Financial incentives are widely used to get people to adopt desirable behaviors. Many small landholders in developing countries, for example, receive multiyear payments to engage in conservation behaviors, and the hope is that they will continue to engage in these behaviors after the program ends. Although effective in the short term, financial incentives rarely lead to long-term behavior change because program participants tend to revert to their initial behaviors soon after the payments stop. In this article, we propose that four psychological constructs can be leveraged to increase the long-term effectiveness of financial-incentive programs: motivation, habit formation, social norms, and recursive processes. We review successful and unsuccessful behavior-change initiatives involving financial incentives in several domains: public health, education, sustainability, and conservation. We make concrete recommendations on how to implement the four above-mentioned constructs in field settings. Finally, we identify unresolved issues that future research might want to address to advance knowledge, promote theory development, and understand the psychological mechanisms that can be used to improve the effectiveness of incentive programs in the real world.

We identify points of conflict and consensus regarding (a) controversial empirical claims and (b) normative preferences for how controversial scholarship-and scholars-should be treated. In 2021, we conducted qualitative interviews (n = 41) to generate a quantitative survey (N = 470) of U.S. psychology professors' beliefs and values. Professors strongly disagreed on the truth status of 10 candidate taboo conclusions: For each conclusion, some professors reported 100% certainty in its veracity and others 100% certainty in its falsehood. Professors more confident in the truth of the taboo conclusions reported more self-censorship, a pattern that could bias perceived scientific consensus regarding the inaccuracy of controversial conclusions. Almost all professors worried about social sanctions if they were to express their own empirical beliefs. Tenured professors reported as much self-censorship and as much fear of consequences as untenured professors, including fear of getting fired. Most professors opposed suppressing scholarship and punishing peers on the basis of moral concerns about research conclusions and reported contempt for peers who petition to retract papers on moral grounds. Younger, more left-leaning, and female faculty were generally more opposed to controversial scholarship. These results do not resolve empirical or normative disagreements among psychology professors, but they may provide an empirical context for their discussion.

Because the term “diversity” has two related but different meanings, what authors mean when they use the term is inherently unclear. In its broad form, it refers to vast variety. In its narrow form, it refers to human demographic categories deemed deserving of special attention by social justice–oriented activists. In this article, I review Hommel’s critique of Roberts et al. (2020), which, I suggest, essentially constitutes two claims: that Roberts et al.’s (2020) call for diversity in psychological science focuses exclusively on the latter narrow form of diversity and ignores the scientific importance of diversity in the broader sense, and ignoring diversity in the broader sense is scientifically unjustified. Although Hommel’s critique is mostly justified, this is not because Roberts et al. (2020) are wrong to call for greater social justice–oriented demographic diversity in psychology but because Hommel’s call for the broader form of diversity subsumes that of Roberts et al. (2020) and has other aspects critical to creating a valid, generalizable, rigorous, and inclusive psychological science. In doing so, I also highlight omissions, limitations, and potential downsides to the narrow manner in which psychology and the broader academy are currently implementing diversity, equity, and inclusion.

The public will rightly not value a science that is more concerned with demographic population matching than with ideas. Taking further steps in the direction of identity politics will reduce public confidence in psychology’s conclusions and reduce trust and respect. If psychology embraces demographic quotas, there will be self-selection out of the discipline, and that self-selection will harm our science.

Roberts and colleagues focus on two aspects of racial inequality in psychological research, namely an alleged underrepresentation of racial minorities and the effects attributed to this state of affairs. My comment focuses only on one aspect, namely the assumed consequences of the lack of diversity in subject populations. Representativeness of samples is essential in survey research or applied research that examines whether a particular intervention will work for a particular population. Representativeness or diversity is not necessary in theory-testing research, where we attempt to establish laws of causality. Because theories typically apply to all of humanity, all members of humanity (even American undergraduates) are suitable for assessing the validity of theoretical hypotheses. Admittedly, the assumption that a theory applies to all of humanity is also a hypothesis that can be tested. However, to test it, we need theoretical hypotheses about specific moderating variables. Supporting a theory with a racially diverse sample does not make conclusions more valid than support from a nondiverse sample. In fact, cause-effect conclusions based on a diverse sample might not be valid for any member of that sample.

The increasing use of political activist arguments and reasoning in scientific communication about diversity is criticized. Based on an article of Roberts et al. (2020) on “racial inequality in psychological research,” three hallmarks of the intrusion of activist thinking into science are described: blindness to the multidimensional nature of diversity, the failure to distinguish psychological mechanisms from the impact of moderators, and a blindness to agency as an explanation for psychological observations. It is argued that uncritically accepting and introducing political activist arguments into science is likely to damage scientific freedom and independence.

In the spirit of America’s Shakespeare, August Wilson (1997), I have written this article as a testimony to the conditions under which I, and too many others, engage in scholarly discourse. I hope to make clear from the beginning that although the ideas presented here are not entirely my own—as they have been inherited from the minority of scholars who dared and managed to bring the most necessary, unpalatable, and unsettling truths about our discipline to the broader scientific community—I do not write for anyone but myself and those scholars who have felt similarly marginalized, oppressed, and silenced. And I write as a race scholar, meaning simply that I believe that race—and racism—affects the sociopolitical conditions in which humans, and scholars, develop their thoughts, feelings, and actions. I believe that it is important for all scholars to have a basic understanding of these conditions, as well as the landmines and pitfalls that define them, as they shape how research is conducted, reviewed, and disseminated. I also believe that to evolve one’s discipline into one that is truly robust and objective, it must first become diverse and self-aware. Any effort to suggest otherwise, no matter how scholarly it might present itself, is intellectually unsound.

A recent article in Perspectives on Psychological Science (Webb & Tangney, 2022) reported a study in which just 2.6% of participants recruited on Amazon’s Mechanical Turk (MTurk) were deemed “valid.” The authors highlighted some well-established limitations of MTurk, but their central claims—that MTurk is “too good to be true” and that it captured “only 14 human beings . [out of] N = 529”—are radically misleading, yet have been repeated widely. This commentary aims to (a) correct the record (i.e., by showing that Webb and Tangney’s approach to data collection led to unusually low data quality) and (b) offer a shift in perspective for running high-quality studies online. Negative attitudes toward MTurk sometimes reflect a fundamental misunderstanding of what the platform offers and how it should be used in research. Beyond pointing to research that details strategies for effective design and recruitment on MTurk, we stress that MTurk is not suitable for every study. Effective use requires specific expertise and design considerations. Like all tools used in research—from advanced hardware to specialist software—the tool itself places constraints on what one should use it for. Ultimately, high-quality data is the responsibility of the researcher, not the crowdsourcing platform.