Do Conspiracy Theories Shape or Rationalize Vaccination Hesitancy Over Time?

Conspiracy beliefs are associated with vaccination hesitancy, which is commonly interpreted as evidence that conspiracy theories contribute to a worldview that damages public health. An alternative, and often ignored, explanation for this relationship is that people may rationalize their existing vaccination hesitancy through conspiracy theories. In two panel studies conducted at multiple time points during the vaccination campaign of 2021, we tested the temporal relationships between COVID-19 conspiracy beliefs and vaccination intentions. Study 1 (three waves in a Dutch sample) provided evidence for temporal effects of conspiracy beliefs on vaccination hesitancy and of vaccination hesitancy on conspiracy beliefs. Study 2 (two waves in a U.S. sample), however, only supported an effect of vaccination hesitancy on conspiracy beliefs over time. Although these findings provide some support for the idea that conspiracy beliefs shape increased vaccination hesitancy, they more consistently support the alternative idea that vaccination hesitancy shapes increased conspiracy beliefs.</p

Measuring the Semantic Priming Effect Across Many Languages

Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages