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Abstract While a robust literature on the psychology of conspiracy theories has identified dozens of characteristics correlated with conspiracy theory beliefs, much less attention has been paid to understanding the generalized predisposition towards interpreting events and circumstances as the product of supposed conspiracies. Using a unique national survey of 2015 U.S. adults from October 2020, we investigate the relationship between this predisposition—conspiracy thinking—and 34 different psychological, political, and social correlates. Using conditional inference tree modeling—a machine learning-based approach designed to facilitate prediction using a flexible modeling methodology—we identify the characteristics that are most useful for orienting individuals along the conspiracy thinking continuum, including (but not limited to): anomie, Manicheanism, support for political violence, a tendency to share false information online, populism, narcissism, and psychopathy. Altogether, psychological characteristics are much more useful in predicting conspiracy thinking than are political and social characteristics, though even our robust set of correlates only partially accounts for variance in conspiracy thinking.
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Modeling Second-Language Learning from a Psychological Perspective
Psychological research on learning and memory has tended to emphasize small-scale laboratory studies. However, large datasets of people using educational software provides opportunities to explore these issues from a new perspective. In this paper we describe our approach to the Duolingo Second Language Acquisition Modeling (SLAM) competition which was run in early 2018. We used a well-known class of algorithms (gradient boosted decision trees), with features partially informed by theories from the psychological literature. After detailing our modeling approach and a number of supplementary simulations, we reflect on the degree to which psychological theory aided the model, and the potential for cognitive science and predictive modeling competitions to gain from each other.
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- Award ID(s):
- 1631436
- PAR ID:
- 10062637
- Date Published:
- Journal Name:
- Proceedings of the NAACL-HLT Workshop on Innovative Use of NLP for Building Educational Applications (BEA).
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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