Recursive retrospective revaluation of causal judgments

Recursive causal evaluation is an iterative process in which the evaluation of a target cause, T, is based on the outcome of the evaluation of another cause, C, the evaluation of which itself depends on the evaluation of a 3rd cause, D. Retrospective revaluation consists of backward processing of information as indicated by the fact that the evaluation of T is influenced by subsequent information that is not concerned with T directly. Two experiments demonstrate recursive retrospective revaluation with contingency information presented in list format as well as with trial-by-trial acquisition. Existing associative models are unable to predict the results. The model of recursive causal disambiguation that conceptualizes the revaluation as a recursive process of disambiguation predicts the pattern of results correctly.     

Re-examination of the role of within-compound associations in the retrospective revaluation of causal judgements

We investigated blocking and retrospective revaluation of causal judgements using a scenario in which food cues acted as potential causes of an allergic reaction as the outcome. In the blocking contingency,the treatment cues were either paired or unpaired with the outcome prior to a second stage in which sequential compounds of treatment and target cues were paired with the outcome. The order of this compound and treatment training was reversed in retrospective revaluation contingencies. When the interstimulus interval between the treatment and target cues was unfilled on compound trials (Experiments 1 and 3), both blocking and retrospective revaluation were observed in that the target cue trained in compound with the paired treatment cue attracted lower causal ratings than the target cue trained in compound with the unpaired treatment cue. By contrast, performing a mental arithmetic task using numerals presented during the interstimulus interval had no effect on the magnitude of blocking but rendered retrospective revaluation unreliable (Experiments 2 and 3). These results provide further support for accounts of revaluation based upon within-compound associations.     

Higher-order retrospective revaluation in human causal learning

Previous studies demonstrated that participants will retrospectively adjust their ratings about the relation between a target cue and an outcome on the basis of information about the causal status of a competing cue that was previously paired with the target cue. We demonstrate that such retrospective revaluation effects occur not only for target cues with which the competing cue was associated directly, but also for target cues that were associated indirectly with the competing cue. These second-order and third-order retrospective revaluation effects are compatible with certain implementations of the probabilistic contrast model and with a modified, extended comparator model, but cannot be explained on the basis of a revised Rescorla-Wagner model or a revised SOP model.     

Aging and retrospective revaluation of causal learning

In a 2-stage causal learning task, young and older participants first learned which foods presented in compound were followed by an allergic reaction (e.g., STEAK-BEANS→ REACTION) and then the causal efficacy of 1 food from these compounds was revalued (e.g., BEANS→ NO REACTION). In Experiment 1, unrelated food pairs were used, and although there were no age differences in compound- or single-cue-outcome learning, older adults did not retrospectively revalue the causal efficacy of the absent target cues (e.g., STEAK). However, they had weaker within-compound associations for the unrelated foods, and this may have prevented them from retrieving the representations of these cues. In Experiment 2, older adults still showed no retrospective revaluation of absent cues even though compound food cues with pre-existing associations were used (e.g., STEAK-POTATO), and they received additional learning trials. Finally, in Experiment 3, older adults revalued the causal efficacy of the target cues when small, unobtrusive icons of these cues were present during single-cue revaluation. These findings suggest that age-related deficits in causal learning for absent cues are due to ineffective associative binding and reactivation processes.     

The hide-and-seek of retrospective revaluation: recovery from blocking is context dependent in human causal learning

In a typical blocking procedure, pairings of a compound consisting of 2 stimuli, A and X, with the outcome are preceded by pairings of only A with the outcome (i.e., A+ then AX+). This procedure is known to diminish responding to the target cue (X) relative to a control group that does not receive the preceding training with blocking cue A. We report 2 experiments that investigated the effect of extinguishing a blocking cue on responding to the target cue in a human causal learning paradigm (i.e., A+ and AX+ training followed by A- training). The results indicate that extinguishing a blocking cue increases conditioned responding to the target cue. Moreover, this increase appears to be context dependent, such that increased responding to the target is limited to the context in which extinction of the blocking cue took place. We discuss these findings in the light of associative and propositional learning theories.     

The effect of causal knowledge on judgments of the likelihood of unknown features

People frequently infer unknown aspects of an entity based on their knowledge about that entity. The current study reports a novel phenomenon, an inductive bias people have in making such inferences. Upon learning that one symptom causes another in a person, both undergraduate students (Experiment 1) and clinicians (Experiment 2) judged that an unknown feature associated with the cause-symptom was more likely to be present in that person than an unknown feature associated with the effect-symptom. Thus, these findings suggest a specific mechanism in which causal explanations influence one’s representation of and inferences about an entity. Implications for clinical reasoning and associative models of conceptual knowledge are discussed.     

