Causal Judgments Research Articles

Aging and integration of contingency evidence in causal judgment.

Age differences in causal judgment are consistently greater for preventative/negative relationships than for generative/positive relationships. In this study, a feature analytic procedure (Mandel & Lehman, 1998) was used to determine whether this effect might be due to differences in young and older adults' integration of contingency evidence during causal induction. To reduce the impact of age-related changes in learning/memory, the authors presented contingency evidence for preventative, noncontingent, and generative relationships in summary form; the meaningfulness of causal context was varied to induce participants to integrate greater or lesser amounts of this evidence. Young adults showed greater flexibility in their integration processes than did older adults. In an abstract causal context, there were no age differences in causal judgment or integration, but in meaningful contexts, young adults' judgments for preventative relationships were more accurate than older adults' and young adults assigned more weight to the contingency evidence confirming these relationships. These differences were mediated by age-related changes in processing speed. The decline in this basic cognitive resource may place boundaries on the amount or type of evidence that older adults can integrate for causal judgment.

Multiple Cue Extinction Effects on Recovery of Responding in Causal Judgments

Many experiments have demonstrated recovery of extinguished responding following a context change and some experiments have shown that extinction in multiple contexts can reduce this response recovery. We report two additional experiments which both showed reduced response recovery following extinction in the presence of multiple partner cues. These experiments also showed reduced response recovery following acquisition in the presence of multiple partner cues. The effect of the multiple extinction treatment was present in tests carried out in presence of the original training cue (ABA design) as well as in the presence of a novel test cue (ABC design), suggesting the effect was mediated by the associative strength of the target cue, rather than by the strength of the partner cue. However, the effect of the multiple acquisition treatment was only present in the ABA design, suggesting this effect was mediated by the associative strength of the partner cues, not by the strength of the target cue.

Folk judgments of causation

Folk judgments of causation

Competition between multiple causes of a single outcome in causal reasoning.

A strong positive predictor of an outcome modulates the causal judgments of a moderate predictor. To study the empirical basis of this modulation, we compared treatments with one and with two strong competing (i.e., modulating) causes. This allowed us to vary the frequency of outcome occurrences or effects paired with the predictors. We investigated causal competition between positive predictors (those signaling the occurrence of the outcome), between negative predictors (those signaling the absence of the outcome) and between predictors of opposite polarity (positive and negative). The results are consistent with a contrast rather than a reduced associative strength or conditional contingency account, because a strong predictor of opposite polarity enhances rather than reduces causal estimates of moderate predictors. In addition, we found competition effects when the strong predictor predicted fewer outcome occurrences than the moderate predictor, thus implying that cue competition is, at least sometimes, a consequence of contingency rather than total cue-outcome pairings.

The influence of virtual sample size on confidence and causal-strength judgments.

The authors investigated whether confidence in causal judgments varies with virtual sample size--the frequency of cases in which the outcome is (a) absent before the introduction of a generative cause or (b) present before the introduction of a preventive cause. Participants were asked to evaluate the influence of various candidate causes on an outcome as well as to rate their confidence in those judgments. They were presented with information on the relative frequencies of the outcome given the presence and absence of various candidate causes. These relative frequencies, sample size, and the direction of the causal influence (generative vs. preventive) were manipulated. It was found that both virtual and actual sample size affected confidence. Further, confidence affected estimates of strength, but confidence and strength are dissociable. The results enable a consistent explanation of the puzzling previous finding that observed causal-strength ratings often deviated from the predictions of both of the 2 dominant models of causal strength.

Driven by power? Probe question and presentation format effects on causal judgment.

It has been proposed that causal power (defined as the probability with which a candidate cause would produce an effect in the absence of any other background causes) can be intuitively computed from cause-effect covariation information. Estimation of power is assumed to require a special type of counterfactual probe question, worded to remove potential sources of ambiguity. The present study analyzes the adequacy of such questions to evoke normative causal power estimation. The authors report that judgments to counterfactual probes do not conform to causal power and that they strongly depend on both the probe question wording and the way that covariation information is presented. The data are parsimoniously accounted for by an alternative model of causal judgment, the Evidence Integration rule.

Short Article: Extinction in human learning and memory

In two experiments, participants were given extinction training in a human causal learning task. In both experiments, three critical experimental cues were paired with different outcomes in a first phase of training and were then extinguished in a second phase. Three control cues were given the same treatment in the first phase of training, but were not then presented in the second phase. Participants' ability to correctly identify the outcome with which each cue had been paired in the first phase was lower for extinguished than for control cues. Causal attributions to the extinguished cues were also lower than those to the control cues, a difference that correlated with outcome memory. These data are consistent with the idea that extinction in causal judgement is due, at least in part, to a failure to remember the cue-outcome relationship encoded in the first phase of training.

