Blocking of human causal learning involves learned changes in stimulus processing

Several theories of associative learning propose that blocking reflects changes in the processing devoted to learning about cues. The results of the only direct test of this suggestion in human learning (Kruschke & Blair, 2000) could equally well be explained in terms of, among others, interference in learning or memory. The present study tested this suggestion in a situation in which processing-change and interference accounts predict opposing results. Results support the idea that blocking in human learning can reflect a change in processing of the cues involved.     

Blocking and unblocking in human causal learning

Three experiments sought to develop the suggestion that, under some circumstances, common associative learning mechanisms might underlie animal conditioning and human causal learning, by demonstrating, in humans, an effect analogous to the unblocking by reinforcer omission observed in animal conditioning. Experiment 1 found no such effect. Experiment 2, designed to prevent inhibitory influences that might have masked excitatory unblocking in Experiment 1, demonstrated unblocking, indicating common human-animal associative learning mechanisms in which the associability of a stimulus varies as a function of its predictive history. Experiment 3, using a similar design but with a procedure promoting application of rational inference processes, failed to detect the same unblocking effect, indicating that associative and cognitive mechanisms may influence human causal learning.     

Perceptual learning in flavor aversion: evidence for learned changes in stimulus effectiveness

Rats were exposed to the compound flavors AX and BX, presented in alternation, and to CX on a separate block of trials. Generalization to BX after aversion conditioning with AX was less than to CX. An equivalent effect was found when the nature of the common element was changed after preexposure but not when the common element was omitted during preexposure, during conditioning and test, or both. Rats conditioned with X alone again showed less aversion to BX than to CX; similarly, rats conditioned with a novel flavor (Y) showed less aversion to BY than to CY. These effects support the proposal that intermixed preexposure to AX and BX enhances the perceptual effectiveness of their unique features, A and B.     

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.     

The outcome specificity of learned predictiveness effects: parallels between human causal learning and animal conditioning

Two experiments examined the outcome specificity of a learned predictiveness effect in human causal learning. Experiment 1 indicated that prior experience of a cue-outcome relation modulates learning about that cue with respect to a different outcome from the same affective class but not with respect to an outcome from a different affective class. Experiment 2 ruled out an interpretation of this effect in terms of context specificity. These results indicate that learned predictiveness effects in human causal learning index an associability that is specific to a particular class of outcomes. Moreover, they mirror demonstrations of the reinforcer specificity of analogous effects in animal conditioning, supporting the suggestion that, under some circumstances, human causal learning and animal conditioning reflect the operation of common associative mechanisms.     

Second-order conditioning of human causal learning

This article provides the first demonstration of a reliable second-order conditioning (SOC) effect in human causal learning tasks. It demonstrates the human ability to infer relationships between a cause and an effect that were never paired together during training. Experiments 1a and 1b showed a clear and reliable SOC effect, while Experiments 2a and 2b demonstrated that first-order extinction did not affect SOC. These results were similar to those found in animal and human conditioning and suggested that a similar associative mechanism could explain these effects. However, they can also be used to look into the underlying causal mental model people build and store while they are learning this task. From a cognitive view, overall results suggest that an independent rather than a chain causal mental model is stored after second-order learning in human causal tasks.     

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.     

Cue competition effects in human causal learning

Five experiments involving human causal learning were conducted to compare the cue competition effects known as blocking and unovershadowing, in proactive and retroactive instantiations. Experiment 1 demonstrated reliable proactive blocking and unovershadowing but only retroactive unovershadowing. Experiment 2 replicated the same pattern and showed that the retroactive unovershadowing that was observed was interfered with by a secondary memory task that had no demonstrable effect on either proactive unovershadowing or blocking. Experiments 3a, 3b, and 3c demonstrated that retroactive unovershadowing was accompanied by an inflated memory effect not accompanying proactive unovershadowing. The differential pattern of proactive versus retroactive cue competition effects is discussed in relationship to amenable associative and inferential processing possibilities.     

