Amazon Mechanical Turk (MTurk) is widely used by behavioral scientists to recruit research participants. MTurk offers advantages over traditional student subject pools, but it also has important limitations. In particular, the MTurk population is small and potentially overused, and some groups of interest to behavioral scientists are underrepresented and difficult to recruit. Here we examined whether online research panels can avoid these limitations. Specifically, we compared sample composition, data quality (measured by effect sizes, internal reliability, and attention checks), and the non-naivete of participants recruited from MTurk and Prime Panels—an aggregate of online research panels. Prime Panels participants were more diverse in age, family composition, religiosity, education, and political attitudes. Prime Panels participants also reported less exposure to classic protocols and produced larger effect sizes, but only after screening out several participants who failed a screening task. We conclude that online research panels offer a unique opportunity for research, yet one with some important trade-offs.Electronic supplementary materialThe online version of this article (10.3758/s13428-019-01273-7) contains supplementary material, which is available to authorized users.

The COVID-19 pandemic has made the world seem less predictable. Such crises can lead people to feel that others are a threat. Here, we show that the initial phase of the pandemic in 2020 increased individuals' paranoia and made their belief updating more erratic. A proactive lockdown made people's belief updating less capricious. However, state-mandated mask-wearing increased paranoia and induced more erratic behaviour. This was most evident in states where adherence to mask-wearing rules was poor but where rule following is typically more common. Computational analyses of participant behaviour suggested that people with higher paranoia expected the task to be more unstable. People who were more paranoid endorsed conspiracies about mask-wearing and potential vaccines and the QAnon conspiracy theories. These beliefs were associated with erratic task behaviour and changed priors. Taken together, we found that real-world uncertainty increases paranoia and influences laboratory task behaviour. ArticlesNATure HumAN BeHAVIOur (BF 10 = 0.163, strong evidence for the null hypothesis) or reversals achieved (BF 10 = 0.210, strong evidence for the null hypothesis) between social and non-social tasks. Computational modelling. Probabilistic reversal learning involves decision-making under uncertainty. The reasons for decisions may not be manifest in simple counts of choices or errors. By modelling Reward probability (%) 10

Bullying has been the topic of much debate and empirical investigations over the past decade. Contemporary literature contends that students with disabilities may be overrepresented within the bullying dynamic as both perpetrators and victims. Unfortunately, prevalence rates associated with the representation of students with disabilities is limited due to measurement, disability status identification, and definition issues. The present study attempted to address these issues by assessing the prevalence rates of specific subgroups of students with disabilities in a large-scale cross-sectional study with 13,325 students without disabilities and 1,183 students with disabilities in Grades 6 through 12. Results suggest that overall, students with disabilities reported proportionally higher rates of bullying, fighting, relational aggression, victimization, online victimization, and relational victimization than did their peers without disabilities. These findings suggest that schools must begin to establish targeted interventions to support skill development based on characteristics associated with specific disability identification. C 2015 Wiley Periodicals, Inc.

Mechanical Turk (MTurk) is a common source of research participants within the academic community. Despite MTurk’s utility and benefits over traditional subject pools some researchers have questioned whether it is sustainable. Specifically, some have asked whether MTurk workers are too familiar with manipulations and measures common in the social sciences, the result of many researchers relying on the same small participant pool. Here, we show that concerns about non-naivete on MTurk are due less to the MTurk platform itself and more to the way researchers use the platform. Specifically, we find that there are at least 250,000 MTurk workers worldwide and that a large majority of US workers are new to the platform each year and therefore relatively inexperienced as research participants. We describe how inexperienced workers are excluded from studies, in part, because of the worker reputation qualifications researchers commonly use. Then, we propose and evaluate an alternative approach to sampling on MTurk that allows researchers to access inexperienced participants without sacrificing data quality. We recommend that in some cases researchers should limit the number of highly experienced workers allowed in their study by excluding these workers or by stratifying sample recruitment based on worker experience levels. We discuss the trade-offs of different sampling practices on MTurk and describe how the above sampling strategies can help researchers harness the vast and largely untapped potential of the Mechanical Turk participant pool.

To understand human behavior, social scientists need people and data. In the last decade, Amazon’s Mechanical Turk (MTurk) emerged as a flexible, affordable, and reliable source of human participants and was widely adopted by academics. Yet despite MTurk’s utility, some have questioned whether researchers should continue using the platform on ethical grounds. The brunt of their concern is that people on MTurk are financially insecure, subjected to abuse, and earning inhumane wages. We investigated these issues with two random and representative surveys of the U.S. MTurk population (N = 4,094). The surveys revealed: 1) the financial situation of people on MTurk mirrors the general population, 2) the vast majority of people do not find MTurk stressful or requesters abusive, and 3) MTurk offers flexibility and benefits that most people value above more traditional work. In addition, people reported it is possible to earn about 9 dollars per hour and said they would not trade the flexibility of MTurk for less than 25 dollars per hour. Altogether, our data are important for assessing whether MTurk is an ethical place for behavioral research. We close with ways researchers can promote wage equity, ensuring MTurk is a place for affordable, high-quality, and ethical data.

a Numbers are percentages of unique participants within each month from the CloudResearch database. A description of this database and how well it captures the Mechanical Turk population appears in Robinson et al. [7] and Moss et al. [8]. To examine the percentage of studies completed by people within each demographic group each month, visit https://metrics.cloudresearch.com/ Trends in Cognitive Sciences

Mechanical Turk (MTurk) is a common source of research participants within the academic community. Despite MTurk's utility and benefits over traditional subject pools some researchers have questioned whether it is sustainable. Specifically, some have asked whether MTurk workers are too familiar with manipulations and measures common in the social sciences, the result of many researchers relying on the same small participant pool.Here, we show that concerns about non-naivete on MTurk are due less to the MTurk platform itself and more to the way researchers use the platform. Specifically, we find that there are at least 250,000 MTurk workers worldwide and that a large majority of US workers are new to the platform each year and therefore relatively inexperienced as research participants. We describe how inexperienced workers are excluded from studies, in part, because of the worker reputation qualifications researchers commonly use. Then, we propose and evaluate an alternative approach to sampling on MTurk that allows researchers to access inexperienced participants without sacrificing data quality. We recommend that in some cases researchers should limit the number of highly experienced workers allowed in their study by excluding these workers or by stratifying sample recruitment based on worker experience levels. We discuss the trade-offs of different sampling practices on MTurk and describe how the above sampling strategies can help researchers harness the vast and largely untapped potential of the Mechanical Turk participant pool.

How do people judge the intentions of a perpetrator and the harm experienced by a victim in cases of racial discrimination? How do these judgments influence attributions to discrimination? We examined these questions in 4 studies, predicting that Whites’ and Blacks’ judgments would reflect different group-based perspectives. Supporting our hypotheses, White authors describing an arrest denied intent and ignored harm relative to Black authors (Study 1). When judging whether an event was discrimination, Whites were influenced by intent, but Blacks were influenced by intent and harm (Study 2). Finally, instructing people to take the victim’s perspective increased Whites’ judgments of intent, harm, and discrimination (Studies 3 and 4), while Blacks’ judgments generally remained the same (Study 4). Our results demonstrate one reason why Whites and Blacks judge discrimination differently—they adopt different perspectives when evaluating intent and harm—and offer a way to increase Whites’ recognition of discrimination: perspective-taking.