Manipulating Causal Priors Affects the Outcome-Density Bias

Non-contingent learning data can lead to the (seemingly) illusory perception of causality if the potential cause and effect often co-occur – an effect known as “outcome-density bias.” Bayesian models of causal induction explain this effect as the result of a rational learning process in which the data do not fully override non-zero causal priors. Convincing evidence for this rational explanation requires the demonstration of an experimentally manipulated effect of causal priors, which has been lacking. We successfully manipulated participants’ (𝑁 = 300) causal priors through visually conveyed causal mechanism information. This manipulation influenced the outcome-density effect in the predicted way: in a condition inducing low causal priors, the effect disappeared. The results demonstrate the effectiveness of visual mechanism information in manipulating priors and strengthen the Bayesian view of causal induction.