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; …
Causal knowledge serves two functions: it allows us to predict future events on the basis of observations and to plan actions. Although associative learning theories traditionally differentiate between learning based on observations (classical …