Aging Online: Characterizing Attention, Reward Function, And Mental Health In Older Online Workers

Background: Identifying predictors of healthy aging is extremely timely, as older adults will soon outnumber younger In this preregistered study of adults age 30+ we tested whether attention and reward function predict depression and anxiety, drawing on socioemotional selectivity theory Methods: Participants (N=265, 102 male, 163 female, mean age=53 28, sd=11 3) were recruited using Amazon Mechanical Turk Participants completed online versions of the PHQ9, GAD7, and a behavioral battery including: Delay Discount task, selective visual attention (Flanker, Visual Search, and Simons), and Willingness to Pay Generalized linear models and linear mixed effects models were used for hypothesis testing Results: We replicated a relationship between older age and lower depression (r=- 17, FDRp 001) Relative to a pre-COVID sample (N=799), the COVID-era participants did not show a significant change in depression (PHQ9) or anxiety (GAD7, p\u003e 05), disconfirming hypotheses Reaction time significantly increased with age for the Visual Search (p 05) Reward function did not change significantly with age (p\u003e 05) Exploratory analyses identified a relationship between response time to incongruent stimuli and symptoms of anxiety (significant with age included as a potential confound, p\u003c 05) Conclusions: Mental health of older online workers remained stable despite the COVID19 pandemic Attention functions change with age, but reward function remains stable Response to incongruent stimuli was predictive of anxiety scores on the GAD7 Supported By: VA Career Development Award, Kaggle Open Data Research Grant, Gorilla Grant Keywords: Cognition, Aging, Depression, Reward, Attention