Using Networks to Understand Language Learning
By Hannah Merseal and Catherine Pham
We live in a highly interconnected, complex world. From the global supply chain to the groups of friends and colleagues we interact with day-to-day—to the brain areas and cognitive processes underlying human thinking—we are constantly surrounded by complex, multidimensional systems. Network science approaches, which are based in something called mathematical graph theory, provide researchers with a powerful way to study the variety of complex systems in our world, by modeling them as networks[1,2].
Due to recent and rapid developments in powerful computing, network science approaches have become more accessible than ever. Indeed, in the last twenty years, network science methods have been increasingly applied to analyze complex systems across diverse research areas[3] including transit[4], public utilities[5], sports[6], medicine[7], bioinformatics[8], innovation[9], neuroscience[10], music[11], and human cognition[12,13,14]. The ongoing “network science revolution” in the cognitive sciences has changed the way the field studies a number of topics[15], including the study of knowledge[16], mental disorders[17], aging[18], creativity[19], and learning[20]. In this research summary, we’d like to share with you some insights from a particularly
fruitful area of network science research: language learning.
First, what is a network?
A network consists of nodes (the blue circles in the picture below) and edges (the pink lines). Nodes represent items that a researcher wants to study. They could be individuals, cities, subway stops, or even words. The relationships between nodes are represented by the edges connecting them. As an example, let’s pretend that each node in our image represents a student in a fifth-grade class, and the edges connecting them represent friendships. Student A is quite popular: they are friends with students B, C, D, and E (as can be seen from the edges connecting them to each other student). Student D, by contrast, is only friends with students A and E (perhaps they are newer to the class).
Network science has also been applied to the study of language learning. Children learn words in distinct patterns. More frequently encountered words are learned earlier than less frequently encountered words[21]. However, beyond these frequency effects, what other factors may affect word learning?
One type of relationship that researchers have examined is the relationship between word meanings. For example, if someone is given the cue word dog, another word that might come to mind is cat. Therefore, if dog and cat are two nodes in a network, an edge would be drawn between these nodes because they are related in meaning. Words that are related in meaning to each other are connected to one another to create a “semantic” network. One common method of using networks to understand meaning relationships between words is by considering the number of possible associations between words[22].
Using powerful computational tools, researchers are able to study large networks by examining every possible combination of two nodes, and whether those nodes are connected by an edge. They can also represent the strengths of connections (perhaps dog and cat are more strongly related than dog and mouse) and directionality of relationships (perhaps the word squirrel comes to mind when given the cue word dog, but dog does not come to mind when given the cue word squirrel). Researchers can calculate many properties of the network, such as whether a node (e.g., word) is connected to many or few other nodes, whether nodes group together into smaller communities, and how easy it is to travel from one node in the network to another node based on the connection between nodes.
Applying these methods to the study of word learning in children, researchers have found that the underlying structure of the language input children receive has an influence on word learning. Words in a network that exhibit a greater number of meaning-based connections to other words are acquired earlier in development than less connected words[23]. These findings have been observed across ten different languages[24]. Additionally, semantic associations between words have been shown to largely explain patterns of word learning at later stages of learning in young children after twenty-three months of age[25]. The distance between words in a network is also an important factor, with distantly related words often acquired earlier than
closely related words in semantic networks[26].
The field of network science has revolutionized the way researchers have approached the study of language learning as a whole. While the present article focuses on contributions of semantic networks to the study of word learning, the tools of network sciences can and have been implemented across a broad range of topics. Check out our interview with Roger Beaty, Michele Diaz, and Chaleece Sandberg to learn more about the various ways researchers at Penn State are using network science to address a variety of language-related research questions.
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