Cultural consensus theory is a statistical framework (CCT) for the study of individual differences in the knowledge of culturally shared opinions. In this article, we demonstrate how a CCT analysis can be used to study individual differences and cultural consensus on what makes people feel loved, or more generally any social behaviors that are governed by cognitive schemata. To highlight the advantages of the method, we describe a study in which people reported on their everyday experiences of feeling loved. Our unique approach to understanding this topic is to focus on people’s cognitive evaluations on what feeling loved (both romantically and nonromantically) entails by exploring the shared agreement regarding when one is most likely to feel loved and the individual differences that influence knowledge of these shared agreements. Our results reveal that people converge on a consensus about indicators of expressed love and that these scenarios are both romantic and nonromantic. Moreover, people show individual differences in (1) the amount of knowledge they have about this consensus and (2) their guessing biases in responding to items on love scenarios, depending on personality and demographics—all conclusions made possible by the CCT method.
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