Temporal predictability facilitates causal learning.

Abstract

Temporal predictability refers to the regularity or consistency of the time interval separating events. When encountering repeated instances of causes and effects, we also experience multiple cause-effect temporal intervals. Where this interval is constant it becomes possible to predict when the effect will follow from the cause. In contrast, interval variability entails unpredictability. Three experiments investigated the extent to which temporal predictability contributes to the inductive processes of human causal learning. The authors demonstrated that (a) causal relations with fixed temporal intervals are consistently judged as stronger than those with variable temporal intervals, (b) that causal judgments decline as a function of temporal uncertainty, and (c) that this effect remains undiminished with increased learning time. The results therefore clearly indicate that temporal predictability facilitates causal discovery. The authors considered the implications of their findings for various theoretical perspectives, including associative learning theory, the attribution shift hypothesis, and causal structure models.