Summary. In the practice of point prediction, it is desirable that forecasters receive a directive in the form of a statistical functional. For example, forecasters might be asked to report the mean or a quantile of their predictive distributions. When evaluating and comparing competing forecasts, it is then critical that the scoring function used for these purposes be consistent for the functional at hand, in the sense that the expected score is minimized when following the directive. We show that any scoring function that is consistent for a quantile or an expectile functional can be represented as a mixture of elementary or extremal scoring functions that form a linearly parameterized family. Scoring functions for the mean value and probability forecasts of binary events constitute important examples. The extremal scoring functions admit appealing economic interpretations of quantiles and expectiles in the context of betting and investment problems. The Choquet-type mixture representations give rise to simple checks of whether a forecast dominates another in the sense that it is preferable under any consistent scoring function. In empirical settings it suffices to compare the average scores for only a finite number of extremal elements. Plots of the average scores with respect to the extremal scoring functions, which we call Murphy diagrams, permit detailed comparisons of the relative merits of competing forecasts.

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If we observe an ambiguous figure, our percept is unstable and alternates between the possible interpretations. Periodically interrupting the presentation sizably modulates the spontaneous reversal rate. We here studied event-related potential (ERP) correlates of the neural processes underlying these strong modulations. An ambiguous Necker stimulus was presented discontinuously with four randomly varying interstimulus intervals (ISI; 14, 43, 130, 390 ms) while participants indicated perceptual reversals. EEG was selectively averaged with respect to the participants' percept and ISI. ERP traces varied markedly between ISIs. A simple model explained a major part of this variation and showed that the ISI-dependent ERP modulation occurs after disambiguation has already taken place. We suggest that perceptual stability (or reversal) depends on a system state, slowly changing from one reversal to the next. ISI can shift this state on a scale between stability and instability.