On Similarities between Causal Judgments in Experienced and Described Situations

Causal Illusions in the Service of Political Attitudes in Spain and the United Kingdom

The causal illusion is a cognitive bias that results in the perception of causality where there is no supporting evidence. We show that people selectively exhibit the bias, especially in those situations where it favors their current worldview as revealed by their political orientation. In our two experiments (one conducted in Spain and one conducted in the United Kingdom), participants who self-positioned themselves on the ideological left formed the illusion that a left-wing ruling party was more successful in improving city indicators than a right-wing party, while participants on the ideological right tended to show the opposite pattern. In sum, despite the fact that the same information was presented to all participants, people developed the causal illusion bias selectively, providing very different interpretations that aligned with their previous attitudes. This result occurs in situations where participants inspect the relationship between the government's actions and positive outcomes (improving city indicators) but not when the outcomes are negative (worsening city indicators).

Causal illusions in children when the outcome is frequent

Causal illusions occur when people perceive a causal relation between two events that are actually unrelated. One factor that has been shown to promote these mistaken beliefs is the outcome probability. Thus, people tend to overestimate the strength of a causal relation when the potential consequence (i.e. the outcome) occurs with a high probability (outcome-density bias). Given that children and adults differ in several important features involved in causal judgment, including prior knowledge and basic cognitive skills, developmental studies can be considered an outstanding approach to detect and further explore the psychological processes and mechanisms underlying this bias. However, the outcome density bias has been mainly explored in adulthood, and no previous evidence for this bias has been reported in children. Thus, the purpose of this study was to extend outcome-density bias research to childhood. In two experiments, children between 6 and 8 years old were exposed to two similar setups, both showing a non-contingent relation between the potential cause and the outcome. These two scenarios differed only in the probability of the outcome, which could either be high or low. Children judged the relation between the two events to be stronger in the high probability of the outcome setting, revealing that, like adults, they develop causal illusions when the outcome is frequent.

Human instrumental performance in ratio and interval contingencies: A challenge for associative theory

Associative learning theories regard the probability of reinforcement as the critical factor determining responding. However, the role of this factor in instrumental conditioning is not completely clear. In fact, free-operant experiments show that participants respond at a higher rate on variable ratio than on variable interval schedules even though the reinforcement probability is matched between the schedules. This difference has been attributed to the differential reinforcement of long inter-response times (IRTs) by interval schedules, which acts to slow responding. In the present study, we used a novel experimental design to investigate human responding under random ratio (RR) and regulated probability interval (RPI) schedules, a type of interval schedule that sets a reinforcement probability independently of the IRT duration. Participants responded on each type of schedule before a final choice test in which they distributed responding between two schedules similar to those experienced during training. Although response rates did not differ during training, the participants responded at a lower rate on the RPI schedule than on the matched RR schedule during the choice test. This preference cannot be attributed to a higher probability of reinforcement for long IRTs and questions the idea that similar associative processes underlie classical and instrumental conditioning.

How One Cause Discounts or Augments Another: A Connectionist Account of Causal Competition

The authors investigated the degree of discounting and augmentation of a target cause by an alternative cause given a varying number of observations on the alternative cause while holding its degree of covariation constant. Two experiments showed that more observations of the alternative cause resulted in greater discounting or augmentation of a target cause. This sample size effect cannot be explained by current attribution theories based on statistical notions or belief updating but can be accounted for by a connectionist framework. In addition, the authors found that the sample size effect was stronger when the information was presented in a sequential trial-by-trial format as opposed to a summarized format but found no effect of information order. Possible extensions of statistical models with confidence weights that take account of sample size were considered and simulated but none accommodated the data as well as connectionist models.

