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We argue that the way ambiguity has been operationalized throughout the literature on ambiguity effects has an important limitation, insofar as ambiguity in outcomes has been neglected. We report two studies where judges do encounter ambiguity in the sampled outcomes and find evidence that ambiguity aversion is not less than when judges are given a range of outcomes without reference to ambiguous outcomes themselves. This result holds regardless of whether people are presented with a sample all at once or sample outcomes sequentially. Our experiments also investigate the effects of conflicting information about outcomes, finding that conflict aversion also does not decrease. Moreover, ambiguity and conflict aversion do not seem to arise as a consequence of judges ignoring uncertain outcomes and thereby treating outcome sets as reduced samples of unambiguous (or unconflicting) information. Instead, ambiguity and conflict aversion are partly explained by more pessimistic outcome forecasts by judges. This pessimism, in turn, may be due to the judges' uncertainty about how the chance of a desirable outcome from an ambiguous or conflictive alternative compares with an equivalent risky alternative. Both studies used hypothetical scenarios, and no incentives were provided for participants' decisions.

Infant sociability is generally conceived in terms of dyadic capacities and behaviours. Recently, quantitative evidence has been published to support arguments that infants achieve a criterion for groupness: the capacity to interact simultaneously with two others. Such studies equate this capacity with alternating dyadic acts to the two other members of an interacting trio. Here we propose a stricter threefold criterion for infant groupness, of which the crux is whether the social behaviour of an infant at time B is shown to be influenced by what two or more group-members were previously doing at time A. We test the viability of this conceptualisation: (a) through its justification of the novel laboratory procedure of studying infant sociability in infant-peer quartets (rather than dyads); and, (b) in an analysis of a pilot study of gaze-behaviour recorded in 5-minute interactions among two quartets of infants aged 6-9 months. We call this a ‘proof of concept’ because our aim is to show that infants are capable of groupness, when groupness is conceptualised in a supra-dyadic way—not that all infants will manifest it, nor that all conditions will produce it, nor that it is commonplace in infants’ everyday lives. We found that both quartets did achieve the minimum criterion of groupness that we propose: mutual gaze predicting coordinated gaze (where two babies, A & B, are looking at each other, and B is then looked at by C, and sometimes D) more strongly than the reverse. There was an almost complete absence of ‘parallel mutual gaze,’ where the four babies pair off. We conclude that, under specific conditions, preverbal infants can manifest supra-dyadic groupness. Infants’ capacities to exhibit groupness by nine months of age, and the paucity of parallel mutual gaze in our data, run counter to the assumption that infant sociability, when in groups, is always generated by a dyadic program. Our conceptualisation and demonstration of groupness in eight-month-olds thus opens a host of empirical, theoretical, and practical questions about the sociability and care of young babies.

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.

Whether conflict and ambiguity are distinct kinds of uncertainty remains an open question, as does their joint impact on judgments of overall uncertainty. This paper reviews recent advances in our understanding of human judgment and decision making when both ambiguity and conflict are present, and presents two types of testable models of judgments under conflict and ambiguity. The first type concerns estimate-pooling to arrive at “best” probability estimates. The second type is models of subjective assessments of conflict and ambiguity. These models are developed for dealing with both described and experienced information. A framework for testing these models in the described-information setting is presented, including a reanalysis of a multi-nation data-set to test best-estimate models, and a study of participants' assessments of conflict, ambiguity, and overall uncertainty reported by Smithson (2013). A framework for research in the experienced-information setting is then developed, that differs substantially from extant paradigms in the literature. This framework yields new models of “best” estimates and perceived conflict. The paper concludes with specific suggestions for future research on judgment and decision making under conflict and ambiguity.

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.

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.

Given a linear relationship between two continuous random variables $X$ and $Y$ that may be moderated by a third, $Z$, the extent to which the correlation $rho$ is (un)moderated by $Z$ is equivalent to the extent to which the regression coefficients $beta_y$ and $beta_x$ are (un)moderated by $Z$ iff the variance ratio $sigma_y^2/sigma_x^2$ is constant over the range or states of $Z$. Otherwise, moderation of slopes and of correlations must diverge. Most of the literature on this issue focuses on tests for heterogeneity of variance in $Y$, and a test for this ratio has not been investigated. Given that regression coefficients are proportional to $rho$ via this ratio, accurate tests and estimations of it would have several uses. This paper presents such a test for both a discrete and continuous moderator and evaluates its Type I error rate and power under unequal sample sizes and departures from normality. It also provides a unified approach to modeling moderated slopes and correlations with categorical moderators via structural equations models.