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BackgroundRandomized controlled trials (RCTs) are essential for determining the safety and efficacy of healthcare interventions. However, both laypeople and clinicians often demonstrate experiment aversion: preferring to implement either of two interventions for everyone rather than comparing them to determine which is best. We studied whether clinician and layperson views of pragmatic RCTs for Covid-19 or other interventions became more positive early in the pandemic, which increased both the urgency and public discussion of RCTs.MethodsWe conducted several survey studies with laypeople (totaln=2,909) and two with clinicians (n=895;n=1,254) in 2020 and 2021. Participants read vignettes in which a hypothetical decision-maker who sought to improve health could choose to implement intervention A for all, implement intervention B for all, or experimentally compare A and B and implement the superior intervention. Participants rated and ranked the appropriateness of each decision.ResultsCompared to our pre-pandemic results, we found no decrease in laypeople’s aversion to non-Covid-19 experiments involving catheterization checklists and hypertension drugs. Nor were either laypeople or clinicians less averse to Covid-19 RCTs (concerning corticosteroid drugs, vaccines, intubation checklists, proning, school reopening, and mask protocols), on average. Across all vignettes and samples, levels of experiment aversion ranged from 28% to 57%, while levels of experiment appreciation (in which the RCT is rated higher than the participant’s highest-rated intervention) ranged from only 6% to 35%.ConclusionsAdvancing evidence-based medicine through pragmatic RCTs will require anticipating and addressing experiment aversion among both patients and healthcare professionals.

This study introduces a novel, game-like method for measuring social intelligence: the Social Shapes Test. Unlike other existing video or game-based tests, the Shapes Test uses animations of abstract shapes to represent social interactions. We explore demographic differences in Shapes Test scores compared to a written situational judgment test. Gender and race/ethnicity only had meaningful effects on written SJT scores while no effects were found for Shapes Test scores. This pattern of results remained after controlling for general mental ability and English language exposure. We also found metric invariance between demographic groups for both tests. Our results demonstrate the potential for using animated shape tasks as an alternative to written SJTs when designing future game-based assessments.

Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources.This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Consortium across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center).Predictive models were developed and validated with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015.In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82 - 0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites.In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Consortium website.

In 1944, Heider and Simmel reported that observers could perceive simple animated geometric shapes as characters with emotions, intentions, and other social attributes. This work has been cited over 3,000 times and has had wide and ongoing influence on the study of social cognition and social intelligence. However, many researchers in this area have continued to use the original Heider and Simmel black-and-white video. We asked whether the original findings could be reproduced 75 years later by creating 32 new colored animated shape videos designed to depict various social plots and testing whether they can evoke similar spontaneous social attributions. Participants (N = 66) viewed our videos and were asked to write narratives which we coded for indicia of different types of social attributions. Consistent with Heider and Simmel, we found that participants spontaneously attributed social meaning to the videos. We observed that responses to our videos were also similar to responses to the original video reported by Klin (2000), despite being only 13-23 s and portraying a broader range of social plots. Participants varied in how many social attributions they made in response, and the videos varied in how much they elicited such responses. Our set of animated shape videos is freely available online for all researchers to use and forms the basis of a multiple-choice assessment of social intelligence (Brown et al., 2019).

Traditionally, scientists have placed more emphasis on communicating inferential uncertainty (i.e., the precision of statistical estimates) compared to outcome variability (i.e., the predictability of individual outcomes). Here we show that this can lead to sizable misperceptions about the implications of scientific results. Specifically, we present three pre-registered, randomized experiments where participants saw the same scientific findings visualized as showing only inferential uncertainty, only outcome variability, or both, and answered questions about the size and importance of findings they were shown. Our results, comprised of responses from medical professionals, professional data scientists, and tenure-track faculty, show that the prevalent form of visualizing only inferential uncertainty can lead to significant overestimates of treatment effects, even among highly trained experts. In contrast, we find that depicting both inferential uncertainty and outcome variability leads to more accurate perceptions of results while appearing to leave other subjective impressions of the results unchanged, on average.

