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Peter Anthony White - One of the best experts on this subject based on the ideXlab platform.
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Causal Judgments about empirical information in an interrupted time series design.
Quarterly journal of experimental psychology (2006), 2017Co-Authors: Peter Anthony WhiteAbstract:Empirical information available for Causal Judgment in everyday life tends to take the form of quasi-experimental designs, lacking control groups, more than the form of contingency information that is usually presented in experiments. Stimuli were presented in which values of an outcome variable for a single individual were recorded over six time periods, and an intervention was introduced between the fifth and sixth time periods. Participants judged whether and how much the intervention affected the outcome. With numerical stimulus information, Judgments were higher for a pre-intervention profile in which all values were the same than for pre-intervention profiles with any other kind of trend. With graphical stimulus information, Judgments were more sensitive to trends, tending to be higher when an increase after the intervention was preceded by a decreasing series than when it was preceded by an increasing series ending on the same value at the fifth time period. It is suggested that a feature-analytic model, in which the salience of different features of information varies between presentation formats, may provide the best prospect of explaining the results.
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Singular Clues to Causality and Their Use in Human Causal Judgment
Cognitive science, 2013Co-Authors: Peter Anthony WhiteAbstract:It is argued that Causal understanding originates in experiences of acting on objects. Such experiences have consistent features that can be used as clues to Causal identification and Judgment. These are singular clues, meaning that they can be detected in single instances. A catalog of 14 singular clues is proposed. The clues function as heuristics for generating Causal Judgments under uncertainty and are a pervasive source of bias in Causal Judgment. More sophisticated clues such as mechanism clues and repeated interventions are derived from the 14. Research on the use of empirical information and conditional probabilities to identify causes has used scenarios in which several of the clues are present, and the use of empirical association information for Causal Judgment depends on the presence of singular clues. It is the singular clues and their origin that are basic to Causal understanding, not multiple instance clues such as empirical association, contingency, and conditional probabilities.
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Accounting for occurrences: an explanation for some novel tendencies in Causal Judgment from contingency information.
Memory & cognition, 2009Co-Authors: Peter Anthony WhiteAbstract:Contingency information is information about empirical associations between possible causes and outcomes. In the present research, it is shown that, under some circumstances, there is a tendency for negative contingencies to lead to positive Causal Judgments and for positive contingencies to lead to negative Causal Judgments. If there is a high proportion of instances in which a candidate cause (CC) being judged is present, these tendencies are predicted by weighted averaging models of Causal Judgment. If the proportion of such instances is low, the predictions of weighted averaging models break down. It is argued that one of the main aims of Causal Judgment is to account for occurrences of the outcome. Thus, a CC is not given a high Causal Judgment if there are few or no occurrences of it, regardless of the objective contingency. This argument predicts that, if there is a low proportion of instances in which a CC is present, Causal Judgments are determined mainly by the number of Cell A instances (i.e., CC present, outcome occurs), and that this explains why weighted averaging models fail to predict Judgmental tendencies under these circumstances. Experimental results support this argument.
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Accounting for occurrences: a new view of the use of contingency information in Causal Judgment.
Journal of experimental psychology. Learning memory and cognition, 2008Co-Authors: Peter Anthony WhiteAbstract: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.
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Causal Judgment from contingency information: a systematic test of the pCI rule.
Memory & cognition, 2004Co-Authors: Peter Anthony WhiteAbstract:Contingency information is information about the occurrence or nonoccurrence of an effect when a possible cause is present or absent. Under the evidential evaluation model, instances of contingency information are transformed into evidence and Causal Judgment is based on the proportion of relevant instances evaluated as confirmatory for the candidate cause. In this article, two experiments are reported that were designed to test systematic manipulations of the proportion of confirming instances in relation to other variables: the proportion of instances on which the candidate cause is present, the proportion of instances in which the effect occurs when the cause is present, and the objective contingency. Results showed that both unweighted and weighted versions of the proportion-of-confirmatoryinstances rule successfully predicted the main features of the results, with the weighted version proving more successful. Other models, including the power PC theory, failed to predict the results.
