Biases in Improvement Decisions: People Focus on the Relative Reduction in Bad Outcomes.

People often decide whether to invest scarce resources-such as time, money, or energy-to improve their chances of a positive outcome. For example, a doctor might decide whether to utilize scarce medicine to improve a patient's chances of recovery, or a student might decide whether to study a few additional hours to increase their chances of passing an exam. We conducted 11 studies (N = 5,342 adults) and found evidence that people behave as if they focus on the relative reduction in bad outcomes caused by such improvements. As a consequence, the same improvements (e.g., 10-percentage-point improvements) are valued very differently depending on whether one's initial chances of success are high or low. This focus on the relative reduction of bad outcomes drives risk preferences that violate normative standards (Studies 1a-1g and 2a), is amplified when decisions become more consequential (Study 2b), and leads even experienced professionals to make suboptimal decisions (Study 3).

Arbitrary fairness in reward and punishments.

People have a strong preference for fairness. For many, fairness means equal reward and punishments for equal efforts and offences. However, this belief does not specify the units in which equality should be expressed. We show that people generally fail to take the interchangeability of units into account when judging and assigning fair punishments and reward. Therefore, judgments about and distributions of resources are strongly influenced by arbitrary decisions about which unit to express them in. For example, if points represent different monetary values for different recipients, people attempt to distribute money equally if money is salient but attempt to distribute points equally if points are salient. Because beliefs about fairness are a fundamental principle in many domains, the implications of these findings are broad. Essentially any distribution of outcomes can be made to appear more or less fair by changing the units these outcomes are expressed in. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

On the role of similarity in mental accounting and hedonic editing.

The theory of mental accounting is often used to understand how people evaluate multiple outcomes or events. However, a model predicting which outcomes are associated with the same mental account and evaluated jointly, versus different accounts and evaluated separately, has remained elusive. We develop a framework that incorporates an online, bottom-up process of similarity and categorization into mental accounting operations. In this categorization-based model of mental accounting, outcomes that overlap on salient attributes are automatically categorized and assigned to the same mental account while outcomes that do not overlap on salient attributes are assigned to different accounts. We use this model to derive the hedonic accounting hypothesis, which generates testable behavioral predictions on people's preferences over the timing of outcomes given similarity-based constraints on mental accounting operations. Six studies provide support for the predictions: People prefer to experience similar losses close together in time and spread dissimilar losses apart; the reverse is true for gains, with a preference for dissimilar gains close together in time and similar gains spread apart across time. Importantly, our model is able to rationalize prior evidence that has found only limited support for the predictions of mental accounting and hedonic editing. Once the psychological process of similarity and categorization is explicitly incorporated into a formal model of mental accounting, its predictions are supported by the data. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A pre-registered, multi-lab non-replication of the action-sentence compatibility effect (ACE).

The Action-sentence Compatibility Effect (ACE) is a well-known demonstration of the role of motor activity in the comprehension of language. Participants are asked to make sensibility judgments on sentences by producing movements toward the body or away from the body. The ACE is the finding that movements are faster when the direction of the movement (e.g., toward) matches the direction of the action in the to-be-judged sentence (e.g., Art gave you the pen describes action toward you). We report on a pre-registered, multi-lab replication of one version of the ACE. The results show that none of the 18 labs involved in the study observed a reliable ACE, and that the meta-analytic estimate of the size of the ACE was essentially zero.

The Bias of Individuals (in Crowds): Why Implicit Bias Is Probably a Noisily Measured Individual-Level Construct.

Payne, Vuletich, and Lundberg's bias-of-crowds model proposes that a number of empirical puzzles can be resolved by conceptualizing implicit bias as a feature of situations rather than a feature of individuals. In the present article we argue against this model and propose that, given the existing evidence, implicit bias is best understood as an individual-level construct measured with substantial error. First, using real and simulated data, we show how each of Payne and colleagues' proposed puzzles can be explained as being the result of measurement error and its reduction via aggregation. Second, we discuss why the authors' counterarguments against this explanation have been unconvincing. Finally, we test a hypothesis derived from the bias-of-crowds model about the effect of an individually targeted "implicit-bias-based expulsion program" within universities and show the model to lack empirical support. We conclude by considering the implications of conceptualizing implicit bias as a noisily measured individual-level construct for ongoing implicit-bias research. All data and code are available at https://osf.io/tj8u6/.

Revisiting Tversky's diagnosticity principle.

Similarity is a fundamental concept in cognition. In 1977, Amos Tversky published a highly influential feature-based model of how people judge the similarity between objects. The model highlights the context-dependence of similarity judgments, and challenged geometric models of similarity. One of the context-dependent effects Tversky describes is the diagnosticity principle. The diagnosticity principle determines which features are used to cluster multiple objects into subgroups. Perceived similarity between items within clusters is expected to increase, while similarity between items in different clusters decreases. Here, we present two pre-registered replications of the studies on the diagnosticity effect reported in Tversky (1977). Additionally, one alternative mechanism that has been proposed to play a role in the original studies, an increase in the choice for distractor items (a substitution effect, see Medin et al., 1995), is examined. Our results replicate those found by Tversky (1977), revealing an average diagnosticity-effect of 4.75%. However, when we eliminate the possibility of substitution effects confounding the results, a meta-analysis of the data provides no indication of any remaining effect of diagnosticity.

Set-fit effects in choice.

In 4 experiments, we investigate how the "fit" of an item with a set of similar items affects choice. We find that people have a notion of a set that "fits" together--one where all items are the same, or all items differ, on salient attributes. One consequence of this notion is that in addition to preferences over the set's individual items, choice reflects set-fit. This leads to predictable shifts in preferences, sometimes even resulting in people choosing normatively inferior options over superior ones.

Sailing From the Seas of Chaos Into the Corridor of Stability: Practical Recommendations to Increase the Informational Value of Studies.

Recent events have led psychologists to acknowledge that the inherent uncertainty encapsulated in an inductive science is amplified by problematic research practices. In this article, we provide a practical introduction to recently developed statistical tools that can be used to deal with these uncertainties when performing and evaluating research. In Part 1, we discuss the importance of accurate and stable effect size estimates as well as how to design studies to reach a corridor of stability around effect size estimates. In Part 2, we explain how, given uncertain effect size estimates, well-powered studies can be designed with sequential analyses. In Part 3, we (a) explain what p values convey about the likelihood that an effect is true, (b) illustrate how the v statistic can be used to evaluate the accuracy of individual studies, and (c) show how the evidential value of multiple studies can be examined with a p-curve analysis. We end by discussing the consequences of incorporating our recommendations in terms of a reduced quantity, but increased quality, of the research output. We hope that the practical recommendations discussed in this article will provide researchers with the tools to make important steps toward a psychological science that allows researchers to differentiate among all possible truths on the basis of their likelihood.