This chapter discusses some widely used strategies (not just in psychiatry but elsewhere) for inferring causal relations, including randomized controlled trials and instrumental variables. The author emphasizes the advantages of these design-based strategies over more traditional strategies based on identifying and conditioning on possible confounders. However, these design-based strategies can come with costs, including failures of generalizability and interpretability, as well as inattention to patient heterogeneity. The role of such considerations as stability and specificity in controlling for possible confounders, as well as the benefits of triangulation strategies, is also emphasized.

Work on causation in psychopathology often emphasizes variation in the causes, but variation in what is to be explained further complicates matters. Focusing on the protean nature of psychopathology, this chapter explores different ways classificatory variation is generated. For example, choices about what features of disorders to foreground and background can produce variation. The chapter also examines, from the perspective of scientific conventionalism, how classificatory decisions made at choice points partly constitute what is classified, but not in the sense of making it up. In contrast to the view that conventions are neither true nor false and thus isolated from the domain of facts, the chapter argues that scientific conventions are implemented to promote the discovery of facts. Scientific conventions must also answer to conceptual and factual constraints. The chapter concludes by looking at how classificatory choices can produce different versions of a psychiatric, which may also result in variations in causal models across those versions. In agreement with the ideas articulated by Putnam, the chapter argues that we cannot divide the language of psychopathology into a part that describes disorders as they are in themselves and a part that contains our conceptual contributions to what we know about disorders.

Shaun Gallagher in his chapter argues that those who study psychopathology can adopt a level-free vocabulary without having to give up the explanatory virtues of levels of analysis thinking. In doing so, they can potentially make new contributions to our knowledge of psychopathology.

Psychopathology is the scientific exploration of abnormal mental states and behavior. The phenomena under consideration are first-person (i.e., lived) experiences and third-person clinical descriptions, which can be studied and explained in two different ways. On the one hand, descriptive psychopathology aims to capture these phenomena without any preconceived notions of cause or mechanism (Häfner, 2015). On the other hand, theoretical psychopathology explores the etiology of abnormal mental states, applying methods ranging from the social sciences to neurobiology and genetics (Schultze-Lutter et al., 2018). Here I will focus on the descriptive and theoretical psychopathology of panic disorder given that Russo and van Eck use it as the paradigmatic psychiatric illness in their chapter, “Charting the Explanatory Potential of Network Models in Psychopathology.”

Psychiatry has a core goal of understanding the mechanisms that produce, underlie, and maintain psychiatric disorders. But what is a mechanism? And how should the answer to that question be justified? In this chapter Craver defends a practice-first direction of fit, which involves building a notion of “mechanism” for psychiatry by charitably reconstructing what the term appears to mean from the functions it serves for psychiatrists and researchers. Taking this approach, many philosophical objections to mechanism (i.e., that it is at odds with a process metaphysics and that it is committed to reductionist obsession with detail) turn out to be predicated on misunderstandings. Some directions for a future mechanistic philosophy of psychiatry are sketched out.

Can we use neuroimaging to study the causes of psychiatric disorders? If so, how does neuroimaging compare to other methods in psychiatric research that allow for strong causal inferences? Neuroimaging study designs have evolved from cross-sectional, providing only correlational evidence, to longitudinal and interventional, which have strengthened the inferences we can draw from brain images. In this chapter, Heckers shows that researchers are using neuroimaging tools to pursue three very different goals. The techniques are similar, but they aim for different – at times conflicting – inferences. The three types of psychiatric neuroimaging studies pursue distinct aspects of causality, with different levels of explanation and applications for clinical practice. Much of current psychiatric neuroimaging does not study the causes of psychiatric disorders. However, the inclusion of neuroimaging methods in intervention trials has the promise to reveal causal relationships in psychiatric disorders.

Descriptive psychiatry has served our field well for more than half a century. The need to rely on phenomenology cannot be blamed for the structural errors built into the DSM system, such as choosing categories over dimensions, pseudo-precision to serve interrater reliability, and the arbitrary division of psychopathology into nearly three hundred distinct categories. Like all of medicine, psychiatric treatments exhibit unacceptable levels of variability in patient outcomes, consistent with cryptic mechanistic heterogeneity underlying indistinguishable clinical presentations. The result is an aspiration for precision medicine using biomarkers to select the right treatment for the right patient at the right time. Unlike most of medicine, however, psychiatric disorders lack clear natural anchors, like amyloid and tau in Alzheimer’s disease, that nominate mechanisms, biomarkers, and treatment targets. Hyman argues that it is now scientifically possible, albeit still very difficult, to begin identifying mechanisms and biomarkers, and that a concerted effort is warranted.

The main question of this chapter is how group-based emotions are involved in multicultural relations. Group-based emotions help people to withdraw within their safe group boundaries, which may lead to both stronger ingroup identifications and perceptions of other groups that are characterized by distrust and negative emotions. Moreover, prolonged and enduring angry sentiments can easily lead to violent outbursts, and include feelings of contempt, hate, or moral disgust. Experiencing these group-based emotions implies that group members no longer value their relationship with the outgroup, do not foresee any future positive interactions, and display low levels of outgroup trust. In multicultural societies, such negative emotion cycles in intergroup relations can be diminished by changing one’s perspectives about others’ emotions and engaging in pro-active emotion regulation. This may create more empathy for other groups and smoother social interactions across groups.

Both linguistic and psychological constructionist approaches to emotion research recognize the crucial role of language in shaping emotion experience and communication. Multilingual individuals navigate multiple languages, and often multiple cultures, making it essential to understand how emotions are perceived, processed, experienced, and communicated in first and later learned languages. This chapter reviews previous findings from linguistics and psychology, shedding light on the complex and multifaceted relationships among emotion, language, and culture. While it is clear that multilinguals perceive, process, experience, and communicate emotions differently across their various languages, the chapter outlines possible directions for future research to further explore the impact of multilingualism and multiculturalism on emotions.