Stereotype-Based Blocking in Social Explanation

Explanations for performance and behavior are often biased by stereotypes held by social perceivers. Stereotypes may affect not only judgments of the causal role of members of the stereotyped group but also assessments of the causal influence of people and events that are unrelated to the stereotype. The findings from three experiments indicate that an important mechanism through which stereotypes affect judgments of the causality of stereotype-irrelevant factors is blocking as described by Kamin. Patterns of causality consistent with gender stereotypes blocked, or attenuated, the perception of covariation between causes unrelated to gender and performance outcomes. The blocking of this covariation evidence, in turn, decreased the causality attributed to gender-irrelevant events. The findings suggest that blocking is a distinct process from discounting through which stereotypes and other expectancies bias causal attributions and persevere.

Interactive effects of the probability of the cue and the probability of the outcome on the overestimation of null contingency

Overestimations of null contingencies between a cue, C, and an outcome, O, are widely reported effects that can arise for multiple reasons. For instance, a high probability of the cue, P(C), and a high probability of the outcome, P(O), are conditions that promote such overestimations. In two experiments, participants were asked to judge the contingency between a cue and an outcome. Both P(C) and P(O) were given extreme values (high and low) in a factorial design, while maintaining the contingency between the two events at zero. While we were able to observe main effects of the probability of each event, our experiments showed that the cue- and outcome-density biases interacted such that a high probability of the two stimuli enhanced the overestimation beyond the effects observed when only one of the two events was frequent. This evidence can be used to better understand certain societal issues, such as belief in pseudoscience, that can be the result of overestimations of null contingencies in high-P(C) or high-P(O) situations.

Two heads are better than one, but how much? Evidence that people's use of causal integration rules does not always conform to normative standards

Many theories of causal learning and causal induction differ in their assumptions about how people combine the causal impact of several causes presented in compound. Some theories propose that when several causes are present, their joint causal impact is equal to the linear sum of the individual impact of each cause. However, some recent theories propose that the causal impact of several causes needs to be combined by means of a noisy-OR integration rule. In other words, the probability of the effect given several causes would be equal to the sum of the probability of the effect given each cause in isolation minus the overlap between those probabilities. In the present series of experiments, participants were given information about the causal impact of several causes and then they were asked what compounds of those causes they would prefer to use if they wanted to produce the effect. The results of these experiments suggest that participants actually use a variety of strategies, including not only the linear and the noisy-OR integration rules, but also averaging the impact of several causes.

Implementation and assessment of an intervention to debias adolescents against causal illusions

Researchers have warned that causal illusions are at the root of many superstitious beliefs and fuel many people’s faith in pseudoscience, thus generating significant suffering in modern society. Therefore, it is critical that we understand the mechanisms by which these illusions develop and persist. A vast amount of research in psychology has investigated these mechanisms, but little work has been done on the extent to which it is possible to debias individuals against causal illusions. We present an intervention in which a sample of adolescents was introduced to the concept of experimental control, focusing on the need to consider the base rate of the outcome variable in order to determine if a causal relationship exists. The effectiveness of the intervention was measured using a standard contingency learning task that involved fake medicines that typically produce causal illusions. Half of the participants performed the contingency learning task before participating in the educational intervention (the control group), and the other half performed the task after they had completed the intervention (the experimental group). The participants in the experimental group made more realistic causal judgments than did those in the control group, which served as a baseline. To the best of our knowledge, this is the first evidence-based educational intervention that could be easily implemented to reduce causal illusions and the many problems associated with them, such as superstitions and belief in pseudoscience.

The role of learning data in causal reasoning about observations and interventions

Recent studies have shown that people have the capacity to derive interventional predictions for previously unseen actions from observational knowledge, a finding that challenges associative theories of causal learning and reasoning (e.g., Meder, Hagmayer, & Waldmann, 2008). Although some researchers have claimed that such inferences are based mainly on qualitative reasoning about the structure of a causal system (e.g., Sloman, 2005), we propose that people use both the causal structure and its parameters for their inferences. We here employ an observational trial-by-trial learning paradigm to test this prediction. In Experiment 1, the causal strength of the links within a given causal model was varied, whereas in Experiment 2, base rate information was manipulated while keeping the structure of the model constant. The results show that learners' causal judgments were strongly affected by the observed learning data despite being presented with identical hypotheses about causal structure. The findings show furthermore that participants correctly distinguished between observations and hypothetical interventions. However, they did not adequately differentiate between hypothetical and counterfactual interventions.