Ralph R. Miller - One of the best experts on this subject based on the ideXlab platform.
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Competition between antecedent and between subsequent stimuli in Causal Judgments.
Journal of experimental psychology. Learning memory and cognition, 2005Co-Authors: Francisco Arcediano, Helena Matute, Martha Escobar, Ralph R. MillerAbstract: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.
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Conditioned inhibition produced by extinction-mediated recovery from the relative stimulus validity effect: a test of acquisition and performance models of empirical retrospective revaluation.
Journal of experimental psychology. Animal behavior processes, 2001Co-Authors: Aaron P. Blaisdell, Ralph R. MillerAbstract:Empirical retrospective revaluation is a phenomenon of Pavlovian conditioning and human Causal Judgment in which posttraining changes in the conditioned response (Pavlovian task) or Causal rating (Causal Judgment task) of a cue occurs in the absence of further training with that cue. Two experiments tested the contrasting predictions made by 2 families of models concerning retrospective revaluation effects. In a conditioned lick-suppression task, rats were given relative stimulus validity training, consisting of reinforcing a compound of conditioned stimuli (CSs) A and X and nonreinforcement of a compound of CSs B and X, which resulted in low conditioned responding to CS X. Massive posttraining extinction of CS A not only enhanced excitatory responding to CS X, but caused CS B to pass both summation (Experiment 1) and retardation (Experiment 2) tests for conditioned inhibition. The inhibitory status of CS B is predicted by the performance-focused extended comparator hypothesis (J. C. Denniston, H. I. Savastano, & R. R. Miller, 2001), but not by acquisition-focused models of empirical retrospective revaluation (e.g., A. Dickinson & J. Burke, 1996; L. J. Van Hamme & E. A. Wasserman, 1994).
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Animal Analogues of Causal Judgment
Psychology of Learning and Motivation, 1996Co-Authors: Ralph R. Miller, Helena MatuteAbstract:This chapter reviews that nonverbal behavioral assessment of Causal Judgment is apt to be more veridical than is verbal assessment, which is compromised by the demand characteristics and ambiguities of language. Organisms presumably evolved the ability to learn cause-effect relationships in order to prepare for and sometimes influence future events in the real world, not in order to verbally describe these Causal relationships. The use of nonverbal behavioral assessment invites direct comparisons between human Causal Judgment behavior and animal behavior in similar situations. Cues of high biological relevance appear to be relatively invulnerable to cue competition compared to cues of low biological relevance, which are quite susceptible to cue competition. It discusses that this convergence of findings in the Causal Judgment and animal learning literatures suggests that the two fields can each benefit by attending to the findings of the other. Another likely finding from studies of cue competition in animals that is profitably examined in Causal Judgment situations with humans is the learning-performance distinction. There is also some discussion that Causal Judgments results from those associations that have a forward relationship from one event to another and that are not nor in competition with other associations that are active at the time the target association is tested.
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Biological significance in forward and backward blocking: Resolution of a discrepancy between animal conditioning and human Causal Judgment
Journal of experimental psychology. General, 1996Co-Authors: Ralph R. Miller, Helena MatuteAbstract:Similarities between Pavlovian conditioning in nonhumans and Causal Judgment by humans suggest that similar processes operate in these situations. Notably absent among the similarities is backward blocking (i.e., retrospective devaluation of a signal due to increased valuation of another signal that was present during training), which has been observed in Causal Judgment by humans but not in Pavlovian responding by animals. The authors used rats to determine if this difference arises from the target cue being biologically significant in the Pavlovian case but not in Causal Judgment. They used a sensory preconditioning procedure in Experiments 1 and 2, in which the target cue retained low biological significance during the treatment, and obtained backward blocking. The authors found in Experiment 3 that forward blocking also requires the target cue to be of low biological significance. Thus, low biological significance is a necessary condition for a stimulus to be vulnerable to blocking.