Influenza vaccinations are recommended for high-risk individuals, but few population-based strategies exist to identify individual risks. Patient-level data from unvaccinated individuals, stratified into retrospective cases (n = 111,022) and controls (n = 2,207,714), informed a machine learning model designed to create an influenza risk score; the model was called the Geisinger Flu-Complications Flag (GFlu-CxFlag). The flag was created and validated on a cohort of 604,389 unique individuals. Risk scores were generated for influenza cases; the complication rate for individuals without influenza was estimated to adjust for unrelated complications. Shapley values were used to examine the model’s correctness and demonstrate its dependence on different features. Bias was assessed for race and sex. Inverse propensity weighting was used in the derivation stage to correct for biases. The GFlu-CxFlag model was compared to the pre-existing Medial EarlySign Flu Algomarker and existing risk guidelines that describe high-risk patients who would benefit from influenza vaccination. The GFlu-CxFlag outperformed other traditional risk-based models; the area under curve (AUC) was 0.786 [0.783–0.789], compared with 0.694 [0.690–0.698] (p-value < 0.00001). The presence of acute and chronic respiratory diseases, age, and previous emergency department visits contributed most to the GFlu-CxFlag model’s prediction. When higher numerical scores were assigned to more severe complications, the GFlu-CxFlag AUC increased to 0.828 [0.823–0.833], with excellent discrimination in the final model used to perform the risk stratification of the population. The GFlu-CxFlag can better identify high-risk individuals than existing models based on vaccination guidelines, thus creating a population-based risk stratification for individual risk assessment and deployment in vaccine hesitancy reduction programs in our health system.

Encouraging vaccination is a pressing policy problem. To assess whether text-based reminders can encourage pharmacy vaccination and what kinds of messages work best, we conducted a megastudy. We randomly assigned 689,693 Walmart pharmacy patients to receive one of 22 different text reminders using a variety of different behavioral science principles to nudge flu vaccination or to a business-as-usual control condition that received no messages. We found that the reminder texts that we tested increased pharmacy vaccination rates by an average of 2.0 percentage points, or 6.8%, over a 3-mo follow-up period. The most-effective messages reminded patients that a flu shot was waiting for them and delivered reminders on multiple days. The top-performing intervention included two texts delivered 3 d apart and communicated to patients that a vaccine was "waiting for you." Neither experts nor lay people anticipated that this would be the best-performing treatment, underscoring the value of simultaneously testing many different nudges in a highly powered megastudy.

During the past decade the idea that expertise is dead, or at best moribund, has become commonplace. Knowledge resistance appears to be growing more politicized and is increasing across a wide range of science-based topics, such as agriculture, evolution and genetics, vaccination, and climate change; even flat-earth beliefs are undergoing a renaissance. But in many of these areas, denying expert authority is cost-free in everyday behavior, making it more rational for people to prize identity and group affiliation over realism. To probe the health of expertise in a domain with everyday consequences for knowledge resistance, we conducted three incentive-compatible studies of laypeople’s preferences for sources of information they would read about specific medical conditions (e.g., heart disease, cancer, COVID-19). We found quite rational preference patterns, by which people preferred sources based on experts (physicians and scientists) over non-experts (celebrities and politicians) and group consensus (professional societies, polls) over individual opinions. These findings held most strongly for issues of personal medical concern, but were robust for less concerning health conditions, and for the highly politicized topic of COVID-19. Individuals who scored higher in intellectual humility and preferences for rational over experiential thinking were more likely to prefer the most expert sources. Expertise retains broad respect in the medical domain, at least when one’s own health is at stake.

