Thank for commenting. First of all I agree that the claims of (A) and (B) do need to be distinguished, and I admit I didn’t make that conceptual distinction very clear in the article. I agree that the most important takeaway from the piece is (B), and I also think that this alone is already enough to challenge EA’s prioritization methods (i.e. ultra-painful experiences are completely flying under the radar from the point of view of QALYs and similar metrics; reducing the incidence of cluster headaches, migraines, kidney stones, etc. could be an extremely cost-effective EA objective).
With that said, I would claim that (1) the article does actually provide evidence for (A), (2) taking this seriously clarifies a lot of puzzling facts about experience and how it is reported, and (3) understanding that pain and pleasure follows a long-tail (most likely a log-normal distribution) gives us a new principled way to approach cause prioritization.
I understand the fact that the pain scales of stings and cluster headaches are *by construction* logarithmic. But you have to understand that such a scale would only ever be “filled to the top” if experiences actually differed in intensity also by the same amount. The article (and presentation, which I strongly recommend you watch) explain that all of the following are consistent with the pain scales (as reported!) are actually logarithmic:
(a) the characteristic distribution of neural activity is log-normal, and under the modest assumption that intensity of experience is roughly proportional (or at least polynomially proportional) to intensity of experience, that entails the distribution of intensity is also log-normal.
(b) the above can be further understood as a kind of “neuronal weather” (see the “avalanches” metaphor in the video presentation)
(c) the predictions of the log-normal world are held by the data, and in particular:
(c1) there are few categories of experiences that capture most of the extremely good and extremely bad sensations
(c2) there is consistency in the deference judgements of the quality of experience (as seen in the deference graph), and importantly
(c3) The ratio of “1st worst or best experience vs. 2nd worst or best experience” fits a log-normal distribution and it does not fit a normal distribution.
For the above reasons, bringing up the Fechner-Weber is not, I would claim, a red-herring. Rather, I think it ties together the whole argument. Here is why:
I understand that Fechner-Weber’s law maps physical intensity to subjective intensity, and that valence is not externally driven a lot of the time. But you may have missed the argument I’m making here. And that is that in one interpretation of the law, a pre-conscious process does a log transform on the intensity of the input and that by the time we are aware of it, what we become aware of are the linear differences in our experience. In the alternate interpretation of the law, which I propose, the senses (within the window of adaptation) translate the intensity of the input into an equivalent intensity of experience. And the reason *why* we can only detect multiplicative differences in the input *is because* we can only notice consciously multiplicative differences in the intensity of experience. Do you see what I am saying? In this account, the fact that people would naturally and spontaneously use a logarithmic scale to report their level of pain is a simple implication of the fact that you can only definitively tell that “the pain got worse” when it got 10% worse and not when it became 1 unit worse (which soon becomes hard to notice when you talk about experiences with e.g. 1000 pain units per second).
In other words, the scales are logarithmic because we can only notice with confidence multiplicative increments in the intensity of experience. And while this is fine and does not seem to have strong implications on the lower end of the scale, it very quickly escalates, to the point where by the time you are in 7⁄10 pain you live in a world with orders of magnitude more pain units per second than you did when you were in 2⁄10 pain.
Finally, you really need the logarithmic scales to make room for the ultra-intense levels of pleasure and pain that I highlighted in the “existence of extremes” section. If people reported their pain on a linear scale, they would quickly run into the problem that they cannot describe even something as painful as a broken bone, let along something like a cluster headache.
Hey Michael,
Thank for commenting. First of all I agree that the claims of (A) and (B) do need to be distinguished, and I admit I didn’t make that conceptual distinction very clear in the article. I agree that the most important takeaway from the piece is (B), and I also think that this alone is already enough to challenge EA’s prioritization methods (i.e. ultra-painful experiences are completely flying under the radar from the point of view of QALYs and similar metrics; reducing the incidence of cluster headaches, migraines, kidney stones, etc. could be an extremely cost-effective EA objective).
With that said, I would claim that (1) the article does actually provide evidence for (A), (2) taking this seriously clarifies a lot of puzzling facts about experience and how it is reported, and (3) understanding that pain and pleasure follows a long-tail (most likely a log-normal distribution) gives us a new principled way to approach cause prioritization.
I understand the fact that the pain scales of stings and cluster headaches are *by construction* logarithmic. But you have to understand that such a scale would only ever be “filled to the top” if experiences actually differed in intensity also by the same amount. The article (and presentation, which I strongly recommend you watch) explain that all of the following are consistent with the pain scales (as reported!) are actually logarithmic:
(a) the characteristic distribution of neural activity is log-normal, and under the modest assumption that intensity of experience is roughly proportional (or at least polynomially proportional) to intensity of experience, that entails the distribution of intensity is also log-normal.
(b) the above can be further understood as a kind of “neuronal weather” (see the “avalanches” metaphor in the video presentation)
(c) the predictions of the log-normal world are held by the data, and in particular:
(c1) there are few categories of experiences that capture most of the extremely good and extremely bad sensations
(c2) there is consistency in the deference judgements of the quality of experience (as seen in the deference graph), and importantly
(c3) The ratio of “1st worst or best experience vs. 2nd worst or best experience” fits a log-normal distribution and it does not fit a normal distribution.
For the above reasons, bringing up the Fechner-Weber is not, I would claim, a red-herring. Rather, I think it ties together the whole argument. Here is why:
I understand that Fechner-Weber’s law maps physical intensity to subjective intensity, and that valence is not externally driven a lot of the time. But you may have missed the argument I’m making here. And that is that in one interpretation of the law, a pre-conscious process does a log transform on the intensity of the input and that by the time we are aware of it, what we become aware of are the linear differences in our experience. In the alternate interpretation of the law, which I propose, the senses (within the window of adaptation) translate the intensity of the input into an equivalent intensity of experience. And the reason *why* we can only detect multiplicative differences in the input *is because* we can only notice consciously multiplicative differences in the intensity of experience. Do you see what I am saying? In this account, the fact that people would naturally and spontaneously use a logarithmic scale to report their level of pain is a simple implication of the fact that you can only definitively tell that “the pain got worse” when it got 10% worse and not when it became 1 unit worse (which soon becomes hard to notice when you talk about experiences with e.g. 1000 pain units per second).
In other words, the scales are logarithmic because we can only notice with confidence multiplicative increments in the intensity of experience. And while this is fine and does not seem to have strong implications on the lower end of the scale, it very quickly escalates, to the point where by the time you are in 7⁄10 pain you live in a world with orders of magnitude more pain units per second than you did when you were in 2⁄10 pain.
Finally, you really need the logarithmic scales to make room for the ultra-intense levels of pleasure and pain that I highlighted in the “existence of extremes” section. If people reported their pain on a linear scale, they would quickly run into the problem that they cannot describe even something as painful as a broken bone, let along something like a cluster headache.