Erratum to “Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics”

Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.

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Revealing semantic and emotional structure of suicide notes with cognitive network science

Scientific Reports ◽

10.1038/s41598-021-98147-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Andreia Sofia Teixeira ◽

Szymon Talaga ◽

Trevor James Swanson ◽

Massimo Stella

Keyword(s):

Suicidal Ideation ◽

Network Science ◽

Meaning Making ◽

Null Model ◽

Future Research ◽

Emotional States ◽

Cognitive Network ◽

Cognitive States ◽

Suicide Notes ◽

Scientific Challenge

AbstractUnderstanding how people who commit suicide perceive their cognitive states and emotions represents an important open scientific challenge. We build upon cognitive network science, psycholinguistics and semantic frame theory to introduce a network representation of suicidal ideation as expressed in multiple suicide notes. By reconstructing the knowledge structure of such notes, we reveal interconnections between the ideas and emotional states of people who committed suicide through an analysis of emotional balance motivated by structural balance theory, semantic prominence and emotional profiling. Our results indicate that connections between positively- and negatively-valenced terms give rise to a degree of balance that is significantly higher than in a null model where the affective structure is randomized and in a linguistic baseline model capturing mind-wandering in absence of suicidal ideation. We show that suicide notes are affectively compartmentalized such that positive concepts tend to cluster together and dominate the overall network structure. Notably, this positive clustering diverges from perceptions of self, which are found to be dominated by negative, sad conceptual associations in analyses based on subject-verb-object relationships and emotional profiling. A key positive concept is “love”, which integrates information relating the self to others and is semantically prominent across suicide notes. The emotions constituting the semantic frame of “love” combine joy and trust with anticipation and sadness, which can be linked to psychological theories of meaning-making as well as narrative psychology. Our results open new ways for understanding the structure of genuine suicide notes and may be used to inform future research on suicide prevention.

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Cognitive Network Science: A New Frontier

Complexity ◽

10.1155/2020/6870278 ◽

2020 ◽

Vol 2020 ◽

pp. 1-4

Author(s):

Yoed N. Kenett ◽

Nicole M. Beckage ◽

Cynthia S. Q. Siew ◽

Dirk U. Wulff

Keyword(s):

Network Science ◽

Cognitive Network ◽

New Frontier

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Cognitive Network Science Reconstructs How Experts, News Outlets and Social Media Perceived the COVID-19 Pandemic

Systems ◽

10.3390/systems8040038 ◽

2020 ◽

Vol 8 (4) ◽

pp. 38

Author(s):

Massimo Stella

Keyword(s):

Social Media ◽

Language Processing ◽

News Media ◽

Network Science ◽

Wide Spectrum ◽

Emotional States ◽

Cognitive Network ◽

Public Speech ◽

Health Crisis ◽

Semantic Associations

This work uses cognitive network science to reconstruct how experts, influential news outlets and social media perceived and reported the news “COVID-19 is a pandemic”. In an exploratory corpus of 1 public speech, 10 influential news media articles on the same news and 37,500 trending tweets, the same pandemic declaration elicited a wide spectrum of perceptions retrieved by automatic language processing. While the WHO adopted a narrative strategy of mitigating the pandemic by raising public concern, some news media promoted fear for economic repercussions, while others channelled trust in contagion containment through semantic associations with science. In Italy, the first country to adopt a nationwide lockdown, social discourse perceived the pandemic with anger and fear, emotions of grief elaboration, but also with trust, a useful mechanism for coping with threats. Whereas news mostly elicited individual emotions, social media promoted much richer perceptions, where negative and positive emotional states coexisted, and where trust mainly originated from politics-related jargon rather than from science. This indicates that social media linked the pandemics to institutions and their intervention policies. Since both trust and fear strongly influence people’s risk-averse behaviour and mental/physical wellbeing, identifying evidence for these emotions is key under a global health crisis. Cognitive network science opens the way to unveiling the emotional framings of massively read news in automatic ways, with relevance for better understanding how information was framed and perceived by large audiences.

