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Blocking in human causal learning is affected by outcome assumptions manipulated through causal structure

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Blocking in human causal learning is affected by outcome assumptions manipulated through causal structure

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Abstract

Additivity-related assumptions have been proven to modulate blocking in human causal learning. Typically, these assumptions are manipulated by means of pretraining phases (including exposure to different outcome magnitudes), or through explicit instructions. In two experiments, we used a different approach that involved neither pretraining nor instructional manipulations. Instead, we manipulated the causal structure in which the cues were embedded, thereby appealing directly to the participants' prior knowledge about causal relations and how causes would add up to yield stronger outcomes. Specifically, in our "different-system" condition, the participants should assume that the outcomes would add up, whereas in our "same-system" condition, a ceiling effect would prevent such an assumption. Consistent with our predictions, Experiment 1 showed that, when two cues from separate causal systems were combined, the participants did expect a stronger outcome on compound trials, and blocking was found, whereas when the cues belonged to the same causal system, the participants did not expect a stronger outcome on compound trials, and blocking was not observed. The results were partially replicated in Experiment 2, in which this pattern was found when the cues were tested for the second time. This evidence supports the claim that prior knowledge about the nature of causal relations can affect human causal learning. In addition, the fact that we did not manipulate causal assumptions through pretraining renders the results hard to account for with associative theories of learning.

