Does alleviating poverty increase cognitive performance? Short- and long-term evidence from a randomized controlled trial.

In this Registered Report, we investigated the impact of a cash transfer based poverty alleviation program on cognitive performance. We analyzed data from a randomized controlled trial conducted on low-income, high-risk individuals in Liberia where a random half of the participants (n = 251) received a $200 lump-sum unconditional cash transfer - equivalent approximately to 300% of their monthly income - while the other half (n = 222) did not. We tested both the short-term (2-5 weeks) and the long-term (12-13 months) impact of the treatment via several executive function measures. The observed effect sizes of cash transfers on cognitive performance (b = .13 for the short- and b = .08 for the long-term) were roughly three and four times smaller than suggested by prior non-randomized research. Bayesian analyses revealed that the overall evidence supporting the existence of these effects is inconclusive. A multiverse analysis showed that neither alternative analytical specifications nor alternative processing of the dataset changed the results consistently. However cognitive performance varied between the executive function measures, suggesting that cash transfers may affect the subcomponents of executive function differently.

Correction to: 'Raising the value of research studies in psychological science by increasing the credibility of research reports: The Transparent Psi Project' (2023) by Kekecs et al.

[This corrects the article DOI: 10.1098/rsos.191375.][This corrects the article DOI: 10.1098/rspb.2022.2500.].

Raising the value of research studies in psychological science by increasing the credibility of research reports: the transparent Psi project.

The low reproducibility rate in social sciences has produced hesitation among researchers in accepting published findings at their face value. Despite the advent of initiatives to increase transparency in research reporting, the field is still lacking tools to verify the credibility of research reports. In the present paper, we describe methodologies that let researchers craft highly credible research and allow their peers to verify this credibility. We demonstrate the application of these methods in a multi-laboratory replication of Bem's Experiment 1 (Bem 2011 J. Pers. Soc. Psychol. 100, 407-425. (doi:10.1037/a0021524)) on extrasensory perception (ESP), which was co-designed by a consensus panel including both proponents and opponents of Bem's original hypothesis. In the study we applied direct data deposition in combination with born-open data and real-time research reports to extend transparency to protocol delivery and data collection. We also used piloting, checklists, laboratory logs and video-documented trial sessions to ascertain as-intended protocol delivery, and external research auditors to monitor research integrity. We found 49.89% successful guesses, while Bem reported 53.07% success rate, with the chance level being 50%. Thus, Bem's findings were not replicated in our study. In the paper, we discuss the implementation, feasibility and perceived usefulness of the credibility-enhancing methodologies used throughout the project.

Contextual factors predicting compliance behavior during the COVID-19 pandemic: A machine learning analysis on survey data from 16 countries.

Voluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someone had left their home during a one week period during which they were asked to voluntarily isolate themselves. The analyses indicated that overall, an increase in the feeling of being caged leads to an increased probability of leaving home. In addition, an increased feeling of responsibility and an increased fear of getting infected decreased the probability of leaving home. The models predicted compliance behavior with between 54% and 91% accuracy within each country's sample. In addition, we modeled factors leading to risky behavior in the pandemic context. We observed an increased probability of visiting risky places as both the anticipated number of people and the importance of the activity increased. Conversely, the probability of visiting risky places increased as the perceived putative effectiveness of social distancing decreased. The variance explained in our models predicting risk ranged from < .01 to .54 by country. Together, our findings can inform behavioral interventions to increase adherence to lockdown recommendations in pandemic conditions.

A machine learning analysis of the relationship of demographics and social gathering attendance from 41 countries during pandemic.

Knowing who to target with certain messages is the prerequisite of efficient public health campaigns during pandemics. Using the COVID-19 pandemic situation, we explored which facets of the society-defined by age, gender, income, and education levels-are the most likely to visit social gatherings and aggravate the spread of a disease. Analyzing the reported behavior of 87,169 individuals from 41 countries, we found that in the majority of the countries, the proportion of social gathering-goers was higher in male than female, younger than older, lower-educated than higher educated, and low-income than high-income subgroups of the populations. However, the data showed noteworthy heterogeneity between the countries warranting against generalizing from one country to another. The analysis also revealed that relative to other demographic factors, income was the strongest predictor of avoidance of social gatherings followed by age, education, and gender. Although the observed strength of these associations was relatively small, we argue that incorporating demographic-based segmentation into public health campaigns can increase the efficiency of campaigns with an important caveat: the exploration of these associations needs to be done on a country level before using the information to target populations in behavior change interventions.

A billion-dollar donation: estimating the cost of researchers' time spent on peer review.

BACKGROUND: The amount and value of researchers' peer review work is critical for academia and journal publishing. However, this labor is under-recognized, its magnitude is unknown, and alternative ways of organizing peer review labor are rarely considered. METHODS: Using publicly available data, we provide an estimate of researchers' time and the salary-based contribution to the journal peer review system. RESULTS: We found that the total time reviewers globally worked on peer reviews was over 100 million hours in 2020, equivalent to over 15 thousand years. The estimated monetary value of the time US-based reviewers spent on reviews was over 1.5 billion USD in 2020. For China-based reviewers, the estimate is over 600 million USD, and for UK-based, close to 400 million USD. CONCLUSIONS: By design, our results are very likely to be under-estimates as they reflect only a portion of the total number of journals worldwide. The numbers highlight the enormous amount of work and time that researchers provide to the publication system, and the importance of considering alternative ways of structuring, and paying for, peer review. We foster this process by discussing some alternative models that aim to boost the benefits of peer review, thus improving its cost-benefit ratio.

