Visual causal models enhance clinical explanations of treatments for generalized anxiety disorder.

A daily challenge in clinical practice is to adequately explain disorders and treatments to patients of varying levels of literacy in a time-limited situation. Drawing jointly upon research on causal reasoning and multimodal theory, the authors asked whether adding visual causal models to clinical explanations promotes patient learning. Participants were 86 people currently or formerly diagnosed with a mood disorder and 104 lay people in Boston, Massachusetts, USA, who were randomly assigned to receive either a visual causal model (dual-mode) presentation or auditory-only presentation of an explanation about generalized anxiety disorder and its treatment. Participants' knowledge was tested before, immediately after, and 4 weeks after the presentation. Patients and lay people learned significantly more from visual causal model presentations than from auditory-only presentations, and visual causal models were perceived to be helpful. Participants retained some information 4 weeks after the presentation, although the advantage of visual causal models did not persist in the long term. In conclusion, dual-mode presentations featuring visual causal models yield significant relative gains in patient comprehension immediately after the clinical session, at a time when the authors suggest that patients may be most willing to begin the recommended treatment plan.

How do practising clinicians and students apply newly learned causal information about mental disorders?

RATIONALE, AIMS AND OBJECTIVES: New causal theories explaining the aetiology of psychiatric disorders continuously appear in the literature. How might such new information directly impact clinical practice, to the degree that clinicians are aware of it and accept it? We investigated whether expert clinical psychologists and students use new causal information about psychiatric disorders according to rationalist norms in their diagnostic reasoning. Specifically, philosophical and Bayesian analyses suggest that it is rational to draw stronger inferences about the presence of a disorder when a client's presenting symptoms are from disparate locations in a causal theory of the disorder than when they are from proximal locations. METHOD: In a controlled experiment, we presented experienced clinical psychologists and students with recently published causal theories for different disorders; specifically, these theories proposed how the symptoms of each disorder stem from a root cause. Participants viewed hypothetical clients with presenting proximal or diverse symptoms, and indicated either the likelihood that the client has the disorder, or what additional information they would seek out to help inform a diagnostic decision. RESULTS: Clinicians and students alike showed a strong preference for diverse evidence, over proximal evidence, in making diagnostic judgments and in seeking additional information. They did not show this preference in the control condition, in which they gave their own opinions prior to learning the causal information. CONCLUSION: These findings suggest that experienced clinical psychologists and students are likely to use newly learned causal knowledge in a normative, rational way in diagnostic reasoning.

Hindsight bias and causal reasoning: a minimalist approach.

What factors contribute to hindsight bias, the phenomenon whereby the known outcome of an event appears obvious only after the fact? The Causal Model Theory (CMT) of hindsight bias (Nestler et al. in Soc Psychol 39:182-188, 2008a; in J Expl Psychol: Learn Mem Cog 34:1043-1054, 2008b; Pezzo in Mem 11:421-441, 2003; Wasserman et al. in Pers Soc Psychol Bull 17:30-35, 1991) posits that hindsight bias can occur when people have the opportunity to identify potential causal antecedents and evaluate whether they could have led to the outcome. Two experiments incorporating highly controlled minimalist scenarios supported the CMT. As predicted by the CMT, hindsight bias occurred when the causal factor explained the actual outcome better than the alternative outcome, and reverse hindsight bias occurred when the causal factor explained the alternative outcome better than the actual outcome. Moreover, we found new evidence that outcome knowledge alone was insufficient to elicit hindsight bias in the absence of a potential causal antecedent. Implications for future directions in hindsight bias research are discussed.

The influence of causal information on judgments of treatment efficacy.

How does the causal structure of a problem concept influence judgments of treatment efficacy? We argue that the task of evaluating treatment efficacy involves a combination of causal reasoning and categorization. After an exemplar has been categorized, a treatment task involves judging where to intervene in the causal structure to eradicate the problem, removing the exemplar from category membership. We hypothesized that the processes underlying such category membership removal tasks are not identical to those underlying categorization. Whereas previous experiments have shown that both the root cause (as the most generative feature) and the coherence of the exemplar heavily influence categorization, Experiments 1 and 2 showed that people base category membership removal judgments on the root cause. In Experiment 3, people spontaneously chose to remove an exemplar from category membership when asked to treat the terminal effect. We discuss how our findings are compatible with existing models of categorization. A description of pilot studies for Experiment 1 may be downloaded as supplemental materials from mc.psychonomic-journals.org.

Causal diversity effects in information seeking.

When trying to determine the root cause of an observed effect, people may seek out information with which to test a candidate hypothesis. In two studies, we investigated how knowledge of causal structure influences this information-seeking process. Specifically, we asked whether people would choose to test for pieces of evidence that were far apart or close together in the learned causal structure of a disease category. In parallel with findings showing people's tendency to select diverse evidence in argument testing (López, 1995), our participants tested for evidence distantly located within the causal structure. Simultaneously, they rated the probability of occurrence of such diverse evidence as comparatively low. These findings suggest that rather than seeking out information most likely to confirm the hypothesis, people seek out evidence that they believe will most strongly support the hypothesis if present but that they also believe is relatively unlikely to be present (that is, might disconfirm the hypothesis).