The Impact of Object Animacy on the Appraisal of Causality

The goal of this study was to examine whether animacy of objects affected the appraisal of a causal relationship when one object was observed to propel the other either immediately, after a delay, or at a distance. Participants rated the degree of causal interaction either before or after an extended experience with observing the interactions. We expected that spatial distance would have little effect when objects were seen as animate (because social interactions often span spatial gaps) and would have a degrading effect when objects were appraised as inanimate. Motion animacy appeared to attenuate the impact of a gap and to decrease initial causal judgments for direct collisions. Explicitly informing participants about the nature of the objects had a strong impact. Experience with the causal task affected ratings to a greater extent when the objects were explicitly described as nonliving rather than living.

Causal judgments of positive mood in relation to self-regulation: A case study with Flemish students

To examine students’ causal judgements of positive mood in relation to self-regulation, 128 participants from two different schools representing two distinct educational environments (Technical/Vocational School (TSO/BSO): N = 63; General Secondary School (ASO): N = 65) were asked to judge 45 statements containing three possible relationships (A → B; A ← B; A <> B) for all iterations of 5 constructs associated with positive mood, namely Hope, Optimism, Resilience, Confidence, Persistence, 4 constructs associated with self-regulation, namely Motivation, Social support, Problem- solving Learning goals, and 1 construct representing Academic performance. Based on a Pareto analysis, mental models were constructed for each school. An analysis of the mental models indicates that all students believe positive mood constructs to be causally related to self-regulation constructs with Academic performance identified as the main driver and Learning goals as the primary outcome for both schools. It is concluded that an appreciation of participants’ causal attributions of positive mood states in relation to self-regulation can present a clearer picture of the conditions under which participants’ causal understanding of positive mood and self-regulation constructs can be activated and used as an interpretive and evaluative framework in learning encounters.

Recency and primacy in causal judgments: Effects of probe question and context switch on latent inhibition and extinction

Traditional associative models assume that associative weights are updated on a trial-by-trial basis. As a result, it is usually expected that responses based on these weights will tend to reflect the most recently presented contingencies. However, a number of studies of human causal judgments have shown primacy effects, wherein judgments obtained at the end of a series of trials are more strongly influenced by a contingency that was in force early in the sequence than by a contingency that was in force later in the sequence. The experiments described in this article replicated other work showing that requesting causal judgments during a sequence can reverse primacy and produce strong recency effects. Evidence was also obtained to suggest that primacy effects are produced by an interaction between latent inhibition and extinction processes and that requesting a judgment affects both of these processes.

Launching at a distance: The effect of spatial markers

It has been widely reported that spatial contiguity is important to judgements of causality involving one object launching another [Michotte's "launching effect" (1963, 1991)]. The present study examined the impact of different types of spatial markers on causal judgements of a distal launch (one object approaching other, stopping short of it, and the second object subsequently moving along the same trajectory). The spatial markers were objects that either partially or completely bridged the spatial gap between two objects (Experiment 1), or they were dashed lines that marked the stopping location of the first object or the starting location of the second object (Experiment 2). The presence of either objects or dashed lines could produce higher causal ratings, but the location of the marker mattered. The results suggest that altering a cause's ability to predict when the effect would occur (via a spatial marker) and the presence of a conduit for energy transmission have independent effects on causal judgements of object interaction.