ImportanceDevelopmental language disorder (DLD) is a common (with up to 7% prevalence) yet underdiagnosed childhood disorder whose underlying biological profile and comorbidities are not fully understood, especially at the population level.ObjectiveTo identify clinically relevant conditions that co-occur with DLD at the population level.Design, Setting, and ParticipantsThis case-control study used an electronic health record (EHR)–based population-level approach to compare the prevalence of comorbid health phenotypes between DLD cases and matched controls. These cases were identified using the Automated Phenotyping Tool for Identifying Developmental Language Disorder algorithm of the Vanderbilt University Medical Center EHR, and a phenome enrichment analysis was used to identify comorbidities. An independent sample was selected from the Geisinger Health System EHR to test the replication of the phenome enrichment using the same phenotyping and analysis pipeline. Data from the Vanderbilt EHR were accessed between March 2019 and October 2020, while data from the Geisinger EHR were accessed between January and March 2022.Main Outcomes and MeasuresCommon and rare comorbidities of DLD at the population level were identified using EHRs and a phecode-based enrichment analysis.ResultsComorbidity analysis was conducted for 5273 DLD cases (mean [SD] age, 16.8 [7.2] years; 3748 males [71.1%]) and 26 353 matched controls (mean [SD] age, 14.6 [5.5] years; 18 729 males [71.1%]). Relevant phenotypes associated with DLD were found, including learning disorder, delayed milestones, disorders of the acoustic nerve, conduct disorders, attention-deficit/hyperactivity disorder, lack of coordination, and other motor deficits. Several other health phenotypes not previously associated with DLD were identified, such as dermatitis, conjunctivitis, and weight and nutrition, representing a new window into the clinical complexity of DLD.Conclusions and RelevanceThis study found both rare and common comorbidities of DLD. Comorbidity profiles may be leveraged to identify risk of additional health challenges, beyond language impairment, among children with DLD.

The compromise effect arises when being close to the "middle" of a choice set makes an option more appealing. The compromise effect poses conceptual and practical problems for economic research: by influencing choices, it can bias researchers' inferences about preference parameters. To study this bias, we conduct an experiment with 550 participants who made choices over lotteries from multiple price lists (MPLs). Following prior work, we manipulate the compromise effect to influence choices by varying the middle options of each MPL. We then estimate risk preferences using a discrete-choice model without a compromise effect embedded in the model. As anticipated, the resulting risk preference parameter estimates are not robust, changing as the compromise effect is manipulated. To disentangle risk preference parameters from the compromise effect and to measure the strength of the compromise effect, we augment our discrete-choice model with additional parameters that represent a rising penalty for expressing an indifference point further from the middle of the ordered MPL. Using this method, we estimate an economically significant magnitude for the compromise effect and generate robust estimates of risk preference parameters that are no longer sensitive to compromise-effect manipulations.

Although individual differences in the ability known as social intelligence are important to many areas of psychological science, there are few validated measures of this capacity. Existing measures often involve complex verbal materials, focus on images of human faces, or are based on self-report judgments. Here we describe the development and validation of a new measure of social intelligence: the Social Shapes Test (SST). The SST is inspired by the work of Heider and Simmel (1944), Klin (2000), and recent applications of their work in social and clinical neuroscience studies of mentalizing and Theory of Mind. Each SST item consists of a short video of 4–6 simple geometric shapes that are animated to simulate social interactions, paired with an objectively scored, multiple-choice question. Scores on the SST predict performance on measures of social intelligence beyond the effects of cognitive ability. SST scores are also distinct from cognitive abilities and do not appear to vary based on sex, race/ethnicity, or current affect. With just 23 engaging items and a duration of about 10 minutes, the SST is a convenient tool for researchers to measure social intelligence, alone or in combination with other established measures.

