Rational theories of diagnostic reasoning assume that the reasoner’s goal is to infer the conditional probability of a cause given an effect from the available data. Typically, diagnostic reasoning is modeled within a statistical inference framework, …
In diagnostic causal reasoning, the goal is to infer the probability of causes from one or multiple observed effects. Typically, studies investigating such tasks provide subjects with precise quantitative information regarding the strength of the …
This chapter discusses diagnostic reasoning from the perspective of causal inference. The computational framework that provides the foundation for the analyses--probabilistic inference over graphical causal structures--can be used to implement …
A probabilistic causal chain A-B-C may intuitively appear to be transitive: If A probabilistically causes B, and B probabilistically causes C, A probabilistically causes C. However, probabilistic causal relations can only guaranteed to be transitive …
Our research examines the normative and descriptive adequacy of alternative computational models of diagnostic reasoning from single effects to single causes. Many theories of diagnostic reasoning are based on the normative assumption that inferences …
Many of our decisions refer to actions that have a causal impact on the external environment. Such actions may not only allow for the mere learning of expected values or utilities but also for acquiring knowledge about the causal structure of our …
In sequential diagnostic reasoning, the goal is to infer the probability of a cause event from sequentially observed effects. Typically, studies investigating such tasks provide subjects with precise quantitative information regarding the strength of …
When dealing with a dynamic causal system people may employ a variety of different strategies. One of these strategies is causal learning, that is, learning about the causal structure and parameters of the system acted upon. In two experiments we …
Whereas the traditional normative benchmark for diagnostic reasoning from effects to causes is provided by purely statistical norms, we here approach the task from the perspective of rational causal inference. The core feature of the presented model …
The present paper examines the interplay between causal reasoning and decision making. We use a repeated decision mak- ing paradigm to investigate how people adapt their choice behavior when being confronted with changes in the decision environment. …