Rethinking temporal contiguity and the judgement of causality: effects of prior knowledge, experience, and reinforcement procedure.

Time plays a pivotal role in causal inference. Nonetheless most contemporary theories of causal induction do not address the implications of temporal contiguity and delay, with the exception of associative learning theory. Shanks, Pearson, and Dickinson (1989) and several replications (Reed, 1992, 1999) have demonstrated that people fail to identify causal relations if cause and effect are separated by more than two seconds. In line with an associationist perspective, these findings have been interpreted to indicate that temporal lags universally impair causal induction. This interpretation clashes with the richness of everyday causal cognition where people apparently can reason about causal relations involving considerable delays. We look at the implications of cause-effect delays from a computational perspective and predict that delays should generally hinder reasoning performance, but that this hindrance should be alleviated if reasoners have knowledge of the delay. Two experiments demonstrated that (1) the impact of delay on causal judgement depends on participants' expectations about the timeframe of the causal relation, and (2) the free-operant procedures used in previous studies are ill-suited to study the direct influences of delay on causal induction, because they confound delay with weaker evidence for the relation in question. Implications for contemporary causal learning theories are discussed.     

Abolishing the effect of reinforcement delay on human causal learning

Associative learning theory postulates two main determinants for human causal learning: contingency and contiguity. In line with such an account, participants in Shanks, Pearson, and Dickinson (1989) failed to discover causal relations involving delays of more than two seconds. More recent research has shown that the impact of contiguity and delay is mediated by prior knowledge about the timeframe of the causal relation in question. Buehner and May (2002, 2003) demonstrated that the detrimental effect of delay can be significantly reduced if reasoners are aware of potential delays. Here we demonstrate for the first time that the negative influence of delay can be abolished completely by a subtle change in the experimental instructions. Temporal contiguity is thus not essential for human causal learning.     

Judgement of two causal candidates from contingency information: II. Effects of information about one cause on judgements of the other cause

When judgements are being made about two causes there are eight possible kinds of contingency information: occurrences and nonoccurrences of the outcome when both causes are present, when Cause 1 alone is present, when Cause 2 alone is present, and when neither cause is present. It is proposed that contingency information is used to some extent to judge proportionate strength, which is the proportion of occurrences of the outcome that each cause can account for. This leads to a prediction that judgements of one cause will be influenced by information about occurrences, but not nonoccurrences, of the outcome when only the other cause is present. In six experiments consistent support was found for this prediction when the cause being judged had a positive relation with the outcome, but no consistent tendency was found when the cause being judged had a negative relation with the outcome. The effects found for causes with positive contingency cannot be explained by the Rescorla-Wagner model of causal judgement nor by the hypothesis that causal judgements are based on conditional contingencies.     

The Accuracy of Causal Learning Over Long Timeframes: An Ecological Momentary Experiment Approach

The ability to learn cause–effect relations from experience is critical for humans to behave adaptively — to choose causes that bring about desired effects. However, traditional experiments on experience-based learning involve events that are artificially compressed in time so that all learning occurs over the course of minutes. These paradigms therefore exclusively rely upon working memory. In contrast, in real-world situations we need to be able to learn cause–effect relations over days and weeks, which necessitates long-term memory. 413 participants completed a smartphone study, which compared learning a cause–effect relation one trial per day for 24 days versus the traditional paradigm of 24 trials back- to- back. Surprisingly, we found few differences between the short versus long timeframes. Subjects were able to accurately detect generative and preventive causal relations, and they exhibited illusory correlations in both the short and long timeframe tasks. These results provide initial evidence that experience-based learning over long timeframes exhibits similar strengths and weaknesses as in short timeframes. However, learning over long timeframes may become more impaired with more complex tasks.     

Statistical contingency has a different impact on preparation judgements than on causal judgements

Previous studies on causal learning showed that judgements about the causal effect of a cue on an outcome depend on the statistical contingency between the presence of the cue and the outcome. We demonstrate that statistical contingency has a different impact on preparation judgements (i.e., judgements about the usefulness of responses that allow one to prepare for the outcome). Our results suggest that preparation judgements primarily reflect information about the outcome in prior situations that are identical to the test situation. These findings also add to previous evidence showing that people can use contingency information in a flexible manner depending on the type of test question.     