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Biological Significance as a Determinant of Cue Competition
Psychological Science, 1996Co-Authors: James C. Denniston, Ralph R. Miller, Helena MatuteAbstract:Many researchers have noted the similarities between Causal Judgment in humans and Pavlovian conditioning in animals One recently noted discrepancy between these two forms of learning is the absence of backward blocking in animals, in contrast with its occurrence in human Causality Judgment Here we report two experiments that investigated the role of biological significance in backward blocking as a potential explanation of this discrepancy With rats as subjects, we used sensory preconditioning and second-order conditioning procedures, which allowed the to-be-blocked cue to retain low biological significance during training for some animals, but not for others Backward blocking was observed only when the target cue was of low biological significance during training These results suggest that the apparent discrepancy between human Causal Judgment and animal Pavlovian conditioning arises not because of a species difference, but because human Causality studies ordinarily use stimuli of low biological signifi...
Tobias Gerstenberg - One of the best experts on this subject based on the ideXlab platform.
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A counterfactual simulation model of causation by omission
2020Co-Authors: Tobias Gerstenberg, Simon StephanAbstract:When do people say that an event that didn't happen was a cause? We extend the counterfactual simulation model (CSM) of Causal Judgment and test it in a series of three experiments that look at people's Causal Judgments about omissions in dynamic physical interactions. The problem of omissive causation highlights a series of sub-problems that need to be addressed in order to give an adequate Causal explanation of why something happened: what are the relevant variables, what are their possible values, how are putative Causal relationships evaluated, and how is the Causal responsibility for an outcome attributed to multiple causes? The CSM predicts that people make Causal Judgments about omissions by mentally simulating what would have happened in relevant counterfactual situations. People use their intuitive understanding of physics to run these mental simulations. While prior work has argued that normative expectations affect Judgments of omissive causation, we suggest a concrete mechanism of how this happens: expectations affect what counterfactuals people consider, and the more certain people are that the counterfactual outcome would have been different from what actually happened, the more Causal they judge the omission to be. Our experiments show that both the structure of the physical situation as well as expectations about what will happen affect people's Judgments.
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A counterfactual simulation model of Causal Judgment
2020Co-Authors: Tobias Gerstenberg, Noah D Goodman, David A Lagnado, Joshua TenenbaumAbstract:How do people make Causal Judgments? We introduce the counterfactual simulation model (CSM) which predicts Causal Judgments by comparing what actually happened with what would have happened in relevant counterfactual situations. The CSM postulates different aspects of causation that capture the extent to which a cause made a difference to whether and how the outcome occurred, and whether the cause was sufficient and robust. We test the CSM in three experiments in which participants make Causal Judgments about dynamic collision events. Experiment 1 establishes a very close quantitative mapping between Causal Judgments and counterfactual simulations. Experiment 2 demonstrates that counterfactuals are necessary for explaining Causal Judgments. Participants' Judgments differed dramatically between pairs of situations in which what actually happened was identical, but where what would have happened differed. Experiment 3 features two candidate causes and shows that participants' Judgments are sensitive to different aspects of causation. The CSM provides a better fit to participants' Judgments than a heuristic model which uses features based on what actually happened. We discuss how the CSM can be used to model the semantics of different Causal verbs, how it captures related concepts such as physical support, and how its predictions extend beyond the physical domain.
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Quantitative Causal selection patterns in token causation
PloS one, 2019Co-Authors: Adam Morris, Tobias Gerstenberg, Jonathan Phillips, Fiery CushmanAbstract:When many events contributed to an outcome, people consistently judge some more Causal than others, based in part on the prior probabilities of those events. For instance, when a tree bursts into flames, people judge the lightning strike more of a cause than the presence of oxygen in the air—in part because oxygen is so common, and lightning strikes are so rare. These effects, which play a major role in several prominent theories of token causation, have largely been studied through qualitative manipulations of the prior probabilities. Yet, there is good reason to think that people’s Causal Judgments are on a continuum—and relatively little is known about how these Judgments vary quantitatively as the prior probabilities change. In this paper, we measure people’s Causal Judgment across parametric manipulations of the prior probabilities of antecedent events. Our experiments replicate previous qualitative findings, and also reveal several novel patterns that are not well-described by existing theories.
