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 becoming more effective through repeated use). In Experiment 1, participants observed either of these cause-effect data patterns unfolding over time and exhibiting the tolerance or sensitization schemata. Participants inferred stronger causal efficacy and made more confident and more extreme predictions about novel cases than in a condition with the same data appearing in a random order over time. In Experiment 2, the same tolerance/sensitization scenarios occurred either within one entity or across many entities. In the many entity conditions, when the schemata were violated, participants made much weaker inferences. Implications for causal learning are discussed.
Whereas most causal learning research has focused on bottom-up inferences of causal relations from data (e.g., Cheng, 1997; Dickinson, Shanks, & Evenden, 1984; Jenkins & Ward, 1965), some has also examined how abstract, domain-general knowledge can shape causal inference. A classic example is Kelley's (1972) "causal schemata," which people can use to make inferences from limited data. For example, if a person believes that his or her stomachache could be caused by eating spoiled shrimp or catching a stomach virus (a multiple sufficient causes schema) and subsequently learns that the shrimp were, indeed, spoiled, he or she might discount the possibility of also having a stomach virus. More recently, Waldmann and colleagues (for a review, see Waldmann, 1996) have shown that causal learning is influenced by knowledge about causal structures. For example, when estimating causal strength, people account for other potential causes of one effect, but not for other effects of one cause (Waldmann & Hagmayer, 2001).
Following Kelley's (1972) suggestion to uncover more causal schemata, the present article introduces two new schemata. The first schema is tolerance (see Figure 1). For instance, the first time a person drinks a cup of coffee, he or she may feel very awake; but after repeatedly drinking one cup of coffee, he or she becomes tolerant. The person may then drink two cups of coffee and initially feel very alert; but after repeatedly drinking two cups of coffee, he or she again becomes tolerant.
The second schema is sensitization (see Figure 1). For example, two antidepressant pills may initially have no effect; but after repeated exposure, two pills may be sufficient to make a person very happy. If the person cuts down to one pill, the decrease may initially result in lowered happiness; but as the person becomes sensitized to the reduced amount of antidepressant, one pill may become sufficient.
Note that, for the tolerance/sensitization scenarios in Figure 1, the strength of the simple correlations between causes and effects is 0. Yet, one may still conclude that coffee causes wakefulness and antidepressants cause happiness. Indeed, in real life, people have experienced and can easily understand tolerance/sensitization situations: A drug addict may become so habituated that he consumes doses sufficient to kill an unhabituated person (Carpenter, 1855, as cited in Goudie & Emmett-Oglesby, 1989), and people may not find such an event to be impossible. Many diverse phenomena exhibit tolerance/sensitization patterns (Peeke & Petrinovich, 1984), but no prior studies have examined whether people use tolerance/sensitization schemata when learning novel causal relations.
There are two key attributes of the tolerance/sensitization schemata that inform our experiments. First, the tolerance/ sensitization schemata have signature cause-effect, temporal patterns. When the cause is held fixed at the same level across repeated exposures, tolerance situations have a decreasing effect, and sensitization situations have an increasing effect. …