Evaluating performance over time: Is improving better than being consistently good?

In many decision contexts, people evaluate others based on intertemporal performance records and commonly face a choice between two distinct profiles: performance that is consistently high versus performance that improves over time to that high level. We proposed that these two profiles could be appealing for different reasons, and thus evaluators’ preferences will differ across decision contexts. In three studies, participants were presented with candidates (e.g., students, employees) displaying the two profiles, and evaluated each candidate in terms of performance, future expectations, and deservingness. The consistent candidate was rated higher on performance, but lower on future expectations, than the improved candidate. Consequently, in achievement-based decisions (e.g., selecting a student for a scholarship), the consistent candidate was viewed as most deserving, whereas in potential-based decisions (e.g., selecting an employee for promotion), the improved candidate was preferred. These effects were mediated by the relative weight that evaluators placed on performance and future expectations.     

Do tests facilitate decision‐making performance on a transfer task?

Testing can improve later recall of information. However, much less is known about the potential use of testing in promoting the transfer of learning. In this study, we investigated whether testing improves decision‐making performance on a transfer task in a sample of 98 university students using a between‐subjects design. After studying several statements about a fictional disease under different learning conditions (restudy, free recall, and multiple‐choice), participants were asked to recall this information and subsequently make medical decisions concerning the fictional disease (i.e., the transfer task). The present study found no advantage of testing conditions over restudy condition on the 30‐min delayed memory task. However, participants in the active retrieval practice (i.e., free recall) group performed significantly better on the transfer task over those in both restudy and multiple‐choice groups. These results suggest free‐recall tests promote the transfer of learning.     

Why Improvement Can Trump Consistent Strong Performance: The Role of Effort Perceptions

How do evaluators form comparative judgments of improved versus consistently strong performance records with equivalent recent performance? We propose that evaluators judge those with improved records to be more deserving of future opportunities (e.g., a promotion) and that this can be explained – at least in part – by perceptions of effort investments. Specifically, evaluators rely on improvement to judge effort and hence devalue consistent strong performance. Five studies supported these propositions. Evaluators perceived greater effort investment and trait effort in individuals with improved profiles than those with consistent profiles and consequently thought that those with improved profiles were more deserving of future opportunities. We discuss implications of these results across various decision contexts. Copyright © 2017 John Wiley & Sons, Ltd.     

Evidence for Opportunity Cost Neglect in the Poor

People often neglect opportunity costs: They do not fully take into account forgone alternatives outside of a particular choice set. Several scholars have suggested that poor people should be more likely to spontaneously consider opportunity costs, because budget constraints should lead to an increased focus on trade‐offs. We did not find support for this hypothesis in five high‐powered experiments (total N = 2325). The experiments used different products (both material and experiential) with both high and low prices (from $8.50 to $249.99) and different methods of reminding participants of opportunity costs. High‐income and low‐income participants showed an equally strong decrease in willingness to buy when reminded of opportunity costs, implying that both the rich and the poor neglect opportunity costs. © 2017 The Authors Journal of Behavioral Decision Making Published by John Wiley & Sons Ltd.     

The Role of Anticipated Regret in Advice Taking

Across five studies, we demonstrate that anticipated future regret influences receptiveness to advice. While making a revision to one's own judgment based on advice, people can anticipate two kinds of future regret: (a) the regret of following non‐beneficial advice and (b) the regret of ignoring beneficial advice. In studies 1a (scenario task) and 1b (judgment task), we find that anticipated regret from erring after following advice is greater than anticipated regret from erring after ignoring advice. Furthermore, receptiveness decreases as the difference between anticipated regret from following and from ignoring advice increases. In study 2, we demonstrate that perceived justifiability of one's own initial decision is greater than that of advice. This difference in perceived justifiability influences anticipated regret and that, in turn, influences receptiveness. In study 3, we investigate the effect of advisor's expertise on perceived justifiability, anticipated regret, and receptiveness. In study 4, we propose and test an intervention to improve receptiveness based on self‐generation of advice justifications. Participants who were asked to self‐generate justifications for the advice were more receptive to it. This effect was mediated by perceived justifiability and anticipated regret. These findings shed further light on what prevents people from being receptive to advice and how this can be improved. Copyright © 2017 John Wiley & Sons, Ltd.     

Are Decisions Made by Group Representatives More Risk Averse? The Effect of Sense of Responsibility

We tested the hypothesis that a sense of responsibility drives group representatives' decisions to be more risk averse compared with decisions made by individuals. The hypothesis was supported when the monetary considerations (i.e., payoff inequality and the magnitude effect) were controlled for in the potential gain domain as well as in the potential loss domain. Evidence showed that this is because the group representatives were concerned about how they would view themselves (e.g., guilt and self‐blame) and also how they would be viewed by others (i.e., to avoid being blamed and looked down upon by others). This study provided new insights into understanding group representatives' decision making under risk. Copyright © 2017 John Wiley & Sons, Ltd.     

Discounting model selection with area‐based measures: A case for numerical integration

A novel method for analyzing delay discounting data is proposed. This newer metric, a model‐based Area Under Curve (AUC) combining approximate Bayesian model selection and numerical integration, was compared to the point‐based AUC methods developed by Myerson, Green, and Warusawitharana (2001) and extended by Borges, Kuang, Milhorn, and Yi (2016). Using data from computer simulation and a published study, comparisons of these methods indicated that a model‐based form of AUC offered a more consistent and statistically robust measurement of area than provided by using point‐based methods alone. Beyond providing a form of AUC directly from a discounting model, numerical integration methods permitted a general calculation in cases when the Effective Delay 50 (ED50) measure could not be calculated. This allowed discounting model selection to proceed in conditions where data are traditionally more challenging to model and measure, a situation where point‐based AUC methods are often enlisted. Results from simulation and existing data indicated that numerical integration methods extended both the area‐based interpretation of delay discounting as well as the discounting model selection approach. Limitations of point‐based AUC as a first‐line analysis of discounting and additional extensions of discounting model selection were also discussed.     

The natural mathematics of behavior analysis

Models that generate event records have very general scope regarding the dimensions of the target behavior that we measure. From a set of predicted event records, we can generate predictions for any dependent variable that we could compute from the event records of our subjects. In this sense, models that generate event records permit us a freely multivariate analysis. To explore this proposition, we conducted a multivariate examination of Catania's Operant Reserve on single VI schedules in transition using a Markov Chain Monte Carlo scheme for Approximate Bayesian Computation. Although we found systematic deviations between our implementation of Catania's Operant Reserve and our observed data (e.g., mismatches in the shape of the interresponse time distributions), the general approach that we have demonstrated represents an avenue for modelling behavior that transcends the typical constraints of algebraic models.