¹⁶(Clark, 2013). From the viewpoint of mathematical theory, it might actually be more appropriate to talk about predicted reward instead of expected reward in the definition of reward loss (assuming here that expectation is defined as the mathematical expectation according to probability theory as in the main text). While these are often seen as the same thing—prediction being an expectation of a future quantity—the concepts are not equivalent. In particular, in machine learning theory, a prediction can be considered more general than expectation: a sophisticated prediction will also include an estimate of the uncertainty involved in the prediction, in addition to the mathematical expectation. The importance of such uncertainty of predictions will be seen in Chapter 7 regarding the concept of threat. But here, regarding frustration, such uncertainty is relevant because it seems that the certainty of the prediction affects the level of frustration. I would claim that if you are completely certain that you will get chocolate (say, 5 pieces), but then it turns out you don’t, the frustration will be greater than in the case where there is only some chance of getting any (like the example in the main text, 10 pieces with 50% probability). Crucially, in this example, the expected amount of chocolate, in the sense of the mathematical expectation, is the same in the two cases, and only the uncertainty changes. Therefore, the effect of uncertainty should be taken into account in the definition of reward loss. See footnote 21 in Chapter 16 and the main text preceding that footnote for further developments of this point, as well as Chapter 12.