Comparing associative, statistical, and inferential reasoning accounts of human contingency learning

For more than two decades, researchers have contrasted the relative merits of associative and statistical theories as accounts of human contingency learning. This debate, still far from resolution, has led to further refinement of models within each family of theories. More recently, a third theoretical view has joined the debate: the inferential reasoning account. The explanations of these three accounts differ critically in many aspects, such as level of analysis and their emphasis on different steps within the information-processing sequence. Also, each account has important advantages (as well as critical flaws) and emphasizes experimental evidence that poses problems to the others. Some hybrid models of human contingency learning have attempted to reconcile certain features of these accounts, thereby benefiting from some of the unique advantages of different families of accounts. A comparison of these families of accounts will help us appreciate the challenges that research on human contingency learning will face over the coming years.

Nonnormative discounting: There is more to cue interaction effects than controlling for alternative causes

Several experiments on human causal reasoning have demonstrated "discounting"--that the presence of a strong alternative cause may decrease the perceived efficacy of a moderate target cause. Some, but not all, of these effects have been shown to be attributable to subjects' use of conditional rather than unconditional contingencies (i.e., subjects control for alternative causes). We review experimental results that do not conform to the conditionalizing contingency account of causal judgment. In four experiments, we demonstrate that there is "nonnormative discounting" above what is accounted for by conditionalization, that discounting may depend on the nature of the question put to the subjects, and that discounting can be affected by motivation. We conclude that because nonnormative discounting occurs for summary presentations as well as trial-by-trial presentations of information and because nonnormative discounting depends on motivation, it is not a necessary result of cue competition during the contingency learning process.

Causal judgment from contingency information: a systematic test of the pCI rule

Contingency information is information about the occurrence or nonoccurrence of an effect when a possible cause is present or absent. Under the evidential evaluation model, instances of contingency information are transformed into evidence and causal judgment is based on the proportion of relevant instances evaluated as confirmatory for the candidate cause. In this article, two experiments are reported that were designed to test systematic manipulations of the proportion of confirming instances in relation to other variables: the proportion of instances on which the candidate cause is present, the proportion of instances in which the effect occurs when the cause is present, and the objective contingency. Results showed that both unweighted and weighted versions of the proportion-of-confirmatory-instances rule successfully predicted the main features of the results, with the weighted version proving more successful. Other models, including the power PC theory, failed to predict the results.

Assessing the Causal Structure of Function

Theories typically emphasize affordances or intentions as the primary determinant of an object's perceived function. The HIPE theory assumes that people integrate both into causal models that produce functional attributions. In these models, an object's physical structure and an agent's action specify an affordance jointly, constituting the immediate causes of a perceived function. The object's design history and an agent's goal in using it constitute distant causes. When specified fully, the immediate causes are sufficient for determining the perceived function--distant causes have no effect (the causal proximity principle). When the immediate causes are ambiguous or unknown, distant causes produce inferences about the immediate causes, thereby affecting functional attributions indirectly (the causal updating principle). Seven experiments supported HIPE's predictions.

How temporal assumptions influence causal judgments

Causal learning typically entails the problem of being confronted with a large number of potentially relevant statistical relations. One type of constraint that may guide the choice of appropriate statistical indicators of causality are assumptions about temporal delays between causes and effects. There have been a few previous studies in which the role of temporal relations in the learning of events that are experienced in real time have been investigated. However, human causal reasoning may also be based on verbally described events, rather than on direct experiences of the events to which the descriptions refer. The aim of this paper is to investigate whether assumptions about the temporal characteristics of the events that are being described also affect causal judgment. Three experiments are presented that demonstrate that different temporal assumptions about causal delays may lead to dramatically different causal judgments, despite identical leaning inputs. In particular, the experiments show that temporal assumptions guide the choice of appropriate statistical indicators of causality by structuring the event stream (Experiment 1), by selecting the potential causes among a set of competing candidates (Experiment 2), and by influencing the level of aggregation of events (Experiment 3).

