Neele Engelmann

Neele Engelmann

Cognitive Scientist

Max Planck Institute for Human Development, Center for Humans and Machines, Berlin

Biography

I’m a Research Scientist at the Max Planck Institute for Human Development, Center for Humans and Machines in Berlin, Germany, where I study behavioral AI ethics: how working with and through AI changes what people are willing to do, and how they judge right and wrong. My current work asks why people behave less ethically when they delegate tasks to AI, and what can prevent this, across settings from dishonesty to physical harm and wartime targeting decisions. I also study how people use AI chatbots for ethical advice, and how much that advice shapes their own judgments.

My earlier work focused on moral judgment, causal reasoning, and law. I studied how causal structure and multiple outcomes shape moral judgments, whether lying is morally worse than misleading, and how people think about rule enforcement, for instance when judges should apply laws they consider unjust. I continue to work on questions at the intersection of psychology, philosophy, and law alongside my AI research.

I obtained my Ph.D. in Psychology from Georg-August-University Göttingen in 2022, with a project on the role of causal representations in moral judgment, and was previously a postdoc at the Center for Law, Behaviour, and Cognition at Ruhr University Bochum.

CV

Interests

  • Moral Psychology
  • Human-AI interaction
  • Causal Reasoning
  • Experimental Philosophy
  • Experimental Jurisprudence
  • Computational Modelling
  • Open Science

Education

  • Dr. rer. nat. Psychology, 2022

    Georg-August-University Göttingen, Germany

  • M.Sc. Psychology, 2017

    Georg-August-University Göttingen, Germany

  • B.Sc. Psychology, 2014

    Georg-August-University Göttingen, Germany

Journal Articles

click title for abstracts

Engelmann, N., Schmidt, E.-M., Alhosseini, A., Hannikainen, I. R., & Dong, M. (2026). People expect less but show equal influence of LLM versus human moral advice. Computers in Human Behavior Reports, Article 101413. Advance online publication. https://doi.org/10.1016/j.chbr.2026.101413

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Engelmann, N., Almeida, G. F. C. F., Oliveira de Sousa, F., Prochownik, K., Hannikainen, I., Struchiner, N., & Magen, S. (2024). Apply the laws, if they are good: Moral evaluations linearly predict whether judges should apply the law. Cognitive Science, 48(10), Article e70001. https://doi.org/10.1111/cogs.70001

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Almeida, G. F. C. F., Nunes, J. L., Engelmann, N., Wiegmann, A., & de Araújo, M. (2024). Exploring the psychology of GPT-4's moral and legal reasoning. Artificial Intelligence, 333, Article 104145. https://doi.org/10.1016/j.artint.2024.104145

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Engelmann, N., & Hannikainen, I. R. (2024). Does moral valence influence the construal of alternative possibilities? Possibility Studies & Society, 2(3), 318–333. https://doi.org/10.1177/27538699241231026

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Stephan, S., Engelmann, N., & Waldmann, M. R. (2023). The perceived dilution of causal strength. Cognitive Psychology, 140, Article 101540. https://doi.org/10.1016/j.cogpsych.2022.101540

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Engelmann, N., & Waldmann, M. R. (2022). How causal structure, causal strength, and foreseeability affect moral judgments. Cognition, 226, Article 105167. https://doi.org/10.1016/j.cognition.2022.105167

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Engelmann, N., & Waldmann, M. R. (2022). How to weigh lives. A computational model of moral judgment in multiple-outcome structures. Cognition, 218, Article 104910. https://doi.org/10.1016/j.cognition.2021.104910

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Viebahn, E., Wiegmann, A., Engelmann, N., & Willemsen, P. (2021). Can a question be a lie? An empirical investigation. Ergo, 8, Article 7. https://doi.org/10.3998/ergo.1144

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Hagmayer, Y., & Engelmann, N. (2014). Causal beliefs about depression in different cultural groups — what do cognitive psychological theories of causal learning and reasoning predict? Frontiers in Psychology, 5, Article 1303. https://doi.org/10.3389/fpsyg.2014.01303

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Book Chapters

Shurakov, N., Kolbe, M., Engelmann, N., & Wiegmann, A. (2026). GPT-4's alignment with human lie and falsity attribution in cases of deceptive implicatures. In V. C. Müller, A. R. Dewey, L. Dung, & G. Löhr (Eds.), Philosophy of artificial intelligence: The state of the art (pp. 431–443). Springer Nature.

Engelmann, N., & Kirfel, L. (2025). Who caused it? Different effects of statistical and prescriptive abnormality on causal selection in chains. In K. Tobia (Ed.), The Cambridge handbook of experimental jurisprudence (pp. 481–501). Cambridge University Press. https://doi.org/10.1017/9781009170901.032

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Engelmann, N. (2025). Murderer at the door! To lie or to mislead? In A. Wiegmann (Ed.), Advances in experimental philosophy of lying. Bloomsbury.