Primacy in causal strength judgments: the effect of initial evidence for generative versus inhibitory relationships

The order in which people receive information has a substantial effect on subsequent judgment and inference. Our focus is on the order of covariation evidence in causal learning. The first experiment shows that the initial presentation of evidence suggesting a generative causal relationship (the joint presence or joint absence of cause and effect) leads to higher judged causal strength than does the initial presentation of evidence suggesting an inhibitory relationship (the presence of cause or effect in the absence of the other). Additional studies show that this primacy effect is unlikely to be due to fatigue or to an insufficient number of learning trials. These results are not readily explained by current contingency-based or associative theories of causal induction.     

Inattentional blindness for negative relationships in human causal learning

The present study focuses on the effect of selective attention on causal learning. Three effects of the level of attention to predictive symptoms in positive and negative contingency learning tasks are reported. First, participants accurately detected a positive relationship between an incidental cue and a contingent outcome, although judgements were slightly lower than those for the attended cue. Second, participants were unable to detect negative relationships between incidental cues and outcomes, which suggests a major role of selective attention in this type of learning. Third, participants retrieved the frequency of each trial type more accurately in the attended conditions than in the incidental conditions. These findings show how attention guides and constrains human causal learning and reveal an inattentional blindness effect for negative contingency learning.     

Inattentional blindness for negative relationships in human causal learning

The present study focuses on the effect of selective attention on causal learning. Three effects of the level of attention to predictive symptoms in positive and negative contingency learning tasks are reported. First, participants accurately detected a positive relationship between an incidental cue and a contingent outcome, although judgements were slightly lower than those for the attended cue. Second, participants were unable to detect negative relationships between incidental cues and outcomes, which suggests a major role of selective attention in this type of learning. Third, participants retrieved the frequency of each trial type more accurately in the attended conditions than in the incidental conditions. These findings show how attention guides and constrains human causal learning and reveal an inattentional blindness effect for negative contingency learning.     

Psychological distance and judgments of causal impact

When assessing causal impact, individuals have to consider two pieces of information: the magnitude of the cause that resulted in an effect, and the magnitude of the resulting effect. In the present research, participants judged the causal impact of cause–effect relationships in which the magnitude of causes and effects varied independently. Participants mainly relied on effect magnitude, rating causal impact to be much higher when strong (vs. weak) effects emerged. When participants took cause magnitude into account (which they did, but to a lesser extent), their judgments reflected a covariation rule (i.e., causal impact being maximal for strong causes generating strong effects) rather than a ratio rule (i.e., causal impact being maximal for weak causes generating strong effects). These distinct views on causal impact were moderated by psychological distance: Effect magnitude dominated judgments of proximal events, whereas cause magnitude had relatively more impact on causal judgments of distal events.     

Contrasting cue-density effects in causal and prediction judgments

Many theories of contingency learning assume (either explicitly or implicitly) that predicting whether an outcome will occur should be easier than making a causal judgment. Previous research suggests that outcome predictions would depart from normative standards less often than causal judgments, which is consistent with the idea that the latter are based on more numerous and complex processes. However, only indirect evidence exists for this view. The experiment presented here specifically addresses this issue by allowing for a fair comparison of causal judgments and outcome predictions, both collected at the same stage with identical rating scales. Cue density, a parameter known to affect judgments, is manipulated in a contingency learning paradigm. The results show that, if anything, the cue-density bias is stronger in outcome predictions than in causal judgments. These results contradict key assumptions of many influential theories of contingency learning.     

Perceptual learning enhances retrospective revaluation of conditioned flavor preferences in rats

Forward and backward blocking of taste preference learning was compared in rats. In the forward condition, thirsty rats were exposed to a flavor (A) in sucrose solution (+) or in water (-), after which they were exposed to A in compound with another flavor (B) in sucrose solution (i.e., AB+). In the backward condition, these phases were reversed. Consumption of B alone was assessed when rats were food deprived. In the forward condition, rats given A+ consumed less B than rats given A-, providing evidence of forward blocking, whereas in the backward condition, rats given A+ drank more of B than those given A-. Subsequent experiments found that alternating but not blocked preexposure to A and B, when given prior to training, produced blocking of B whether A+ was given before or after AB+, suggesting that prior failures to observe backward blocking reflect failures of discrimination.     

Inferring action structure and causal relationships in continuous sequences of human action

In the real world, causal variables do not come pre-identified or occur in isolation, but instead are embedded within a continuous temporal stream of events. A challenge faced by both human learners and machine learning algorithms is identifying subsequences that correspond to the appropriate variables for causal inference. A specific instance of this problem is action segmentation: dividing a sequence of observed behavior into meaningful actions, and determining which of those actions lead to effects in the world. Here we present a Bayesian analysis of how statistical and causal cues to segmentation should optimally be combined, as well as four experiments investigating human action segmentation and causal inference. We find that both people and our model are sensitive to statistical regularities and causal structure in continuous action, and are able to combine these sources of information in order to correctly infer both causal relationships and segmentation boundaries.     

