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• W2765394640 abstract "The Impact of the Format of Covariation Information on Causal Inferences Teresa Payton and Frederic Vallee-Tourangeau Psychology Research Unit, Kingston University Kingston-upon-Thames UNITED KINGDOM KT1 2EE t.payton / f.vallee-tourangeau@kingston.ac.uk variations between the regions as well as (possibly) by the seeding. Information was presented either on-line, where the results of individual trials (seeding/no seeding, leading to rain or no rain) were presented one trial at a time, or the information was presented off-line, that is as static frequency summaries (participants received frequency summaries of the days clouds were and were not seeded, and on how many days in each case it rained and did not rain). Only 17% of the participants receiving the covariation information one trial at a time appeared to formulate judgments in line with the ∆P rule compared to 75% of the participants who received summary information. Abstract Representational effects in a fictitious virus-disease causal induction task were examined in three studies. In all three studies, six different judgment conditions were created by crossing two levels of virus-disease covariation (0, .5) with three levels of disease base rate (.25, .5, .75). In Study 1, the covariation information was presented as four propositions summarising the frequencies of the four patient types, namely patients with or without the virus who either did have or did not have the disease. In Study 2 the same information was presented in a 2 x 2 table with the cell frequencies represented iconistically (the presence/absence of virus/disease was shown as schematic faces that varied in expression and colour). In Study 3 the covariation information was presented in terms of a branching tree with the two main branches representing the frequencies of patients with and without the disease from which sprouted smaller branches showing the frequency of those with and without the virus. Causal judgments were poorest in Study 1, reflected significantly improved covariation discrimination in Study 2, but were most normative in Study 3. These results signal the presence of important representational effects in causal induction tasks. ∆ P = Introduction In formulating a judgment of the causal link between a candidate cause and a target effect, information about the frequency of the pairing of four types of events should be considered. These four types of events refer to the pairings of (a) the cause and the effect, (b) the cause with the absence of the effect, (c) the effect in the absence of the cause, and (d) the absence of both the effect and the cause. These event frequencies are often represented in a 2 x 2 table with the columns referring to the presence or absence of the effect and the rows the presence or absence of the cause (see Fig. 1). The contrast between the probability of the effect in the presence of the cause, P(E|C), and the probability of the effect in the absence of the cause, P(E|~C) is taken as a measure of the covariation between the candidate cause and the effect (also referred to as ∆P). Early research on reasoners’ appreciation of covariation in formulating causal judgments examined how the manner with which the covariation information was presented influenced judgments. For example, in Ward and Jenkins (1965), participants were asked to gauge the relationship between seeding clouds and the occurrence of rain in different regions. The instructions made clear that confirming evidence (i.e., rain) was influenced by climatic E ~ E C a b ~ C c d a c = P ( E | C ) − P ( E |~ C ) a + b c + d Figure 1: A 2x2 contingency table. a, b,c, d are cell frequencies; C = candidate cause, E = target effect. Based on the information available from a 2x2 table, Wasserman, Dorner and Kao (1990) explored the relationship between the information participants deemed important in order to formulate a causal judgment and the information they actually used. Using a drug/disease scenario, their participants either decided which of the four categories of information would be necessary for a causal judgement or rated the importance of each category of information. Cell importance was ranked as cell a > cell b > cell c > cell d in both conditions. In a second experiment, using the same causal scenario, problems were structured in quartets of pairs, giving the opportunity to vary the numerical content of one cell while holding constant the contents of the other three. This allowed Wasserman et al. to measure the impact that increasing the frequency in a single cell had on participants’ causal judgements. The numerical information from a 2 x 2 table for each problem was presented as a set of four statements. For each problem participants ranked the value of the drug for treatment of the disease on a scale of -10 (drug worsens disease) to +10 (drug helps cure). The importance ratings of each cell" @default.
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• W2765394640 modified "2023-09-26" @default.
• W2765394640 title "The Impact of the Format of Covariation Information on Causal Inferences - eScholarship" @default.
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