Experimental manipulations of delay discounting & related processes: an introduction to the special issue.

Au-Yeung et al. (2015) wrote the opening essay for a full journal issue. The issue gathered new experiments on delay discounting.

They did not run new data. They mapped the big questions: Which procedures change how steeply people devalue future rewards?

No results to report. The paper is a road map, not a study.

It lists gaps: We need cleaner tasks, better animal models, and ways to translate lab findings to clinics.

Odum et al. (2020) later summed up 15 years of such work. Their review shows food, cigarettes, and sex are discounted more steeply than money, backing the call K et al. made for testing non-monetary rewards.

Nickerson et al. (2015) answered the call in the same year. They showed that simply making adults sit and wait at the computer steepened discounting curves. This extends the editorial by proving small procedural tweaks matter.

McKerchar et al. (2019) tested two question formats the editorial flagged. Double-reward trials fit the classic hyperbolic model; variable-delay trials did not. Again, the details of your task change the curve you get.

Holt et al. (2018) gave pigeons four ways to respond. All produced orderly, hyperbolic curves, showing you can simplify animal setups without losing data. This fills the editorial’s plea for validated non-human tools.

Even though this paper has no numbers, it shapes every discounting study you run. Pick rewards that mean something to your client, lock the task format, and track data with area-under-the-curve if you hate math models. When results look odd, first check the procedure—small waits, wording, or response type can flip the curve.