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Abstract
This dissertation investigates the information accumulation perspective of cognitive aging, which posits that differences in accumulated knowledge, rather than declines in cognitive abilities, may account for age-related variances in linguistic and cognitive task performance. Through four studies, it provides evidence for this perspective and proposes more effective methods to capture individual differences in language experiences. Chapter 2 utilizes a paired-associate learning paradigm to reveal the significant influence of accumulated linguistic knowledge on performance variations between younger and older adults. Chapter 3 revisits the widely used Author Recognition Test in older adults, finding it inadequate for differentiating high levels of print exposure and highlighting the complex role of author characteristics. Chapter 4 introduces the Author Production Test as a promising alternative by implementing it as an unconstrained semantic fluency task and leveraging state-of-the-art natural language processing (NLP) techniques to uncover fascinating patterns in author production. Chapter 5 builds on this work by quantifying individuals’ unique reading experiences through vector comparisons, affirming the validity of this efficient methodology. Collectively, these investigations highlight the benefit of employing advanced computational tools, from distributional semantics to network analysis, to achieve more precise assessments that further illuminate the role of lifelong knowledge accumulation in cognitive aging. This dissertation makes significant contributions to understanding age-related cognitive performance differences as shaped by experience rather than physiological decline. The innovative methodologies pave the way for richer insights into the complex interplay between language exposure and cognition across the lifespan.
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