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The use of semantic networks to represent search patterns in memory has gained momentum in recent years. Building on a modified version of Haantjes-Ricci path-based curvature, this article offers a novel way to represent information retrieval in memory. Our measure considers the direction of a stream of associations and estimates the extent to which any single association attracts the upcoming associations to its semantic environment—in other words, the extent to which one explores the semantic vicinity of a specific association. After presenting our measure, we demonstrate empirically that it differs from Haantjes-Ricci curvature and confirm that it expresses the extent to which a stream of associations remains close to its starting point. Finally, we examine the relationship between a word’s level of attraction, which we use our novel measure to quantify, and accessibility to knowledge stored in memory. We demonstrate that a high degree of attraction significantly facilitates the retrieval of upcoming words in the stream of associations. By applying methods from differential geometry to semantic networks, this study contributes to our understanding of strategic search in memory.
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