2025-03-07 00:11:00
[PROLOGUE – EVERYBODY WANTS A ROCK]
[PART I – THERMOSTAT]
[PART II – MOTIVATION]
[PART III – PERSONALITY AND INDIVIDUAL DIFFERENCES]
Reward and punishment is the lowest form of education.
— Zhuangzi
What is the value of a glass of water? Well, it has great value if you’re in the middle of the desert, but not much value at all on the shores of Lake Champlain.
What’s the value of the action, “put on a heavy down coat”? It has positive value if you find yourself in Saskatchewan on January 3rd, but negative value in the Arizona summer.
And what’s the value of taking an outdoor shower in cool water? This one has negative value in Saskatchewan and positive value in Phoenix.
Building a mind around notions of “value” quickly leads you into contradictions.
Let’s say we have a mouse in a cage. We’ve designed the cage to vary wildly in temperature, so by default, the mouse is uncomfortable.
But we’re not monsters. We’ve given the mouse some control over the temperature: two levers, a red one that raises the temperature a bit, and a blue one that lowers it. If the mouse can learn to operate this system, it will be able to maintain a reasonable temperature with little trouble.
How would a mouse do on this task, if God saw fit to grace it with a brain that runs on reward and punishment?
Well, let’s say that first the temperature got too high. The mouse tries the red lever. This makes things even hotter. Clearly the red lever is a punishment! The mouse assigns the red lever a value like [-1] or something. Next the mouse tries the blue lever. This makes the cage less hot. A reward! The blue lever gets a value like [+1].
Because it is rewarding, the mouse presses the blue lever until the cage is a comfortable temperature. Then what happens? That’s right, the mouse keeps pressing the lever! After all, the mouse is trying to seek rewards and avoid punishments, and the blue lever has always been rewarding in the past.
Soon the cage is too cold. Pressing the blue lever becomes a punishment [-1], since it only makes things colder. The mouse slowly updates the value of the blue lever until it reaches [0], at which point it stops pressing the lever.
Then what happens? Well, it doesn’t press the blue lever, because it has an expected value of [0]. And it doesn’t press the red lever either! After all, the red lever still has an expected value of [-1]. In all past experience, pressing the red lever always makes things “worse”.
This system of reward and punishment has left the mouse entirely confused. Its conclusion is that the blue lever has no value, and that the red lever is always a negative experience.
You can try to solve this with awkward kludges, but most of them don’t work. For example, you might have it so that the mouse learns separate values for the levers in separate environments, the idea being that it will learn that the blue lever is rewarding when it’s too warm, and punishing when it’s too cold. But then the mouse will have to learn thousands of different values for each action in thousands of different environments — a separate value for the blue lever when it is sunny, overcast, breezy, when the mouse is bored, when the lab techs are talking too loud, etc.
Worse, the mouse will have no ability to generalize. If it learns that the blue lever is “punishing” when the cage is cold, it won’t be able to apply this knowledge outside that immediate situation. It will not learn to press the blue lever when the cage is too hot, because it has reduced the experience to an abstract number.
Much easier for the mouse to learn what the blue lever does: it lowers the temperature of the cage, which the mouse experiences as a decline in body temperature.
Is this a reward or a punishment? Neither. What is the value of this action? It has none. Value is absurd. Pushing the blue lever has specific rather than general consequences. It is simply a thing the mouse can do, and the mouse learns how the things it can do affect the things that interest it.
The mouse is naturally endowed with systems interested in its body temperature: at least two governors, one dedicated to keeping it from being too hot, the other keeping it from being too cold. The governors pay attention to things that might knock the mouse’s body temperature away from its set points, and actions that can set the body temperature right again. So the governors are very interested in these levers, and quickly learn their uses.
Both the (keep-mouse-from-getting-too) cold governor and the (keep-mouse-from-getting-too) hot governor track the mouse’s body temperature, though they defend different set points. When the mouse pulls the blue lever, there is a change in the mouse’s body temperature. Since both governors control that variable, both of them learn that the action of pulling the blue lever reduces the mouse’s body temperature. When the mouse pulls the red lever, both governors learn that the action of pulling the red lever increases the mouse’s body temperature.
The governors gain the same information, but they use it in different ways. The cold governor knows to vote for pulling the red lever when the mouse is below its target temperature, and to vote against pulling the blue lever when that would drive the mouse below its target temperature. This is implicit in its design. The hot governor knows to vote for pulling the blue lever when the mouse is above its target temperature, and to vote against pulling the red lever when that would drive the mouse above its target temperature.
From this analysis, we think that the mind doesn’t represent “value” at all (compare: The case against economic values in the orbitofrontal cortex (or anywhere else in the brain)). Instead, governors track how actions affect their error signals.
Each governor learns on its own, and keeps some kind of record of what actions increase or decrease the variables it cares about, and by how much. This is itself a complicated process, and we don’t mean to discount it. But governors clearly learn how actions change the world, not whether or not they are “valuable”. There is no reward and no punishment.
Some experiences are consistently “punishing”, like getting hit in the face by a 2×4. But this is incidental, it’s only because the pain governor has strong opinions about damage to the face — the opinion that this value should always be kept very close to zero. So the pain governor will always oppose such hardware-related incidents.
