Modeling 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.
network science, cognitive science, mental representations, cognitive processes, cognitive structures, mental lexicon
‘Spirals of science’  refers to the continued exploration of research questions over generations, where each new generation of researchers benefits from the knowledge of those who came before. The shape of a spiral depicts how science grows, develops, and evolves over time The resources and knowledge available at a given point in time constrain how much movement up the spiral is possible, which may lead to either period of small, incremental steps or instances of explosive momentum. This paper explores the ‘research spirals’ of cognitive science related to the representation and processes of the human mind and how the application of network science and graph-theoretic approaches, particularly after the publication of seminal papers from Watts & Strogatz , Barabasi & Albert  and Page et al. , has contributed to the upward movement of research spirals in the cognitive sciences.
The field of experimental psychology, which seeks to understand human behaviour, has its early roots in the behaviourist tradition [5–8]. Early behaviourists did not find it necessary to examine the internal properties of the human mind in order to understand and explain behaviour, because they viewed observable behaviours as by-products of reinforcement and punishment schedules in response to external environmental stimuli. Indeed, one of the earliest metaphors of the mind is the notion of a ‘black box’, where input goes in (i.e. stimuli in the environment) and output emerges (i.e. behaviour), and it is often assumed that it is impossible to completely know what is occurring in the black box. Stated differently, behaviourists generally view the processes of the mind as unobservable and unmeasurable.
The cognitive revolution emerged largely in response to the behaviourist perspective and has matured into the interdisciplinary field of cognitive science today, which focuses on understanding how humans think (i.e. the processes of the ‘black box’) through analyses of cognitive representations and structures as well as the computational procedures that operate on those representations . Some of the early work challenging behaviourist principles by focusing on studying the internal properties and structure of the mind includes George Miller’s research on short-term memory , John McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon’s research on artificial intelligence [11–13], and Noam Chomsky’s research on universal grammar [14,15]. Among others, these researchers provided a foundation for new metaphors of the human mind. For example, one prominent metaphor is that the mind is a computer, an information-processing machine. This metaphor captures computationalism approaches to cognition, where information can be represented symbolically and processes of the mind can be described in terms of algorithms that operated on these symbols. Such an approach relied heavily on implementations of cognitive models in computer programs.
Another metaphor describes the ‘black box’ of the mind as the brain. This metaphor captures connectionist approaches to cognition, where researchers view the mind as systems of highly interconnected units of information. One way to compare computationalism and connectionism is through the following (albeit oversimplified) analogy: computationalism is to ‘software’ as connectionism is to ‘hardware’. Computationalism focuses on identifying idealized algorithms that achieve human-like performance in a computer, whereas connectionism focuses on the structure of the mind . Indeed, one of the main draws of the connectionist approach is its potential to connect the mind and brain, since both have apparently compatible architectures (i.e. massively interconnected simple units which mimic the connectivity structure among neurons in the brain), although the implementation is less straightforward than the similarities in structure might suggest. Regardless, a massive body of work on artificial neural networks, beginning with McCullock & Pitts , has proven fruitful in understanding many aspects of human cognition, such as intelligence and language. For example, today artificial neural networks are a prominent feature of many everyday technologies, ranging from voice recognition systems to search engines.
An important point to note is that metaphors of the mind shape the theoretical and methodological approaches adopted to investigate the properties of the mind. Each perspective influences a researcher’s decisions on how cognitive representations are defined and how cognitive processes are computationally implemented . In this review, we show that network science has been and will continue to be a fruitful theoretical and methodological framework that advances our understanding of human cognition. Cognitive science and network science have surprisingly compatible and complementary aims: cognitive science aims to understand mental representations and processes , and network science provides the means to understand the structure of complex systems and the influence of that structure on processes . Early cognitive models tended to be descriptive and qualitative in nature. Furthermore, such quantitative models tended to be small ‘toy’ models that do not reflect the large-scale and massively complex nature of human cognitive systems. In this paper, we argue that, when used in combination with exponentially increasing computational power and the availability of big data, network science provides a powerful mathematical framework for modeling the structure and processes of the mind, propelling cognitive scientists up the research spirals.
The remainder of this paper delves into two research spirals of cognitive science, one of representation and one of process, and explores how network science has been and is currently being used, to further our understanding of these fundamental aspects of cognitive science. The first spiral specifically addresses issues related to defining cognitive representations, with a particular focus on conceptual and lexical representations. For example, without a clear understanding of how words and concepts are represented in the mind, our ability to understand language processes is hampered. The second spiral specifically addresses issues related to the dynamical processes that occur on cognitive representations (e.g. retrieving a word from the mental lexicon), and the dynamics of how cognitive representations themselves change over time (e.g. language acquisition and development). We discuss the beginnings of these research spirals, how network science has helped cognitive scientists progress up the spirals, and speculate on where these research spirals are headed.
To recapitulate, this paper provided a comprehensive review of the two research spirals of cognitive science, representation, and process, through the lens of network science. As seen from the studies discussed, the application of modern network science has been crucial in enhancing our understanding of such fundamental aspects of cognitive science. The first section focused on the research spiral of how cognitive structure, specifically the mental lexicon, can be represented and quantified using networks. The rise of modern network science, along with the availability of more data and computational power, has given rise to a new wave of research that applies network science methods to quantitatively model the mental lexicon and semantic memory.
The second section focused on the research spiral of the dynamics on and of cognitive structures. The fast-growing body of research adopting modern network science approaches to study a diverse array of topics in the cognitive sciences, ranging from lexical retrieval, vocabulary development, cognitive aging, learning, and creativity to problem-solving, clearly demonstrates how the interaction between structure and process can be investigated via a network science framework that focuses on the dynamics that operate on the network (i.e. spreading activation and random walks) and the dynamics of the network representation itself (i.e. network growth, development, and change). Looking ahead, in order to make continued progress up the research spirals, we suggest that, given the inherently abstract nature of cognitive representations, researchers applying network science to the field of cognitive science need to recognize the limitations of relying on behavioral data in the construction of cognitive network representations and to also recognize the inherently dynamic structure of networks and the dynamic processes that occur within networks. Furthermore, we call to action greater exploration into the possibility of using the network science framework to explicitly connect the structure and processes of the mind and the brain. Indeed, network science itself could serve as the necessary theoretical and conceptual linkage between human cognition and the human brain.
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FULL Paper PDF file:Contributions of modern network science to the cognitive sciences: revisiting research spirals of representation and process
Contributions of modern network science to the cognitive sciences: revisiting research spirals of representation and process
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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.