Recently, a number of rational theories have been put forward which provide a coherent formal framework for modeling different types of causal inferences, such as prediction, diagnosis, and action planning. A hallmark of these theories is their …
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 …
The main goal of the present research was to demonstrate the interaction between category and causal induction in causal model learning. We used a two-phase learning procedure in which learners were presented with learning input referring to two …
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 …
In deterministic causal chains the relations "A causes B" and "B causes C" imply that "A causes C". However, this is not necessarily the case for probabilistic causal relationships: A may probabilistically cause B, and B may probabilistically cause …
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. …
Recent studies have shown that people have the capacity to derive interventional predictions for previously unseen actions from observational knowledge, a finding that challenges associative theories of causal learning and reasoning (e.g., Meder, …
Many decisions refer to actions that have a causal impact on other events. Such actions allow for mere learning of expected values, but also for causal learning about the structure of the decision context. Whereas most theories of decision making …
Previous research has shown that people are capable of deriving correct predictions for previously unseen actions from passive observations of causal systems (Waldmann \& Hagmayer, 2005). However, these studies were limited, since learning data were …
This dissertation is concerned with the question of how people infer the consequences of active interventions in causal systems when only knowledge from passive observations is available. Causal Bayes nets theory (Spirtes, Glymour & Scheines, 1993; …