When Success Is Not Enough: The Symptom Base-Rate Can Influence Judgments of Effectiveness of a Successful Treatment.

Fernando Blanco,María Manuela Moreno-Fernández,Helena Matute

doi:10.3389/fpsyg.2020.560273

Fernando Blanco, María Manuela Moreno-Fernández + Show 1 more

Open Access

https://doi.org/10.3389/fpsyg.2020.560273

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Abstract

Patients’ beliefs about the effectiveness of their treatments are key to the success of any intervention. However, since these beliefs are usually formed by sequentially accumulating evidence in the form of the covariation between the treatment use and the symptoms, it is not always easy to detect when a treatment is actually working. In Experiments 1 and 2, we presented participants with a contingency learning task in which a fictitious treatment was actually effective to reduce the symptoms of fictitious patients. However, the base-rate of the symptoms was manipulated so that, for half of participants, the symptoms were very frequent before the treatment, whereas for the rest of participants, the symptoms were less frequently observed. Although the treatment was equally effective in all cases according to the objective contingency between the treatment and healings, the participants’ beliefs on the effectiveness of the treatment were influenced by the base-rate of the symptoms, so that those who observed frequent symptoms before the treatment tended to produce lower judgments of effectiveness. Experiment 3 showed that participants were probably basing their judgments on an estimate of effectiveness relative to the symptom base-rate, rather than on contingency in absolute terms. Data, materials, and R scripts to reproduce the figures are publicly available at the Open Science Framework: https://osf.io/emzbj/.

Highlights

A great deal of health-related decisions, such as deciding whether or not to quit a treatment, or whether to replace it by an alternative option, depend on the patients’ beliefs about their symptoms and diseases, and about the effectiveness of their treatments
In contrast with most previous studies on causal learning, we provide the information of the treatment effectiveness on a more natural fashion, which implies: (a) describing first how likely symptoms are before the treatment, and how they respond to the introduction of the treatment, and (b) that the information given groups have identical trait distributions, it is often concluded that the majority group possesses the common trait to a greater extent than does the minority group (Hamilton and Gifford, 1976)
Beliefs of treatment effectiveness can be understood as the result of causal learning (Rottman et al, 2017), under the assumption that an effective treatment produces a change in the likelihood of symptom improvement compared to a control condition

Summary

Introduction

A great deal of health-related decisions, such as deciding whether or not to quit a treatment, or whether to replace it by an alternative option, depend on the patients’ beliefs about their symptoms and diseases, and about the effectiveness of their treatments. We can study the formation of beliefs under highly controlled settings, by using fictitious scenarios and computerized tasks. This would be impossible in real life, in which researchers cannot manipulate parameters such as the frequency with which a treatment is used, its actual effectiveness, or the severity of symptoms. It is sometimes possible to use samples of real patients who deal with fictitious or imagined health outcomes in the context of a causal learning experiment (Meulders et al, 2018), which helps to alleviate the limitations of ecological validity while using highly controlled procedures

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