Judgement frequency, belief revision, and serial processing of causal information

The main aim of this research was to study the cognitive architecture underlying causal/covariation learning by investigating the frequency of judgement effect. Previous research has shown that decreasing the number of trials between opportunities to make a judgement in a covariation learning task led to a higher score after an a or d type of trial (positive cases) than after b and c trials (negative cases). Experiment I replicated this effect using a trial-by-trial procedure and examined the conditions under which it occurs. Experiment 2 demonstrated a similar frequency of judgement effect when the information was presented in the form of contingency tables. Associative or statistical single-mechanism accounts of causal and covariation learning do not provide a satisfactory explanation for these findings. An alternative belief revision model is presented.

Human agency and associative learning: Pavlovian principles govern social process in causal relationship detection

Estimates of a worker's causal relationship (CR) to production obeyed associative principles, despite the participants' a priori beliefs that workers are responsible or "at cause" for production. In three experiments, social analogues of conditioned stimuli (workers) and unconditioned stimuli (company production information) were manipulated in familiar Pavlovian paradigms. The findings included (1) CR acquisition, (2) unconditioned stimulus-intensity effects, and (3) CR blocking. The research plan employed an approach that Neal Miller (1959) termed "extension of liberalized S-R theory" and drew on the Rescorla-Wagner model to integrate the experimental results, to illuminate the empirical data of social attribution research, and to guide the study of causal relationship detection using social stimuli.

Verbal and visual causal arguments

The present paper analyzes how verbalizations and visualizations can be used to justify and dispute causal claims. The analysis is based on a taxonomy of 27 causal arguments as they appear in ordinary language. It is shown how arguments from spatio-temporal contiguity, covariation, counterfactual necessity, and causal mechanisms, to name only a few, are visualized in persuasive uses of tables, graphs, time series, causal diagrams, drawings, maps, animations, photos, movies, and simulations. The discussion centers on how these visual media limit the argumentative moves of justifying, disputing, and qualifying claims; how they constrain the representation of observational, explanatory, and abstract knowledge in the premises of causal arguments; and how they support and externalize argument-specific inferences, namely generalizations, comparisons, mental simulations, and causal explanations.

Base-rate training without case cues reduces base-rate neglect

Base-rate neglect is a persistent phenomenon in which subjects do not place sufficient weight on the probabilities of occurrence of relevant events. Two experiments with college students support the hypothesis that base-rate neglect may be minimized by providing base-rate training in the absence of case, or witness, cues, prior to introducing (or reintroducing) these cues. In Experiment 1, the hypothesis was supported by both within-subjects and between-groups assessments; in Experiment 2, the hypothesis was supported while the effects of instructions and a correction procedure were found to be minimal. In Experiment 1, but not in Experiment 2, training with case cues present also reduced base-rate neglect, but this effect was not sufficient to account for the effect of cue-absent base-rate training. Correction trials led some subjects to detect that the task contingencies were random; however, neither this nor actually telling subjects after the experiment that the task was indeed random led invariably to subjects' describing the optimal strategy (which was to choose the richer alternative exclusively).

Judging interevent relations: from cause to effect and from effect to cause

Stimulus competition was studied in college students' correlational judgments in a medical decision-making setting. In accord with prior findings, subjects making cause-to-effect (predictive) judgments discounted a stimulus event that was moderately correlated with a target event when rival stimuli were more highly correlated with the effect. However, subjects making effect-to-cause (diagnostic) judgments were not at all disposed to discount a stimulus event which was moderately correlated with a target event when rival stimuli were more highly correlated with the cause. The theoretical implications of these results are considered in connection with associative and mentalistic models of causal attribution.