Further Reading
This guide deliberately keeps its reference list short. The following are the books, papers, and theses most worth reading alongside it — selected for usefulness to PhD students and postdocs, not for comprehensiveness.
On writing and structure
Mensh, B., & Kording, K. (2017). Ten simple rules for structuring papers. PLOS Computational Biology, 13(9), e1005619. https://doi.org/10.1371/journal.pcbi.1005619
The paper that most directly inspired this guide. Concise, practical, and applicable across disciplines. Read it before writing your first manuscript and again before submitting your first revision.
Mensh and Kording address the structure of a paper as a whole — abstract, introduction, results, discussion — at the level of sections, paragraphs, and sentences. This guide addresses a narrower problem they deliberately set aside: how to decide which theoretical content belongs in the paper in the first place, and how to write it without letting the theoretical landscape swallow the argument. The two are complementary. If you find yourself asking how to structure what you have decided to include, Mensh and Kording is the next thing to read.
Schimel, J. (2012). Writing science: How to write papers that get cited and proposals that get funded. Oxford University Press.
The best full-length book on scientific writing for researchers. Particularly strong on narrative structure and why papers fail to communicate. Recommended for anyone who wants to go deeper than this guide.
Pinker, S. (2014). The sense of style: The thinking person’s guide to writing in the 21st century. Viking.
Broader in scope than this guide, but the best book on clear prose available. The chapter on the curse of knowledge is essential reading for anyone who has ever been told their writing is too technical.
On theory in psychology
Meehl, P. E. (1978). Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. Journal of Consulting and Clinical Psychology, 46(4), 806–834. https://doi.org/10.1037/0022-006X.46.4.806
A foundational paper on why psychological theories are hard to test and why the field accumulates theories faster than it retires them. Difficult but worth the effort.
Oberauer, K., & Lewandowsky, S. (2019). Addressing the theory crisis in psychology. Psychonomic Bulletin & Review, 26(5), 1596–1618. https://doi.org/10.3758/s13423-019-01645-2
A more recent and accessible treatment of the same problem. Directly relevant to the argument in Chapter 1 of this guide.
McPhetres, J., Albayrak-Aydemir, N., Barbosa Mendes, A., et al. (2021). A decade of theory as reflected in Psychological Science (2009–2019). PLOS ONE, 16(3), e0247986. https://doi.org/10.1371/journal.pone.0247986
Empirical evidence for the theory inflation problem. Across 2,225 articles in Psychological Science, the authors identified 359 distinct theories mentioned in text — most referred to only once. Theories proliferate faster than they are tested or retired.
Platt, J. R. (1964). Strong inference. Science, 146(3642), 347–353. https://doi.org/10.1126/science.146.3642.347
Platt observed that fields advancing quickly shared a specific habit: devising competing hypotheses and designing experiments that could exclude at least one of them. Written for bench scientists in 1964, but the core question — what result would count against your hypothesis? — applies directly to the problem of theoretical framing in psychology.
On the review process
Sternberg, R. J. (2002). On civility in reviewing. APS Observer. https://www.psychologicalscience.org/observer/on-civility-in-reviewing
Short and direct. Required reading before responding to your first hostile review.
On writing with AI
Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., & Jurafsky, D. (2026). Sycophantic AI decreases prosocial intentions and promotes dependence. Science, 391, eaec8352. https://doi.org/10.1126/science.aec8352
The empirical basis for the sycophancy problem described in Chapter 10. Across 11 state-of-the-art models, AI affirmed users’ positions nearly 50% more often than humans — even when those positions involved deception or harm.
Uhler, L., Jordan, V., Buder, J., Huff, M., & Papenmeier, F. (2026). Influence of solution efficiency and valence of instruction on additive and subtractive solution strategies in humans, GPT-4, and GPT-4o. Communications Psychology, 5(1), 41. https://doi.org/10.1038/s44271-026-00403-0
Shows that AI tools systematically favour additive over subtractive solutions — directly relevant to why AI expands theoretical framings rather than cutting them.
Suggestions for additions to this list are welcome — contact markus.huff@uni-tuebingen.de.