Accessing causal relations in semantic memory

Most studies investigating semantic memory have focused on taxonomic or associative relations. Little is known about how other relations, such as causal relations, are represented and accessed. In three experiments, we presented participants with pairs of words one after another, describing events that referred to either a cause (e.g., spark) or an effect (e.g., fire). We manipulated the temporal order of word presentation and the question participants had to respond to. The results revealed that questions referring to the existence of a causal relation are answered faster when the first word refers to a cause and the second word refers to its effect than vice versa. However, no such asymmetry was observed with questions referring to the associative relation. People appear to distinguish the roles of cause and effect when queried specifically about a causal relation, but not when the same information is evaluated for the presence of an associative relation.     

Predictions and causal estimations are not supported by the same associative structure

Studies performed by different researchers have shown that judgements about cue-outcome relationships are systematically influenced by the type of question used to request those judgements. It is now recognized that judgements about the strength of the causal link between a cue and an outcome are mostly determined by the cue-outcome contingency, whereas predictions of the outcome are more influenced by the probability of the outcome given the cue. Although these results make clear that those different types of judgement are mediated by some knowledge of the normative differences between causal estimations and outcome predictions, they do not speak to the underlying processes of these effects. The experiment presented here reveals an interaction between the type of question and the order of trials that challenges standard models of causal and predictive learning that are framed exclusively in associative terms or exclusively in higher order reasoning terms. However, this evidence could be easily explained by assuming the combined intervention of both types of process.     

Discounting in causality and covariation judgements

In two experiments, humans were asked to judge the strength of a moderate contingency between a cue and an outcome in the presence of a second strong contingency between another cue and the outcome. The first experiment replicated the discounting effect whereby a strong contingency causes subjects to reduce or discount judgements of a moderate contingency. This experiment used a video-game procedure in which subjects camouflaged a tank to make it safe from mines. The second causal cue was the presence or absence of a spotter plane. Experiment 1 also ruled out the possibility that judgements might be determined by the number of co-occurrences of the cue and outcome as opposed to the level of contingency. The second experiment used an abstract scenario in which discounting was demonstrated when subjects were asked to judge the relationship between the occurrence of geometric objects. The instructions were neutral to discourage causal hypotheses. These results support the notion that judgements result from associative or connectionist processes and not from a two-step cognitive retrospective process in which an estimate of covariation is calculated between cue and outcome and then in a second step this information is used in a normative manner when a decision is made.     

Acting knowingly: effects of the agent's awareness of an opportunity on causal attributions

According to difference-based (e.g. counterfactual/covariational) models of causal judgement, the epistemic state of the agent should not affect judgements of cause. Four experiments examined opportunity chains in which a physical event (distal cause) enabled a subsequent proximal cause to produce an outcome. All four experiments showed that when the proximal cause was a human action, it was judged as more causal if the agent was aware of his opportunity than if he was not or if the proximal cause was a physical event. The first two experiments showed that these preferences could not be explained in terms of differences in perceived conditional probability (whether from the observer's or the agent's point of view), social controllability or perceptions of the causal sequence as forming a single unit. The third experiment showed that awareness affected the perceived deliberateness with which the action brought the outcome about but not its perceived voluntariness. The fourth experiment showed that when the outcome was intended, the perceived deliberateness of the agent's action was a plausible mediator of the effect of awareness of opportunity on causal preference. We conclude that awareness of the opportunity allows inferences about the deliberate production of the outcome when the action is voluntary, which in turn influence causal judgements.     

Short article conformity to the power PC theory of causal induction depends on the type of probe question

P. W. Cheng's () power PC theory of causal induction proposes that causal estimates are based on the power (P) of a potential cause, where P is the contingency between the cause and effect normalized by the base rate of the effect. Most previous research using a standard causal probe question has failed to support the predictions of the power PC model but recently Buehner, Cheng, and Clifford () found that participants responded in terms of causal power when probed with a counterfactual test question, which they argued prompted participants to consider the base rate of the effect. However, Buehner et al. framed their counterfactual question in terms of frequency, a factor that has been demonstrated to decrease base rate neglect in judgements under uncertainty. In the experiment reported here, we sought to disentangle the influence of counterfactual and frequency framing of the probe question to determine which factor is responsible for encouraging responses in terms of causal power.     