Professional Development for Prospective Epidemiology Teachers in Grades 6–12

“I have to think about why anyone would want to remember particular pieces of information. What does this fact help you understand? What problems does it help you address?” —As quoted in Ken Bain's What the Best College Teachers Do, 20041 How old were you when you heard the word epidemiology for the first time? At what stage in your career did you take your first epidemiology course? What events and decisions had to align for these events to occur? Did that alignment occur because of a well-conceived plan or was it more or less by serendipity? Many, if not most, answers to these questions from members of the public health community will include “graduate school” and/or “serendipity.” As the size of the public health workforce dwindles and its responsibilities increase, it is time to ensure that the answers future cohorts give to these questions will include “middle and high school” and “a well-conceived plan.” Younger students can learn epidemiology and understand its role in public health. The inductive and deductive reasoning of epidemiologic sleuthing captures students' curiosity. The limitations of epidemiologic study designs and the making of causal judgments when evidence is missing and/or flawed can challenge the critical-thinking skills of the best of students. Designing public health strategies based on such evidence can stir even the most laid-back student's sense of right and wrong. And all of this engagement is heightened when the issues being explored are of immediate relevance, whether it be backpacks and back pain, watching television and being overweight, or sleep deprivation and academic performance. If students have these experiences when the career-path playing field is still level, it seems likely that more of them will consider careers in public health. More importantly, perhaps, is the benefit to the majority of these students, whether or not they opt for public health careers. Knowledge of epidemiology arguably will prepare students to become more scientifically literate personal and public health decision makers, who will be able to appreciate epidemiologic evidence and provide support for public health initiatives. While epidemiology can be viewed as a required core course for public health professionals, it has more than simply vocational value. Even if public health workforce issues were no longer a problem, there would still be a legitimate scientific literacy argument for infusing epidemiology education into the curricula of grades 6–12. Are biology, chemistry, and physics taught for merely vocational purposes? This article begins to address what will be a necessary, but not sufficient, component of a well-conceived plan—deciding what sort of professional development experiences need to be created to prepare middle school and high school teachers to teach epidemiology, a science about which few have had previous knowledge. What epidemiologic knowledge is needed to teach epidemiology effectively? How can teachers be prepared to teach this science? To begin to plan the professional development experiences that prospective grade 6–12 epidemiology teachers would need, we might take a few epidemiology textbooks off our shelves and, paying particular attention to the tables of contents, begin to create a list of topics. We might recall some favorite epidemiology lessons we experienced as students. We might start to create a collection of assigned readings such as a textbook, a combination of historical epidemiology-related documents, some dramatic case studies, and some current newspaper columns. For a change of pace, we might think of a video and a guest speaker or two. In short, we might begin to plan this professional development by thinking about what we, as providers of professional development, will do. We might plan this professional development as a series of activities during which we cover an assortment of epidemiologic topics that are sure to engage budding teachers of epidemiology. All of this may be of value, but the National Research Council (NRC) warns that learners “… presented with vast amounts of content knowledge that is not organized into meaningful patterns are likely to forget what they have learned and to be unable to apply the knowledge to new problems or unfamiliar contexts.”2 Needless to say, a new teacher of epidemiology does not want to be in this situation when a student asks a question. The NRC concludes, “Learning with understanding is facilitated when new and existing knowledge is structured around the major concepts and principles of a discipline.”2

Interaction between previous beliefs and cue predictive value in covariation-based causal induction

The main aim of this work was to show the impact of preexisting causal beliefs on causal induction from cause–effect co-occurrence information, when several cues compete with each other for predicting the same effect. Two different causal scenarios – one social (a), the other medical (b) – were used to check the generality of the effects. In Experiments 1a and 1b, participants were provided information on the co-occurrence of a two-cause compound and an effect, but not about the potential relationship between each cause by its own and the effect. As expected, prior beliefs – induced by means of instructions – strongly modulated the causal strength assigned to each element of the compound. In Experiments 2a and 2b, covariation evidence was provided, not only about the predictive value of the two-cause compound, but also about one of the elements of the compound. When this evidence was available, prior beliefs had less impact on judgments, and these were mostly guided by the relative predictive value of the cue. These results demonstrate the involvement of inferential integrative mechanisms in the generation of causal knowledge and show that single covariation detection mechanisms – either rule-based or associative – are insufficient to account for human causal judgment. At the same time, the fact that the power of new covariational evidence to change prior beliefs depended on the availability of information on the relative (conditional) predictive value of the target candidate cause suggests that causal knowledge derived from information on causal mechanisms and from covariation probably share a common representational basis.

Color Change as a Causal Agent Revisited

Michotte (1946/1963, p. 235) concluded that color "has no bearing whatever on the question of qualitative causality". Surprisingly, this claim has received little empirical investigation in the 60 years since its publication. In 2 experiments, 2 balls struck a cylinder and changed color (either continuously or in a stepped progression) for 2 s. After the 2-s interval, the cylinder disgorged a purple substance. Participants chose which of the 2 balls was most likely to have caused the disgorging effect. An object that changed color was favored as the cause much more often than one that did not, and participants generally preferred an object that reached its terminal color immediately before the effect over objects that reached their terminal colors earlier. When participants performed a causal ratings task, color change produced moderate judgments of causation, with little response differentiation as a function of color dynamics.

EL PROCESAMIENTO DE COMPUESTOS ESTIMULARES EN JUICIOS DE CAUSALIDAD

Our research focuses on learning about causal relationships between events when a candidate cause is a compound integrated by several individual causes. In two experiments, we compared the predictions of the Associative Models of Rescorla and Wagner (1972) and Pearce (1994), the Inductive Models of Cheng and Novick (1992) and Novick and Cheng (2004). In contrast with previous research about this topic, in these experiments, a causality judgments task was used in which the information about the presence/absence of the causes and the effect was presented through small samples of cases. Our results showed that the learning mechanisms involved in compound cue processing could be associative in origin.