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Causal Judgments approximate the effectiveness of future interventions
2018Co-Authors: Adam Morris, Joshua Knobe, Tobias Gerstenberg, Jonathan Scott Phillips, Thomas Icard, Fiery Andrews CushmanAbstract:When many things contributed to an outcome, people consistently judge certain ones to be more Causal than others. For instance, people believe that a fire was more caused by the lit match than by the surrounding oxygen that fueled it. Why? Here, we offer a functional account of such patterns in Causal Judgment: By selecting causes as people naturally do, repeated Judgments of whether something (e.g. the match) was the cause of an outcome (e.g. the fire) can be averaged to obtain the probability that introducing those things would produce the outcome (e.g., that lighting a match would start a fire). In other words, token Causal Judgments accumulate evidence about the general effectiveness of potential future interventions. We offer a formal account of this process, and show how it explains three basic qualitative features of Causal Judgment: why the causes people select tend (1) to be necessary, (2) to be abnormal, and (3) to lack abnormal counterparts.
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CogSci - How, whether, why: Causal Judgments as counterfactual contrasts.
Cognitive Science, 2015Co-Authors: Tobias Gerstenberg, Noah D Goodman, David A Lagnado, Joshua B TenenbaumAbstract:How do people make Causal Judgments? Here, we propose a counterfactual simulation model (CSM) of Causal Judgment that unifies different views on causation. The CSM predicts that people’s Causal Judgments are influenced by whether a candidate cause made a difference to whether the outcome occurred as well as to how it occurred. We show how whethercausation and how-causation can be implemented in terms of different counterfactual contrasts defined over the same intuitive generative model of the domain. We test the model in an intuitive physics domain where people make Judgments about colliding billiard balls. Experiment 1 shows that participants’ counterfactual Judgments about what would have happened if one of the balls had been removed, are well-explained by an approximately Newtonian model of physics. In Experiment 2, participants judged to what extent two balls were Causally responsible for a third ball going through a gate or missing the gate. As predicted by the CSM, participants’ Judgments increased with their belief that a ball was a whether-cause, a how-cause, as well as sufficient for bringing about the outcome.
Helena Matute - One of the best experts on this subject based on the ideXlab platform.
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Contrasting cue-density effects in Causal and prediction Judgments
Psychonomic bulletin & review, 2010Co-Authors: Miguel A. Vadillo, Serban C. Musca, Fernando Blanco, Helena MatuteAbstract: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.
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Competition between antecedent and between subsequent stimuli in Causal Judgments.
Journal of experimental psychology. Learning memory and cognition, 2005Co-Authors: Francisco Arcediano, Helena Matute, Martha Escobar, Ralph R. MillerAbstract: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.
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Animal Analogues of Causal Judgment
Psychology of Learning and Motivation, 1996Co-Authors: Ralph R. Miller, Helena MatuteAbstract:This chapter reviews that nonverbal behavioral assessment of Causal Judgment is apt to be more veridical than is verbal assessment, which is compromised by the demand characteristics and ambiguities of language. Organisms presumably evolved the ability to learn cause-effect relationships in order to prepare for and sometimes influence future events in the real world, not in order to verbally describe these Causal relationships. The use of nonverbal behavioral assessment invites direct comparisons between human Causal Judgment behavior and animal behavior in similar situations. Cues of high biological relevance appear to be relatively invulnerable to cue competition compared to cues of low biological relevance, which are quite susceptible to cue competition. It discusses that this convergence of findings in the Causal Judgment and animal learning literatures suggests that the two fields can each benefit by attending to the findings of the other. Another likely finding from studies of cue competition in animals that is profitably examined in Causal Judgment situations with humans is the learning-performance distinction. There is also some discussion that Causal Judgments results from those associations that have a forward relationship from one event to another and that are not nor in competition with other associations that are active at the time the target association is tested.