Flexible use of recent information in causal and predictive judgments

Associative and statistical theories of causal and predictive learning make opposite predictions for situations in which the most recent information contradicts the information provided by older trials (e.g., acquisition followed by extinction). Associative theories predict that people will rely on the most recent information to best adapt their behavior to the changing environment. Statistical theories predict that people will integrate what they have learned in the two phases. The results of this study showed one or the other effect as a function of response mode (trial by trial vs. global), type of question (contiguity, causality, or predictiveness), and postacquisition instructions. That is, participants are able to give either an integrative judgment, or a judgment that relies on recent information as a function of test demands. The authors concluded that any model must allow for flexible use of information once it has been acquired.     

The role of causal models in multiple judgments under uncertainty

Two studies examined a novel prediction of the causal Bayes net approach to judgments under uncertainty, namely that causal knowledge affects the interpretation of statistical evidence obtained over multiple observations. Participants estimated the conditional probability of an uncertain event (breast cancer) given information about the base rate, hit rate (probability of a positive mammogram given cancer) and false positive rate (probability of a positive mammogram in the absence of cancer). Conditional probability estimates were made after observing one or two positive mammograms. Participants exhibited a causal stability effect: there was a smaller increase in estimates of the probability of cancer over multiple positive mammograms when a causal explanation of false positives was provided. This was the case when the judgments were made by different participants (Experiment 1) or by the same participants (Experiment 2). These results show that identical patterns of observed events can lead to different estimates of event probability depending on beliefs about the generative causes of the observations.     

The power of causal beliefs and conflicting evidence on causal judgments and decision making

In two experiments, we investigated the relative impact of causal beliefs and empirical evidence on both decision making and causal judgments, and whether this relative impact could be altered by previous experience. Participants had to decide which of two alternatives would attain a higher outcome on the basis of four cues. After completing the decision task, they were asked to estimate to what extent each cue was a reliable cause of the outcome. Participants were provided with instructions that causally related two of the cues to the outcome, whereas they received neutral information about the other two cues. Two of the four cues—a causal and a neutral cue—had high validity and were both generative. The remaining two cues had low validity, and were generative in Experiment 1, but almost not related to the outcome in Experiment 2. Selected groups of participants in both experiments received pre-training with either causal or neutral cues, or no pre-training was provided. Results revealed that the impact of causal beliefs and empirical evidence depends on both the experienced pre-training and cue validity. When all cues were generative and participants received pre-training with causal cues, they mostly relied on their causal beliefs, whereas they relied on empirical evidence when they received pre-training with neutral cues. In contrast, when some of the cues were almost not related to the outcome, participants’ responses were primarily influenced by validity and—to a lesser extent—by causal beliefs. In either case, however, the influence of causal beliefs was higher in causal judgments than in decision making. While current theoretical approaches in causal learning focus either on the effect of causal beliefs or empirical evidence, the present research shows that both factors are required to explain the flexibility involved in human inferences.     

Competition between antecedent and between subsequent stimuli in causal judgments

In the analysis of stimulus competition in causal judgment, 4 variables have been frequently confounded with respect to the conditions necessary for stimuli to compete: causal status of the competing stimuli (causes vs. effects), temporal order of the competing stimuli (antecedent vs. subsequent) relative to the noncompeting stimulus, directionality of training (predictive vs. diagnostic), and directionality of testing (predictive vs. diagnostic). In a factorial study using an overshadowing preparation, the authors isolated the role of each of these variables and their interactions. The results indicate that competition may be obtained in all conditions. Although some of the results are compatible with various theories of learning, the observation of stimulus competition in all conditions calls for a less restrictive reformulation of current learning theories that allows similar processing of antecedent and subsequent events, as well as of causes and effects.     

A comparison between elemental and compound training of cues in retrospective revaluation

Associative learning theories assume that cue interaction and, specifically, retrospective revaluation occur only when the target cue is previously trained in compound with the to-be-revalued cue. However, there are recent demonstrations of retrospective revaluation in the absence of compound training (e.g., Matute & Pine?o, 1998a, 1998b). Nevertheless, it seems reasonable to assume that cue interaction should be stronger when the cues are trained together than when they are trained apart. In two experiments with humans, we directly compared compound and elemental training of cues. The results showed that retrospective revaluation in the elemental condition can be as strong as and, sometimes, stronger than that in the compound condition. This suggests that within-compound associations are not necessary for retrospective revaluation to occur and that these effects can possibly be best understood in the framework of general interference theory.