The Impact of Region and Urbanicity on the Discrimination-Cognitive Health Link Among Older Blacks

Little research has examined how the link between discrimination and cognitive health varies by where people live. This study investigates how living in non-urban versus urban areas in different regions in the United States moderates the discrimination-cognitive health link among older non-Hispanic Blacks. Data are from the 2012 and 2014 waves of the Health and Retirement Study (HRS; N = 2,347). Regression analyses indicate that experiencing more everyday discrimination is significantly associated with lower episodic memory when living in urban areas. Among non-Hispanic Blacks, the discrimination-episodic memory link does not significantly vary across U.S. regional contexts. Findings highlight variation in the association between everyday discrimination and cognitive health by where older non-Hispanic Blacks live. Results suggest the importance of socio-environmental factors in shaping how stressful experiences such as discrimination are linked to cognitive health in later life.     

Modeling Misretrieval and Feature Substitution in Agreement Attraction: A Computational Evaluation

We present computational modeling results based on a self-paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k-fold cross-validation. We find that our data are better accounted for by an encoding-based model of agreement attraction, compared to a retrieval-based model. A novel methodological contribution of our study is the use of comprehension questions with open-ended responses, so that both misinterpretation of the number feature of the subject phrase and misassignment of the thematic subject role of the verb can be investigated at the same time. We find evidence for both types of misinterpretation in our study, sometimes in the same trial. However, the specific error patterns in our data are not fully consistent with any previously proposed model.