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Biological significance in forward and backward blocking: Resolution of a discrepancy between animal conditioning and human Causal Judgment
Journal of experimental psychology. General, 1996Co-Authors: Ralph R. Miller, Helena MatuteAbstract:Similarities between Pavlovian conditioning in nonhumans and Causal Judgment by humans suggest that similar processes operate in these situations. Notably absent among the similarities is backward blocking (i.e., retrospective devaluation of a signal due to increased valuation of another signal that was present during training), which has been observed in Causal Judgment by humans but not in Pavlovian responding by animals. The authors used rats to determine if this difference arises from the target cue being biologically significant in the Pavlovian case but not in Causal Judgment. They used a sensory preconditioning procedure in Experiments 1 and 2, in which the target cue retained low biological significance during the treatment, and obtained backward blocking. The authors found in Experiment 3 that forward blocking also requires the target cue to be of low biological significance. Thus, low biological significance is a necessary condition for a stimulus to be vulnerable to blocking.
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Biological Significance as a Determinant of Cue Competition
Psychological Science, 1996Co-Authors: James C. Denniston, Ralph R. Miller, Helena MatuteAbstract:Many researchers have noted the similarities between Causal Judgment in humans and Pavlovian conditioning in animals One recently noted discrepancy between these two forms of learning is the absence of backward blocking in animals, in contrast with its occurrence in human Causality Judgment Here we report two experiments that investigated the role of biological significance in backward blocking as a potential explanation of this discrepancy With rats as subjects, we used sensory preconditioning and second-order conditioning procedures, which allowed the to-be-blocked cue to retain low biological significance during training for some animals, but not for others Backward blocking was observed only when the target cue was of low biological significance during training These results suggest that the apparent discrepancy between human Causal Judgment and animal Pavlovian conditioning arises not because of a species difference, but because human Causality studies ordinarily use stimuli of low biological signifi...
David R Shanks - One of the best experts on this subject based on the ideXlab platform.
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Causal Learning Beyond Causal Judgment: An Overview
2010Co-Authors: José C. Perales, David R ShanksAbstract:Most research articles studying how people learn to detect Causal relationships in their environments commence with some sort of example to illustrate the relevance of Causality in our daily lives. These examples allude to routine problems faced by doctors, economists, social psychologists, and others and emphasize the importance of deepening our understanding of Causal reasoning. But despite these frequent applied examples, it is somewhat surprising that research on Causal learning has only had a modest impact in applied disciplines. After three decades or so of intense study, it is probably time to wonder why this is the case. Plainly, we do not want Causal learning to become a super-specialized topic, perfectly constructed but unable to generate useful knowledge of wider relevance. In our view, cross-boundary work to fulfill this ambition is being undertaken, but to make a full impact it requires a reformulation of the implicit paradigm for Causal learning research. The core of this paradigm is simple and can be summarized in two principles: first, Causal knowledge can be assessed by means of verbal or numerical Judgments of Causal strength, and second, a single mental algorithm is sufficient to account for how environmental conditions (including covariation between cues and outcomes, time delays, and statistical interactions) map onto Judgments. This research program has produced an impressive corpus of data (see [1] for a recent review), but also a current feeling that wider progress and impact is not being achieved. This special issue is a joint attempt to present a vision of how research on Causal learning might develop in the future, and to push that process forward. With regard to the first principle, it is important to acknowledge that Judgments are not the only way to assess Causal knowledge. Judgments reflect Causal beliefs, and Causal beliefs are probably the basis for other responses, such as decisions or interventions. But it is not possible to predict decisions or interventions on the basis of Judgments alone. It would be naive to think that Causal beliefs reflected in simple Causal Judgments are the sole input to decision-making and intervention processes. Much effort is necessary to ascertain how Causal knowledge is employed in all of these competencies, so we can build bridges between what we have discovered in recent decades and other aspects of behavior. With regard to the second principle, we argue that a reconsideration of how theory