Methodology · References

Complete bibliography

Every published work cited in Red Stet's methodology documentation, organized by topic area. Open-access links are included where stable URLs exist; otherwise the citation alone is enough to locate the paper through a university library or DOI lookup.

Sections

Keystroke dynamics
Cognitive psychology of writing
Mouse and click dynamics
HCI and motor control
AI-content detection
Stylometry & authorship attribution
Academic integrity research

How to read this bibliography. Each section corresponds to one or more methodology pages. The keystroke-dynamics section grounds Keystroke cadence variance and Correction rate. The cognitive psychology of writing section grounds Correction rate and Thinking pauses. The mouse dynamics section grounds Mouse path geometry and Click patterns. The AI-detection section grounds the Comparisons page.

Where a paper is cited on multiple methodology pages, it appears once here under its primary topic.

Keystroke dynamics

The behavioral biometric with the longest research history — 45+ years from foundational work in the late 1970s through modern continuous authentication.

Card, S.K., Moran, T.P., & Newell, A. (1980). The keystroke-level model for user performance time with interactive systems. Communications of the ACM, 23(7), 396–410.
Gaines, R., Lisowski, W., Press, S., & Shapiro, N. (1980). Authentication by keystroke timing: Some preliminary results. RAND Corporation Report R-2526-NSF.
Joyce, R., & Gupta, G. (1990). Identity authentication based on keystroke latencies. Communications of the ACM, 33(2), 168–176.
Monrose, F., & Rubin, A. (1997). Authentication via keystroke dynamics. Proceedings of the 4th ACM Conference on Computer and Communications Security, 48–56.
Bergadano, F., Gunetti, D., & Picardi, C. (2002). User authentication through keystroke dynamics. ACM Transactions on Information and System Security, 5(4), 367–397.
Gunetti, D., & Picardi, C. (2005). Keystroke analysis of free text. ACM Transactions on Information and System Security, 8(3), 312–347.
Killourhy, K.S., & Maxion, R.A. (2009). Comparing anomaly-detection algorithms for keystroke dynamics. Proceedings of the IEEE/IFIP International Conference on Dependable Systems & Networks (DSN'09), 125–134.
Banerjee, S.P., & Woodard, D.L. (2012). Biometric authentication and identification using keystroke dynamics: A survey. Journal of Pattern Recognition Research, 7(1), 116–139.
Ahmed, A.A.E., & Traore, I. (2014). Biometric recognition based on free-text keystroke dynamics. IEEE Transactions on Cybernetics, 44(4), 458–472.

Cognitive psychology of writing

The cognitive-process model of writing and the keystroke-logging methodology that derives from it. Foundational for correction-rate and thinking-pauses signals.

Hayes, J.R., & Flower, L.S. (1980). Identifying the organization of writing processes. In L.W. Gregg & E.R. Steinberg (Eds.), Cognitive Processes in Writing (pp. 3–30). Lawrence Erlbaum.
Bereiter, C., & Scardamalia, M. (1987). The Psychology of Written Composition. Lawrence Erlbaum.
Schilperoord, J. (1996). It's About Time: Temporal Aspects of Cognitive Processes in Text Production. PhD dissertation, Tilburg University. Rodopi.
Galbraith, D. (1999). Writing as a knowledge-constituting process. In M. Torrance & D. Galbraith (Eds.), Knowing What to Write: Conceptual Processes in Text Production. Amsterdam University Press.
Sullivan, K.P.H., & Lindgren, E. (Eds.) (2006). Computer Keystroke Logging and Writing: Methods and Applications. Elsevier, Studies in Writing vol. 18.
Wengelin, Å. (2006). Examining pauses in writing: Theory, methods and empirical data. In K.P.H. Sullivan & E. Lindgren (Eds.), Computer Keystroke Logging and Writing. Elsevier.
Alves, R.A., & Castro, S.L. (2011). Pauses in adolescents' writing. In V.W. Berninger (Ed.), Past, Present, and Future Contributions of Cognitive Writing Research to Cognitive Psychology. Psychology Press.
Leijten, M., & Van Waes, L. (2013). Keystroke logging in writing research: Using Inputlog to analyze and visualize writing processes. Written Communication, 30(3), 358–392.
Olive, T. (2014). Toward a parallel-processing model of writing. Journal of Writing Research, 6(2), 173–194.
Conijn, R., Roeser, J., & van Zaanen, M. (2019). Understanding the keystroke log: The effect of writing task on keystroke features. Reading and Writing, 32, 2353–2374.
Miller, G.A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97.
Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114 (target article; commentary to 185).
Salthouse, T.A. (1986). Perceptual, cognitive, and motoric aspects of transcription typing. Psychological Bulletin, 99(3), 303–319.