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... On the other hand, the approach taken by these researchers falls short of fully accommodating the effect of additivity assumptions, because the sensitivity to additivity assumptions is observed even without explicit pretraining with actual exposure to stimuli. It also occurs just with instructional manipulations (Mitchell & Lovibond, 2002), or with implicit causal structure manipulations (Blanco, Baeyens, & Beckers, 2014), which might be viewed as challenging associative attempts to capture the effect. ...
... Nevertheless, Waldmann and Holyoak's results have been replicated using different paradigms and designs (Booth & Buehner, 2007;Waldmann, 2000Waldmann, , 2001, and further experiments have documented sensitivity of cue competition effects to a number of different causal structure manipulations. For example, Blanco et al. (2014) found that blocking was sensitive to outcome maximality manipulations conducted through changes in the causal structures, using neither explicit instruction nor pretraining (only previous knowledge about causal structures). When two independent causes from separate causal structures were combined (i.e., two water taps were opened on different, disconnected pipe networks with independent water reservoirs), they were assumed to linearly increment the outcome magnitude, whereas when two independent causes from the same causal structure were combined (i.e., two water taps were opened within the same pipe network, connected to a single water reservoir), an implicit ceiling effect was assumed. ...
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Many experiments have shown that humans and other animals can detect contingency between events accurately. This learning is used to make predictions and to infer causal relationships, both of which are critical for survival. Under certain conditions, however, people tend to overestimate a null contingency. We argue that a successful theory of contingency learning should explain both results. The main purpose of the present review is to assess whether cue-outcome associations might provide the common underlying mechanism that would allow us to explain both accurate and biased contingency learning. In addition, we discuss whether associations can also account for causal learning. After providing a brief description on both accurate and biased contingency judgments, we elaborate on the main predictions of associative models and describe some supporting evidence. Then, we discuss a number of findings in the literature that, although conducted with a different purpose and in different areas of research, can also be regarded as supportive of the associative framework. Finally, we discuss some problems with the associative view and discuss some alternative proposals as well as some of the areas of current debate. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
... blocking; Kruschke, Kappenman & Hetrick, 2005) seem to be affected by the explicit causal relationship implied by the task scenario (e.g. Blanco, Baeyens & Beckers, 2014;Luque, Cobos, and López, 2008;Waldmann, 2000;Waldmann & Holyoak, 1998). The robustness of the effect across various task scenarios suggests this may not be the case for the inverse base-rate effect, although this has yet to be directly tested. ...
Article
People often fail to use base-rate information appropriately in decision-making. This is evident in the inverse base-rate effect, a phenomenon in which people tend to predict a rare outcome for a new and ambiguous combination of cues. While the effect was first reported in 1988, it has recently seen a renewed interest from researchers concerned with learning, attention and decision-making. However, some researchers have raised concerns that the effect arises in specific circumstances and is unlikely to provide insight into general learning and decision-making processes. In this review, we critically evaluate the evidence for and against the main explanations that have been proposed to explain the effect, and identify where this evidence is currently weak. We argue that concerns about the effect are not well supported by the data. Instead, the evidence supports the conclusion that the effect is a result of general mechanisms that provides a useful opportunity to understand the processes involved in learning and decision making. We discuss gaps in our knowledge and some promising avenues for future research, including the relevance of the effect to models of attentional change in learning, an area where the phenomenon promises to contribute new insights.
... The demonstration of asymmetry in cue competition in predictive and diagnostic models has been taken as key evidence for causal model theories. Causal model effects have been replicated in several cue competition paradigms (e.g., Blanco, Baeyens & Beckers, 2014;Luque, Cobos, and López, 2008;Waldmann, 2000;2001). However, other studies have failed to find this asymmetry (e.g. ...
Conference Paper
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Cue competition effects in human contingency learning appear to be sensitive to the causal nature of cue-outcome relationships. While blocking effects are reliably demonstrated in scenarios where cues are presented as causes of outcomes, several studies have failed to find blocking in scenarios where cues are presented as effects of outcomes, a finding that is typically taken as evidence for the involvement of controlled reasoning processes in cue competition. These studies typically measure blocking with continuous causal ratings about individual cues. Previous studies have found that sensitivity to causal model may depend on how the test question is phrased. In contrast, the current study tested the sensitivity of blocking to causal scenarios across different formats of the same test question. Participants completed a causal learning task with instructions suggesting either a predictive (i.e. cue causes outcome) or diagnostic (cue is caused by outcome) cue-outcome relationship. Participants were then asked about the likelihood of outcomes occurring by either giving a continuous rating of each outcome or a discrete choice about the most likely outcome. When measured by continuous ratings of individual cues, blocking was evident in predictive, but not diagnostic scenarios. However, when measured by discrete choice or using a compound negation test, blocking was robust and insensitive to causal scenario. The results suggest that contributions of predictive memory and causal reasoning to cue competition effects may depend substantially on the type of measure used.
... As Waldman and Holyoak's study did not include the appropriate control cues for blocking, C and D, drawing a conclusion on the blocking effect is not possible. However, similar subsequent experiments have replicated the effect of causal model, implemented through instructions and prior knowledge, on blocking and other effects (e.g., Waldmann, 2000Waldmann, , 2001Luque et al., 2008;Blanco et al., 2014; but see Shanks and Lopez, 1996;Thorwart and Lachnit, 2010). ...
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A paperback edition of the translation by Anrep, first published in 1927 by the Oxford University Press, containing a series of 23 lectures on the research of Pavlov's laboratory in the 1st quarter of the 20th century. From Psyc Abstracts 36:05:5CG30P. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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We have recently demonstrated that pre-training of additivity (the outcome of two causal cues is larger than one causal cue) greatly enhances blocking. This manipulation could work by removing a ceiling effect on the outcome, as proposed by Cheng (1997). Alternatively, it could remove the logical ambiguity associated with blocking under non-additive conditions, thus permitting blocking as a deductive inference. We used a counterintuitive combination rule—subtractivity rather than additivity—to discriminate between these two accounts. Prior to a backward blocking causal judgment procedure (AB+, A+), we trained participants that a compound of one causal and one non-causal cue leads to the outcome, but that a compound of two causal cues leads to no outcome. This design allows the logical deduction that cue B is non-causal (blocking), but retains the ceiling effect on outcome magnitude. Blocking was strong following both subtractivity and additivity training, supporting the deductive reasoning account.
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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.
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In everyday life, people typically observe fragments of causal networks. From this knowledge, people infer how novel combinations of causes they may never have observed together might behave. I report on 4 experiments that address the question of how people intuitively integrate multiple causes to predict a continuously varying effect. Most theories of causal induction in psychology and statistics assume a bias toward linearity and additivity. In contrast, these experiments show that people are sensitive to cues biasing various integration rules. Causes that refer to intensive quantities (e.g., taste) or to preferences (e.g., liking) bias people toward averaging the causal influences, whereas extensive quantities (e.g., strength of a drug) lead to a tendency to add. However, the knowledge underlying these processes is fallible and unstable. Therefore, people are easily influenced by additional task-related context factors. These additional factors include the way data are presented, the difficulty of the inference task, and transfer from previous tasks. The results of the experiments provide evidence for causal model and related theories, which postulate that domain-general representations of causal knowledge are influenced by abstract domain knowledge, data-driven task factors, and processing difficulty.
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Participants saw a series of situations in which a cue (a light appearing at a certain position) could be followed by an outcome (a drawing of a tank that exploded) and were afterwards asked to rate the likelihood of the outcome in the presence of the cue. In Experiments 1 and 2, the compound cues AT and KL were always followed by the outcome (AT+, KL+). During an elemental phase that either preceded or followed the compound phase, Cue A was also paired with the outcome (A+). Cue T elicited a lower rating than Cues K and L when cues were described as being weapons but not when the cues were said to be indicators. The magnitude of this blocking effect was also influenced by whether the outcome occurred to a maximal or submaximal extent. Experiment 3 replicated the effect of cue instructions on blocking (A+, AT+) but showed that cue instructions had no impact on reduced overshadowing (B-, BT+). The results shed new light on previous findings and support probabilistic contrast models of human contingency judgements.
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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.
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An eyetracking version of the classic Shepard, Hovland, and Jenkins (1961) experiment was conducted. Forty years of research has assumed that category learning often involves learning to selectively attend to only those stimulus dimensions useful for classification. We confirmed that participants learned to allocate their attention optimally. We also found that learners tend to fixate all stimulus dimensions early in learning. This result obtained despite evidence that participants were also testing one-dimensional rules during this period. Finally, the restriction of eye movements to only relevant dimensions tended to occur only after errors were largely (or completely) eliminated. We interpret these findings as consistent with multiple-systems theories of learning which maximize information input in order to maximize the number of learning modules involved, and which focus solely on relevant information only after one module has solved the learning problem.
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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.
From covariation to causation: A causal power theory Outcome and cue properties modulate blocking
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Associative accounts of causality judgement The psychology of learning and motivation Cue competition in causality judgments: The role of nonpresentation of compound stimulus elements
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