Scale-Free Superiority, Egocentric Bias and Network Centrality Heuristics in Social Graph Learning
31 Pages Posted: 22 Aug 2012
Date Written: August 21, 2012
This study examined factors predicted to affect how well the connections between people would be learned in a social graph learning task. Participants learned “who is friends with whom” in three social graphs of varying internal structure. The description of the task was varied between subjects. The structure of the graph predicted how quickly knowledge of the graph was acquired. Scale-free graphs were acquired more quickly than random graphs and “caveman” graphs. The surface description of the task had little or no effect on the speed of acquisition. Framing the task such that it seemed more personally relevant did not augment performance. Neither did making the task (hypothetically) survival-relevant to the learner. Nevertheless, within a graph, participants displayed egocentric bias in their attention and learning – they learned best about relationships which involved a node labeled “You.” Additionally, participants employed network centrality heuristics. They learned best about relationships involving nodes of extreme network centrality – those nodes that were either extremely well-connected or poorly-connected to the rest of the graph.
Keywords: learning, memory, social networks
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