Delay and knowledge mediation in human causal reasoning
Contemporary theories of causal induction have focussed largely on the question of how evidence in the form of covariations between causes and effects is used to compute measures of causal strength. A very important precursor enabling such computations is that the reasoner notices that a cause and effect have co-occurred. Standard laboratory experiments have usually bypassed this problem by presenting participants directly with covariational information. As a result, relatively little is known about how humans identify causal relations in real time. What evidence exists, however, paints a rather unflattering picture of human causal induction and converges to the conclusion that humans cannot identify causal relations if cause and effect are separated by more than a few seconds. Associative learning theory has interpreted these findings to indicate that temporal contiguity is essential to causal inference. I argue instead that contiguity is not essential, but that the influence of time in causal inference is crucially dependent on people's beliefs and expectations about the timeframe of the causal relation in question. First I demonstrate that humans are capable of dissociating temporal contiguity from causal strength; more specifically, they can learn that a given event exerts a stronger causal influence when it is temporally separated from the effect than when it is contiguous with it. Then I re-investigate a paradigm commonly used to study the effects of delay on human causal induction. My experiments employed one crucial additional manipulation regarding participants' awareness of potential delays. This manipulation was sufficient to reduce the detrimental effects of delay. Three other experiments employed a similar strategy, but relied on implicit instructions about the timeframe of the causal relation in question. Overall, results support the hypothesis that knowledge mediates the timeframe of covariation assessment in human causal induction. Implications for associative learning and causal power theories are discussed.