Previous work has suggested that abstraction should not be treated as a unitary construct, but it should be split in at least two components. Concreteness is based on the proportion of sensory information in a concept, while precision is based on the aggregation of information corresponding to the concept position in a semantic taxonomy. This line of work showed that while both of these components have been used as operationalizations of abstraction, they can have opposite effects on cognitive performance. In this paper we extend those findings by showing that precision and concreteness are also differently related to affective content. Our current findings are relevant to some inconsistencies and puzzles in the existing research on the link between abstraction and affective content, and further emphasize the multi-component structure of abstraction.

Battery electric vehicles (BEVs) play an important role in efforts to reduce carbon emissions but widespread adoption is hindered by people's perceptions of BEVs. Here we examine the role of familiarity in influencing preferences for BEVs. Using a US-based survey, we measured people's familiarity with BEVs, BEV beliefs, belief uncertainty, and perceived barriers and measured how these cognitive factors influence preferences. We first find that familiarity increases BEV preferences independent of its effect through other factors. Second, exploratory mediation analyses find that familiarity also indirectly increases BEV preferences by increasing positive BEV beliefs. Third, although familiarity reduces belief uncertainty, the influence of uncertainty on preferences depends on belief valence. Taken together, these results propose that familiarity plays a unique role in improving people's perceptions and attitudes towards BEVs. We situate our findings within the broader cognitive science literature and highlight a familiarity-targeted intervention aimed at improving more widespread BEV adoption.

Recently Milkman et al. (2021) reported that heterogeneity of outcomes is extremely difficult to predict in advance and that behavioral experts, despite their educational background or their extensive applied research experience, might not be better in their predictions than non-experts. Such findings are rather concerning and deserve more attention. In the current project we present three studies aimed at assessing the quality of human predictions in respect to outcome heterogeneity of behavioral interventions. We borrow methods from anthropology and cognitive psychology to asses both consensus and accuracy of such predictions as well as to evaluate the potential role of behavioral expertise. Our results are more optimistic than previous findings and reveal that experts’ predictions are significantly better than a chance level. These findings have implications for applied behavioral research and further advance our theoretical understanding of expertise.