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).     

People want to see information that will help them make valid inferences in human causal learning

According to higher order reasoning accounts of human causal learning (e.g., Lovibond, Been, Mitchell, Bouton, and Frohardt, 2003; Waldmann and Walker, 2005) ceiling effects in forward blocking (i.e., smaller blocking effects when the outcome occurs with a maximal intensity on A+ and AX+ trials) are due to the fact that people are uncertain about the causal status of a blocked cue X in a forward blocking design when the outcome is always fully present on A+ and AX+ trials. This should not be the case for a reduced overshadowing cue Y (B- trials followed by BY+ trials). We tested this hypothesis by asking participants which additional information they preferred to see after seeing all learning trials. Results showed (1) that all participants preferred to see the effect of the blocked cue X over seeing the effect of the reduced overshadowing cue Y (Experiment 1), and (2) that more participants preferred to see the blocked cue X on its own when the outcome on A+ and AX+ trials was fully present than when the outcome on those trials had a submaximalintensity (Experiment 2).     

A comparison of cue competition in a simple and a complex design

Recent evidence suggests that controlled reasoning processes play an important role in cue competition in human causal learning (see De Houwer, J., Beckers, T., & Vandorpe, S. (2005). Evidence for the role of higher-order reasoning processes in cue competition and other learning phenomena. Learning &Behavior, 33(2), 239-249, for a review). Until now, this evidence comes almost exclusively from studies with simple designs that involved only a limited number of cues. Little is known about the role of controlled reasoning processes when the design is more complex. It is important to examine this issue because the complexity of the design could determine the resources that are available for reasoning and thus the role that reasoning plays in cue competition. We directly compared cue competition in a simple and a complex design. The results showed that complexity of the design affected retrospective cue competition but not forward cue competition. More fine grained analyses with respect to retrospective cue competition showed that unovershadowing but not backward blocking differed significantly between complexity conditions.     

Secondary task difficulty modulates forward blocking in human contingency learning

The influence of a secondary task on forward blocking of human contingency ratings was examined. A smaller blocking effect was found when participants performed a highly demanding secondary task than when they performed a less demanding secondary task. The modulatory effect of secondary task difficulty was significant only when the secondary task was administered during both the learning and the test phase of the contingency judgement task. The results suggest that forward blocking in human contingency learning cannot be fully accounted for by associative processes. Instead, forward blocking seems to depend at least partially on deliberate deductive reasoning processes.     

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.     

Are preventive and generative causal reasoning symmetrical? Extinction and competition

We tested whether preventive and generative reasoning processes are symmetrical by keeping the training and testing of preventive (inhibitory) and generative (excitatory) causal cues as similar as possible. In Experiment 1, we extinguished excitors and inhibitors in a blocking design, in which each extinguished cause was presented in compound with a novel cause, with the same outcome occurring following the compound and following the novel cause alone. With this novel extinction procedure, the inhibitory cues seemed more likely to lose their properties than the excitatory cues. In Experiment 2, we investigated blocking of excitatory and inhibitory causes and found similar blocking effects. Taken together, these results suggest that acquisition of excitation and inhibition is similar, but that inhibition is more liable to extinguish with our extinction procedure. In addition, we used a variable outcome, and this enabled us to test the predictions of an inferential reasoning account about what happens when the outcome level is at its minimum or maximum (De Houwer, Beckers, & Glautier, 2002). We discuss the predictions of this inferential account, Rescorla and Wagner's (1972) model, and a connectionist model-the auto-associator.     

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.     

Contingency bias in probability judgement may arise from ambiguity regarding additional causes

We tested whether preventive and generative reasoning processes are symmetrical by keeping the training and testing of preventive (inhibitory) and generative (excitatory) causal cues as similar as possible. In Experiment 1, we extinguished excitors and inhibitors in a blocking design, in which each extinguished cause was presented in compound with a novel cause, with the same outcome occurring following the compound and following the novel cause alone. With this novel extinction procedure, the inhibitory cues seemed more likely to lose their properties than the excitatory cues. In Experiment 2, we investigated blocking of excitatory and inhibitory causes and found similar blocking effects. Taken together, these results suggest that acquisition of excitation and inhibition is similar, but that inhibition is more liable to extinguish with our extinction procedure. In addition, we used a variable outcome, and this enabled us to test the predictions of an inferential reasoning account about what happens when the outcome level is at its minimum or maximum (De Houwer, Beckers, & Glautier, 2002). We discuss the predictions of this inferential account, Rescorla and Wagner's (1972) model, and a connectionist model—the auto-associator.     