needs to develop in the future is also necessary. Most researchers now accept that people interpret the world as Causal, and build mental representations of their environment in which events are Causally related. Still, these Causal models must be constructed from some sort of evidence, and that evidence is provided by basic coding mechanisms capturing regularities in the environment. In other words, Causal learning not only serves to detect and code statistical regularities, but also to uncover the hidden Causal structure that generates those regularities. For example, the correlation between smoking and lung cancer has been known for a long time. However, some scientists and tobacco manufacturers denied the existence of a Causal link between the two variables, because they believed that some other Causal factor was responsible for the co-occurrence (for example, populations from certain social origins could be more likely both to smoke and suffer cancer). Obviously, if smoking were not a direct cause of cancer, it would be useless to recommend people to quit smoking. In theoretical terms, we need some basic coding mechanism(s) to capture statistical regularities, and some other mechanism(s) to infer Causal structures from them. Most psychologists and neuroscientists would accept that the brain is a hugely sophisticated form of connectionist net, capable of building quite reliable models of the regularities in our experiences and interactions with the world (see Moris, Cobos, & Luque’s paper in this volume). Miller’s comparator model [2], and Allan’s [3] recent work, emphasize the idea that basic coding processes, either associative or episodic, can generate representations of the world in which, with sufficient attentional resources, most relevant events, their conjunctions, their relations of time and order, and their statistical dependencies, are conserved. Additionally, a number of algorithms have been postulated in artificial intelligence that are capable of using the sort of output generated by these basic coding mechanisms to build structural Causal representations [4, 5]. The limits of bounded rationality and actual research indicate, however, that the use of such algorithms requires the management of quantities of information that are beyond human processing limits. So, Causal induction is also a learning problem: certain second-order cues (abstract features of interrelations among cues) can indicate what is a cause and what is not. We, and most of the authors in this volume, support this cues-to-Causality approach (see the papers by Lagnado & Speekenbrink, and Hagmayer et al. in this volume, and [6], for more detailed discussions). Much research is needed to ascertain how we learn to manage these cues, the quantitative
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Driven by power? Probe question and presentation format effects on Causal Judgment.
Journal of experimental psychology. Learning memory and cognition, 2008Co-Authors: José C. Perales, David R ShanksAbstract: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.
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Models of covariation-based Causal Judgment: A review and synthesis
PSYCHON B REV, 2007Co-Authors: David R ShanksAbstract:Causal Judgment is assumed to play a central role in prediction, control, and explanation. Here, we consider the function or functions that map contingency information concerning the relationship between a single cue and a single outcome onto Causal Judgments. We evaluate normative accounts of Causal induction and report the findings of an extensive meta-analysis in which we used a cross-validation model-fitting method and carried out a qualitative analysis of experimental trends in order to compare a number of alternative models. The best model to emerge from this competition is one in which Judgments are based on the difference between the amount of confirming and disconfirming evidence. A rational justification for the use of this model is proposed.
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Momentary and integrative response strategies in Causal Judgment
Memory & cognition, 2002Co-Authors: Darrell J. Collins, David R ShanksAbstract:Associative models of Causal learning predict recency effects: Judgments at the end of a trial series should be strongly biased by recently presented information. Prior research, however, presents a contrasting picture of human performance. Lopez, Shanks, Almaraz, and Fernandez (1998) observed recency, whereas Dennis and Ahn (2001) found the opposite, primacy. Here we replicate both of these effects and provide an explanation for this paradox. Four experiments show that the effect of trial order on Judgments is a function of Judgment frequency, where incremental Judgments lead to recency while single final Judgments abolish recency and lead instead to integration of information across trials (i.e., primacy). These results challenge almost all existing accounts of Causal Judgment. We propose a modified associative account in which participants can base their Causal Judgments either on current associative strength (momentary strategy) or on the cumulative change in associative strength since the previous Judgment (integrative strategy).