Effects of question formats on causal judgments and model evaluation

Evaluation of causal reasoning models depends on how well the subjects' causal beliefs are assessed. Elicitation of causal beliefs is determined by the experimental questions put to subjects. We examined the impact of question formats commonly used in causal reasoning research on participant's responses. The results of our experiment (Study 1) demonstrate that both the mean and homogeneity of the responses can be substantially influenced by the type of question (structure induction versus strength estimation versus prediction). Study 2A demonstrates that subjects' responses to a question requiring them to predict the effect of a candidate cause can be significantly lower and more heterogeneous than their responses to a question asking them to diagnose a cause when given an effect. Study 2B suggests that diagnostic reasoning can strongly benefit from cues relating to temporal precedence of the cause in the question. Finally, we evaluated 16 variations of recent computational models and found the model fitting was substantially influenced by the type of questions. Our results show that future research in causal reasoning should place a high priority on disentangling the effects of question formats from the effects of experimental manipulations, because that will enable comparisons between models of causal reasoning uncontaminated by method artifact

Asymmetries in responses to attitude statements: the example of "zero-sum" beliefs

While much has been written about the consequences of zero-sum (or fixed-pie) beliefs, their measurement has received almost no systematic attention. No researchers, to our awareness, have examined the question of whether the endorsement of a zero-sum-like proposition depends on how the proposition is formed. This paper focuses on this issue, which may also apply to the measurement of other attitudes. Zero-sum statements have a form such as "The more of resource X for consumer A, the less of resource Y for consumer B." X and Y may be the same resource (such as time), but they can be different (e.g., "The more people commute by bicycle, the less revenue for the city from car parking payments"). These statements have four permutations, and a strict zero-sum believer should regard these four statements as equally valid and therefore should endorse them equally. We find, however, that three asymmetric patterns routinely occur in people's endorsement levels, i.e., clear framing effects, whereby endorsement of one permutation substantially differs from endorsement of another. The patterns seem to arise from beliefs about asymmetric resource flows and power relations between rival consumers. We report three studies, with adult samples representative of populations in two Western and two non-Western cultures, demonstrating that most of the asymmetric belief patterns are consistent across these samples. We conclude with a discussion of the implications of this kind of "order-effect" for attitude measurement.The research for this project was supported by Australian Research Council Discovery Project grant DP102101095, awarded to MS in 2012

Randomly stopped sums: models and psychological applications

This paper describes an approach to modeling the sums of a continuous random variable over a number of measurement occasions when the number of occasions also is a random variable. A typical example is summing the amounts of time spent attending to pieces of information in an information search task leading to a decision to obtain the total time taken to decide. Although there is a large literature on randomly stopped sums in financial statistics, it is largely absent from psychology. The paper begins with the standard modeling approaches used in financial statistics, and then extends them in two ways. First, the randomly stopped sums are modeled as "life distributions" such as the gamma or log-normal distribution. A simulation study investigates Type I error rate accuracy and power for gamma and log-normal versions of this model. Second, a Bayesian hierarchical approach is used for constructing an appropriate general linear model of the sums. Model diagnostics are discussed, and three illustrations are presented from real datasets

Moderator effects differ on alternative effect-size measures

This paper discusses largely ignored issues regarding moderation of effect-sizes. We show that, under commonly-occurring conditions, popular alternatives for effect-size measures in ANOVA and multiple regression are not moderated identically across independent samples. Effects may appear to be unmoderated according to one effect-size measure but not according to another, or may even be moderated in opposite directions. We identify the conditions under which differential effect-size moderation can occur, and show that they are commonplace. We then review techniques for detecting and dealing with differential moderation of alternative effect-size measures. Finally, we discuss implications for research practice, reporting, replication, and meta-analysis

Adapting to an uncertain world: Cognitive capacity and causal reasoning with ambiguous observations

Ambiguous causal evidence in which the covariance of the cause and effect is partially known is pervasive in real life situations. Little is known about how people reason about causal associations with ambiguous information and the underlying cognitive mechanisms. This paper presents three experiments exploring the cognitive mechanisms of causal reasoning with ambiguous observations. Results revealed that the influence of ambiguous observations manifested by missing information on causal reasoning depended on the availability of cognitive resources, suggesting that processing ambiguous information may involve deliberative cognitive processes. Experiment 1 demonstrated that subjects did not ignore the ambiguous observations in causal reasoning. They also had a general tendency to treat the ambiguous observations as negative evidence against the causal association. Experiment 2 and Experiment 3 included a causal learning task requiring a high cognitive demand in which paired stimuli were presented to subjects sequentially. Both experiments revealed that processing ambiguous or missing observations can depend on the availability of cognitive resources. Experiment 2 suggested that the contribution of working memory capacity to the comprehensiveness of evidence retention was reduced when there were ambiguous or missing observations. Experiment 3 demonstrated that an increase in cognitive demand due to a change in the task format reduced subjects' tendency to treat ambiguous-missing observations as negative cues. Copyright: © 2015 Shou, Smithson.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Modelling causal reasoning under ambiguity

