scholarly journals Contributions of Modern Network Science to the Cognitive Sciences: Revisiting research spirals of representation and process

2019 ◽  
Author(s):  
Nichol Castro ◽  
Cynthia S. Q. Siew
Keyword(s):  
Network Science ◽  
Human Mind ◽  
Cognitive Networks ◽  
Cognitive Systems ◽  
Mathematical Framework ◽  
Psychological Processes ◽  
Cognitive Network ◽  
Cognitive Sciences ◽  
Graph Theoretic ◽  
Classic Papers

Modelling the structure of cognitive systems is a central goal of the cognitive sciences—a goal that has greatly benefitted from the application of network science approaches. This paper provides an overview of how network science has been applied to the cognitive sciences, with a specific focus on the two research “spirals” of cognitive sciences related to the representation and processes of the human mind. For each spiral, we first review classic papers in the psychological sciences that have drawn on graph-theoretic ideas or frameworks before the advent of modern network science approaches. We then discuss how current research in these areas have been shaped by modern network science, which provide the mathematical framework and methodological tools for psychologists to (i) represent cognitive network structure, and (ii) investigate and model the psychological processes that occur in these cognitive networks. Finally, we briefly comment on the future of, and the challenges facing, cognitive network science.

scholarly journals Contributions of modern network science to the cognitive sciences: revisiting research spirals of representation and process

2020 ◽  
Vol 476 (2238) ◽  
pp. 20190825 ◽  
Author(s):  
Nichol Castro ◽  
Cynthia S. Q. Siew
Keyword(s):  
Network Science ◽  
Human Mind ◽  
Cognitive Networks ◽  
Cognitive Systems ◽  
Mathematical Framework ◽  
Psychological Processes ◽  
Cognitive Network ◽  
Cognitive Sciences ◽  
Graph Theoretic ◽  
Classic Papers

Modelling the structure of cognitive systems is a central goal of the cognitive sciences—a goal that has greatly benefitted from the application of network science approaches. This paper provides an overview of how network science has been applied to the cognitive sciences, with a specific focus on the two research ‘spirals’ of cognitive sciences related to the representation and processes of the human mind. For each spiral, we first review classic papers in the psychological sciences that have drawn on graph-theoretic ideas or frameworks before the advent of modern network science approaches. We then discuss how current research in these areas has been shaped by modern network science, which provides the mathematical framework and methodological tools for psychologists to (i) represent cognitive network structure and (ii) investigate and model the psychological processes that occur in these cognitive networks. Finally, we briefly comment on the future of, and the challenges facing, cognitive network science.

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

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.

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

2018 ◽  
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.

scholarly journals Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Cynthia S. Q. Siew ◽  
Dirk U. Wulff ◽  
Nicole M. Beckage ◽  
Yoed N. Kenett ◽  
Ana Meštrović
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.

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

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.

The 7E Model of the Human Mind: Articulating a Plastic Self for the Cognitive Science of Religion

2019 ◽  
Vol 19 (5) ◽  
pp. 450-476
Author(s):  
Flavio A. Geisshuesler
Keyword(s):  
Cognitive Science ◽  
Human Mind ◽  
Religious Studies ◽  
The Self ◽  
Cognitive Science Of Religion ◽  
Cognitive Sciences ◽  
Religious Mind ◽  
Science Of Religion ◽  
Concrete Model

AbstractThis article proposes a 7E model of the human mind, which was developed within the cognitive paradigm in religious studies and its primary expression, the Cognitive Science of Religion (CSR). This study draws on the philosophically most sophisticated currents in the cognitive sciences, which have come to define the human mind through a 4E model as embodied, embedded, enactive, and extended. Introducing Catherine Malabou’s concept of “plasticity,” the study not only confirms the insight of the 4E model of the self as a decentered system, but it also recommends two further traits of the self that have been overlooked in the cognitive sciences, namely the negativity of plasticity and the tension between giving and receiving form. Finally, the article matures these philosophical insights to develop a concrete model of the religious mind, equipping it with three further Es, namely emotional, evolved, and exoconscious.

scholarly journals Cognitive Network Neuroscience

2015 ◽  
Vol 27 (8) ◽  
pp. 1471-1491 ◽  
Author(s):  
John D. Medaglia ◽  
Mary-Ellen Lynall ◽  
Danielle S. Bassett
Keyword(s):  
Cognitive Neuroscience ◽  
Network Science ◽  
Structural Connectivity ◽  
Cognitive Network ◽  
Network Characteristics ◽  
Time Dynamics ◽  
Neuroimaging Data ◽  
Control Learning ◽  
And Function ◽  
Network Neuroscience

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.

scholarly journals Cognitive networks identify dimensions of distress in suicide notes: Anxiety, emotional profiles, and "words not said"

2021 ◽  
Author(s):  
Trevor Swanson ◽  
Andreia Sofia Teixeira ◽  
Brianne N. Richson ◽  
Ying Li ◽  
Thomas Hills ◽  
...  
Keyword(s):  
Public Health ◽  
Suicidal Behavior ◽  
Real World ◽  
Network Science ◽  
Cognitive Networks ◽  
Health Concern ◽  
Public Health Concern ◽  
High Anxiety ◽  
Emotion Words ◽  
Suicide Notes

Suicide remains a serious public-health concern that is difficult to accurately predict in real-world settings. To identify potential predictors of suicide, we examined the emotional content of suicide notes using methods from cognitive network science. Specifically, we compared the co-occurrence networks of suicide notes with those constructed out of emotion words written by individuals scoring low or high on measures of depression, anxiety, and stress. Our objective was to identify which networks were most similar to the suicide notes network, in particular with regard to the connectivity between words and their emotional contents. We also investigated what types of words remained in the high/low emotion networks after controlling for the words present in the suicide notes, which we conceptualize as the “words not said” in the suicide notes. We found that patterns of connectivity among emotion words in suicide notes were most similar to those in texts written by low-anxiety individuals. However, upon analyzing the “words not said” in suicide notes, we observed that the remaining collection of emotions in suicide notes was most similar to those expressed by high-anxiety individuals. We discuss how these findings relate with existing clinical psychological literature as well as their potential implications for predicting suicidal behavior.

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