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Science is among humanity's greatest achievements, yet scientific censorship is rarely studied empirically. We explore the social, psychological, and institutional causes and consequences of scientific censorship (defined as actions aimed at obstructing particular scientific ideas from reaching an audience for reasons other than low scientific quality). Popular narratives suggest that scientific censorship is driven by authoritarian officials with dark motives, such as dogmatism and intolerance. Our analysis suggests that scientific censorship is often driven by scientists, who are primarily motivated by self-protection, benevolence toward peer scholars, and prosocial concerns for the well-being of human social groups. This perspective helps explain both recent findings on scientific censorship and recent changes to scientific institutions, such as the use of harm-based criteria to evaluate research. We discuss unknowns surrounding the consequences of censorship and provide recommendations for improving transparency and accountability in scientific decision-making to enable the exploration of these unknowns. The benefits of censorship may sometimes outweigh costs. However, until costs and benefits are examined empirically, scholars on opposing sides of ongoing debates are left to quarrel based on competing values, assumptions, and intuitions.
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Censura de la Investigación , Ciencia , Responsabilidad Social , Costos y Análisis de CostoRESUMEN
We propose a friendly amendment to integrative experiment design (IED), adversarial-collaboration IED, that incentivizes research teams from competing theoretical perspectives to identify zones of the design space where they possess an explanatory edge. This amendment is especially critical in debates that have high policy stakes and carry a strong normative-political charge that might otherwise prevent free exchange of ideas.
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Proyectos de InvestigaciónRESUMEN
Two preregistered studies from two different platforms with representative U.S. adult samples (N = 1,865) tested the harm-hypervigilance hypothesis in risk assessments of controversial behavioral science. As expected, across six sets of scientific findings, people consistently overestimated others' harmful reactions (medium to large average effect sizes) and underestimated helpful ones, even when incentivized for accuracy. Additional analyses found that (a) harm overestimations were associated with support for censoring science, (b) people who were more offended by scientific findings reported greater difficulty understanding them, and (c) evidence was moderately consistent for an association between more conservative ideology and harm overestimations. These findings are particularly relevant because journals have begun evaluating potential downstream harms of scientific findings. We discuss implications of our work and invite scholars to develop rigorous tests of (a) the social pressures that lead science astray and (b) the actual costs and benefits of publishing or not publishing potentially controversial conclusions.
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Ciencias de la Conducta , Censura de la Investigación , Adulto , Humanos , Ansiedad , Estados Unidos , Medición de Riesgo , ConocimientoRESUMEN
In this paper we investigate the criterion validity of forced-choice comparisons of the quality of written arguments with normative solutions. Across two studies, novices and experts assessing quality of reasoning through a forced-choice design were both able to choose arguments supporting more accurate solutions-62.2% (SE = 1%) of the time for novices and 74.4% (SE = 1%) for experts-and arguments produced by larger teams-up to 82% of the time for novices and 85% for experts-with high inter-rater reliability, namely 70.58% (95% CI = 1.18) agreement for novices and 80.98% (95% CI = 2.26) for experts. We also explored two methods for increasing efficiency. We found that the number of comparative judgments needed could be substantially reduced with little accuracy loss by leveraging transitivity and producing quality-of-reasoning assessments using an AVL tree method. Moreover, a regression model trained to predict scores based on automatically derived linguistic features of participants' judgments achieved a high correlation with the objective accuracy scores of the arguments in our dataset. Despite the inherent subjectivity involved in evaluating differing quality of reasoning, the forced-choice paradigm allows even novice raters to perform beyond chance and can provide a valid, reliable, and efficient method for producing quality-of-reasoning assessments at scale.
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Until social psychology devotes as much attention to construct and external validity as it does to internal validity, the field will continue to produce theories that fail to replicate in the field and cannot be used to meliorate social problems.
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Conducta Obsesiva , Humanos , Reproducibilidad de los ResultadosRESUMEN
In our target article, we made four claims: (1) Social psychology is now politically homogeneous; (2) this homogeneity sometimes harms the science; (3) increasing political diversity would reduce this damage; and (4) some portion of the homogeneity is due to a hostile climate and outright discrimination against non-liberals. In this response, we review these claims in light of the arguments made by a diverse group of commentators. We were surprised to find near-universal agreement with our first two claims, and we note that few challenged our fourth claim. Most of the disagreements came in response to our claim that increasing political diversity would be beneficial. We agree with our critics that increasing political diversity may be harder than we had thought, but we explain why we still believe that it is possible and desirable to do so. We conclude with a revised list of 12 recommendations for improving political diversity in social psychology, as well as in other areas of the academy.
