UC San Diego

OBJECTIVE: The present study investigated cognitive mechanisms underlying the ability to stop autocorrect errors elicited by unexpected words in a read-aloud task, and the utility of autocorrection for predicting Alzheimers disease (AD) biomarkers. METHOD: Cognitively normal participants (total n = 85; n = 64 with cerebrospinal fluid [CSF] biomarkers) read aloud six short paragraphs in which 10 critical target words were replaced with autocorrect targets, for example, The player who scored that final [paint] for the local team reported [him] experience. Autocorrect targets either replaced the most expected/dominant completion (i.e., point) or a less expected/nondominant completion (i.e., basket), and within each paragraph half of the autocorrect targets were content words (e.g., point/paint) and half were function words (e.g., his/him). Participants were instructed to avoid autocorrecting. RESULTS: Participants produced more autocorrect errors in paragraphs with dominant than with nondominant targets, and with function than with content targets. Cognitively normal participants with high CSF Tau/Aβ42 (i.e., an AD-like biomarker profile) produced more autocorrect total errors than those below the Tau/Aβ42 threshold, an effect also significant with dominant-function targets alone (e.g., saying his instead of him). A logistic regression model with dominant-function errors and age showed errors as the stronger predictor of biomarker status (sensitivity 83%; specificity 85%). CONCLUSIONS: Difficulty stopping autocorrect errors is associated with biomarkers indicating preclinical AD, and reveals promise as a diagnostic tool. Greater vulnerability of function over content words to autocorrection in individuals with AD-like biomarkers implicates monitoring and attention (rather than semantic processing) in the earliest of cognitive changes associated with AD risk. (PsycInfo Database Record (c) 2023 APA, all rights reserved).