Failure to stop autocorrect errors in reading aloud increases in aging especially with a positive biomarker for Alzheimer's disease

Language switching and speaking a nondominant language challenge executive control: Preliminary data for novel behavioral markers of Alzheimer's risk in Spanish-English bilinguals

OBJECTIVE

The present study explored psycholinguistic analysis of spoken responses produced in a structured interview and cued linguistic and nonlinguistic task switching as possible novel markers of Alzheimer's disease (AD) risk in Spanish-English bilinguals.

METHOD

Nineteen Spanish-English bilinguals completed an Oral Proficiency Interview (OPI) in both languages, cued-switching tasks, and a battery of traditional neuropsychological tests (in a separate testing session). All were cognitively healthy at the time of testing, but eight decliners were later diagnosed with AD (on average 4.5 years after testing; SD = 2.3), while 11 controls remained cognitively healthy.

RESULTS

Past studies showed picture naming was more sensitive to AD in the dominant than in the nondominant language, but we found the opposite for a composite measure of spoken utterances produced in the OPI that included revisions, repetitions, and filled pauses (RRFPs), which were especially sensitive to AD risk in the nondominant language. Errors produced on language switch trials best discriminated decliners from controls (in receiver operating characteristic curves), and though the nonlinguistic switching task was also sensitive to AD risk, it elicited more errors overall and was also negatively affected by increased age and low education level.

CONCLUSIONS

Speaking a nondominant language and errors in cued language switching provided sensitive and specific markers of pending cognitive decline and AD risk in bilinguals. These measures may reflect early decline in executive control abilities that are needed to plan and monitor the production of connected speech and to manage competition for selection between languages. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Autocorrection if→of function words in reading aloud: A novel marker of Alzheimer's risk

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 Alzheimer's 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).

Domain-general conflict monitoring predicts neural and behavioral indices of linguistic error processing during reading comprehension

The ability to detect and respond to linguistic errors is critical for successful reading comprehension, but these skills can vary considerably across readers. In the current study, healthy adults (age 18-35) read short discourse scenarios for comprehension while monitoring for the presence of semantic anomalies. Using a factor analytic approach, we examined if performance in nonlinguistic conflict monitoring tasks (Stroop, AX-CPT) would predict individual differences in neural and behavioral measures of linguistic error processing. Consistent with this hypothesis, domain-general conflict monitoring predicted both readers' end-of-trial acceptability judgments and the amplitude of a late neural response (the P600) evoked by linguistic anomalies. The influence on the P600 was nonlinear, suggesting that online neural responses to linguistic errors are influenced by both the effectiveness and efficiency of domain-general conflict monitoring. These relationships were also highly specific and remained after controlling for variability in working memory capacity and verbal knowledge. Finally, we found that domain-general conflict monitoring also predicted individual variability in measures of reading comprehension, and that this relationship was partially mediated by behavioral measures of linguistic error detection. These findings inform our understanding of the role of domain-general executive functions in reading comprehension, with potential implications for the diagnosis and treatment of language impairments. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Remote data collection speech analysis and prediction of the identification of Alzheimer's disease biomarkers in people at risk for Alzheimer's disease dementia: the Speech on the Phone Assessment (SPeAk) prospective observational study protocol

INTRODUCTION

Identifying cost-effective, non-invasive biomarkers of Alzheimer's disease (AD) is a clinical and research priority. Speech data are easy to collect, and studies suggest it can identify those with AD. We do not know if speech features can predict AD biomarkers in a preclinical population.

METHODS AND ANALYSIS

The Speech on the Phone Assessment (SPeAk) study is a prospective observational study. SPeAk recruits participants aged 50 years and over who have previously completed studies with AD biomarker collection. Participants complete a baseline telephone assessment, including spontaneous speech and cognitive tests. A 3-month visit will repeat the cognitive tests with a conversational artificial intelligence bot. Participants complete acceptability questionnaires after each visit. Participants are randomised to receive their cognitive test results either after each visit or only after they have completed the study. We will combine SPeAK data with AD biomarker data collected in a previous study and analyse for correlations between extracted speech features and AD biomarkers. The outcome of this analysis will inform the development of an algorithm for prediction of AD risk based on speech features.

ETHICS AND DISSEMINATION

This study has been approved by the Edinburgh Medical School Research Ethics Committee (REC reference 20-EMREC-007). All participants will provide informed consent before completing any study-related procedures, participants must have capacity to consent to participate in this study. Participants may find the tests, or receiving their scores, causes anxiety or stress. Previous exposure to similar tests may make this more familiar and reduce this anxiety. The study information will include signposting in case of distress. Study results will be disseminated to study participants, presented at conferences and published in a peer reviewed journal. No study participants will be identifiable in the study results.