Responding to Causal Uncertainty Through Abstract Thinking

Abstract

The need to understand causality is a powerful motivator. As a result, causal uncertainty, or the sense of not knowing why something happened, can lead to negative psychological consequences and thus activate cognitive processes that can help reduce causal uncertainty. Here, we review the literature that focuses on the relationship between causal uncertainty and abstract thinking. Research shows that causal uncertainty spontaneously motivates people to think more abstractly. This cognitive process has important implications in other domains, such as communication and leadership. For example, when individuals tune in to others on social media during times of causal uncertainty, they prefer more abstract messages, especially when those messages come from socially prominent sources (e.g., leaders). Furthermore, research shows that abstract thinking reduces causal uncertainty by simplifying how causal relationships are cognitively represented. We discuss how these findings relate to previous research and propose directions for future research on the basis of remaining questions.

General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python

“…variators import prm_efficient_gs_xo , p r m _ e f f i c i e n t _ g s _ m t n 5 6 # Creates the search space 7 sspace_sml_gs = { " n_dims " : X . shape [1] , " function_set " : f_set ,…”

“…Thinking about something (an object or an event) in abstract terms involves considering its central meaning or identifying overarching themes and fundamental issues that can apply across contexts. On the other hand, thinking about something in concrete terms has a narrower scope because peripheral details about the event become salient [1]. According to [2], abstract thinking is less constraining than concrete as it involves generalization, which allows for more freedom and flexibility.…”