Research suggests that counterfactuals (i.e., thoughts of how things might have been different) play an important role in determining the perceived cause of a target outcome. Results from 3 scenario studies indicate that counterfactual content overlapped primarily with thoughts of how an outcome might have been prevented (preventability ascriptions) rather than with thoughts of how it might have been caused (causal ascriptions). Counterfactuals and preventability ascriptions focused mainly on controllable antecedents, whereas causal ascriptions focused mainly on antecedents that covaried with the target outcome over a focal set of instances. Contrary to current theorizing, causal ascriptions were unrelated to counterfactual content (Study 3). Results indicate that the primary criterion used to recruit causal ascriptions (covariation) differs from that used to recruit counterfactuals (controllability).

We examined positive and negative life changes reported by bereaved spouses and parents 4-7 years after the sudden loss of a family member (N= 94). Although the bereaved described significantly more positive than negative life changes in response to a series of open-ended interview questions, the number of positive life changes reported was unrelated to reports of psychological symptoms and well-being. Analyses examining the impact of positive life changes within particular domains (e.g., social relations, life orientation) revealed the same null pattern. Those bereaved reporting various positive life changes were no better off than their counterparts who did not mention such changes and, like their 90 LIFE CHANGES 91 bereaved counterparts, were in fact worse off than their non-bereaved matched controls. In contrast, reports of negative life changes were consistently related to psychological symptoms and well-being. Our results raise questions about the significance of reports of positive life changes following victimization. Statements of personal growth may not be reliable indicators of adjustment. Implications for theory and future research are discussed.The past 20 years have witnessed a dramatic increase in research on reactions to stressful life events (see Kessler, Price, & Wortman, 1985, for a review). Investigators have examined the effects of many different events including illness, sexual assault, and bereavement. These studies, employing a wide range of standard functioning measures, have typically focused on documenting the psychological and physical sequelae of the event, usually within one to two years of its occurrence.Researchers have recently begun to broaden the scope of inquiry by focusing on additional implications and consequences of major life events. Within this framework, a new research tradition has emerged which considers whether stressful life experiences prompt any endur ing personal changes. Some investigators have attempted to identify those aspects of a person's life that change following a stressful event

Integration of contingency information underlies many cognitive tasks including causal, covariational, and probability judgments. The authors'feature-analytic approach was used to account for the findings that people differentially weight specific types of conjunctive information in causal (Experiment 1) and noncausal (Experiment 2) contingency judgments. These findings were explained in terms of positive-test and sufficiency-test biases, which were found in both judgment domains. The same biases, however, were not observed in normative conditional-probability judgments (Experiment 3). The authors argue that this discrepancy is owing to the differential clarity of normative criteria in these domains. Much of human learning and inferential thinking depends on the integration of contingency information. To test hypotheses and revise beliefs; to explain past events and predict future ones; to establish categories, form stereotypes, and develop impressions of others, humans integrate a vast amount of information about interevent contingencies. In short, the ability to discriminate contingencies in the physical and social worlds of humans is a basic characteristic of adaptive behavior. It is important, then, to understand how people accomplish this first-order cognitive task in different domains of judgment. Our work addresses the issue of how people go about integrating contingency information in order to arrive at intuitive statistical estimations of causality, covariation, and likelihood. The super-symbolic society (Toffier, 1990) that people now live in affords them a greater number of facts and figures to process than at any other time in history. This is an age of both statistical dependence and statistical distrust. One cannot read the newspaper, watch television, or listen to the radio without receiving a barrage of statistical information in the form of frequencies, means, probabilities, and variances. How do people use the statistical information that they receive to update their beliefs, to evaluate claims,

Framing effects have long been viewed as compelling evidence of irrationality in human decision making, yet that view rests on the questionable assumption that numeric quantifiers used to convey the expected values of choice options are uniformly interpreted as exact values. Two experiments show that when the exactness of such quantifiers is made explicit by the experimenter, framing effects vanish. However, when the same quantifiers are given a lower bound (at least) meaning, the typical framing effect is found. A 3rd experiment confirmed that most people spontaneously interpret the quantifiers in standard framing tests as lower bounded and that their interpretations strongly moderate the framing effect. Notably, in each experiment, a significant majority of participants made rational choices, either choosing the option that maximized expected value (i.e., lives saved) or choosing consistently across frames when the options were of equal expected value.

Research suggests that causal judgment is influenced primarily by counterfactual or covariational reasoning. In contrast, the author of this article develops judgment dissociation theory (JDT), which predicts that these types of reasoning differ in function and can lead to divergent judgments. The actuality principle proposes that causal selections focus on antecedents that are sufficient to generate the actual outcome. The substitution principle proposes that ad hoc categorization plays a key role in counterfactual and covariational reasoning such that counterfactual selections focus on antecedents that would have been sufficient to prevent the outcome or something like it and covariational selections focus on antecedents that yield the largest increase in the probability of the outcome or something like it. The findings of 4 experiments support JDT but not the competing counterfactual and covariational accounts.

The accuracy of 1,514 strategic intelligence forecasts abstracted from intelligence reports was assessed. The results show that both discrimination and calibration of forecasts was very good. Discrimination was better for senior (versus junior) analysts and for easier (versus harder) forecasts. Miscalibration was mainly due to underconfidence such that analysts assigned more uncertainty than needed given their high level of discrimination. Underconfidence was more pronounced for harder (versus easier) forecasts and for forecasts deemed more (versus less) important for policy decision making. Despite the observed underconfidence, there was a paucity of forecasts in the least informative 0.4-0.6 probability range. Recalibrating the forecasts substantially reduced underconfidence. The findings offer cause for tempered optimism about the accuracy of strategic intelligence forecasts and indicate that intelligence producers aim to promote informativeness while avoiding overstatement.forecasting | prediction | intelligence analysis | quality control | recalibration

In judging posterior probabilities, people often answer with the inverse conditional probability-a tendency named the inverse fallacy. Participants (N = 45) were given a series of probability problems that entailed estimating both p(H | D) and p(,H | D). The findings revealed that deviations of participants' estimates from Bayesian calculations and from the additivity principle could be predicted by the corresponding deviations of the inverse probabilities from these relevant normative benchmarks. Methodological and theoretical implications of the distinction between inverse fallacy and base-rate neglect and the generalization of the study of additivity to conditional probabilities are discussed.

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