Conformity to the power PC theory of causal induction depends on the type of probe question

P. W. Cheng's (1997) power PC theory of causal induction proposes that causal estimates are based on the power (P) of a potential cause, where P is the contingency between the cause and effect normalized by the base rate of the effect. Most previous research using a standard causal probe question has failed to support the predictions of the power PC model but recently Buehner, Cheng, and Clifford (2003) found that participants responded in terms of causal power when probed with a counterfactual test question, which they argued prompted participants to consider the base rate of the effect. However, Buehner et al. framed their counterfactual question in terms of frequency, a factor that has been demonstrated to decrease base rate neglect in judgements under uncertainty. In the experiment reported here, we sought to disentangle the influence of counterfactual and frequency framing of the probe question to determine which factor is responsible for encouraging responses in terms of causal power.     

Beliefs about interactions between factors in the natural environment: a causal network study

This research is concerned with the structure of people's beliefs about causal processes in complex natural systems. A set of entities was derived from an expert analysis of important factors in relation to forest ecosystems and climate change. These included human population, atmospheric carbon dioxide levels, fires and several biological features such as extinction rates. In two experiments participants were presented with each pair of entities and asked whether change in one would produce change in the other. From the judgements made, causal networks were constructed that reflected consensual causal beliefs. The resultant causal network was unidirectional, with some entities, such as humans, functioning as causal origins and others, such as extinction rates, functioning as effects. This is consistent with previous research showing unidirectional patterns of thinking about causality in natural systems. Copyright © 2007 John Wiley & Sons, Ltd.     

Temporal contiguity and the judgement of causality by human subjects

Three experiments examined the role of the degree of temporal contiguity between an action and an outcome in human causality judgement. In all the experiments subjects were required to perform an action—pressing a key on a computer keyboard—and to judge the extent to which the action caused an outcome on the computer screen to occur. The action and outcome occurred on a free-operant schedule. In the first experiment a 2-sec delay between the action and outcome reduced causality judgements relative to a situation in which there was no delay. In the second experiment judgements in conditions with delays of 0, 4, 8, and 16 sec were compared with judgements in conditions in which the same pattern of outcomes occurred non-contingently with respect to the subjects' responding. In both of these experiments the events were controlled by random ratio schedules, following the procedure of Wasserman, Chatlosh, and Neunaber (1983), in which each condition was divided into 1-sec intervals. In the third experiment judgements in conditions with delays of 0, 2, 4, or 8 sec were compared in a continuous procedure rather than one divided into 1-sec intervals. In all experiments the increasing delays led to progressively lower judgements of causality. The results are related to three accounts of the mechanism underlying human causality judgement and are also compared with results from analogous animal conditioning studies.     

Causal powers and preventers: An explanatory account of cue interaction effects in human causal judgement

According to the causal powers theory, all causal relations are understood in terms of causal powers of one thing producing an effect by acting on liability of another thing. Powers can vary in strength, and their operation also depends on the presence of preventers. When an effect occurs, there is a need to account for the occurrence by assigning sufficient strength to produce it to its possible causes. Contingency information is used to estimate strengths of powers and preventers and the extent to which they account for occurrences and nonoccurrences of the outcome. People make causal judgements from contingency information by processes of inference that interpret evidence in terms of this fundamental understanding. From this account it is possible to derive a computational model based on a common set of principles that involve estimating strengths, using these estimates to interpret ambiguous information, and integrating the resultant evidence in a weighted averaging model. It is shown that the model predicts cue interaction effects in human causal judgement, including forward and backward blocking, second and third order backward blocking, forward and backward conditioned inhibition, recovery from overshadowing, superlearning, and backward superlearning.     

