Initial assessment of the efficacy of food recovery policies in US States for increasing food donations and reducing waste.

In this paper, we examine the primary impact of two categories of food recovery policies on food donation and the secondary impact on food safety, food waste, and food insecurity in U.S. states. As one method of food recovery, food donation can reduce food waste while mitigating food insecurity, and it can be promoted in U.S. states through strong liability protection policies that provide legal protection to food donors and through tax incentivization policies that financially reward food donors via deductions and/or credits. To provide an initial evaluation of the effects of these policies, we coded each state's food recovery policies in 2012 and 2018 and compared strong policies versus weak policies. Using data from multiple sources, we found that states with stronger liability protection policies had more food donations, and states that provide tax incentivization had more food waste. Although our analyses were correlational, rather than causal, and were reliant upon limited data, our results demonstrate that the current food recovery policy landscape in U.S. states does relate to important food waste outcomes. We discuss the implications of these findings for crafting more effective policies that encourage food recovery.

Nudging Discharge Readiness With a Poster: A Sequential, Exploratory Mixed Methods Pilot Study of Patient Caregivers.

Many hospitals face a common challenge: limited space for a high number of patients. This has led to quick patient throughput, which can impact patient perception of discharge readiness. This study examined whether a poster highlighting tasks to complete as part of the discharge process improved caregiver perception of readiness to transition home. Using a sequential, exploratory mixed methods design, focus groups were convened to explore clinical staff perspective on the discharge process on 3 pediatric inpatient units at a large, urban, pediatric academic medical center in the United States. Analysis of this content informed the design of a poster intervention to "nudge" caregivers (eg, parents, legal guardians) toward readiness and self-efficacy that was then tested in a randomized, controlled experiment. The poster focused on practical knowledge for specific areas of transition adjustment, such as medication and care recipient recovery behaviors, barriers, and enablers. Caregivers (n = 135) completed surveys at discharge indicating their perceived readiness to transition home with their child. Analysis of covariance was used to test the effect of the poster condition (poster vs no poster) on caregiver readiness, preparedness, and confidence for discharge while controlling for previous admission history. Significant effects for poster presence were found on caregivers' perceived readiness for discharge, F 1,125 = 7.75, P = .006, Cohen's d = 0.44; and caregivers' perceived preparedness for the transition home, F 1,121 =7.24, P = .008, Cohen's d = 0.44. Only a marginal effect was found for poster condition on caregivers' confidence ratings, F 1,125 = 2.93, P = .090, Cohen's d = 0.29. The results suggest that simple nudges in the patient care environment may yield measurable improvements in caregiver outcomes.

General fluid/inductive reasoning battery for a high-ability population.

The validity of studies investigating interventions to enhance fluid intelligence (Gf) depends on the adequacy of the Gf measures administered. Such studies have yielded mixed results, with a suggestion that Gf measurement issues may be partly responsible. The purpose of this study was to develop a Gf test battery comprising tests meeting the following criteria: (a) strong construct validity evidence, based on prior research; (b) reliable and sensitive to change; (c) varying in item types and content; (d) producing parallel tests, so that pretest-posttest comparisons could be made; (e) appropriate time limits; (f) unidimensional, to facilitate interpretation; and (g) appropriate in difficulty for a high-ability population, to detect change. A battery comprising letter, number, and figure series and figural matrix item types was developed and evaluated in three large-N studies (N = 3,067, 2,511, and 801, respectively). Items were generated algorithmically on the basis of proven item models from the literature, to achieve high reliability at the targeted difficulty levels. An item response theory approach was used to calibrate the items in the first two studies and to establish conditional reliability targets for the tests and the battery. On the basis of those calibrations, fixed parallel forms were assembled for the third study, using linear programming methods. Analyses showed that the tests and test battery achieved the proposed criteria. We suggest that the battery as constructed is a promising tool for measuring the effectiveness of cognitive enhancement interventions, and that its algorithmic item construction enables tailoring the battery to different difficulty targets, for even wider applications.

Measuring working memory is all fun and games: a four-dimensional spatial game predicts cognitive task performance.

We developed a novel four-dimensional spatial task called Shapebuilder and used it to predict performance on a wide variety of cognitive tasks. In six experiments, we illustrate that Shapebuilder: (1) Loads on a common factor with complex working memory (WM) span tasks and that it predicts performance on quantitative reasoning tasks and Ravens Progressive Matrices (Experiment 1), (2) Correlates well with traditional complex WM span tasks (Experiment 2), predicts performance on the conditional go/no go task (Experiment 3) and N-back (Experiment 4), and showed weak or nonsignificant correlations with the Attention Networks Task (Experiment 5), and task switching (Experiment 6). Shapebuilder shows that it exhibits minimal skew and kurtosis, and shows good reliability. We argue that Shapebuilder has many advantages over existing measures of WM, including the fact that it is largely language independent, is not prone to ceiling effects, and take less than 6 min to complete on average.

