Review
Local Patterns to Global Architectures: Influences of Network Topology on Human Learning

https://doi.org/10.1016/j.tics.2016.06.003Get rights and content

Trends

Descriptive analytical approaches indicate that diverse facets of the environment adhere to a complex network structure.

Recent advances offer insight into how learners might acquire and access network representations. Specifically, higher-order topological properties of networks have been shown to facilitate learning.

Emerging neuroimaging techniques construe the brain itself as complex system, revealing how network dynamics support learning.

We suggest that network science approaches are compatible with statistical learning approaches to knowledge acquisition. That is, local statistical regularities extracted from sensory input form the building blocks of complex network structures. Broader architectural properties of network structures might then explain learning effects beyond sensitivity to local statistical information.

A core question in cognitive science concerns how humans acquire and represent knowledge about their environments. To this end, quantitative theories of learning processes have been formalized in an attempt to explain and predict changes in brain and behavior. We connect here statistical learning approaches in cognitive science, which are rooted in the sensitivity of learners to local distributional regularities, and network science approaches to characterizing global patterns and their emergent properties. We focus on innovative work that describes how learning is influenced by the topological properties underlying sensory input. The confluence of these theoretical approaches and this recent empirical evidence motivate the importance of scaling-up quantitative approaches to learning at both the behavioral and neural levels.

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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