Structural awareness mitigates the effect of delay in human causal learning

Many studies have demonstrated that reinforcement delays exert a detrimental influence on human judgments of causality. In a free-operant procedure, the trial structure is usually only implicit, and delays are typically manipulated via trial duration, with longer trials tending to produce both longer experienced delays and also lower objective contingencies. If, however, a learner can become aware of this trial structure, this may mitigate the effects of delay on causal judgments. Here we tested this “structural-awareness” hypothesis by manipulating whether response–outcome contingencies were clearly identifiable as such, providing structural information in real time using an auditory tone to delineate consecutive trials. A first experiment demonstrated that providing cues to indicate trial structure, but without an explicit indication of their meaning, significantly increased the accuracy of causal judgments in the presence of delays. This effect was not mediated by changes in response frequency or timing, and a second experiment demonstrated that it cannot be attributed to the alternative explanation of enhanced outcome salience. In a third experiment, making trial structure explicit and unambiguous, by telling participants that the tones indicated trial structure, completely abolished the effect of delays. We concluded that, with sufficient information, a continuous stream of causes and effects can be perceived as a series of discrete trials, the contingency nature of the input may be exploited, and the effects of delay may be eliminated. These results have important implications for human contingency learning and in the characterization of temporal influences on causal inference.     

Temporal predictability enhances judgements of causality in elemental causal induction from both observation and intervention

When the temporal interval or delay separating cause and effect is consistent over repeated instances, it becomes possible to predict when the effect will follow from the cause, hence temporal predictability serves as an appropriate term for describing consistent cause-effect delays. It has been demonstrated that in instrumental action-outcome learning tasks, enhancing temporal predictability by holding the cause-effect interval constant elicits higher judgements of causality compared to conditions involving variable temporal intervals. Here, we examine whether temporal predictability exerts a similar influence when causal learning takes place through observation rather than intervention through instrumental action. Four experiments demonstrated that judgements of causality were higher when the temporal interval was constant than when it was variable, and that judgements declined with increasing variability. We further found that this beneficial effect of predictability was stronger in situations where the effect base-rate was zero (Experiments 1 and 3). The results therefore clearly indicate that temporal predictability enhances impressions of causality, and that this effect is robust and general. Factors that could mediate this effect are discussed.     

Spatial separation of target and competitor cues enhances blocking of human causality judgements

Three experiments were carried out. Each required subjects to make judgements about the causal status of cues following a two-stage blocking procedure. In Stage 1 a competitor cue was consistently paired with an outcome, and in Stage 2 the competitor continued to be paired with the outcome but was accompanied by a target cue. It was predicted that causal judgements for the target would be reduced by the presence of the competitor. In Experiments 1 and 2 the blocking procedure was implemented as a computer simulation of a card game during which subjects had to learn which cards produced the best payouts. The cues that subjects used to make their judgement were colours and symbols that appeared on the backs of the cards. When the target and competitor cues appeared on the same card blocking effects did not emerge, but when they appeared as part of different cards blocking effects were found. Thus, spatial separation of target and competitor cues appeared to facilitate blocking. Experiment 3 replicated the blocking result using spatially separated target and competitor cues.     

Effect of counterfactual and factual thinking on causal judgements

The significance of counterfactual thinking in the causal judgement process has been emphasized for nearly two decades, yet no previous research has directly compared the relative effect of thinking counterfactually versus factually on causal judgement. Three experiments examined this comparison by manipulating the task frame used to focus participants' thinking about a target event. Prior to making judgements about causality, preventability, blame, and control, participants were directed to think about a target actor either in counterfactual terms (what the actor could have done to change the outcome) or in factual terms (what the actor had done that led to the outcome). In each experiment, the effect of counterfactual thinking did not differ reliably from the effect of factual thinking on causal judgement. Implications for research on causal judgement and mental representation are discussed.     

特征相似性、类别标签与因果关系在归纳推理中的作用

采用人工材料,比较类别标签、特征相似性与因果关系对归纳推理强度的影响。包括两个实验,实验1比较类别标签与特征相似性对归纳推理的影响,结果表明,当类别标签对归纳推理的影响显著强于特征相似性时,不能将类别标签等同于一个相似性特征。实验2进一步探讨类别标签与因果关系对归纳推理的作用,结果表明,因果关系作用明显强于类别标签的作用。综合两个实验的结果并整合前人相关研究,提出了不同关系影响归纳推理的强度假想。