The Diagnosis of Mental Disorders Is Influenced by Automatic Causal Reasoning

Abstract

, which mirrors the DSM, provide normative frameworks for the diagnosis of mental disorders. Classifications are supposed to be based on diagnostic criteria, most of which are neither necessary nor sufficient. These diagnostic criteria are usually directly observable symptoms. Neither of the two classification systems is based on causal, etiological theories of mental disorders. Potential causal relations between the symptoms listed as criteria are not included in the definition. This means that clinicians should ignore causal considerations when making a diagnosis, but should rely on counting symptoms instead. Research on diagnostic decision making, however, has shown that clinicians (both experienced and in training) take causal considerations into account when making a diagnosis (Kim & Ahn, 2002; Kim & Keil, 2003). For example, Kim and Ahn (2002) asked mental health professionals about their causal assumptions with respect to common disorders from the DSM. They found that clinicians assumed causal relations among the symptoms used as diagnostic criteria. More important, the researchers also showed that these causal assumptions affected diagnostic classifications when participants were later presented with case vignettes. Specifically, participants were more likely to diagnose a hypothetical client with a mental disorder when she or he presented with symptoms that were assumed to affect other symptoms (causally central symptoms) than when the person presented with symptoms that presumably had no impact on other symptoms (causally peripheral symptoms). Later studies replicated these findings (e.g., Kim & Keil, 2003). Assumptions about causal relations have also been shown to affect judgments of treatment efficacy (Ahn,

From symptom to process: Case formulation, clinical utility, and PDM-2.

“…Limitations in prototype approaches to diagnosis notwithstanding (see Spitzer, First, Shedler, Westen, & Skodol, 2008), PDM descriptions dovetail with how clinicians process and encode information. As Flores et al (2017) have shown, when thinking about patients, clinicians do not generate symptom lists, but tend to perceive and classify patients with respect to how well they match exemplars of similar patients.…”

“…Although the theoretical and empirical literature describes case formulation as a deliberate, mindful process—which it is—it is important to recognize that all clinicians reflexively conceptualize the core features of newly encountered patients based on past experiences with other similar patients, even when they are not consciously aware of doing so (Flores, Cobos, & Hagmayer, 2017; Westen & Weinberger, 2004). Put another way, an initial case formulation inevitably occurs implicitly, and when it does it is tainted by all sorts of biases and distortions (e.g., stereotypes, heuristics, attribution errors; see Bornstein, 2017; Croskerry, 2016).…”

Pathways linking clinician demographics to mental health diagnostic accuracy: An international perspective

“…Response latency is a potential operational definition of which system a clinician uses for a diagnostic decision (e.g., Flores, Cobos, & Hagmayer, 2018). Faster response times would indicate the use of System 1, where slower response times would imply the use of System 2.…”

“…We used mediation models to determine whether clinician characteristics influence diagnostic accuracy through response time. Based on past research, it was hypothesized that clinicians with more experience would respond quicker and provide more accurate diagnoses (Croskerry & Nimno, ; Flores et al, ). In addition, we hypothesized that clinician profession would influence diagnostic accuracy through differences in response time.…”

Concluding Commentary: Clinical Decision Making

“…The stimuli used in the first five articles are suitable for empirical research. In four of the five studies, they are vignettes (also called short clinical reports) of real disorders (Flores, Cobos, & Hagmayer, 2018 [this issue]; Marsh & De Los Reyes, 2018 [this issue]; Weine & Kim, 2018 [this issue]) or fictitious diseases (Hayes, 2018 [this issue]). Such stimuli approximate what clinicians will see in practice and allow the researcher to manipulate the relevant variables.…”