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Principal Investigator: Chuchu Li
Organization: UNIVERSITY OF CALIFORNIA, SAN DIEGO
Fiscal Year: 2024
Award: $294,484
Funding agency: National Institute on Aging
Alzheimer’s disease (AD) is known to cause subtle changes in language production years before diagnosis, yet
current behavioral tests take limited advantage of linguistic tools to diagnose AD at early stages. Most
language tasks focus on single word production (e.g., picture naming), missing many critical aspects of
language. Some tasks focus on connected speech (e.g., picture description), but they heavily rely on complex
computational modeling methods, thus are hard to implement in clinical settings. Addressing these issues,
autocorrection is a type of error produced in connected speech that is easy to elicit, easy to analyze, and is
sensitive to AD biomarkers. Thus, the long-term goal of this proposal is to maximize this sensitivity, facilitating
the development of the autocorrect task as a non-invasive, simple, and low-cost diagnostic tool for early
detection of AD. Our proposed study will lay foundation to achieve this goal through investigating the
underlying cognitive mechanisms that drive the sensitivity of autocorrection to AD. In the autocorrect task,
participants read aloud short paragraphs in which some words are replaced by unexpected words that are
similar in form (e.g., Think about they concept replaced the concept). Participants are told to read exactly what
they see, but occasionally, they automatically correct the unexpected words and produce the expected words
instead, i.e., they produce an autocorrect error (e.g., say the concept instead of they concept). Participants with
AD or preclinical AD (i.e., those at risk for AD based on CSF biomarkers) produce more autocorrections than
healthy controls, especially with function word targets with rich syntactic properties (e.g., the, and). This is
probably because 1) AD decreases attention thus eliciting more misperception, 2) AD increases monitoring
difficulty thus making it more difficult to detect planned errors, and/or 3) AD makes it more difficult to overcome
competition from syntactically well-formed expected targets. We will combine behavioral and eye-tracking
measures to test each account. In Aim 1, we will investigate the attention and monitoring accounts by
manipulating the font color of autocorrect targets: they will be either in black or red font. We hypothesize that
the red font will reduce sensitivity to AD, and skipping rate and regression rate in eye movements will reveal
whether the effect is driven by facilitating attention or monitoring or both. In Aim 2, we will investigate the
monitoring vs. syntactic constraints accounts. The autocorrect targets and their corresponding expected words
will either match (e.g., much/more are both adverbs) or mis-match (e.g., then/that is an adverb/pronoun pair) in
syntactic category. Mismatching pairs elicit greater syntactic anomaly than matching pairs, and thus should be
harder to produce but be easier to monitor. This contrast, combining with regression rates in eye movements,
will differentiate the syntactic constraints vs. monitoring account. The proposed project will be the first that
combines the analysis of autocorrect errors with eye-tracking in preclinical AD, shedding light on how complex
aspects of language production are affected by AD in its earliest stages in a simple way.
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