Positive and negative patterning in human causal learning

Investigations of patterning discriminations by nonhuman animals have generally found that positive patterning is easier to learn than negative patterning. Studies of patterning discriminations in human causal learning tasks have failed to document any differences between positive and negative patterning. In the present study, human participants predicted an outcome on trials involving either a compound cue or its elements. Positive and negative patterning problems were successfully solved in a within-subjects design; negative patterning problems proved to be more difficult when an additional, 50% contingent cue was included (Experiment 2), but not when it was excluded (Experiment 1). Possible reasons for these results are discussed. The discussion concludes with an analysis of exemplar models (e.g., Pearce, 1994) of human causal learning and considers the conditions under which these models do and do not anticipate our results.     

Generality of the summation effect in human causal learning

Considerable research has examined the contrasting predictions of the elemental and configural association theories proposed by Rescorla and Wagner (1972) and Pearce (1987), respectively. One simple method to distinguish between these approaches is the summation test, in which the associative strength attributed to a novel compound of two separately trained cues is examined. Under common assumptions, the configural view predicts that the strength of the compound will approximate to the average strength of its components, whereas the elemental approach predicts that the strength of the compound will be greater than the strength of either component. Different studies have produced mixed outcomes. In studies of human causal learning, Collins and Shanks (2006) suggested that the observation of summation is encouraged by training, in which different stimuli are associated with different submaximal outcomes, and by testing, in which the alternative outcomes can be scaled. The reported experiments further pursued this reasoning. In Experiment 1, summation was more substantial when the participants were trained with outcomes identified as submaximal than when trained with simple categorical (presence/absence) outcomes. Experiments 2 and 3 demonstrated that summation can also be obtained with categorical outcomes during training, if the participants are encouraged by instruction or the character of training to rate the separately trained components with submaximal ratings. The results are interpreted in terms of apparent performance constraints in evaluations of the contrasting theoretical predictions concerning summation.     

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.     

Second-order backward blocking and unovershadowing in human causal learning

De Houwer and Beckers (in press, Experiment 1) recently demonstrated that ratings about the relation between a target cue T2 and an outcome are higher when training involves CT1+ and T1T2+ followed by C+ trials than when training involves CT1+ and T1T2+ followed by C- trials. We replicated this study but now explicitly asked participants to rate the causal status of the cues both before and after the C+ or C- trials. Results showed that causal ratings for T2 were significantly higher after C+ trials than before C+ trials and that T2 received significantly lower ratings after C- trials than before C- trials. The results thus provide the first evidence for higher-order unovershadowing and higher-order backward blocking. In addition, the ratings for T1 revealed that first-order backward blocking (i.e., decrease in ratings for T1 as the result of C+ trials) was stronger than first-order unovershadowing (i.e., increase in ratings for T1 as the result of C- trials).     

Outcome maximality and additivity training also influence cue competition in causal learning when learning involves many cues and events

Recent evidence shows that outcome maximality (e.g., De Houwer, Beckers, & Glautier, 2002) and additivity training (e.g., Lovibond, Been, Mitchell, Bouton, & Frohard, 2003) have an influence on cue competition in human causal learning. This evidence supports the idea that cue competition is based on controlled reasoning processes rather than on automatic associative processes. Until now, however, all the evidence for controlled reasoning processes comes from studies with rather simple designs that involved only few cues and events. We conducted two experiments with a complex design involving 24 different cues. The results showed that outcome maximality and additivity training had an influence on cue competition but that this influence was more pronounced for forward cue competition than for retrospective cue competition.     