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Política , Ciencias Sociales , Humanos , Psicología Social , Ciencia , PensamientoRESUMEN
Psychologists have demonstrated the value of diversity--particularly diversity of viewpoints--for enhancing creativity, discovery, and problem solving. But one key type of viewpoint diversity is lacking in academic psychology in general and social psychology in particular: political diversity. This article reviews the available evidence and finds support for four claims: (1) Academic psychology once had considerable political diversity, but has lost nearly all of it in the last 50 years. (2) This lack of political diversity can undermine the validity of social psychological science via mechanisms such as the embedding of liberal values into research questions and methods, steering researchers away from important but politically unpalatable research topics, and producing conclusions that mischaracterize liberals and conservatives alike. (3) Increased political diversity would improve social psychological science by reducing the impact of bias mechanisms such as confirmation bias, and by empowering dissenting minorities to improve the quality of the majority's thinking. (4) The underrepresentation of non-liberals in social psychology is most likely due to a combination of self-selection, hostile climate, and discrimination. We close with recommendations for increasing political diversity in social psychology.
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Política , Psicología Social , HumanosRESUMEN
Five university-based research groups competed to recruit forecasters, elicit their predictions, and aggregate those predictions to assign the most accurate probabilities to events in a 2-year geopolitical forecasting tournament. Our group tested and found support for three psychological drivers of accuracy: training, teaming, and tracking. Probability training corrected cognitive biases, encouraged forecasters to use reference classes, and provided forecasters with heuristics, such as averaging when multiple estimates were available. Teaming allowed forecasters to share information and discuss the rationales behind their beliefs. Tracking placed the highest performers (top 2% from Year 1) in elite teams that worked together. Results showed that probability training, team collaboration, and tracking improved both calibration and resolution. Forecasting is often viewed as a statistical problem, but forecasts can be improved with behavioral interventions. Training, teaming, and tracking are psychological interventions that dramatically increased the accuracy of forecasts. Statistical algorithms (reported elsewhere) improved the accuracy of the aggregation. Putting both statistics and psychology to work produced the best forecasts 2 years in a row.
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Predicción , Técnicas Psicológicas/educación , Adulto , Algoritmos , Sesgo , Femenino , Humanos , Relaciones Interpersonales , Juicio , Masculino , Probabilidad , Conducta SocialRESUMEN
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.
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Research on clinical versus statistical prediction has demonstrated that algorithms make more accurate predictions than humans in many domains. Geopolitical forecasting is an algorithm-unfriendly domain, with hard-to-quantify data and elusive reference classes that make predictive model-building difficult. Furthermore, the stakes can be high, with missed forecasts leading to mass-casualty consequences. For these reasons, geopolitical forecasting is typically done by humans, even though algorithms play important roles. They are essential as aggregators of crowd wisdom, as frameworks to partition human forecasting variance, and as inputs to hybrid forecasting models. Algorithms are extremely important in this domain. We doubt that humans will relinquish control to algorithms anytime soon-nor do we think they should. However, the accuracy of forecasts will greatly improve if humans are aided by algorithms.
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Careful bias management and data fidelity are key.
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Inteligencia Artificial , Ciencias Sociales , Sesgo , HumanosRESUMEN
The authors reanalyzed data from 2 influential studies-A. R. McConnell and J. M. Leibold and J. C. Ziegert and P. J. Hanges-that explore links between implicit bias and discriminatory behavior and that have been invoked to support strong claims about the predictive validity of the Implicit Association Test. In both of these studies, the inclusion of race Implicit Association Test scores in regression models reduced prediction errors by only tiny amounts, and Implicit Association Test scores did not permit prediction of individual-level behaviors. Furthermore, the results were not robust when the impact of rater reliability, statistical specifications, and/or outliers were taken into account, and reanalysis of A. R. McConnell & J. M. Leibold (2001) revealed a pattern of behavior consistent with a pro-Black behavioral bias, rather than the anti-Black bias suggested in the original study.
