This review was written in collaboration with ChatGPT to reduce personal biases, but I take full responsibility for the accuracy of the claims in this review.

Pleskac, T. J., Cesario, J., Johnson, D. J., & Gagnon, G. (2025). Modeling police officers’ deadly force decisions in an immersive shooting simulator. Journal of Experimental Psychology: Applied. Advance online publication. https://dx.doi.org/10.1037/xap0000542

The 2025 article by Pleskac, Cesario, Johnson, and Gagnon presents a large-scale experimental investigation of police officers’ deadly force decisions using an immersive simulator. With a sample of 659 officers from the Milwaukee Police Department, the study represents one of the most ambitious attempts to date to examine racial disparities in shoot/don’t-shoot errors under realistic conditions. The authors report that officers were more likely to mistakenly shoot unarmed Black suspects than unarmed White suspects, but only in non-antagonistic, ambiguous encounters. Computational modeling further suggests that this disparity arises not from a global bias to “shoot Black suspects,” but from differences in evidence accumulation once an object is produced. These findings are an important contribution to understanding how racial bias can emerge in precisely those contexts that mirror real-world disparities in unarmed fatalities.

While the methodological contribution of the 2025 study is considerable, the article’s presentation raises serious concerns about scholarly transparency and the cumulative development of knowledge. The data were collected in 2017, two years before Johnson and Cesario published their now-retracted 2019 PNAS article, and around the same time as their 2019 Social Psychological and Personality Science (SPPS) article. Both of those earlier publications emphasized the absence of systematic racial disparities in police use of deadly force, with the PNAS article going further to assert “no evidence of anti-Black disparities.” Neither article cited or discussed the ongoing experimental work reported in the 2025 paper.

This timeline matters because the 2017 simulator data clearly demonstrate context-dependent racial bias: officers were approximately 1.5 times more likely to mistakenly shoot unarmed, non-threatening Black suspects compared to White suspects (95% CI ~1.0–2.0). That evidence directly undermines the sweeping “no disparity” claims advanced in the 2019 PNAS paper and complicates the more cautious but still minimization-oriented conclusions of the SPPS article. By omitting mention of their own experimental findings, the authors allowed a misleading narrative—that disparities are illusory or absent—to gain traction in both the scientific literature and public debate.

Equally concerning is that the 2025 publication itself does not acknowledge this inconsistency. The authors frame their contribution as filling a methodological gap in the simulator literature, but they do not confront the fact that their own experimental evidence from 2017 contradicts claims they made in widely cited articles. Readers are left without any discussion of why the earlier findings were published without reference to this experiment, or how to reconcile the divergent messages across their corpus of work. This absence undermines the credibility of the current contribution and raises questions about selective framing of evidence in a highly contested area of research.

A more balanced and transparent approach would have explicitly situated the 2025 findings against the backdrop of their earlier claims. Doing so would not only have clarified the scientific record but also demonstrated scholarly accountability in an area where research is closely tied to public trust and policy debates. By failing to address the contradiction between the 2017 data and their 2019 publications, the authors miss an opportunity to advance a genuinely integrative understanding of racial bias in policing.

In sum, the 2025 article provides strong experimental evidence that racial bias shapes police use of force decisions under ambiguity. However, the credibility and impact of this contribution are diminished by the failure to acknowledge how these results, collected in 2017, undermine the claims made in earlier high-profile publications. A critical lesson from this case is that transparency about contradictory findings is not optional; it is central to the integrity of science, especially on issues as socially consequential as racial disparities in police violence.

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Postscript by Ulrich Schimmack:
I like ChatGPT because it writes well, in a neutral, matter-of-fact way. My own blog posts tend to be more emotional and maybe as a result less convincing. For example, I would have said that White researchers who receive funding to study racial biases but fail to report evidence of such biases are not living up to the standards of science and should be held accountable. Having a PhD or a university position is neither a necessary nor a sufficient criterion for being a true scientist; what matters is a commitment to transparency and integrity. Cases like this show the importance of diversity at universities and in sensitive research projects. The general public is already losing trust in universities, and examples of selective reporting only reinforce those concerns by suggesting that academics are sometimes unwilling or unable to confront their own biases.