Trends in Cognitive Sciences
ReviewLocal Patterns to Global Architectures: Influences of Network Topology on Human Learning
Section snippets
Relating Two Approaches
From the earliest stages of development, the human brain is tasked with the monumental feat of building and efficiently accessing an enormously complex constellation of knowledge. Even the most mundane interactions with our environment require a rich understanding of its component parts as well as of the scales at which they relate to form a larger system. Thus, knowledge can be represented at multiple levels, ranging from local associations between elements to complex networks built from those
Complex Networks Are Pervasive
Complex systems approaches rest on the premise, not tied to any particular domain, that the world can be decomposed into parts, and that those parts interact with one another in meaningful ways. Therefore, diverse facets of human knowledge can and have been studied under the lens of network science. Cognitive systems are generally thought to adhere to a complex network structure, a type of graph structure that is neither truly random nor truly regular [26]. Random graphs are collections of
Network Topology Influences Learning and Memory
Historically, complex network analyses of cognitive structures have had a descriptive focus. Naturally, the first step in understanding how networks structures develop 45, 46, 47 is to characterize existing topological properties (e.g., based on text or production-based corpora). Only recently has network topology been linked to empirical evidence of human learning. This relationship is typically examined in one of two ways: (i) by exposing adult learners to a set of tightly controlled stimuli
The Impact of Local Statistics
As introduced previously, statistical learning persists as an influential and well-supported theory of how learners extract structure from our external world. While we mainly focus on the effect of pair-based conditional probabilities, statistical learning fits into a broader distributional learning literature. In fact, longstanding interest in how we compute local contingencies can be traced to even earlier studies of associative learning mechanisms in animals (e.g., [59]). As used to describe
The Human Brain Is a Dynamical Complex System
As with all efforts to understand a cognitive process, probing how that process is implemented in the human brain must be considered in addition to patterns of human behavior. Particularly as related to linguistic functions such as processing and production, this integrative approach between brain and behavior has been applied with success [90]. With the advent of functional magnetic resonance imaging (fMRI), similar strides have been made in increasing our understanding of the neural regions
Concluding Remarks
Learners attain a complex and highly structured representation of the world. Currently, many quantitative approaches to learning hinge on sensitivity to local statistics such as co-occurrence frequencies and transitional probabilities between adjacent elements. While local statistics are clearly one salient source of structural information, evidence reviewed here suggests that learners also perceive global organizational patterns. In fact, exciting new results suggest that learners can acquire
Acknowledgments
This work was supported by a National Institutes of Health grant to S.T.S. (DC-009209-12), an National Science Foundation (NSF) Career Award to D.S.B. (1554488), and NSF workshop award BCS-1430087 ‘Quantitative Theories of Learning Memory, and Prediction’.
Glossary
- Assortative mixing
- a measure of whether nodes with similar properties (e.g., high degree) are more likely to share an edge.
- Clustering coefficient
- the extent to which adjacent neighbors of a given node are also connected to one another. This measure may be calculated for an individual node or expressed as an average across a network.
- Community structure
- a graph property wherein nodes are densely connected in clusters that in turn share only weak connections with one another. Communities are commonly
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