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Población Negra/psicología , Prejuicio , Relaciones Raciales , Inconsciente en Psicología , Población Blanca/psicología , Pruebas de Asociación de Palabras/estadística & datos numéricos , Adulto , Sesgo , Femenino , Humanos , Relaciones Interpersonales , Solicitud de Empleo , Juicio , Masculino , Comunicación no Verbal , Variaciones Dependientes del Observador , Psicometría/estadística & datos numéricos , Reproducibilidad de los ResultadosRESUMEN
From 2011 to 2015, the U.S. intelligence community sponsored a series of forecasting tournaments that challenged university-based researchers to invent measurably better methods of forecasting political events. Our group, the Good Judgment Project, won these tournaments by balancing the collaboration and competition of members across disciplines. At the outset, psychologists were ahead of economists in identifying individual differences in forecasting skill and developing methods of debiasing forecasts, whereas economists were ahead of psychologists in designing simple market mechanisms that distilled predictive signals from noisy individual-level data. Working closely with statisticians, psychologists eventually beat the markets by producing better probability estimates that funneled top forecasters into elite teams and aggregated their judgments using a log-odds formula tuned to the diversity of the forecasters. Our research group performed best when team members strove to get as much as possible from their home disciplines, but acknowledged their limitations and welcomed help from outsiders. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Predicción , Políticas , Política , Personal Administrativo , Humanos , ProbabilidadRESUMEN
Intelligence analysts, like other professionals, form norms that define standards of tradecraft excellence. These norms, however, have evolved in an idiosyncratic manner that reflects the influence of prominent insiders who had keen psychological insights but little appreciation for how to translate those insights into testable hypotheses. The net result is that the prevailing tradecraft norms of best practice are only loosely grounded in the science of judgment and decision-making. The "common sense" of prestigious opinion leaders inside the intelligence community has pre-empted systematic validity testing of the training techniques and judgment aids endorsed by those opinion leaders. Drawing on the scientific literature, we advance hypotheses about how current best practices could well be reducing rather than increasing the quality of analytic products. One set of hypotheses pertain to the failure of tradecraft training to recognize the most basic threat to accuracy: measurement error in the interpretation of the same data and in the communication of interpretations. Another set of hypotheses focuses on the insensitivity of tradecraft training to the risk that issuing broad-brush, one-directional warnings against bias (e.g., over-confidence) will be less likely to encourage self-critical, deliberative cognition than simple response-threshold shifting that yields the mirror-image bias (e.g., under-confidence). Given the magnitude of the consequences of better and worse intelligence analysis flowing to policy-makers, we see a compelling case for greater funding of efforts to test what actually works.
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Political debates often suffer from vague-verbiage predictions that make it difficult to assess accuracy and improve policy. A tournament sponsored by the U.S. intelligence community revealed ways in which forecasters can better use probability estimates to make predictions-even for seemingly "unique" events-and showed that tournaments are a useful tool for generating knowledge. Drawing on the literature about the effects of accountability, the authors suggest that tournaments may hold even greater potential as tools for depolarizing political debates and resolving policy disputes.
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The scientific community often portrays science as a value-neutral enterprise that crisply demarcates facts from personal value judgments. We argue that this depiction is unrealistic and important to correct because science serves an important knowledge generation function in all modern societies. Policymakers often turn to scientists for sound advice, and it is important for the wellbeing of societies that science delivers. Nevertheless, scientists are human beings and human beings find it difficult to separate the epistemic functions of their judgments (accuracy) from the social-economic functions (from career advancement to promoting moral-political causes that "feel self-evidently right"). Drawing on a pluralistic social functionalist framework that identifies five functionalist mindsets-people as intuitive scientists, economists, politicians, prosecutors, and theologians-we consider how these mindsets are likely to be expressed in the conduct of scientists. We also explore how the context of policymaker advising is likely to activate or de-activate scientists' social functionalist mindsets. For instance, opportunities to advise policymakers can tempt scientists to promote their ideological beliefs and values, even if advising also brings with it additional accountability pressures. We end prescriptively with an appeal to scientists to be more circumspect in characterizing their objectivity and honesty and to reject idealized representations of scientific behavior that inaccurately portray scientists as value-neutral virgins.