Encouraging children to think counterfactually enhances blocking in a causal learning task

According to a higher order reasoning account, inferential reasoning processes underpin the widely observed cue competition effect of blocking in causal learning. The inference required for blocking has been described as modus tollens (if p then q, not q therefore not p). Young children are known to have difficulties with this type of inference, but research with adults suggests that this inference is easier if participants think counterfactually. In this study, 100 children (51 five-year-olds and 49 six- to seven-year-olds) were assigned to two types of pretraining groups. The counterfactual group observed demonstrations of cues paired with outcomes and answered questions about what the outcome would have been if the causal status of cues had been different, whereas the factual group answered factual questions about the same demonstrations. Children then completed a causal learning task. Counterfactual pretraining enhanced levels of blocking as well as modus tollens reasoning but only for the younger children. These findings provide new evidence for an important role for inferential reasoning in causal learning.     

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.     

Reasoning rats: forward blocking in Pavlovian animal conditioning is sensitive to constraints of causal inference

Forward blocking is one of the best-documented phenomena in Pavlovian animal conditioning. According to contemporary associative learning theories, forward blocking arises directly from the hardwired basic learning rules that govern the acquisition or expression of associations. Contrary to this view, here the authors demonstrate that blocking in rats is flexible and sensitive to constraints of causal inference, such as violation of additivity and ceiling considerations. This suggests that complex cognitive processes akin to causal inferential reasoning are involved in a well-established Pavlovian animal conditioning phenomenon commonly attributed to the operation of basic associative processes.     

Unintentional processing of motivational valence

Recent motivational affective priming studies (Moors & De Houwer, 2001; Moors, De Houwer, & Eelen, 2004) showed that primes that indicate success on a goal-inducing task facilitate positive target responses whereas primes that indicate failure on that task facilitate negative target responses. In the current studies, we examined whether these priming effects depend on consciously intentional processing of the motivational valence of the primes. In Experiment 1, the outcome of success or failure was presented not only immediately before the target (i.e., the prime) but also a second time after the target response. This should encourage participants to ignore the prime. In Experiment 2, participants were asked to respond to the targets within 600 ms after target onset. As a result, participants had little opportunity to process the motivational prime valence in a consciously intentional way. Nevertheless, strong affective priming effects were found in both studies. These results provide additional support for the claim that motivational valence can be processed without the conscious intention to do so.     

Outcome additivity, elemental processing and blocking in human causality judgements

Informing participants in a causal judgement task that outcomes are additive can increase blocking effects (Experiment 1). Outcome additivity information emphasizes the fact that the outcome following a compound is the sum of the effects of its elements. We suggest that the effect of providing outcome additivity information is to encourage elemental processing and thereby enhance blocking. Experiment 2 showed that blocking could be enhanced by factors encouraging elemental processing, and Experiment 3 demonstrated that blocking was reduced by manipulating the visual presentation of cues to encourage configural processing. While these experiments do not rule out the role of inference in causal judgement tasks, the results are most parsimoniously explained by associative accounts that allow flexibility in the encoding of compound cues.     

Valuing intervention and observation

Understanding causal relations is fundamental to effective action but causal data can be confounded. We examined the value that participants placed on data derived from a hypothetical intervention or observation. Our materials involved a possible cause ("bottled water"), a possible confound ("food"), and a context ("a restaurant"). We supposed that participants seek to draw as specific a causal inference as possible from presented data and value information sources more highly that allow them to do so. On this basis, we predicted that in circumstances where an intervention removed the confounding causal factor but observation did not, participants would prefer data derived from an intervention when the possible cause was present (the bottled water was drunk) but show the reverse preference when the possible cause was absent (the bottled water was not drunk). Experiment 1 confirmed this prediction. Using a between-subjects design, Experiment 2 tested for a difference in confidence in causal judgements given identical data, including data on the confound, as a function of method of data collection (intervention or observation). There was no significant difference in confidence ratings between the two methods but confidence ratings were sensitive to the probability of an effect (illness) given the cause. Using a within-subjects design, Experiment 3 revealed systematic individual differences in preference for the two methods. Participants were divided between those who considered intervention more confounded and those who considered observation more confounded. Our experiments point to the subtleties of participants' evaluation of data from studies of human beings.     

