Age‐Related Diversification and Specialization in the Mental Lexicon: Comparing Aggregate and Individual‐Level Network Approaches

Published on November 5, 2024

Abstract
The mental lexicon changes across the lifespan. Prior work, aggregating data among individuals of similar ages, found that the aging lexicon, represented as a network of free associations, becomes more sparse with age: degree and clustering coefficient decrease and average shortest path length increases. However, because this work is based on aggregated data, it remains to be seen whether or not individuals show a similar pattern of age-related lexical change. Here, we demonstrate how an individual-level approach can be used to reveal differences that vary systematically with age. We also directly compare this approach with an aggregate-level approach, to show how these approaches differ. Our individual-level approach follows the logic of many past approaches by comparing individual data as they are situated within population-level data. To do this, we produce a conglomerate network from population-level data and then identify how data from individuals of different ages are situated within that network. Though we find most qualitative patterns are preserved, individuals produce associates that have a higher clustering coefficient in the conglomerate network as they age. Alongside a reduction in degree, this suggests more specialized but clustered knowledge with age. Older individuals also reveal a pattern of increasing distance among the associates they produce in response to a single cue, indicating a more diverse range of associations. We demonstrate these results for three different languages: English, Spanish, and Dutch, which all show the same qualitative patterns of differences between aggregate and individual network approaches. These results reveal how individual-level approaches can be taken with aggregate data and demonstrate new insights into understanding the aging lexicon.

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