale (OSSS-3) for different age groups for men and women and to further investigate the factor structure in the general population. Methods Nationally representative face-to face household surveys were conducted in Germany in 2008 ( n  = 2524). Results Normative data for the Oslo Social Support Scale were generated for men and women (52.3% female) and different age levels (mean age (SD) of 48.9 (18.3) years). Men had mean scores comparable to women (10.1 [SD = 2.3] vs. 10.2 [SD = 2.2]). The EFA resulted in a clear one-factor solution for the OSSS-3. Conclusions The normative data provide a framework for the interpretation and comparisons of social support with other populations.