Judging the Importance of Constant and Variable Candidate Causes: A Test of the Power PC Theory

Vallée-Tourangeau, Frédéric; Murphy, Robin A.; Drew, Susan; Baker, A. G.

doi:10.1080/713755745

Cited by 61 publications

(53 citation statements)

References 17 publications

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“…Overall, the fit between the predictions derived from the Pearce model and the ratings is impressive. Recent efforts suggest that the Pearce model accounts for other aspects of human causal induction as well (e.g., Lopez, Shanks, Almaraz, & Fernandez, 1998;Vallee-Tourangeau, Murphy, Drew, & Baker, 1998). The conclusion with the most apparent parsimony that can be drawn from our results is that neither the R-W model nor the PCM accounts well for the dynamic properties of discounting or blocking found with our preparation.…”

Section: Discussioncontrasting

confidence: 48%

Asymptotic judgment of cause in a relative validity paradigm

2000

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We report three experiments in which we tested asymptotic and dynamic predictions of the Rescorla-Wagner(R-W) model and the asymptotic predictions of Cheng's probabilistic contrast model (PCM) concerning judgments of causality when there are two possible causal candidates. We used a paradigm in which the presence of a causal candidate that is highlycorrelated with an effect influences judgments of a second, moderately correlated or uncorrelated cause. In Experiment I, which involved a moderate outcome density, judgments of a moderately positive cause were attenuated when it was paired with either a perfect positive or perfect negative cause. This attenuation was robust over a large set of trials but was greater when the strong predictor was positive. In Experiment 2, in which there was a low overall density of outcomes, judgments of a moderately correlated positive cause were elevated when this cause was paired with a perfect negative causal candidate. This elevation was also quite robust over a large set of trials. In Experiment 3, estimates of the strength of a causal candidate that was uncorrelated with the outcome were reduced when it was paired with a perfect cause. The predictions of three theoretical models of causal judgments are considered. Both the R-W model and Cheng's PCMaccounted for some but not all aspects of the data. Pearce's model of stimulus generalization accounts for a greater proportion of the data.In the last decade, research on human judgment ofcontingency has been motivated by a comparative perspective that stresses an analogous associative learning component in both animal conditioning and human judgments of contingency. When a single predictor variable signals the occurrence of an outcome variable, contingency judgments closely parallel the actual one-way contingency, I1P, between the predictor and the outcome. I1P is defined as the difference between the conditional probabilities p(outcome Ipredictor) and p(outcome Ino predictor) (Allan, 1980).This parallel has been observed in both operant learning tasks (see, e.g., Wasserman, Elek, Chatlosh, & Baker, 1993) and video games that are analogous to classical conditioning tasks (see, e.g., Dickinson, Shanks, & Evenden, 1984). These results suggest that subjects might derive their judgments from an unbiased consideration of I1P. called discounting or blocking (we will use these terms interchangeably throughout). In a series of experiments inspired by Kamin's (1969) blocking preparation, Dickinson et at. (1984) demonstrated that contingency estimates of moderately positive predictor A were reliably attenuated by exposing subjects to a strong positive contingency between predictor B and the same outcome in an initial observation stage. In turn, in a series of experiments inspired by the multiple-cue preparation of Wagner, Logan, Haberlandt, and Price (1968), Baker, Mercier, Vallee-Tourangeau, Frank, andPan (1993) established that estimates of moderately positive or negative contingencies between predictor A and the outcome were influenced ...

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Section: Discussioncontrasting

confidence: 48%

Asymptotic judgment of cause in a relative validity paradigm

2000

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“…This effect is illustrated by the judgmental differences between Conditions 1 and 2 and between Conditions 3 and 4 in Experiment 1 from Perales and Shanks's (2003) study; among Conditions 1-3 in Experiment 3 from Lober and Shanks (2000); between Conditions 2 and 3 in Experiments 1 and 2 from Collins and Shanks (2006); among Conditions 2-4 from Experiment 3 in Shanks (2002); among Conditions 1-3 in Experiment 1 and Conditions 1-4 in Experiment 2 from Vallée-Tourangeau et al (1998); between Conditions 1 and 4 and 2 and 3 in Experiment 6 from White (2003c); and between Conditions 2 and 3 and among Conditions 4-6, 7-10, and 11-13 in Experiment 1 from .…”

Section: Notesmentioning

confidence: 95%

Models of covariation-based causal judgment: A review and synthesis

Perales

Shanks

2007

Psychonomic Bulletin & Review

138

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Over the past 25 or so years, many researchers have sought to characterize the mechanisms by which people form causal beliefs from covariation information. This can be seen as a special case of the fundamental problem of associative learning (in a broad sense)-namely, the problem of describing how learned beliefs relate to the environmental contingencies on which they are based. Causal beliefs are influenced by a range of factors such as the temporal (Shanks, Pearson, & Dickinson, 1989; see also Buehner & May, 2003) and spatial (Glautier, 2002) distribution of events, as well as by more top-down factors such as causal expectations (Waldmann & Hagmayer, 2001). Yet within the total space defined by the attributes that influence causal learning, research has tended to occupy a rather restricted region: Specifically, numerous studies have held constant all other factors (e.g., spatial and temporal relationships) and varied only covariation information-that is, information about the frequencies of conjunctions and disjunctions between a target cause and effect. This research has thus been focused on a seemingly rather straightforward problem: to characterize the function that maps covariation information onto causal beliefs. Until an adequate description of this function emerges, we cannot be said to have even the beginnings of an understanding of causal knowledge or, since causal knowledge is central to cognition (Sloman, 2005), of cognition itself.This seemingly simple quest has proven to be a formidable challenge. However, there is no lack of candidate theories. The problem, rather, is that the core components of the theories are surrounded, in Kuhnian fashion, by a penumbra of additional assumptions that can often be manipulated to explain away awkward results. The end result is that we have not even been able to discriminate at the highest level of the theoretical hierarchy between associative and rule-based accounts, never mind to make finer discriminations between theories within each class. Moreover, research practice has exacerbated the problem. Researchers tend to design and run new experiments to distinguish between theories (often only between two theories), focusing on the parts of the experimental space where their favored theory is a priori likely to be most successful. There is little attempt to ensure that the favored theory adequately handles the historical record of prior experiments.In the present article, we take a new approach in an attempt to circumvent, at least partially, these difficulties. We report the results of a meta-analysis of historical causal learning studies that have manipulated contingency information, and we employ a cross-validation methodology, suitable for comparing models that make incommensurable assumptions, to try to find the model that provides the best overall account of the data. Although any given theory can argue away the results of a particular study by changing some of its peripheral assumptions or by arguing that some aspect of the experimental procedure violated ...

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“…The debate over rational models ∆P and causal power make different predictions about the strength of causal relationships, and several experiments have been conducted with the aim of determining which model gives a better account of human data (e.g., Buehner & Cheng, 1997;Collins & Shanks, submitted;Lober & Shanks, 2000;Perales & Shanks, 2003;Shanks, 2002;Vallee Tourangeau, Murphy, Drew, & Baker, 1998). Each model captures some of the trends identified in these experiments, but there are several results that are predicted by only one of the models, as well as phenomena that are predicted by neither.…”

Section: 2mentioning

confidence: 99%