Mouse and click dynamics

Mouse dynamics as a behavioral biometric — younger field than keystroke, but with growing literature.

Pusara, M., & Brodley, C.E. (2004). User re-authentication via mouse movements. Proceedings of the ACM Workshop on Visualization and Data Mining for Computer Security (VizSEC), 1–8.
Gamboa, H., & Fred, A. (2004). A behavioral biometric system based on human-computer interaction. SPIE Proceedings, vol. 5404, 381–392.
Ahmed, A.A.E., & Traore, I. (2007). A new biometric technology based on mouse dynamics. IEEE Transactions on Dependable and Secure Computing, 4(3), 165–179.
Zheng, N., Paloski, A., & Wang, H. (2011). An efficient user verification system via mouse movements. Proceedings of the 18th ACM Conference on Computer and Communications Security, 139–150.
Jorgensen, Z., & Yu, T. (2011). On mouse dynamics as a behavioral biometric. Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security (ASIACCS), 378–383.
Feher, C., Elovici, Y., Moskovitch, R., Rokach, L., & Schclar, A. (2012). User identity verification via mouse dynamics. Information Sciences, 201, 19–36.
Mondal, S., & Bours, P. (2013). Continuous authentication using mouse dynamics. Proceedings of BIOSIG 2013, 1–12.
Antal, M., & Egyed-Zsigmond, E. (2019). Intrusion detection using mouse dynamics. IET Biometrics, 8(5), 285–294.
Acien, A., Morales, A., Vera-Rodriguez, R., & Fierrez, J. (2020). BeCAPTCHA-Mouse: Synthetic mouse trajectories and improved bot detection. arXiv:2005.00890.

HCI and motor control

Foundational human-computer interaction research that grounds the mechanical reasons individual signals discriminate.

Fitts, P.M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47(6), 381–391.
Crossman, E.R.F.W., & Goodeve, P.J. (1963, reprinted 1983). Feedback control of hand-movement and Fitts' Law. Quarterly Journal of Experimental Psychology, 35A, 251–278.
MacKenzie, I.S. (1992). Fitts' law as a research and design tool in human-computer interaction. Human-Computer Interaction, 7(1), 91–139.
Cypher, A. (1991). Eager: Programming repetitive tasks by example. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 33–39.
Miller, R.C., & Myers, B.A. (2002). Outlier finding: Focusing user attention on possible errors. Proceedings of the 15th Annual ACM Symposium on User Interface Software and Technology (UIST'02), 81–90.

AI-content detection

The literature on output-text detection of generated content. Cited in the Comparisons page for the documented failure modes of output-text approaches.

Mitchell, E., Lee, Y., Khazatsky, A., Manning, C.D., & Finn, C. (2023). DetectGPT: Zero-shot machine-generated text detection using probability curvature. Proceedings of the 40th International Conference on Machine Learning (ICML).
Sadasivan, V.S., Kumar, A., Balasubramanian, S., Wang, W., & Feizi, S. (2023). Can AI-generated text be reliably detected? arXiv:2303.11156.
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7).
Krishna, K., Song, Y., Karpinska, M., Wieting, J., & Iyyer, M. (2023). Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense. Advances in Neural Information Processing Systems (NeurIPS).
Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., et al. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(26).
Wang, Y., Mansurov, J., Ivanov, P., Su, J., Shelmanov, A., et al. (2024). M4: Multi-generator, multi-domain, multi-lingual black-box machine-generated text detection. Proceedings of EACL 2024.

Stylometry & authorship attribution

The literary-voice (stylometric) literature. Not part of the current composition fingerprint, but cited as the future direction for "voice profile" work per the 2026-06-08 decision drop.

Mosteller, F., & Wallace, D.L. (1964). Inference and Disputed Authorship: The Federalist. Addison-Wesley.
Burrows, J. (2002). 'Delta': A measure of stylistic difference and a guide to likely authorship. Literary and Linguistic Computing, 17(3), 267–287.

Academic integrity research

Adjacent literature on academic integrity and process-based evidence in integrity decisions.

Stein, B., Meyer zu Eissen, S., & Potthast, M. (2007). Strategies for retrieving plagiarized documents. Proceedings of the 30th Annual International ACM SIGIR Conference, 825–826.