Social Norms – rules that dictate which behaviors are appropriate, permissible, or obligatory in different situations for members of a given community – permeate all aspects of human life. Many researchers have sought to explain the ubiquity of social norms in human life in terms of the psychological mechanisms underlying their acquisition, conformity, and enforcement. Existing theories of the psychology of social norms appeal to a variety of constructs, from prediction-error minimization, to reinforcement learning, to shared intentionality, to evolved psychological adaptations. However, most of these accounts share what we call the psychological unity assumption, which holds that there is something psychologically distinctive about social norms, and that social norm adherence is driven by a single system or process. We argue that this assumption is mistaken. In this paper, we propose a methodological and conceptual framework for the cognitive science of social norms that we call normative pluralism. According to this framework, we should treat norms first and foremost as a community-level pattern of social behavior that might be realized by a variety of different cognitive, motivational, and ecological mechanisms. Norm psychologists should not presuppose that social norms are underpinned by a unified set of processes, nor that there is anything particularly distinctive about normative cognition as such. We argue that this pluralistic approach offers a methodologically sound point of departure for a fruitful and rigorous science of norms.

Detecting testimonial injustice is an essential element of addressing inequities and promoting inclusive healthcare practices, many of which are life-critical. However, using a single demographic factor to detect testimonial injustice does not fully encompass the nuanced identities that contribute to a patient's experience. Further, some injustices may only be evident when examining the nuances that arise through the lens of intersectionality. Ignoring such injustices can result in poor quality of care or life-endangering events. Thus, considering intersectionality could result in more accurate classifications and just decisions. To illustrate this, we use real-world medical data to determine whether medical records exhibit words that could lead to testimonial injustice, employ fairness metrics (e.g. demographic parity, differential intersectional fairness, and subgroup fairness) to assess the severity to which subgroups are experiencing testimonial injustice, and analyze how the intersectionality of demographic features (e.g. gender and race) make a difference in uncovering testimonial injustice. From our analysis, we found that with intersectionality we can better see disparities in how subgroups are treated and there are differences in how someone is treated based on the intersection of their demographic attributes. This has not been previously studied in clinical records, nor has it been proven through empirical study.