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Networks of Cognitive Processes: Functional and Anatomical Correlates of Cognition, Emotions, and Social Cognition

Cognitive Dimensions of Major Depressive Disorder ◽

10.1093/med/9780198810940.003.0013 ◽

2019 ◽

pp. 157-170

Author(s):

John D. Medaglia

Keyword(s):

Neural Networks ◽

Cognitive Function ◽

Human Brain ◽

Cognitive Processes ◽

Network Science ◽

Complex Dynamics ◽

Cognitive Networks ◽

Cognitive Network ◽

The World ◽

Network Neuroscience

Networks of cognitive processes describe some of the key findings emerging from cognitive network neuroscience. Cognition is organized in distinct networks in the human brain. These cognitive networks interact via complex dynamics to process our environments and enact our decisions on the world. Within the emerging subdiscipline known as cognitive network neuroscience, we can connect classical neuroscience approaches to network science. This allows us to consider how major cognitive functions ranging from sensation to cognitive control and emotion are organized in the human brain. Through the lens of network neuroscience, we can enrich our understanding of normal and disordered cognitive function to be manifestations of processes and representations in ordered or disorded neural networks.

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Cognitive Network Science: A review of research on cognition through the lens of network representations, processes, and dynamics

10.31234/osf.io/eu9tr ◽

2018 ◽

Cited By ~ 5

Author(s):

Cynthia S. Q. Siew ◽

Dirk U. Wulff ◽

Nicole Beckage ◽

Yoed Kenett

Keyword(s):

Quantitative Methods ◽

Structural Changes ◽

Network Science ◽

Human Cognition ◽

Cognitive Systems ◽

Cognitive Network ◽

Key Points ◽

Science Approach ◽

Cognitive Theories ◽

Underlying Network

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.

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Cognitive network science as a framework for detecting structural patterns and emotions in suicide letters

10.31234/osf.io/frvta ◽

2021 ◽

Author(s):

Massimo Stella ◽

Trevor Swanson ◽

Ying Li ◽

Thomas Hills ◽

Sofia A. Teixeira

Keyword(s):

Network Science ◽

Balance Theory ◽

Main Body ◽

Emotional States ◽

Healthy Individuals ◽

Cognitive Network ◽

Suicide Notes ◽

Negative Context ◽

Entropy Measures ◽

Complex Relationships

Communicating one's mindset means transmitting complex relationships between concepts and emotions. Using cognitive network science, we reconstruct the mindset around suicide as communicated in 139 genuine suicide notes. Despite their negative context, suicide notes are surprisingly positively valenced and their ending statements are markedly more emotional, i.e. elicit deeper fear/sadness but also stronger joy/trust and anticipation, than their main body. By using emotional states from the Emotional Recall Task, we "open the lid" of suicidal narratives and compare their emotional backbone against emotion recall in mentally healthy individuals. Supported by psychological literature, we introduce emotional complexity as an affective analogue of structural balance theory, measuring how elementary cycles (closed triads) of emotion co-occurrences mix positive, negative and neutral states in narratives and recollections. Both authors of suicide notes and healthy individuals exhibit less complexity and more emotional coherence than expected by chance. However, suicide narratives display higher complexity, i.e. a lower level of coherently valenced triads, than healthy individuals recalling the same states. Entropy measures identified a similar tendency for suicide letters to shift more frequently between contrasting emotional states. Our results demonstrate that suicide notes possess highly contrastive narratives of emotions, more complex than expected by null models and healthy populations.

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What Do Cognitive Networks Do? Simulations of Spoken Word Recognition Using the Cognitive Network Science Approach

Brain Sciences ◽

10.3390/brainsci11121628 ◽

2021 ◽

Vol 11 (12) ◽

pp. 1628

Author(s):

Michael S. Vitevitch ◽

Gavin J. D. Mullin

Keyword(s):

Word Recognition ◽

Network Model ◽

Language Processing ◽

Network Science ◽

Spoken Word Recognition ◽

Cognitive Networks ◽

Spoken Word ◽

Cognitive Network ◽

Behavioral Studies ◽

Trace Model

Cognitive network science is an emerging approach that uses the mathematical tools of network science to map the relationships among representations stored in memory to examine how that structure might influence processing. In the present study, we used computer simulations to compare the ability of a well-known model of spoken word recognition, TRACE, to the ability of a cognitive network model with a spreading activation-like process to account for the findings from several previously published behavioral studies of language processing. In all four simulations, the TRACE model failed to retrieve a sufficient number of words to assess if it could replicate the behavioral findings. The cognitive network model successfully replicated the behavioral findings in Simulations 1 and 2. However, in Simulation 3a, the cognitive network did not replicate the behavioral findings, perhaps because an additional mechanism was not implemented in the model. However, in Simulation 3b, when the decay parameter in spreadr was manipulated to model this mechanism the cognitive network model successfully replicated the behavioral findings. The results suggest that models of cognition need to take into account the multi-scale structure that exists among representations in memory, and how that structure can influence processing.

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