Estimating causal strength: the role of structural knowledge and processing effort
Introduction
Causal learning is central for our survival. Causal knowledge allows us to anticipate harmful or gratifying events, and to plan actions to achieve goals. Despite the fact that a large number of philosophers and psychologists agree on the importance of research on causality, no unitary concept has evolved so far. The main question of how we distinguish causal relations from accidental sequences of events remains highly debated.
Section snippets
Covariation view
In the past 30 years, philosophers and psychologists have become increasingly interested in probabilistic notions of causality. Our knowledge about causal relations, such as “Smoking causes lung cancer”, is often based on the observation of covariations between causes and effects. A number of philosophers have proposed a notion of causality that reduces causal relations to observable statistical laws (e.g. Eells, 1991, Salmon, 1971, Suppes, 1970). Roughly, it has been proposed that causes alter
Overview of experiments
The goal of the experiments that will be presented in this article is to show that prior knowledge and processing effort guide the strategies of causal strength estimation. A number of previous studies have investigated causal structures in which multiple causes converge on a common effect, and have demonstrated that learners take potential causal co-factors into account (e.g. Baker et al., 1993, Price and Yates, 1993, Spellman, 1996a, Spellman, 1996b). In contrast, the aim of the present
Experiment 1
The Cartwright (1983) analysis of the Berkeley admission problem has demonstrated that in situations with multiple potential causes of a common effect, conditional contingencies should be assessed which control for causally relevant co-factors. Causally irrelevant co-factors should be ignored. Controlling for causally irrelevant co-factors may lead to distortions of the underlying causal relations. Because the causal relevance of the co-factors has to be established prior to the induction task,
Experiment 2
In Experiment 1 two different grouping variables were compared, type of fruit (Mamones and Taringes) and investigators (A and B). Experiment 2 attempted to replicate the results of Experiment 1 with a grouping variable that was kept constant across the two conditions. Thus, all participants saw identical cases, received identical rating instructions, and were informed about identical subcategories. The only manipulation involved a hint about the potential causal relevance of the co-factor
Experiment 3
The previous experiments have explored the conditions in which participants calculate contingencies between a potential cause and an effect conditional upon a third correlated event. This third event was either described as causally relevant or irrelevant for the cause–effect relation in question. In these experiments, the co-factor was typically introduced as a variable that potentially interacted with the target cause in producing the effect. This is a situation in which it is appropriate to
General discussion
The goal of causal induction is to arrive at representations of objective causal relations. Typically causal relations cannot be observed directly but must be inferred on the basis of statistical relations between observable events (see Cartwright, 1989, Cheng, 1997). The presented studies focus on the problem that there are multiple ways of extracting statistical information from a set of events. Which method is appropriate is partly determined by the hypothesized causal structure underlying
Acknowledgements
We would like to thank Merideth Gattis for helpful comments. Experiment 1 was presented at the 1995 meeting of the Cognitive Science Society, Pittsburgh, PA. The first two experiments were planned and conducted while the authors were affiliated with the Max Planck Institute for Psychological Research, Munich, and the University of Tübingen.
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2018, Cognitive PsychologyCitation Excerpt :One of the goals of the current research was to better understand how people statistically ‘control’ for alternative cues, since prior experiments mainly focused on whether participants controlled for alternative cues, but not how they did so (e.g., Spellman, 1996, p. 342). Many of the previous experiments that tested whether participants controlled for alternative cues used a design involving Simpson’s paradox with two cues (e.g., Spellman, 1996; Spellman et al., 2001; Waldmann & Hagmayer, 2001). However, we could not use this paradigm in the current experiements: the design was developed for two cues, and even if we could figure out a way to generalize the Simpson’s paradox design to eight cues, it still would not allow us to test the hypotheses about one-change transitions.