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

Abstract

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.

Keywords: complex systems; network science; statistical learning.

Copyright © 2016 Elsevier Ltd. All rights reserved.

Figures

Figure 1

Visualization of networks types. Regular networks, also known as lattices, are collections of nodes with equivalent degree (left panel). Random networks are collections of nodes that are linked by edges selected at random from a uniform distribution of all possible connections. Here we show a random network generated from an Erdős–Rényi model with an edge probability of 0.3 (right panel). In the center panel, we display a complex network with community structure, much like a network that could be derived from a learner’s language environment.

Figure 2

Sample (not exhaustive) phonological networks of two English words, beer and silk, that differ in their clustering coefficient. Note how the phonological neighbors of beer tend also to be phonological neighbors of each other, resulting in a high clustering coefficient. In contrast, the phonological neighbors of silk are not phonological neighbors, resulting in a low clustering coefficient.

Figure 3

Co-occurrence (bigram) statistics underpin network topology. When four pseudowords (tudaro, bikuti, pigola, budopa) are concatenated together to form a continuous stream of syllables, evidence from Saffran et al. (1996) indicates that these words can be segmented via the dip in transitional probabilities at word boundaries. Here, we show that the co-occurrence between syllables can also be used to construct a weighted graph (black lines indicate a high bigram frequency and red lines indicate a low bigram frequency). A community detection algorithm consisting of a series of short random walks through this graph will then reveal robust cluster structure corresponding to each word in the stream (shown in green, pink, purple, and blue).

Figure 4

Evolution of complex network structure in the brain. To investigate topological properties of task-based functional connectivity, the brain is first parcellated into anatomically defined nodes, in this example using the Harvard-Oxford structural atlas (1). Next, the moment-to-moment activity within each of these regions is extracted at different points during a scanning session (2). To construct the edges between brain regions (nodes of the network), the correlation or coherence between any two time series is computed, forming a pairwise adjacency matrix at each time point (3). These matrices can then be used to probe the temporal dynamics of a functional brain network over the course of a given task (4).