Differences between Observation and Intervention in Causal Learning
Differences between Observation and Intervention in Causal Learning Motoyuki Saito (m saito@kwansei.ac.jp) Department of Psychological Science, Kwansei Gakuin University Hyogo 662 8501, JAPAN Tsuneo Shimazaki (shimazaki@kwansei.ac.jp) Department of Psychological Science, Kwansei Gakuin University Hyogo 662 8501, JAPAN Abstract Previous studies have suggested that learning is improved when people actively intervene rather than when they passively observe in causal structure learning tasks. Two experiments were conducted to investigate whether a facilitative effect will occur in the judgment of causal strength. In Experiment 1, participants were asked to learn causal strength in a situation where the target cause and context independently produced the effect. The intervention group could manipulate the state of the cause, which was later presented to the observation group (i.e. yoked control procedure) The results demonstrated that participants made similar evaluations for the target cause, but not for the context. Experiment 2 was designed to examine whether different estimations were because of facilitation or bias in which participants undervalue other causes. The results provide support for a facilitative effect, but suggest that the improvement with intervention may be limited to the estimation of weak causal strength. Keywords: causal reasoning; causal inference; intervention; causal power; yoked control procedure. Introduction The ability to learn causal relations is essential for adapting to complex environments. Causal knowledge enables people to explain past events, to control present situations, and to predict future consequences. When someone catches a cold, for example, this might be attributed to viruses and lack of sleep. The person might take medicine to control this condition, with the expectation of getting better. It has been recognized that both children and adults easily represent causal relations (Gopnik Schulz, 2007; Sloman, 2005; see also Holyoak Cheng, 2011 for a review) Information about causal relations can be acquired by passive observation and active intervention. Whereas learning by observation includes an observation of a system's autonomous behavior, learning by intervention involves an exogenous manipulation to the causal system that changes the state of the variable in some way (Pearl, 2000; Steyvers, Tenenbaum, Wagenmakers, Blum, 2003) In the above example, observation corresponds to seeing your friend take medicine, while intervention corresponds to making your friend take medicine. Both observation and intervention provide the state (i.e. presence or absence) of the cause and its effect, but they differ in how this information is conveyed. Differences between observation and intervention have been well documented in structure learning tasks where participants are required to infer causal directions among events. Steyvers et al. (2003) investigated people's ability to infer causal structure from both observation and intervention. The results demonstrated that performance improved when people were allowed to intervene in the causal system. Similar results were obtained in Lagnado and Sloman (2004) The benefit of intervention is explained by the fact that interventions can discriminate Markov equivalent models which mere observation cannot. For instance, observations about co occurrences imply statistical relations between two events X and Y; however, they cannot differentiate whether X causes Y or Y causes X. If event X is manipulated and nothing happens, the possibility that X causes Y is eliminated. The relation that Y causes X is confirmed by the fact that a manipulation of Y changes the state of X. Sobel and Kushnir (2006) revealed that learners were better at inferring causal structure when they intervened of their own free will, rather than observing data from another's intervention. They discussed that causal learning is facilitated because learners who intervened freely were able to receive the data in a desired order, and concluded that decision making is an important part of causal learning from intervention. In contrast to many studies about causal structure learning, only a few studies have focused on differences between observation and intervention when people make judgments of causal strength. Whether intervention facilitates estimation of causal strength remains unknown. Hattori and Oaksford (2007) investigated the differences between observation and intervention when assessing causal strength. Participants were asked to learn the strength of the causal relation between using a particular type of fertilizer and plants blooming, by either observation or intervention. Their results demonstrated that people tended to ignore the information that both cause and effect were absent (i.e. cell d information in a 2 x 2 contingency table) when learning with observation. However, the study is limited in two ways. First, they did not make comparisons between the causal ratings of the observation group and those of the intervention group, nor between participants' ratings and normative values, such as causal power (Cheng, 1997) because the experiment was conducted to establish the criteria for meta analysis. In addition, covariation information for each group was programmed separately,
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