Many pieces of information are potentially important to causal inference. Determining whether vitamin C prevents colds may entail knowing the frequency with which colds occur without vitamin C, other cold inhibitors, and the frequency of vitamin C use. Do reasoners integrate all this information to create coherent beliefs? In contrast to models emphasizing modular causal learning (e.g., Cheng, ...

When we try to identify causal relationships, how strong do we expect that relationship to be? Bayesian models of causal induction rely on assumptions regarding people's a priori beliefs about causal systems, with recent research focusing on people's expectations about the strength of causes. These expectations are expressed in terms of prior probability distributions. While proposals about the...

BACKGROUND Patients with heart failure in Uganda present for health care with advanced structural heart disease, have repeated hospitalizations and poorly controlled disease symptoms. The reasons for these are unclear. Literature from other settings shows that patients' understanding of their illness and their beliefs influence their health related behaviour. The study aimed to explore the beli...

BACKGROUND Stigma in relation to mental illness is one of the main factors inhibiting people from seeking help. Studies have been undertaken looking into the knowledge, attitudes, and beliefs (KAB) about mental illness among residents in Qatar; however, none have looked specifically at students in higher education. The aim of this study was to understand the KAB toward mental illness among stud...

The verbs cause, enable, and prevent express beliefs about the way the world works. We offer a theory of their meaning in terms of the structure of those beliefs expressed using qualitative properties of causal models, a graphical framework for representing causal structure. We propose that these verbs refer to a causal model relevant to a discourse and that "A causes B" expresses the belief th...

In the reasoning literature, paranormal beliefs have been proposed to be linked to two related phenomena: a biased perception of causality and a biased information-sampling strategy (believers tend to test fewer hypotheses and prefer confirmatory information). In parallel, recent contingency learning studies showed that, when two unrelated events coincide frequently, individuals interpret this ...

BACKGROUND Rheumatoid arthritis (RA) is a common chronic inflammatory disease causing joint damage, disability, and reduced life expectancy. Highly effective drugs are now available for the treatment of RA. However, poor adherence to drug regimens remains a significant barrier to improving clinical outcomes in RA. Poor adherence has been shown to be linked to patients' beliefs about medicines w...

Causal inference is a fundamental component of cognition and perception. Probabilistic theories of causal judgment (most notably causal Bayes networks) derive causal judgments using metrics that integrate contingency information. But human estimates typically diverge from these normative predictions. This is because human causal power judgments are typically strongly influenced by beliefs conce...

Objective: The present study examined whether psychoeducational tools are beneficial in improving awareness and reducing stigma regarding mental health. This aims to increase health reduce the associated with illness. Methods: is quantitative research. In this study, 147 students registered Abnormal Psychology course, which focuses on illness, were recruited. Students signed informed consent pa...

Decision making and causal reasoning are clearly relevant for one another, but have often been studied in relative isolation. In this paper, we report the results of two experiments that investigated whether people can make appropriate decisions using causal beliefs learned from sequences of cases. We found that people behave close-to-optimally for various causal and payoff structures, even tho...