Differences in the weighting and choice of evidence for plausible versus implausible causes.

Abstract

Individuals have difficulty changing their causal beliefs in light of contradictory evidence. We hypothesized that this difficulty arises because people facing implausible causes give greater consideration to causal alternatives, which, because of their use of a positive test strategy, leads to differential weighting of contingency evidence. Across 4 experiments, participants learned about plausible or implausible causes of outcomes. Additionally, we assessed the effects of participants' ability to think of alternative causes of the outcomes. Participants either saw complete frequency information (Experiments 1 and 2) or chose what information to see (Experiments 3 and 4). Consistent with the positive test account, participants given implausible causes were more likely to inquire about the occurrence of the outcome in the absence of the cause (Experiments 3 and 4) than those given plausible causes. Furthermore, they gave less weight to Cells A and B in a 2 × 2 contingency table and gave either equal or less weight to Cells C and D (Experiments 1 and 2). These effects were inconsistently modified by participants' ability to consider alternative causes of the outcome. The total of the observed effects are not predicted by either dominant models of normative causal inference or by the particular positive test account proposed here, but they may be commensurate with a more broadly construed positive test account.

Using unobserved causes to explain unexpected outcomes: The effect of existing causal knowledge on protection from extinction by a hidden cause.

“…Even among participants in the hidden condition who reported inferring a protective cause, ratings to A were numerically much lower than ratings by the medicine subgroup in Experiments 1 and 2, suggesting that even when a hidden protective cue was assumed to be present, the degree to which participants were willing to attribute the sudden change in outcome to the less plausible protective cue was much weaker than when the protective cue was a medicine. These findings are reminiscent of previous studies that have shown differential influence of covariation information in causal inference when candidate causes of the outcome were plausible versus implausible (Fugelsang & Thompson, 2000; Goedert et al, 2014). Fugelsang and Thompson (2000) showed that participants were more effective at using covariation information to form causal judgments when the causal candidate was believable compared to when it was unbelievable, whereas Goedert et al (2014) showed that participants overweighted cue-present trials to a greater extent when causes were plausible, and less so when causes were implausible.…”

“…versus implausible (Fugelsang & Thompson, 2000;Goedert et al, 2014). Fugelsang and Thompson (2000) showed that participants were more effective at using covariation information to form causal judgments when the causal candidate was believable compared to when it was unbelievable, whereas Goedert et al (2014) showed that participants overweighted cue-present trials to a greater extent when causes were plausible, and less so when causes were implausible.…”

“…versus implausible (Fugelsang & Thompson, 2000;Goedert et al, 2014). Fugelsang and Thompson (2000) showed that participants were more effective at using covariation information to form causal judgments when the causal candidate was believable compared to when it was unbelievable, whereas Goedert et al (2014) showed that participants overweighted cue-present trials to a greater extent when causes were plausible, and less so when causes were implausible. Importantly, both studies showed differences in how people interacted with covariation information when the candidate cause was plausible and when it was implausible, suggesting that prior knowledge of causal mechanism is important for causal learning.…”

Base-rate expectations modulate the causal illusion

“…Typically, the rank of subjective trial weights that has been documented in the literature is a > b ≥ c > d [29,50–52], which would only explain the results of those conditions in which we found an illusion of causality (i.e., Control group in Experiment 1, low base-rate in Experiment 2). To account for all the results presented here, the trial weights ranks should be different for each condition.…”

Motivated Reasoning in an Explore‐Exploit Task

“…Even when learning simple stable relations (e.g., the cause has a probabilistic but unchanging weakly positive influence on the effect), prior beliefs and expectations have strong impacts on the assessment of the strength of the relation from very positive to very negative (Alloy & Tabachnik, 1984;Fugelsang & Thompson, 2000, 2003Goedert, Ellefson, & Rehder, 2014). However, in dynamic situations like those mentioned above, the task is considerably harder, and individuals may rely on additional temporal expectations for navigating the task (e.g., Buehner & McGregor, 2006;Hagmayer & Waldmann, 2002).…”