Valuing intervention and observation

Understanding causal relations is fundamental to effective action but causal data can be confounded. We examined the value that participants placed on data derived from a hypothetical intervention or observation. Our materials involved a possible cause (“bottled water”), a possible confound (“food”), and a context (“a restaurant”). We supposed that participants seek to draw as specific a causal inference as possible from presented data and value information sources more highly that allow them to do so. On this basis, we predicted that in circumstances where an intervention removed the confounding causal factor but observation did not, participants would prefer data derived from an intervention when the possible cause was present (the bottled water was drunk) but show the reverse preference when the possible cause was absent (the bottled water was not drunk). Experiment 1 confirmed this prediction. Using a between-subjects design, Experiment 2 tested for a difference in confidence in causal judgements given identical data, including data on the confound, as a function of method of data collection (intervention or observation). There was no significant difference in confidence ratings between the two methods but confidence ratings were sensitive to the probability of an effect (illness) given the cause. Using a within-subjects design, Experiment 3 revealed systematic individual differences in preference for the two methods. Participants were divided between those who considered intervention more confounded and those who considered observation more confounded. Our experiments point to the subtleties of participants' evaluation of data from studies of human beings.     

Causal Induction in the Presence of a Perfect Negative Cue: Contrasting Predictions from Associative and Statistical Models

Two experiments on human causal induction with multiple candidate causes are reported. Experiment 1 investigated the influence of a perfect preventive cause on the ratings of a less contingent cause. Whereas the Rescorla-Wagner model (RWM) and Cheng's probabilistic contrast model predict that the less contingent cause should be completely discounted, the Pearce model predicts, in most cases, an enhancement of that cause's perceived importance. Results corresponded more closely tothe predictions of the Pearce model.The predictions of both the RWM and the Pearce model rely on a constant context cue acquiring associative strength, yet no such cue was explicitly identified in the task scenario employed in Experiment 1. Experiment 2 replicated a number of key conditions of Experiment 1 with a task scenario that afforded ratings of the causal importance of the context in which the effectiveness of the discrete candidate causes was evaluated. In addition, the number of trials was increased to test the possibility that the ratings in Experiment 1 were the product of incomplete learning. The results of the first experiment were replicated and the ratings of the effectiveness of the context cue were anticipated by both the RWM and the Pearce model. Overall, the Pearce model offers a more comprehensive account of the causal inferences recorded in this study.     

类别学习中两种学习模式的比较研究：分类学习与推理学习

采用学习迁移任务范式,使用基于单一特征的类别判断技术,比较了非线性分离结构下,分类学习和推理学习的学习效率、学习过程与策略和学习结果。结果表明：在学习效率上,分类学习比推理学习更好地习得了含有较多样例的类别知识,分类学习的速度上显著快于推理学习。在学习的过程与策略上,推理学习比分类学习更为关注类别内不同特征的相关,但在分类策略的运用上不如分类学习灵活。在学习的结果上,推理学习倾向于原型记忆,分类学习倾向于进行样例记忆,分类学习比推理学习更好地掌握了类别原型     

Induction of implicit evaluation biases by approach–avoidance training: A commentary on Vandenbosch and De Houwer (this issue)

Vandenbosch and De Houwer (this issue) reported a series of failures to induce an implicit evaluation bias by means of an approach–avoidance training paradigm. In this commentary, we point out issues raised by Vandenbosch and De Houwer that we interpret differently or that we would like to emphasise more thoroughly. In addition, we report recent results from a replication study from our lab in which we found the effects in question. Finally, we provide an overview of potential future studies needed to replicate and validate the approach–avoidance training effects.     

Backward and forward blocking in human electrodermal conditioning: Blocking requires an assumption of outcome additivity

Blocking was observed in two human Pavlovian conditioning studies in which colour cues signalled shock. Both forward (Experiment 1) and backward (Experiment 2) blocking was demonstrated, but only when prior verbal and written instructions suggested that if two signals of shock (A+ and B+) were presented together, a double shock would result (AB++). In this case, participants could assume that the outcome magnitude was additive. Participants given non-additivity instructions (A+ and B+ combined would result in the same outcome, a single shock) failed to show blocking. Modifications required for associative models of learning, and normative statistical accounts of causal induction, to account for the impact of additivity instructions on the blocking effect, are discussed. It is argued that the blocking shown in the present experiments resulted from the operation, not of an error-correction learning rule, nor of a simple contingency detection mechanism, but of a more complex inferential process based on propositional knowledge. Consistent with the present data, blocking is a logical outcome of an A+/AB+ design only if participants can assume that outcomes will be additive.     

A comparison of forward blocking and reduced overshadowing in human causal learning

In this study, we directly compared forward blocking with reduced overshadowing in a human causal learning study using an A+, B- (first learning stage), AX+, BY+, KL+ (second learning stage) design. The results showed that reduced overshadowing was significantly stronger than forward blocking. These results are problematic for at least some associative learning models but were predicted on the basis of higher order reasoning accounts of cue competition in human causal learning.