Consensus-based guidance for conducting and reporting multi-analyst studies.

Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research.

Researchers working from home: Benefits and challenges.

The flexibility allowed by the mobilization of technology disintegrated the traditional work-life boundary for most professionals. Whether working from home is the key or impediment to academics' efficiency and work-life balance became a daunting question for both scientists and their employers. The recent pandemic brought into focus the merits and challenges of working from home on a level of personal experience. Using a convenient sampling, we surveyed 704 academics while working from home and found that the pandemic lockdown decreased the work efficiency for almost half of the researchers but around a quarter of them were more efficient during this time compared to the time before. Based on the gathered personal experience, 70% of the researchers think that in the future they would be similarly or more efficient than before if they could spend more of their work-time at home. They indicated that in the office they are better at sharing thoughts with colleagues, keeping in touch with their team, and collecting data, whereas at home they are better at working on their manuscript, reading the literature, and analyzing their data. Taking well-being also into account, 66% of them would find it ideal to work more from home in the future than they did before the lockdown. These results draw attention to how working from home is becoming a major element of researchers' life and that we have to learn more about its influencer factors and coping tactics in order to optimize its arrangements.

Higher economic inequality intensifies the financial hardship of people living in poverty by fraying the community buffer.

The current research investigates whether higher economic inequality disproportionately intensifies the financial hardship of low-income individuals. We propose that higher economic inequality increases financial hardship for low-income individuals by reducing their ability to rely on their community as a buffer against financial difficulties. This may occur, in part, because a frayed community buffer reduces low-income individuals' propensity to seek informal financial support from others. We provide empirical support across eight studies (sample size N = 1,029,900) from the United States, Australia and rural Uganda, through correlational and experimental data, as well as an instrumental variable analysis. On average across our studies, a 1 s.d. increase in economic inequality is associated with an increase of financial hardship among low-income individuals of 0.10 s.d. We discuss the implications of these results for policies aimed to help people living in poverty buffer against the adverse effects higher economic inequality imposes on them.

Author Correction: A consensus-based transparency checklist.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Is action execution part of the decision-making process? An investigation of the embodied choice hypothesis.

In this study, we aimed to explore whether action execution is an inherent part of the decision-making process. According to the hypothesis of embodied choice, the decision-making process is bidirectional as action dynamics exert their backward influence on decision processes through changing the cost and value of the potential options. This influence takes place as moving toward one option increases the commitment to and, therefore, the likelihood of choosing that option. This commitment effect can be the result of either (a) the continuous act of getting closer to this option or (b) the increased movement cost associated with changing the movement direction to select a different option. To disentangle the potential influence of these two factors, we developed the Guided Movement Task, a choice task designed to bias participant's computer-mouse movements by constraining the allowed movement space by a corridor. Using this task, we created different conditions in which the participants' mouse cursor, after being guided toward one of the options, either had equal or unequal distances to the choice options. By this manipulation, we could test whether the continuous act of getting closer to an option in itself is sufficient to influence people's decisions-a claim of "strong embodiment." In two experiments, we found that the likelihood of choosing an option only increased when the distances between the two options were unequal after the initial movement but not when they were equal. These results disagree with the hypothesis that action execution is an inherent part of the decision-making process. (PsycINFO Database Record

Exploring the determinants of confidence in the bat-and-ball problem.

People often fail to solve deceptively simple mathematical problems, a tendency popularly demonstrated by the bat-and-ball problem. The most prominent explanation of this finding is that, to spare cognitive effort, people substitute the difficult task with an easier one, without being aware of the substitution. Despite this latter assumption, recent studies have found decreased levels of post-decision confidence ratings when people gave the answer of an easier calculation, suggesting that people are sensitive to their errors. In the current study, we investigated a mechanism that might be responsible for such a decrease in people's confidence ratings when they make errors: their attempts to make certain that their answer is correct (verification) and the perceived level of task difficulty (verifiability). We found that these two factors predicted people's confidence, suggesting that people's self-assessment of the perceived task difficulty and of their attempt to verify their response might determine their confidence. Implication for current models of post-decision confidence on reasoning problems is discussed.

Estimating the evidential value of significant results in psychological science.

Quantifying evidence is an inherent aim of empirical science, yet the customary statistical methods in psychology do not communicate the degree to which the collected data serve as evidence for the tested hypothesis. In order to estimate the distribution of the strength of evidence that individual significant results offer in psychology, we calculated Bayes factors (BF) for 287,424 findings of 35,515 articles published in 293 psychological journals between 1985 and 2016. Overall, 55% of all analyzed results were found to provide BF > 10 (often labeled as strong evidence) for the alternative hypothesis, while more than half of the remaining results do not pass the level of BF = 3 (labeled as anecdotal evidence). The results estimate that at least 82% of all published psychological articles contain one or more significant results that do not provide BF > 10 for the hypothesis. We conclude that due to the threshold of acceptance having been set too low for psychological findings, a substantial proportion of the published results have weak evidential support.