References

Miller, R. B. (1968). Response time in man-computer conversational transactions. Proceedings of the AFIPS Fall Joint Computer Conference, 33(I), 267–277. The 17-transaction taxonomy that the popularised 0.1 / 1 / 10-second trichotomy is later distilled from.

Card, S. K., Moran, T. P., & Newell, A. (1983). The Psychology of Human-Computer Interaction. Lawrence Erlbaum. Source of the human-processor time constants — perceptual processing ~100 ms, immediate response ~1 s, unit task ~10 s.

Card, S. K., Robertson, G. G., & Mackinlay, J. D. (1991). The information visualizer, an information workspace. Proceedings of CHI '91, 181–188. ~10 Hz / 100-ms-per-frame animation refresh recommendation underlying modern transition-timing defaults.

Doherty, W. J., & Thadani, A. J. (1982). The Economic Value of Rapid Response Time. IBM Technical Report GE20-0752-0. Empirical productivity-vs-response-time curve breaking sharply at ~400 ms — the basis of the 'Doherty threshold' framing. PDF: https://www.ibm.com/support/pages/sites/default/files/inline-files/EconomicValueofResponseTime.pdf

Nielsen, J. (1993). Response Times: The 3 Important Limits. From Usability Engineering, Ch. 5. Morgan Kaufmann. The synthesis that distilled Miller, Card-Moran-Newell, and the field's empirical work into the clean 0.1 / 1 / 10-second trichotomy.

Myers, B. A. (1985). The importance of percent-done progress indicators for computer-human interfaces. Proceedings of CHI '85, 11–17. ~86 % of participants preferred a determinate progress bar over a blank wait.

Harrison, C., Amento, B., Kuznetsov, S., & Bell, R. (2007). Rethinking the progress bar. Proceedings of UIST '07, 115–118. Establishes that animation pattern measurably alters perceived progress-bar duration even with identical real duration.

Harrison, C., Yeo, Z., & Hudson, S. E. (2010). Faster progress bars: Manipulating perceived duration with visual augmentations. Proceedings of CHI '10, 1545–1548. ~11–12 % perceived speed-up from backwards-moving, decelerating ribbed progress bars.

Arapakis, I., Bai, X., & Cambazoglu, B. B. (2014). Impact of response latency on user behavior in web search. Proceedings of SIGIR '14, 103–112. Below ~500 ms added latency, users rarely consciously notice; above ~1,000 ms detection is high.

Ng, A., Lepinski, J., Wigdor, D., Sanders, S., & Dietz, P. H. (2012). Designing for low-latency direct-touch input. Proceedings of UIST '12, 453–464. Experienced users can detect latency improvements down to single-millisecond ranges in some direct-touch contexts.

Jota, R., Ng, A., Dietz, P., & Wigdor, D. (2013). How fast is fast enough? A study of the effects of latency in direct-touch pointing tasks. Proceedings of CHI '13, 2291–2300. Direct-touch dragging JND ~33 ms; tapping ~82 ms.

Deber, J., Jota, R., Forlines, C., & Wigdor, D. (2015). How much faster is fast enough? Proceedings of CHI '15, 1827–1836. Confirms and extends Jota et al.; quantifies the gap between direct- and indirect-touch latency JNDs.

James, W. (1890). The Perception of Time. The Principles of Psychology, Ch. 15. Holt. Classical statement of the prospective–retrospective asymmetry — empty time feels long while passing and short in memory; filled time the reverse.

Ornstein, R. E. (1969). On the Experience of Time. Penguin Books. Storage-size account of remembered duration.

Block, R. A., & Zakay, D. (1997). Prospective and retrospective duration judgments: A meta-analytic review. Psychonomic Bulletin & Review, 4(2), 184–197.

Anstis, S. M. (2001). Footsteps and inchworms: Illusions show that contrast modulates motion salience. Perception, 30(7), 785–794.

Anstis, S. M. (2003). Moving objects appear to slow down at low contrasts. Neural Networks, 16(5), 933–938.

Anstis, S. M. (2004). Factors affecting footsteps: contrast can change the apparent speed, amplitude and direction of motion. Vision Research, 44(18), 2171–2178.

Weber, E. H. & Fechner, G. T. (c. 1834+). The Weber–Fechner Law of perceived stimulus intensity. Foundational psychophysics; UX-readable application: Mishunov 2015.

Church, R. M., MacInnis, M., & Guilhardi, P. Work on animal interval timing at Brown University. The geometric-mean indifference property between two intervals; cited via Mishunov 2015 for its UX framing.

Fitch, E. Perceived Performance: The Only Kind That Really Matters (conference talk). Source for the active-to-passive transition, the mousedown / pointerdown head-start study, the 200 ms active-state sweet spot, predictive preloading via mouse deceleration, and the performance-scaler concept.

Eizenberg, E. When Actual Performance Is More Important Than Perceived Performance (Medium). The argument that placeholders are not a substitute for interactivity on production surfaces.

Mishunov, D. (2015). Why Performance Matters, Part 1: The Perception Of Time. Smashing Magazine. Surfaces the Weber–Fechner law and the geometric-mean indifference threshold for the UX community. https://www.smashingmagazine.com/2015/09/why-performance-matters-the-perception-of-time/

Wroblewski, L. (2013). Mobile Design Details: Avoid The Spinner. The post that named the skeleton-screen pattern — argues blank-template UIs that load content incrementally shift user attention from the wait to the arriving content. Used as the canonical naming citation in place of the unrecoverable Mobify Engineering article. https://www.lukew.com/ff/entry.asp?1797

Cox, S. R., Martin-Lise, J., Hosio, S., & van Berkel, N. (2026). Watching AI Think: User Perceptions of Visible Thinking in Chatbots. arXiv preprint. Mixed-design experiment varying "thinking content" (none / emotionally-supportive / expertise-supportive) finds visible pre-response reflections anthropomorphise the agent and shift perceived empathy, warmth, and competence vs. a typing indicator. https://arxiv.org/abs/2601.16720

The Impact of Response Latency and Task Type on Human–LLM Interaction and Perception (CHI 2026). Controlled experiment with TTFT at 2 s / 9 s / 20 s across Creation vs. Advice tasks: participants who experienced 2 s latencies rated outputs less thoughtful and useful than those at 9 s or 20 s — moderate delay raises perceived quality, very long delay shifts attribution from "deliberation" to "unreliability." https://dl.acm.org/doi/full/10.1145/3772318.3790716

Joshi, N., & Vogel, D. (2025). While We Wait… How Users Perceive Waiting Times and Generation Cues during AI Image Generation. CHI EA '25. Semi-structured interviews (n = 11): users accept and sometimes value wait time when it is framed as part of the creative process; generation cues (progress bars, descriptive text, skeleton-style cues) shape that framing. https://dl.acm.org/doi/10.1145/3706599.3719725

Epperson, W., et al. (2025). Interactive Debugging and Steering of Multi-Agent AI Systems (AGDebugger). CHI 2025. User-study finding: ability to steer agents was gated by users' technical understanding of how each agent processes instructions and uses tools — supports tool-call transparency in agent UIs as necessary for non-expert oversight, not just preference. https://dl.acm.org/doi/10.1145/3706598.3713581