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Wiegmann, A., & Engelmann, N. (2022). Is lying morally different from misleading? An empirical investigation. In L. Horn (Ed.), From lying to perjury: Linguistic and legal perspectives on lies and other falsehoods (pp. 89–111). De Gruyter. https://doi.org/10.1515/9783110733730-005

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Wiegmann, A., & Engelmann, N. (2020). Entwicklungen und Probleme der Moralpsychologie zu Beginn des 21. Jahrhunderts. In N. Paulo & J. C. Bublitz (Eds.), Empirische Ethik - Grundlagentexte aus Psychologie und Philosophie (pp. 139–175). Suhrkamp.

Hagmayer, Y., & Engelmann, N. (2020). Asking questions to provide a causal explanation – Do people search for the information required by cognitive psychological theories? In E. A. Bar-Asher Siegal & N. Boneh (Eds.), Perspectives on causation: Selected papers from the Jerusalem 2017 workshop (pp. 121–147). Springer. https://doi.org/10.1007/978-3-030-34308-8_4

DOI

Conference Proceedings

Kirfel, L., Engelmann, N., Maaß, A., Nussberger, A.-M., & Phillips, J. S. (2026). LLM-generated possibilities increase blame attribution. In G. Boleda, I. Dautriche, M. Maldonado, C. Saldana, & T. Torrent (Eds.), Proceedings of the 48th Annual Meeting of the Cognitive Science Society (pp. 4680–4686).

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Engelmann, N., Kirfel, L., Nussberger, A.-M., Rilla, R., & Rahwan, I. (2025). Framing, not transparency, reduces cheating in algorithmic delegation. In D. Barner, N. Bramley, A. Ruggeri, & C. Walker (Eds.), Proceedings of the 47th Annual Meeting of the Cognitive Science Society (pp. 908–914).

PDF Project

Engelmann, N., Hannikainen, I. R., González-García, C., & Ruz, M. (2024). Understanding rule enforcement using drift diffusion models. In L. Samuelson, S. Frank, M. Toneva, A. Mackey, & E. Hazeltine (Eds.), Proceedings of the 46th Annual Meeting of the Cognitive Science Society (pp. 114–120).

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Engelmann, N., & Waldmann, M. R. (2021). A causal proximity effect in moral judgment. In T. Fitch, C. Lamm, H. Leder, & K. Teßmar-Raible (Eds.), Proceedings of the 43rd Annual Meeting of the Cognitive Science Society (pp. 2330–2336).

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Engelmann, N., & Waldmann, M. R. (2019). Moral reasoning with multiple effects: Justification and moral responsibility for side effects. In W. D. Gray & C. D. Schunn (Eds.), Proceedings of the 41st Annual Meeting of the Cognitive Science Society (pp. 1703–1709).

PDF Project

Neth, H., Engelmann, N., & Mayrhofer, R. (2014). Foraging for alternatives: Ecological rationality in keeping options viable. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Meeting of the Cognitive Science Society (pp. 1078–1083).

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Preprints

Schmidt, E.-M., Engelmann, N., & Dong, M. (2026). Rethinking human responses to AI: A person-machine-context framework for human-AI interaction [Preprint]. ResearchGate. https://doi.org/10.13140/RG.2.2.35387.37929

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Hannikainen, I. R., Engelmann, N., González-García, C., & Ruz, M. (2026). Textualism as an effort-accuracy trade-off in rule enforcement [Preprint]. ResearchGate. https://doi.org/10.13140/RG.2.2.28676.49284

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Reports

Max Planck Institute for Human Development, Max Planck Society, Bengfort, T., Blunk, J., Engelmann, N., Feg, T., Herzog, S., Kleemeyer, M., Schwarze, S., Nix, S., Peikert, A., Pit, I., & Tilsley, P. (2025). Open Science and Research Data Management Guidelines [Version 1]. https://doi.org/10.17617/2.3682163

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Teaching & Supervision

In summer 2026, I co-taught the seminar “Machine + Behavior” at TU Berlin, which covers research at the intersection of AI and behavioral science; my sessions focused on experimental design and causal inference, delegation to AI, and developing research plans. From 2017 to 2022, I taught the introductory statistics courses “Quantitative Methods I and II” for psychology students at the University of Göttingen, covering research design, hypothesis testing, and the General Linear Model, with practical exercises in Excel and R.

At the Max Planck Institute for Human Development, I am the daily supervisor of two PhD students working on delegation to AI and human-centered AI, and I supervise Master’s students, lab rotations, and interns. Previously, I supervised and co-supervised several Bachelor’s and Master’s theses on causal and moral reasoning and experimental philosophy at the University of Göttingen.

More details are listed in my CV.