Accounting for occurrences: A new view of the use of contingency information in causal judgment.

When people make causal judgments from contingency information, a principal aim is to account for occurrences of the outcome. When 2 causes are under consideration, the capacity of either to account for occurrences is judged from how likely the cause is to be present when the outcome occurs and from the rate at which the outcome occurs when that cause alone is present, which gives an estimate of the strength of the cause. These propositions are formalized in a weighted averaging model, which successfully predicted several judgmental phenomena not predicted by other models of causal judgment. These include a tendency for judgment of one cause (A) to be reduced as the number of occurrences of when only the other one (B) increases and a tendency for A to receive higher judgments than B if A is better able to account for occurrences than B is even if B has a higher contingency with the outcome than A does. Overshadowing, a tendency for judgments of B to be depressed if A has a higher contingency, is weak or absent when B is better able to account for occurrences than A. Results of several experiments support these and related predictions derived from the accounting for occurrences hypothesis.

The impact of presentation format on causal inferences

The impact of the presentation format of covariation information on causal judgements was examined in a fictitious virus-disease causal induction task. Six different judgement conditions were created by crossing two levels of virus-disease covariation (0, .5) with three levels of disease base rate (.25, .5, .75). The covariation information was presented differently to three groups of participants. In a first group, it was presented in propositional format summarising the frequencies of the four patient types, namely patients with or without the virus who either did have or did not have the disease. In a second group the same information was presented in a 2×2 table with the cell frequencies represented in terms of countable objects (the presence/absence of virus/disease was shown as schematic faces that varied in expression and colour). In a third group the covariation information was presented in terms of a cumulative frequency tree with the two main branches representing the frequencies of patients with and without the disease which further decomposed into subgroups of those with and without the virus. The ability to discriminate between the levels of covariation was poorest in Group 1; it was significantly improved in Group 2, and was best in Group 3. These results suggest that the format with which covariation information is presented to reasoners exerts a significant influence on causal judgements.

Selective attention in human associative learning and recognition memory.

Four experiments examined the role of selective attention in a new causal judgment task that allowed measurement of both causal strength and cue recognition. In Experiments 1 and 2, blocking was observed; pretraining with 1 cue (A) resulted in reduced learning about a 2nd cue (B) when those 2 cues were trained in compound (AB+). Participants also demonstrated decreased recognition performance for the causally redundant Cue B, suggesting that less attention had been paid to it in training. This is consistent with the idea that attention is preferentially allocated toward the more predictive Cue A, and away from the less predictive Cue B (e.g., N. J. Mackintosh, 1975). Contrary to this hypothesis, in Experiments 3 and 4, participants demonstrated poorer recognition for the most predictive cues, relative to control cues. A new model, which is based on N. J. Mackintosh's (1975) model, is proposed to account for the observed relationship between the extent to which each cue is attended to, learned about, and later recognized

Comparing patterning and biconditional discriminations in humans.

In two experiments, human participants performed a causal judgment task that simultaneously comprised two reciprocal patterning discriminations and a biconditional discrimination. They learned both patterning discriminations more quickly than the biconditional discrimination. Postdiscrimination tests were used to identify participants who had, or had not, learned to apply the patterning rules, as well as participants who continued to expect summation when presented with two cues that predicted the same outcome. All groups were faster to learn the patterning than the biconditional discriminations. These results are inconsistent with models of stimulus representation that invoke configural representations (e.g., Pearce, 1987, 1994; Rescorla & Wagner, 1972) because these models solve biconditional discriminations more readily than patterning discriminations.

Cognitive Biases in Human Causal Learning

The main aim of this work was to look for cognitive biases in human inference of causal relationships in order to emphasize the psychological processes that modulate causal learning. From the effect of the judgment frequency, this work presents subsequent research on cue competition (overshadowing, blocking, and super-conditioning effects) showing that the strength of prior beliefs and new evidence based upon covariation computation contributes additively to predict causal judgments, whereas the balance between the reliability of both, beliefs and covariation knowledge, modulates their relative weight. New findings also showed "inattentional blindness" for negative or preventative causal relationships but not for positive or generative ones, due to failure in codifying and retrieving the necessary information for its computation. Overall results unveil the need of three hierarchical levels of a whole architecture for human causal learning: the lower one, responsible for codifying the events during the task; the second one, computing the retrieved information; finally, the higher level, integrating this evidence with previous causal knowledge. In summary, whereas current theoretical frameworks on causal inference and decision-making usually focused either on causal beliefs or covariation information, the present work shows how both are required to be able to explain the complexity and flexibility involved in human causal learning.