Reply to Rouder (2014): good frequentist properties raise confidence.

Established psychological results have been called into question by demonstrations that statistical significance is easy to achieve, even in the absence of an effect. One often-warned-against practice, choosing when to stop the experiment on the basis of the results, is guaranteed to produce significant results. In response to these demonstrations, Bayes factors have been proposed as an antidote to this practice, because they are invariant with respect to how an experiment was stopped. Should researchers only care about the resulting Bayes factor, without concern for how it was produced? Yu, Sprenger, Thomas, and Dougherty (2014) and Sanborn and Hills (2014) demonstrated that Bayes factors are sometimes strongly influenced by the stopping rules used. However, Rouder (2014) has provided a compelling demonstration that despite this influence, the evidence supplied by Bayes factors remains correct. Here we address why the ability to influence Bayes factors should still matter to researchers, despite the correctness of the evidence. We argue that good frequentist properties mean that results will more often agree with researchers' statistical intuitions, and good frequentist properties control the number of studies that will later be refuted. Both help raise confidence in psychological results.

When decision heuristics and science collide.

The ongoing discussion among scientists about null-hypothesis significance testing and Bayesian data analysis has led to speculation about the practices and consequences of "researcher degrees of freedom." This article advances this debate by asking the broader questions that we, as scientists, should be asking: How do scientists make decisions in the course of doing research, and what is the impact of these decisions on scientific conclusions? We asked practicing scientists to collect data in a simulated research environment, and our findings show that some scientists use data collection heuristics that deviate from prescribed methodology. Monte Carlo simulations show that data collection heuristics based on p values lead to biases in estimated effect sizes and Bayes factors and to increases in both false-positive and false-negative rates, depending on the specific heuristic. We also show that using Bayesian data collection methods does not eliminate these biases. Thus, our study highlights the little appreciated fact that the process of doing science is a behavioral endeavor that can bias statistical description and inference in a manner that transcends adherence to any particular statistical framework.

Generating and evaluating options for decision making: the impact of sequentially presented evidence.

We examined how decision makers generate and evaluate hypotheses when data are presented sequentially. In the first 2 experiments, participants learned the relationship between data and possible causes of the data in a virtual environment. Data were then presented iteratively, and participants either generated hypotheses they thought caused the data or rated the probability of possible causes of the data. In a 3rd experiment, participants generated hypotheses and made probability judgments on the basis of previously stored general knowledge. Findings suggest that both the hypotheses one generates and the judged probability of those hypotheses are heavily influenced by the most recent evidence observed and by the diagnosticity of the evidence. Specifically, participants generated a narrow set of possible explanations when the presented evidence was diagnostic compared with when it was nondiagnostic, suggesting that nondiagnostic evidence entices participants to cast a wider net when generating hypotheses.

Implications of cognitive load for hypothesis generation and probability judgment.

We tested the predictions of HyGene (Thomas et al., 2008) that both divided attention at encoding and judgment should affect the degree to which participants' probability judgments violate the principle of additivity. In two experiments, we showed that divided attention during judgment leads to an increase in subadditivity, suggesting that the comparison process for probability judgments is capacity limited. Contrary to the predictions of HyGene, a third experiment revealed that divided attention during encoding leads to an increase in later probability judgment made under full attention. The effect of divided attention during encoding on judgment was completely mediated by the number of hypotheses participants generated, indicating that limitations in both encoding and recall can cascade into biases in judgments.

Diagnostic hypothesis generation and human judgment.

Diagnostic hypothesis-generation processes are ubiquitous in human reasoning. For example, clinicians generate disease hypotheses to explain symptoms and help guide treatment, auditors generate hypotheses for identifying sources of accounting errors, and laypeople generate hypotheses to explain patterns of information (i.e., data) in the environment. The authors introduce a general model of human judgment aimed at describing how people generate hypotheses from memory and how these hypotheses serve as the basis of probability judgment and hypothesis testing. In 3 simulation studies, the authors illustrate the properties of the model, as well as its applicability to explaining several common findings in judgment and decision making, including how errors and biases in hypothesis generation can cascade into errors and biases in judgment.

The influence of improper sets of information on judgment: how irrelevant information can bias judged probability.

This article introduces 2 new sources of bias in probability judgment, discrimination failure and inhibition failure, which are conceptualized as arising from an interaction between error prone memory processes and a support theory like comparison process. Both sources of bias stem from the influence of irrelevant information on participants' probability judgments, but they postulate different mechanisms for how irrelevant information affects judgment. The authors used an adaptation of the proactive interference (PI) and release from PI paradigm to test the effect of irrelevant information on judgment. The results of 2 experiments support the discrimination failure account of the effect of PI on probability judgment. In addition, the authors show that 2 commonly used measures of judgment accuracy, absolute and relative accuracy, can be dissociated. The results have broad implications for theories of judgment.