Significance Randomized experiments—long the gold standard in medicine—are increasingly used throughout the social sciences and professions to evaluate business products and services, government programs, education and health policies, and global aid. We find robust evidence—across 16 studies of 5,873 participants from three populations spanning nine domains—that people often approve of untested policies or treatments (A or B) being universally implemented but disapprove of randomized experiments (A/B tests) to determine which of those policies or treatments is superior. This effect persists even when there is no reason to prefer A to B and even when recipients are treated unequally and randomly in all conditions (A, B, and A/B). This experimentation aversion may be an important barrier to evidence-based practice., Randomized experiments have enormous potential to improve human welfare in many domains, including healthcare, education, finance, and public policy. However, such “A/B tests” are often criticized on ethical grounds even as similar, untested interventions are implemented without objection. We find robust evidence across 16 studies of 5,873 participants from three diverse populations spanning nine domains—from healthcare to autonomous vehicle design to poverty reduction—that people frequently rate A/B tests designed to establish the comparative effectiveness of two policies or treatments as inappropriate even when universally implementing either A or B, untested, is seen as appropriate. This “A/B effect” is as strong among those with higher educational attainment and science literacy and among relevant professionals. It persists even when there is no reason to prefer A to B and even when recipients are treated unequally and randomly in all conditions (A, B, and A/B). Several remaining explanations for the effect—a belief that consent is required to impose a policy on half of a population but not on the entire population; an aversion to controlled but not to uncontrolled experiments; and a proxy form of the illusion of knowledge (according to which randomized evaluations are unnecessary because experts already do or should know “what works”)—appear to contribute to the effect, but none dominates or fully accounts for it. We conclude that rigorously evaluating policies or treatments via pragmatic randomized trials may provoke greater objection than simply implementing those same policies or treatments untested.

Abstract. Williams and Bargh (2008) reported that holding a hot cup of coffee caused participants to judge a person’s personality as warmer and that holding a therapeutic heat pad caused participants to choose rewards for other people rather than for themselves. These experiments featured large effects ( r = .28 and .31), small sample sizes (41 and 53 participants), and barely statistically significant results. We attempted to replicate both experiments in field settings with more than triple the sample sizes (128 and 177) and double-blind procedures, but found near-zero effects ( r = −.03 and .02). In both cases, Bayesian analyses suggest there is substantially more evidence for the null hypothesis of no effect than for the original physical warmth priming hypothesis.

Many Americans fail to get life-saving vaccines each year, and the availability of a vaccine for COVID-19 makes the challenge of encouraging vaccination more urgent than ever. We present a large field experiment ( N = 47,306) testing 19 nudges delivered to patients via text message and designed to boost adoption of the influenza vaccine. Our findings suggest that text messages sent prior to a primary care visit can boost vaccination rates by an average of 5%. Overall, interventions performed better when they were 1) framed as reminders to get flu shots that were already reserved for the patient and 2) congruent with the sort of communications patients expected to receive from their healthcare provider (i.e., not surprising, casual, or interactive). The best-performing intervention in our study reminded patients twice to get their flu shot at their upcoming doctor’s appointment and indicated it was reserved for them. This successful script could be used as a template for campaigns to encourage the adoption of life-saving vaccines, including against COVID-19.

For many vaccine-preventable diseases like influenza, vaccination rates are lower than optimal to achieve community protection. Those at high risk for infection and serious complications are especially advised to be vaccinated to protect themselves. Using influenza as a model, we studied one method of increasing vaccine uptake: informing high-risk patients, identified by a machine learning model, about their risk status. Patients (N=39,717) were evenly randomized to (1) a control condition (exposure only to standard direct mail or patient portal vaccine promotion efforts) or to be told via direct mail, patient portal, and/or SMS that they were (2) at high risk for influenza and its complications if not vaccinated; (3) at high risk according to a review of their medical records; or (4) at high risk according to a computer algorithm analysis of their medical records. Patients in the three treatment conditions were 5.7% more likely to get vaccinated during the 112 days post-intervention (p < .001), and did so 1.4 days earlier (p < .001), on average, than those in the control group. There were no significant differences among risk messages, suggesting that patients are neither especially averse to nor uniquely appreciative of learning their records had been reviewed or that computer algorithms were involved. Similar approaches should be considered for COVID-19 vaccination campaigns.

