Prediction in Language Across the Lifespan - Cognitive functioning fluctuates over the lifespan, across myriad domains. Though many aspects of language comprehension are preserved and/or continue to improve with age (e.g., vocabulary), older adults (OA) do not appear to predict upcoming linguistic input to the same extent as younger adults (YA). This finding is surprising given current views that prediction is a fundamental aspect of human language comprehension. On a Resource-based account, this alleged decline in linguistic prediction is due to a general age-related decline in executive functions. However, the evidence for this account is mixed. The present work tests key predictions of a competing Experience-based Account account whereby linguistic prediction is intact in OA, but the nature of the predictions differs from those of YA. This proposal builds on recent information-theoretic accounts of language processing, according to which comprehenders derive linguistic predictions using the local context (e.g., preceding words) along with their long-term knowledge of the language, accumulated across their lifespan. The critical observation at the core of the Experience-based proposal is that OA may make different predictions given the many additional years of language exposure over different time periods (over which language statistics fluctuate). In order to test the Resource-based and Experience-based accounts, the current research proposes two complementary approaches: Aim 1: Reveal how language statistics change over a person’s lifespan. Large-scale, diachronic language corpora will be used to characterize the changes in semantic and syntactic co-occurrences over the course of several decades. Language models (n-gram, RNN) will then be used to test the hypothesis that the predictability of a word/sentence changes depending on the source of language experience (e.g., the time period from which training data are sampled). Aim 2: Generate a large-scale stimulus set for the investigation of predictability across the lifespan. A series of behavioral studies will be conducted online, in order to compare predictions generated by OA and YA given the same contexts and develop age-specific norms for a large set of stimuli which can be used for designing future experiments on semantic and syntactic predictability. Critically, these norms will be collected in a large age-continuous sample (age 18-80) and will provide a direct measurement of contextual language statistics across the lifespan. The research proposed here will shed critical new light on the effects of aging on language processing. The findings will inform a) early detection of dementia by delineating the effects of healthy aging and neurodegeneration on language comprehension and b) strategies for supporting cognitive health across the lifespan (e.g., through linguistic or executive function-based training).