Causal reasoning with ambiguous observations requires subjects to estimate and evaluate ambiguous observations.This paper proposes a hierarchical model that accounts for the uncertainty of both the distribution of the functional form selection and distribution of the ambiguity treatment selection. The posterior distribution of the causal estimates is determined by both the functional form and the ambiguity processing strategy adopted by the reasoner. A model is tested in a simulation study for its ability to recover the strategy and functional form adopted by subjects across a range of hypothetical conditions. In addition, the model is applied to the results of an experimental study

cdfquantreg: An R Package for CDF-Quantile Regression

The CDF-quantile family of two-parameter distributions with support (0, 1) described in Smithson and Merkle (2014) and recently elaborated by Smithson and Shou (2017), considerably expands the variety of distributions available for modeling random variables on the unit interval. This family is especially useful for modeling quantiles, and also sometimes out-performs the other distributions. The distributions are very tractable, with a location and dispersion parameter, explicit probability distribution functions, cumulative distribution functions, and quantiles. They enable a wide variety of quantile regression models with predictors for the location and dispersion parameters, and simple interpretations of those parameters. The R package cdfquantreg (Shou and Smithson 2019) (at least R 3.2.0) presented in this paper includes 36 distributions from the CDF-quantile family. Separate submodels may be specified for the location and for the dispersion parameters, with different or overlapping sets of predictors in each. The package offers maximum likelihood, Bayesian MCMC, and bootstrap estimation methods. Model diagnostics, including the gradient, three types of residuals, and the dfbeta influence measures, are available for evaluating models. The package also provides pseudo-random generators for all of its distributions. Many of its functions and their usage have forms familiar to R users, and the documentation is extensive. We also present a SAS macro for general linear models using the CDF-quantile family that includes many of the same capabilities as the cdfquantreg package. The paper provides examples of applications to real data-sets

Assessing a domain-specific risk-taking construct: A meta-analysis of reliability of the DOSPERT scale

The DOSPERT scale has been used extensively to understand individual differences in risk attitudes across varying decision domains since 2002. The present study reports a reliability generalization meta-analysis to summarize the internal consistency of both the initial and the revised versions of DOSPERT. It also examined factors that can influence the reliability of the DOSPERT and its subscales. A total of 104 samples (N = 30,109) that reported 465 coefficient alphas were analyzed. Results of meta-regression models showed that the overall coefficient alpha of the DOSPERT total scores was satisfactory, regardless of the scale and study characteristics. Coefficient alphas varied significantly across domain subscales, with values ranging from .68 for the social domain to .80 for the recreational domain. In addition, the alpha coefficients of subscales varied significantly depending on various study characteristics. Finally, we report the meta-analysis of the intercorrelations among DOSPERT subscales and reveal that intercorrelations among the subscales are heterogeneous. We discuss the theoretical implications of the present findings.This research was supported by the Australian Government through the Australian Research Council (Project number DE180100015

Attitudes toward risk and uncertainty: The role of subjective knowledge and affect

An individual's attitude toward risk is often measured by their behavioral tendency in risky situations. However, commonly used self-report measures of risk attitudes often do not explicitly specify "risk" in all the items, which results in an unsystematic mix of both perceived uncertainty and risk (as loss). Thus, an individual’s endorsement of those items can vary as a function of not only the latent construct of attitudes toward risk, but also factors including prior knowledge and affective reaction to uncertainty. Two studies were carried out to examine the extent to which participants perceive behavioral tendency items as entailing uncertainty or risk (as loss), and how behavioral tendency can be influenced by prior knowledge. Results indicate that endorsement of behavioral tendency was significantly greater when "risk" information was implicit compared to items that had explicit information to contextualize the uncertainty or risk. Furthermore, prior knowledge had a significantly stronger influence on the endorsement of items in which risk information was implicit than on the explicit uncertainty/risk items. Finally, uncertainty and risk in the items appeared to influence behavioral tendency significantly via emotional responses to the items. This research highlights the need for researchers to more adequately control for different sources of variability when measuring the desired construct of attitude towards risk.This research was supported by the Australian Government through the Australian Research Council (Project number DE180100015)

Immediate emotions and subjective stakes in risky decision-making under uncertainty

BACKGROUND Previous research has shown that immediate emotions and cognitive processing of the stakes of outcomes influence decision-making under uncertainty. The effect of perceived beneficial stakes and different types of immediate emotions on decision-making is an important topic that has received little attention in the literature. This study investigated the effects of trait anxiety and anticipatory emotions (fear, sadness, excitement and comfortability) on the perception of thee stakes of outcomes and behavioral intentions. METHOD Participants from the community completed a task measuring anticipatory emotions and their perceived stakes of risky and beneficial outcomes in a range of uncertain situations. Trait anxiety was also measured. RESULTS Results revealed that anticipatory emotions (except for sadness), trait anxiety and subjective stakes all demonstrated significant associations with risky behavioral intention in uncertain situations. Anticipatory emotions, but not trait anxiety, had stable effects on stake perceptions. However, trait anxiety moderated the effect of excitement on risky behavioral intention. In addition, positive emotions (comfortability and excitement) and beneficial stakes demonstrated consistent effects in the decision-making process. CONCLUSIONS The current study sheds light on future immediate-emotion-based interventions for deficits in uncertain decision-making.This research was supported by the Australian Government through the Australian Research Council (Project number DE180100015)