Memory encoding of stimulus features in human perceptual learning

Two experiments analysed memory encoding in human perceptual learning. Both experiments started with preexposure without feedback to four checkerboards composed by a unique feature each and sharing a common feature (AX, BX, CX, and DX). Elements of one pair were presented intermixed and elements of the other pair were presented in separate blocks. Immediately after preexposure participants completed a memory recognition task in which the characteristics of the distractors were manipulated. Experiment 1 showed that only intermixed presentation results in good encoding of the unique features of the stimuli. Experiment 2 demonstrated that intermixed preexposure results in different encoding of unique versus common features of the stimuli: Participants are able to remember A and B better than they remember X, whereas for the blocked condition memory for C, D, and X does not differ. Overall, the results presented here support the proposal that intermixing stimuli results in differential memory traces for unique versus common features and that this contributes to the intermixed/blocked effect.     

The nonindependence of stimulus properties in human category learning

Typically, models of category learning are verified through behavioral experiments with stimuli consisting of putatively independent dimensions such as shape, size, and color. The assumption of independence is critical in both the design of behavioral experiments and the development of models and theories of learning. Using the standard classification learning paradigm and a common stimulus set, the present work demonstrates that the assumption of independence is unwarranted. Systematic relations span stimulus dimensions and govern learning performance. For example, shape is not independent of size and color, because humans quantify size and color over shape when shape is relevant to the categorization. This quantification is reflected in natural language use (e.g., "blue triangle" as opposed to "triangle and blue"). In this example, color and size are predicates and shape is the argument. Across four experiments, the difficulty of mastering a classification rule can be predicted by the number of predicates that must be unbound in order to free rule-relevant stimulus dimensions.     

Blocking in category learning

Many theories of category learning assume that learning is driven by a need to minimize classification error. When there is no classification error, therefore, learning of individual features should be negligible. The authors tested this hypothesis by conducting three category-learning experiments adapted from an associative learning blocking paradigm. Contrary to an error-driven account of learning, participants learned a wide range of information when they learned about categories, and blocking effects were difficult to obtain. Conversely, when participants learned to predict an outcome in a task with the same formal structure and materials, blocking effects were robust and followed the predictions of error-driven learning. The authors discuss their findings in relation to models of category learning and the usefulness of category knowledge in the environment.     

Changes in cue associability across training in human causal learning

A series of experiments studied the amount learned about two food cues (A and B) whose presentation in a meal was followed by an allergy (+) in a fictitious patient. Participants were trained with A+ and C+ in Phase 1 and then with AB+ or AB++ in Phase 2. Subsequent testing revealed that BC was more allergenic than AD, showing that more had been learned about B than A in Phase 2. Participants were also trained with A+, then with AB+, and finally with AB++. The results of interpolating AB+ between A+ and AB++ training were consistent with the hypothesis that pretraining with Cue A selectively suppressed attention to its associate across the AB+ trials and, thereby, reduced the amount subsequently learned about B on AB++ trials.     

Learned associability and associative change in human causal learning.

The Mackintosh (1975) model of associative learning specifies that processing of both the cues presented on a trial and the outcome of that trial will interact to determine the amount of associative change undergone by a given cue. Experiments looking at the distribution of associative change among the elements of a reinforced compound in animal conditioning studies indicate that processing of the outcome of a trial does indeed influence associative change. The work reported here investigates the distribution of associative change among the elements of a reinforced compound in a human causal judgement paradigm, and it indicates that processing of the cues presented on a trial also plays a role in determining associative change (in terms of changes in the associability of cues as a result of experience). Taken in combination, these results provide good support for Mackintosh (1975) and the characterizations of both cue and outcome processing that it offers.     

The experimental task influences cue competition in human causal learning

Participants were shown A+ and C- trials followed by AB+ and CD+ trials. These trials were embedded in a causal learning task in which participants had to learn either the relationship between different foods and allergic reactions or the relationship between different stocks and an increase in the stock market index. The authors orthogonally varied the manner in which the different cues were presented to participants during training. Cue competition was related to the causal learning scenario but not to the manner in which the different cues were presented. These results question claims of a human bias toward configural processing that were based on difficulties in finding cue competition in some previous causal learning experiments.