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This article uses methods drawn from perceptual psychology to answer a basic social psychological question: Do people process the faces of norm violators differently from those of others--and, if so, what is the functional significance? Seven studies suggest that people process these faces different and the differential processing makes it easier to punish norm violators. Studies 1 and 2 use a recognition-recall paradigm that manipulated facial-inversion and spatial frequency to show that people rely upon face-typical processing less when they perceive norm violators' faces. Study 3 uses a facial composite task to demonstrate that the effect is actor dependent, not action dependent, and to suggest that configural processing is the mechanism of perceptual change. Studies 4 and 5 use offset faces to show that configural processing is only attenuated when they belong to perpetrators who are culpable. Studies 6 and 7 show that people find it easier to punish inverted faces and harder to punish faces displayed in low spatial frequency. Taken together, these data suggest a bidirectional flow of causality between lower-order perceptual and higher-order cognitive processes in norm enforcement.
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Reconocimiento Facial , Principios Morales , Castigo/psicología , Adulto , Deshumanización , Femenino , Humanos , Masculino , Adulto JovenRESUMEN
Five studies tested four hypotheses on the drivers of punitive judgments. Study 1 showed that people imposed covertly retributivist physical punishments on extreme norm violators when they could plausibly deny that is what they were doing (attributional ambiguity). Studies 2 and 3 showed that covert retributivism could be suppressed by subtle accountability manipulations that cue people to the possibility that they might be under scrutiny. Studies 4 and 5 showed how covert retributivism can become self-sustaining by biasing the lessons people learn from experience. Covert retributivists did not scale back punitiveness in response to feedback that the justice system makes false-conviction errors but they did ramp up punitiveness in response to feedback that the system makes false-acquittal errors. Taken together, the results underscore the paradoxical nature of covert retributivism: It is easily activated by plausible deniability and persistent in the face of false-conviction feedback but also easily deactivated by minimalist forms of accountability.
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Juicio , Castigo , Adulto , Toma de Decisiones , Femenino , Humanos , Relaciones Interpersonales , Masculino , Conducta Social , Adulto JovenRESUMEN
[Correction Notice: An Erratum for this article was reported in Vol 100(5) of Journal of Applied Psychology (see record 2015-40760-001). there are errors in some of the values listed in Table 6 that do not alter any of the conclusions or substantive statements in the original article. The corrected portion of Table 6 is in the correction. The positive intercepts in this table represent the estimated IAT score when the criterion has a value of zero (suggesting attitudinal neutrality), except in the equation examining voter preference in Greenwald et al. (2009), where the intercept estimated the IAT score of Obama voters.] The modal distribution of the Implicit Association Test (IAT) is commonly interpreted as showing high levels of implicit prejudice among Americans. These interpretations have fueled calls for changes in organizational and legal practices, but such applications are problematic because the IAT is scored on an arbitrary psychological metric. The present research was designed to make the IAT metric less arbitrary by determining the scores on IAT measures that are associated with observable racial or ethnic bias. By reexamining data from published studies, we found evidence that the IAT metric is "right biased," such that individuals who are behaviorally neutral tend to have positive IAT scores. Current scoring conventions fail to take into account these dynamics and can lead to faulty inferences about the prevalence of implicit prejudice.
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Prejuicio , Psicometría/instrumentación , Pruebas de Asociación de Palabras/normas , Adulto , Humanos , Racismo/psicologíaRESUMEN
Intelligence analysis plays a vital role in policy decision making. Key functions of intelligence analysis include accurately forecasting significant events, appropriately characterizing the uncertainties inherent in such forecasts, and effectively communicating those probabilistic forecasts to stakeholders. We review decision research on probabilistic forecasting and uncertainty communication, drawing attention to findings that could be used to reform intelligence processes and contribute to more effective intelligence oversight. We recommend that the intelligence community (IC) regularly and quantitatively monitor its forecasting accuracy to better understand how well it is achieving its functions. We also recommend that the IC use decision science to improve these functions (namely, forecasting and communication of intelligence estimates made under conditions of uncertainty). In the case of forecasting, decision research offers suggestions for improvement that involve interventions on data (e.g., transforming forecasts to debias them) and behavior (e.g., via selection, training, and effective team structuring). In the case of uncertainty communication, the literature suggests that current intelligence procedures, which emphasize the use of verbal probabilities, are ineffective. The IC should, therefore, leverage research that points to ways in which verbal probability use may be improved as well as exploring the use of numerical probabilities wherever feasible.