Science is often perceived to be a self-correcting enterprise. In principle, the assessment of scientific claims is supposed to proceed in a cumulative fashion, with the reigning theories of the day progressively approximating truth more accurately over time. In practice, however, cumulative self-correction tends to proceed less efficiently than one might naively suppose. Far from evaluating new evidence dispassionately and infallibly, individual scientists often cling stubbornly to prior findings. Here we explore the dynamics of scientific self-correction at an individual rather than collective level. In 13 written statements, researchers from diverse branches of psychology share why and how they have lost confidence in one of their own published findings. We qualitatively characterize these disclosures and explore their implications. A cross-disciplinary survey suggests that such loss-of-confidence sentiments are surprisingly common among members of the broader scientific population yet rarely become part of the public record. We argue that removing barriers to self-correction at the individual level is imperative if the scientific community as a whole is to achieve the ideal of efficient self-correction. ispartof: PERSPECTIVES ON PSYCHOLOGICAL SCIENCE vol:16 issue:6 pages:1255-1269 ispartof: location:United States status: published

We partnered with Walmart to test 22 nudges designed to boost vaccination rates in their pharmacies. Nudges were delivered via text message to over 650,000 Walmart pharmacy patients in the fall of 2020 and encouraged patients to visit Walmart for a flu vaccine. We demonstrate that behaviorally informed messages increase pharmacy vaccination rates by an average of 6.7% over a roughly three-month follow-up period. The most effective messages in our field experiment matched the tone of typical pharmacy communications and reminded patients that a flu shot was waiting for them. These insights suggest that carefully crafted messages informed by the results of this study could nudge the adoption of other vaccines for other infectious diseases, including COVID-19.

There exists a moderate correlation between MRI-measured brain size and the general factor of IQ performance (g), but the question of whether the association reflects a theoretically important causal relationship or spurious confounding remains somewhat open. Previous small studies (n < 100) looking for the persistence of this correlation within families failed to find a tendency for the sibling with the larger brain to obtain a higher test score. We studied the within-family relationship between brain volume and intelligence in the much larger sample provided by the Human Connectome Project (n = 1,022) and found a highly significant correlation (disattenuated ρ = 0.18, p < .001). We replicated this result in the Minnesota Center for Twin and Family Research (n = 2,698), finding a highly significant within-family correlation between head circumference and intelligence (disattenuated ρ = 0.19, p < .001). We also employed novel methods of causal inference relying on summary statistics from genome-wide association studies (GWAS) of head size (n ≈ 10,000) and measures of cognition (257,000 < n < 767,000). Using bivariate LD Score regression, we found a genetic correlation between intracranial volume (ICV) and years of education (EduYears) of 0.41 (p < .001). Using the Latent Causal Variable method, we found a genetic causality proportion of 0.72 (p < .001); thus the genetic correlation arises from an asymmetric pattern, extending to sub-significant loci, of genetic variants associated with ICV also being associated with EduYears but many genetic variants associated with EduYears not being associated with ICV. This is the pattern of genetic results expected from a causal effect of brain size on intelligence. These findings give reason to take up the hypothesis that the dramatic increase in brain volume over the course of human evolution has been the result of natural selection favoring general intelligence.

Intelligence is associated with important economic and health-related life outcomes. Despite intelligence having substantial heritability (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered. Here we report a meta- A nalysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10-8) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10-6), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10-6). Despite the well-known difference in twin-based heritability for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg = 0.89, LD score regression P = 5.4 × 10-29). These findings provide new insight into the genetic architecture of intelligence.

Note: A final version of this research has been published (link and reference below).Link: https://www.pnas.org/content/pnas/early/2020/07/24/2009030117.full.pdfReference: Heck, P.R., Watts, D., Chabris, C.F., & Meyer, M.N. (2020). Objecting to experiments even while approving of the policies or treatments they compare. Proceedings of the National Academy of Sciences. Online first. https://doi.org/10.1073/pnas.2009030117Across three studies that revealed the A/B Effect using a between-subjects design in Meyer et al. (2019), we find via corresponding within-subjects designs that the A/B Effect persists even when participants (1) have complete information about the options available to a decision-maker (i.e., to implement policy A for everyone or policy B for everyone, or to run an A/B test between them and use the results to choose the best policy), (2) rate the appropriateness of these decisions after being able to consider all of them, and (3) rank-order the soundness of the decisions to run an A/B test or implement an untested policy. The result held across three domains: two healthcare (a hospital and a walk-in clinic) and one corporate (a direct-to-consumer genetic testing company). Importantly, the results were robust to increasing levels of conservativeness in the critical test for the presence of an effect.

