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The theory-based model of categorization posits that concepts are represented as theories, not feature lists. Thus, it is interesting that the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) established atheoretical guidelines for mental disorder diagnosis. Five experiments investigated how(More)
Two experiments, incorporating both real-life (Experiment 1) and artificial (Experiment 2) stimuli, demonstrated that lay concepts of mental disorders can be reliably predicted from subjects' naive causal theories about those disorders. Symptoms that are deeper causes (X, where X causes Y, which causes Z) are more important in lay concepts than intermediate(More)
The current experiments examine mental health clinicians' beliefs about biological, psychological, and environmental bases of the DSM-IV-TR mental disorders and the consequences of those causal beliefs for judging treatment effectiveness. Study 1 found a large negative correlation between clinicians' beliefs about biological bases and(More)
In the present study, we examine what types of feature correlations are salient in our conceptual representations. It was hypothesized that of all possible feature pairs, those that are explicitly recognized as correlated (i.e., explicit pairs) and affect typicality judgments are the ones that are more likely theory based than are those that are not(More)
Dealing with alternative causes is necessary to avoid making inaccurate causal inferences from covariation data. However, information about alternative causes is frequently unavailable, rendering them unobserved. The current article reviews the way in which current learning models deal, or could deal, with unobserved causes. A new model of causal learning,(More)
In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to expectations(More)
Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be(More)
Mental disorders are increasingly understood in terms of biological mechanisms. We examined how such biological explanations of patients' symptoms would affect mental health clinicians' empathy--a crucial component of the relationship between treatment-providers and patients--as well as their clinical judgments and recommendations. In a series of studies,(More)
Do people believe mental disorders are real and possess underlying essences? The current study found that both novices and practicing clinicians held weaker essentialist beliefs about mental disorders than about medical disorders. They were also unwilling to endorse the idea that mental disorders are real and natural. Furthermore, compared with novices,(More)
We introduce two abstract, causal schemata used during causal learning. (1) Tolerance is when an effect diminishes over time, as an entity is repeatedly exposed to the cause (e.g., a person becoming tolerant to caffeine). (2) Sensitization is when an effect intensifies over time, as an entity is repeatedly exposed to the cause (e.g., an antidepressant(More)