Cue competition in human categorization: Contingency or the Rescorla-Wagner Learning Rule? Comment on Shanks (1991).

Melz, Eric; Cheng, Patricia W.; Holyoak, Keith J.; Waldmann, Michael R.

doi:10.1037/0278-7393.19.6.1398

Cited by 39 publications

(56 citation statements)

References 28 publications

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“…As Melz, Cheng, Holyoak, and Waldmann (1993) have pointed out, the Rescorla-Wagner model in fact predicts rather more extreme ratings than those observed in the present experiments. At asymptote, the associative strength of a noncontingent cue should be zero and that of a contingent cue should be at ceiling, yet judgments for these cues were much less extreme than zero and 100.…”

Section: Discussionsupporting

confidence: 60%

Causal order does not affect cue selection in human associative learning

1996

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Waldmann and Holyoak (1992) presented evidence in support of the claim that cue selection does not emerge in "diagnostic" human learning tasks in which the cues are interpretable as effects and the outcomes as the causes of those effects. Waldmann and Holyoak argued that this evidence presents a major difficulty for associationist theories oflearning and instead supports a "causal model" theory. We identify a number of flaws in Waldmann and Holyoak's experimental procedures and report three new experiments designed to test their claim. In Experiment 1, cue selection was observed regardless of causal order and regardless of whether the cues were abstractly or concretely specified. In Experiments 2 and 3, cue selection was again observed when subjects predicted causes from effects. Weconclude that our results are consistent with simple associationist theories of learning but contradict Waldmann and Holyoak's causal model theory.511

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Section: Discussionsupporting

confidence: 60%

Causal order does not affect cue selection in human associative learning

1996

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“…Both conditional probabilities are directly estimable by observable frequencies, and ⌬P i is a measure of covariation between i and e. Cheng and Holyoak (1995) and Melz, Cheng, Holyoak, and Waldmann (1993) modified the model by specifying that the focal sets reasoners prefer for computing simple contrasts are those in which every plausible alternative cause remains constant. When applied to the focal set consisting of the first two panels of Figure 1, Equation 1 predicts that i causes e. Likewise, this equation predicts that j causes e in the same figure and that i and j each prevents e in Figure 2.…”

Section: Outcomes Illustrating the Independent Influence Of Twomentioning

confidence: 99%

Assessing interactive causal influence.

Novick

Cheng

2004

Psychological Review

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178

187

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The discovery of conjunctive causes-factors that act in concert to produce or prevent an effect-has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-power theory that overcomes the problems, and reports empirical evidence favoring the new theory. Unlike earlier models, the new theory derives (a) the conditions under which covariation implies conjunctive causation and (b) functions relating observable events to unobservable conjunctive causal strength. This psychological theory, which concerns simple cases involving 2 binary candidate causes and a binary effect, raises questions about normative statistics for testing causal hypotheses regarding categorical data resulting from discrete variables.A single causal factor is often perceived as contributing toward producing an effect yet insufficient to produce it on its own. Low body resistance in the absence of a flu virus is not by itself sufficient to cause one to have the flu; neither, typically, is the presence of a flu virus per se. The two in conjunction, however, often do cause one to come down with the flu. Likewise, striking a match per se does not cause it to light-there must be oxygen in the environment, the match must be combustible, and so forth. Cigarette smoke per se does not cause lung cancer-the smoke must be inhaled over a relatively long interval, the smoker must be susceptible to the disease, and so forth. The susceptibility itself is probably in turn specified by multiple genetic factors. Hard work alone typically does not produce success; it must be combined with talent and opportunity. Most causes in the real world, like these examples, are complex, involving a conjunction of factors acting in concert, rather than simple, involving a single factor acting alone. How do reasoners come to know that there is something special about the conjunction of several factors such that it can produce or prevent an effect?We first present some phenomena that are inexplicable by previous psychological accounts of conjunctive causation. To explain these phenomena, as well as to solve other problems that beset previous accounts, we propose our causal-power theory of the assessment of interactive causal influence. We then review previous findings in the literature in light of our new theory and report new empirical evidence in support of the theory. Finally, we discuss some implications of our approach for the normative testing of causal hypotheses regarding data resulting from discrete variables.Our new theory, like many previous psychological accounts of conjunctive causation (Cheng & Novick, 1990Forsterling, 1989;Hewstone & Jaspars, 1987;Hilton & Slugoski, 1986;Kelley, 1967), is covariational in that it bases causal inferences on concomitant variations in observed events as well as on other observable features such as t...

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“…In fact, the data from several articles that claimed to show that people were poor reasoners because they deviated far from D P when reasoning about multiple causes of effects (e.g., Baker, Mercier, Vallée-Tourangeau, Frank, & Pan, 1993;Chapman, 1991;Chapman & Robbins, 1990;Price & Yates, 1993 can be reanalyzed to show that people seem to be using the "smarter" conditional contingency strategy (Cheng, 1993;Melz, Cheng, Holyoak, & Waldmann, 1993;Shanks, 1993Shanks, , 1995Spellman, 1993Spellman, , 1996aSpellman, , 1996b Schaller and colleagues (Schaller, 1992a(Schaller, , 1992bSchaller & O' Brien, 1992) have investigated the use of something akin to conditionalization-what he calls "intuitive analysis of covariance"-in tasks that do not involve causal reasoning. For instance, in a study that is reminiscent of our baseball example, subjects were presented with information about the racquetball prowess of two potential doubles partners.…”

Section: Simpson's Paradox: the Mathematical Problem Of Differing Basmentioning

confidence: 99%

How two causes are different from one: The use of (un)conditional information in Simpson's paradox

2001

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In a causally complex world, two (or more) factors may simultaneously be potential causes of an effect. To evaluate the causal efficacy of a factor, the alternative factors must be controlled for (or conditionalized on). Subjects judged the causal strength of two potential causes of an effect that covaried with each other, thereby setting up a Simpson's paradox-a situation in which causal judgments should vary widely depending on whether or not they are conditionalized on the alternative potential cause. In Experiments 1 (table format) and 2 (trial-by-trialformat), the subjects did conditionalize their judgments for one causal factor on a known alternative cause. The subjects also demonstrated that they knew what information was needed to properly make causal judgments when two potential causes are available. In Experiment 3 (trial-by-trial),those subjects who were not told about the causal mechanism by which the alternative cause operated were less likely to conditionalize on it. However, the more a subject recognized the covariation between the alternative cause and the effect, the more the subject conditionalized on it. Such behavior may arise from the interaction between bottom-up and top-down processing.

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Cue competition in human categorization: Contingency or the Rescorla-Wagner Learning Rule? Comment on Shanks (1991).

Cited by 39 publications

References 28 publications

Causal order does not affect cue selection in human associative learning

Causal order does not affect cue selection in human associative learning

Assessing interactive causal influence.

How two causes are different from one: The use of (un)conditional information in Simpson's paradox

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