Objective: Individuals at high risk for schizophrenia may benefit from early intervention, but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts. The authors sought to test the utility of this approach in clinical settings and to evaluate the broader health consequences of high genetic risk for schizophrenia. Methods: The authors used electronic health records for 106,160 patients from four health care systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1,359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites. Results: PRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS, 1.55; 95% CI=1.4, 1.7), and patients in the highest risk decile of the PRS distribution had up to 4.6-fold higher odds of schizophrenia compared with those in the bottom decile (95% CI=2.9, 7.3). PRSs were also positively associated with other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity. Conclusions: The study demonstrates that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in health care settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. The results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in health care systems.

The first opportunities to field test interventions to increase COVID-19 vaccination were among health care workers (HCWs), who were among the first to be offered COVID-19 vaccines. After 1 large Pennsylvania health system sent 36 vaccine-related mass emails to employees over 5 weeks (eAppendix in Supplement 1), 9723 of 23 700 HCWs (41%) had still not scheduled their vaccination. We sought to determine whether individually addressed emails designed with behaviorally informed features1,2,3,4,5 could increase vaccination registration compared with a delayed control group.

In 2014, two groups of scientists published open letters on the efficacy of brain-training interventions, or “brain games,” for improving cognition. The first letter, a consensus statement from an international group of more than 70 scientists, claimed that brain games do not provide a scientifically grounded way to improve cognitive functioning or to stave off cognitive decline. Several months later, an international group of 133 scientists and practitioners countered that the literature is replete with demonstrations of the benefits of brain training for a wide variety of cognitive and everyday activities. How could two teams of scientists examine the same literature and come to conflicting “consensus” views about the effectiveness of brain training?In part, the disagreement might result from different standards used when evaluating the evidence. To date, the field has lacked a comprehensive review of the brain-training literature, one that examines both the quantity and the quality of the evidence according to a well-defined set of best practices. This article provides such a review, focusing exclusively on the use of cognitive tasks or games as a means to enhance performance on other tasks. We specify and justify a set of best practices for such brain-training interventions and then use those standards to evaluate all of the published peer-reviewed intervention studies cited on the websites of leading brain-training companies listed on Cognitive Training Data ( www.cognitivetrainingdata.org ), the site hosting the open letter from brain-training proponents. These citations presumably represent the evidence that best supports the claims of effectiveness.Based on this examination, we find extensive evidence that brain-training interventions improve performance on the trained tasks, less evidence that such interventions improve performance on closely related tasks, and little evidence that training enhances performance on distantly related tasks or that training improves everyday cognitive performance. We also find that many of the published intervention studies had major shortcomings in design or analysis that preclude definitive conclusions about the efficacy of training, and that none of the cited studies conformed to all of the best practices we identify as essential to drawing clear conclusions about the benefits of brain training for everyday activities. We conclude with detailed recommendations for scientists, funding agencies, and policymakers that, if adopted, would lead to better evidence regarding the efficacy of brain-training interventions.

What individual factors predict success? We tested Chua and Rubenfeld's (2014) widely publicized “Triple Package” hypothesis that a tendency toward impulse control, personal insecurity, and a belief in the superiority of one's cultural or ethnic group combine to increase the odds that individuals will attain exceptional achievement. Consistent with previous research, we found in two sizable samples (combined N = 1258) that parents' level of education and individuals' own cognitive ability robustly predicted a composite measure of success that included income, education, and awards. Other factors such as impulse control and emotional stability also appeared to be salutary. But despite measuring personal insecurity in four different ways and measuring success in three different ways, we did not find support for any plausible version of Chua and Rubenfeld's proposed synergistic trinity of success-engendering personality traits.

