Craig R. M. McKenzie

What a speaker's choice of frame reveals: Reference points, frame selection, and framing effects

Framing effects are well established: Listeners’ preferences depend on how outcomes are described to them, or framed. Less well understood is what determines how speakers choose frames. Two experiments revealed that reference points systematically influenced speakers’ choices between logically equivalent frames. For example, speakers tended to describe a 4-ounce cup filled to the 2-ounce line as half full if it was previously empty but described it as half empty if it was previously full. Similar results were found when speakers could describe the outcome of a medical treatment in terms of either mortality or survival (e.g., 25% die vs. 75% survive). Two additional experiments showed that listeners made accurate inferences about speakers’ reference points on the basis of the selected frame (e.g., if a speaker described a cup as half empty, listeners inferred that the cup used to be full). Taken together, the data suggest that frames reliably convey implicit information in addition to their explicit content, which helps explain why framing effects are so robust.

The Accuracy of Intuitive Judgment Strategies: Covariation Assessment and Bayesian Inference

Abstract Most of people′s apparent strategies for covariation assessment and Bayesian inference can lead to errors. However, it is unclear how often and to what degree the strategies are inaccurate in natural contexts. Through Monte Carlo simulation, the respective normative and intuitive strategies for the two tasks were compared over many different situations. The results indicate that (a) under some general conditions, all the intuitive strategies perform much better than chance and many perform surprisingly well, and (b) some simple environmental variables have large effects on most of the intuitive strategies′ accuracy, not just in terms of the number of errors, but also in terms of the kinds of errors (e.g., incorrectly accepting versus incorrectly rejecting a hypothesis). Furthermore, common to many of the intuitive strategies is a disregard for the strength of the alternative hypothesis. Thus, a key to better performance in both tasks lies in considering alternative hypotheses, although this does not necessarily imply using a normative strategy (i.e., calculating the φ coefficient or using Bayes′ theorem). Some intuitive strategies take into account the alternative hypothesis and are accurate across environments. Because they are presumably simpler than normative strategies and are already part of people′s repertoire, using these intuitive strategies may be the most efficient means of ensuring highly accurate judgment in these tasks.

Learning from feedback: exactingness and incentives

In a series of five experiments, exactingness, or the extent to which deviations from optimal decisions are punished, is studied within the context of learning a repetitive decision-making task together with the effects of incentives. Results include the findings that (a) performance is an inverted-U shaped function of exactingness, (b) performance is better under incentives when environments are lenient but not when they are exacting, (c) the interaction between exactingness and incentives does not obtain when an incentives function fails to discriminate sharply between good and bad performance, and (d) when the negative effects of exactingness on performance are eliminated, performance increases with exactingness.

Underestimating the Duration of Future Events: Memory Incorrectly Used or Memory Bias?

People frequently underestimate how long it will take them to complete a task. The prevailing view is that during the prediction process, people incorrectly use their memories of how long similar tasks have taken in the past because they take an overly optimistic outlook. A variety of evidence is reviewed in this article that points to a different, although not mutually exclusive, explanation: People base predictions of future duration on their memories of how long past events have taken, but these memories are systematic underestimates of past duration. People appear to underestimate future event duration because they underestimate past event duration.

Taking into account the strength of an alternative hypothesis

A common phenomenon in judgment under uncertainty is that alternative hypotheses are underweighted or ignored. This article addresses when and how the strength of the alternative is taken into account when there are 2 hypotheses. A learning manipulation was used to invoke 2 representations of 2 illnesses in a medical diagnosis task. One representation tended to lead to consideration of the alternative when, for example, requesting new information, reporting confidence, and making diagnoses. The other representation tended to result in ignoring or underweighting the alternative, but a simple change in how confidence was probed increased consideration of the alternative. Costs and benefits of each representation are discussed.

Framing effects in inference tasks—and why they are normatively defensible

Framing effects occur when logically equivalent redescriptions of objects or outcomes lead to different behaviors, and, traditionally, such effects have been seen as irrational. However, recent evidence has shown that a speaker’s choice among logically equivalent attribute frames can implicitly convey (or “leak”) normatively relevant information about the speaker’s reference point, among other things. In a reinterpretion of data published elsewhere, in this article it is shown that some common effects in inference tasks (covariation assessment and hypothesis testing) can also be seen as framing effects, thereby expanding the domain of framing. It is also shown that these framing effects are normatively defensible because normatively relevant information aboutevent rarity is leaked through the description of data and through the phrasing of hypotheses, thereby broadening the information leakage approach to explaining framing effects. Information leakage can also explain why framing effects in such inference tasks disappear under certain conditions.

Experience Matters Information Acquisition Optimizes Probability Gain

Deciding which piece of information to acquire or attend to is fundamental to perception, categorization, medical diagnosis, and scientific inference. Four statistical theories of the value of information—information gain, Kullback-Liebler distance, probability gain (error minimization), and impact—are equally consistent with extant data on human information acquisition. Three experiments, designed via computer optimization to be maximally informative, tested which of these theories best describes human information search. Experiment 1, which used natural sampling and experience-based learning to convey environmental probabilities, found that probability gain explained subjects’ information search better than the other statistical theories or the probability-of-certainty heuristic. Experiments 1 and 2 found that subjects behaved differently when the standard method of verbally presented summary statistics (rather than experience-based learning) was used to convey environmental probabilities. Experiment 3 found that subjects’ preference for probability gain is robust, suggesting that the other models contribute little to subjects’ search behavior.

The psychological side of Hempel’s paradox of confirmation

People often test hypotheses about two variables (X andY), each with two levels (e.g.,X1 andX2). When testing “IfX1, thenY1,” observing the conjunction ofX1 andY1 is overwhelmingly perceived as more supportive than observing the conjunction ofX2 andY2, although both observations support the hypothesis. Normatively, theX2&Y2 observation provides stronger support than theX1&Y1 observation if the former is rarer. Because participants in laboratory settings typically test hypotheses they are unfamiliar with, previous research has not examined whether participants are sensitive to the rarity of observations. The experiment reported here showed that participants were sensitive to rarity, even judging a rareX2&Y2 observation more supportive than a commonX1&Y1 observation under certain conditions. Furthermore, participants’ default strategy of judgingX1&Y1 observations more informative might be generally adaptive because hypotheses usually regard rare events.

Rational models as theories - not standards - of behavior.

When peoples behavior in laboratory tasks systematically deviates from a rational model, the implication is that real-world performance could be improved by changing the behavior. However, recent studies suggest that behavioral violations of rational models are at least sometimes the result of strategies that are well adapted to the real world (and not necessarily to the laboratory task). Thus, even if one accepts that certain behavior in the laboratory is irrational, compelling evidence that real-world behavior ought to change accordingly is often lacking. It is suggested here that rational models be seen as theories, and not standards, of behavior.

Misunderstanding Savings Growth: Implications for Retirement Savings Behavior

People systematically underestimate exponential growth. This article illustrates this phenomenon, its implications, and some potential interventions in the context of saving for retirement, where savings grow exponentially over long periods of time. Experiment 1 shows that a majority of participants expect savings over 40 years to grow linearly rather than exponentially, leading them to grossly underestimate their account balance at retirement. Experiment 2 demonstrates that this misunderstanding leads to underestimates of the cost of waiting to save, which makes putting off saving more attractive than it should be. Finally, Experiments 3–5 show that highlighting the exponential growth of savings motivates both college students and employees to save more for retirement. Making clear to employees the exponential growth of savings before they make crucial decisions about how much to save may be a simple and effective means of increasing retirement savings.