In a choice experiment with 1866 Amazon Mechanical Turk workers in the domain of cars, we test some of the predictions of a family of multi-attribute choice models that incorporate a menu-dependent reference point whose value in every attribute is the minimum value of that attribute among the available options. In particular, these models predict that in two of the scenarios we consider in this paper, one should expect a significant attraction effect when an asymmetrically dominated option is added to the choice set, due to a change in the reference point. However, in the third scenario, these models predict no change in the choice probabilities of the existing options after adding the asymmetrically dominated option, and thus, no attraction effect, as the reference point remains intact. These models also make a number of predictions regarding the presence or the absence of another context effect called the compromise effect, which are tested in this paper. This amounts to a total of 8 predictions. We find support for 7 of these predictions, suggesting the aforementioned reference point has a significant impact on preferences and choices.

Williams and Bargh (2008) reported that holding a hot cup of coffee caused participants to judge a person’s personality as warmer, and that holding a therapeutic heat pad caused participants to choose rewards for other people rather than for themselves. These experiments featured large effects (r = .28 and .31), small sample sizes (41 and 53 participants), and barely statistically significant results. We attempted to replicate both experiments in field settings with more than triple the sample sizes (128 and 177) and double-blind procedures, but found near-zero effects (r = –.03 and .02). In both cases, Bayesian analyses suggest there is substantially more evidence for the null hypothesis of no effect than for the original physical warmth priming hypothesis.

OBJECTIVEIndividuals at high risk for schizophrenia may benefit from early intervention but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts, but its utility in clinical settings remains largely unexplored. Moreover, the broad health consequences of a high genetic risk of schizophrenia are poorly understood, despite being relevant to treatment decisions.METHODWe used electronic health records for 106,160 patients from four healthcare systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites.RESULTSPRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS=1.55 [95% confidence interval (CI), 1.4-1.7], p=4.48 x 10-16) and patients in the highest risk decile of the PRS distribution had up to 4.6-fold increased odds of schizophrenia compared to those in the bottom decile (95% CI, 2.9-7.3, p=1.37 x 10-10). PRSs were also positively associated with a range of other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity.CONCLUSIONSWe demonstrate that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in healthcare settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. Our results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in healthcare systems.

In response to our article (1), Mislavsky et al. (2) claim that “experiment aversion” does not exist because they found no evidence of it in their own research on low-stakes corporate experiments (3) and because our studies used between- rather than within-subjects designs. First, as we noted, we do not expect (and did not ourselves find) an A/B effect in every scenario, and we called for research on how the effect might vary across contexts. Second, we deliberately used a between-subjects design to maximize external validity: Universal implementation of policies usually occurs without mention of foregone alternatives, whereas A/B tests inherently acknowledge those alternatives. The belief that experiments deprive people of potentially beneficial interventions, but universally implemented policies do not, is not a “confound” to be avoided (2) but, rather, a key … [↵][1]1To whom correspondence may be addressed. Email: michellenmeyer{at}gmail.com. [1]: #xref-corresp-1-1

Groups typically express more confidence than individuals, yet how individual-level confidence combines during collaborative decision tasks is not well understood. We prescreened 686 community members using a novel confidence measure (a true/false trivia test) intentionally designed to be difficult (accuracy rates were not significantly better than chance) and randomly assigned 72 individuals to collaborate on a matched version of the same test in dyads composed of two low-confidence individuals, two high-confidence individuals, or one of each (“mixed”). Consistent with past research, we found that the confidence expressed by dyads was higher than the confidence expressed by individuals; importantly, however, this pattern varied markedly by dyad type, with low-confidence dyads showing the largest increase, mixed dyads showing a moderate increase, and high-confidence dyads showing no increase—despite the fact that all dyads showed similarly low accuracy (about 55%). These results highlight the conditions under which groups express greater confidence than individuals and offer insights for the composition of collaborative decision-making teams. Copyright © 2015 John Wiley & Sons, Ltd.