And in fact, even this is not always punishing. If you are born without a pain governor, or your pain governor is temporarily turned off (by drugs, for example), then getting hit in the face by a 2×4 is no longer “punishing”. More like a gentle romp.
And there is nothing at all that is always “rewarding”. Your first donut after a long day at work will be rewarding, but by the 10th donut you will start to find donuts “punishing”. By the 100th donut, anyone would find additional donuts excruciating (ok, almost anyone).
Even with that said, there are still a lot of open questions. It may be, for example, that governors learn more quickly when an action actually corrects their error, compared to when they observe it in a neutral situation.
Imagine it’s 20 °F outside and you go and stand near a campfire. Up to that point you were pretty cold, so your cold governor had a pretty big error. When you walk over to the campfire, your cold governor will be very interested — it will notice that standing near a campfire is a good way to warm up.
But what will your hot governor learn? Hopefully it will learn something. After all, standing near a campfire affects the variable it cares about, body temperature. It would be good for the hot governor to remember this, so it can avoid standing near campfires in the future when it’s hot out. But in this moment, the hot governor’s error is zero. So it’s possible that the hot governor doesn’t learn such a strong lesson about the effect of campfires as the cold governor did.
If some day it is 98 °F outside, and there’s a campfire, will the hot governor remember what it learned? At 98 °F, you are too hot, the hot governor has an error. Will it remember that standing near the campfire will increase your body temperature, and so will increase its error? Or will it have to learn that lesson all over again, because last time you encountered a campfire, it was sleeping, because it had no error.
Similarly, we don’t know if a governor will learn more when its error is bigger. But it seems plausible. If it is 78 °F and you go stand near a campfire, that will increase your hot governor’s error from small to medium, and it will remember that. But if it is 98 °F and you go stand near a campfire, that will increase your hot governor’s error from large to extra large! It seems possible that the hot governor will remember that even more, that increasing an error will be remembered more seriously when the error is already somewhat large.
We probably won’t have to invent the exact rules that run inside a mouse’s head when it’s learning to manage all those levers. Our guess is that many of these algorithms have already been discovered, in past research on reinforcement learning.
A complete recap of reinforcement learning is beyond the scope of this book, but we can give you a rough sense, and suggest the few tweaks it might need to fit into our new paradigm.
There are many kinds of reinforcement learning algorithms, but the difference between them isn’t our current focus. For today we’ll use Q-learning as our example, a model-free algorithm that uses this update function:
Q-learning works by keeping track of the value of different actions A the agent can take in states S. The function Q(S, A) gives us the value of taking action A in state S.
This update function describes how the value representation of Q(S, A), the current estimate of the value of choosing action A in state S, changes in the light of new evidence.
The core of the equation is very simple. The new Q(S, A) is equal to the old Q(S, A) plus some change (for now ERROR) times a learning rate:
Q_new(S, A) = Q(S, A) + α [ERROR]
The learning rate α is a parameter between 0 and 1 that controls how much the Q-value is updated in each step. If the learning rate is higher, then more weight is given to new information and the mouse learns faster. But it might learn too much from the most recent example, and ignore past experience. If the learning rate is lower, then less weight is given to new information, the mouse learns slower, and updates are more conservative.
Hopefully that makes sense so far. You update the new value based on the old value, adjusting it by some amount, tuned by a parameter that controls whether the update is fast or slow. But what about this error? Let’s break it down.
R_t+1 is the immediate value the animal just experienced after taking action A in state S.
Next we see γ, which is the discount function. This is another parameter between 0 and 1, and this one controls how much future rewards are valued compared to immediate rewards. If γ is close to 1, the agent considers long-term rewards heavily; if close to 0, it focuses mainly on immediate rewards.
The next term, max_aQ(S_t+1,a), is a little trickier but not actually that bad. This looks ahead to the next step (t+1, where t stands for time), so the state we’ll be in next. Then it estimates the maximum value of the possible actions available at that state S_t+1. So this represents the agent’s best estimate of the value of future actions from the next state onward. This is important because if an action puts us in situations that lead to future rewards, we should learn that action is rewarding even if it doesn’t lead to a reward directly; it sets us up for success, which is nearly as good.
Finally, this is subtracted from the current estimate, Q(S, A), because what we want here is to know how far off is the current reward plus expected future rewards from the existing estimate of the value of this action.
Let’s take a few perverse examples that will make this equation transparent. To keep things simple, we’ll assume that the discount function is exactly 1.
Let’s start by considering a situation where we have already learned the correct value. The expected value of action A is 10, and we’ll see that that is perfectly correct. When we take action A in state S, we get a reward of 8, and that puts us in a new state S_t+1 with a maximum expected value of 2. This was all anticipated, so the existing value of Q(S, A) is 10:
NEW = 10 + α(8 + 2 – 10)
This gives:
NEW = 10 + α(0)
So we see that the weight of α doesn’t matter, because the error was zero, and anything multiplied by zero is zero. The organism was entirely correct in its expectations, so there will be no update at all. The reward from this action in this state (including anticipated future rewards) was 10, the old estimate was 10. The new value will be 10, the same as the old value.
But let’s say the estimate is off, and the mouse expected a value of 7 from the action A in state S. Then the function is:
NEW = 7 + α(8 + 2 – 7)
Now the learning rate matters. If the learning rate is 1, then the new estimate of this action in this state will be changed to the exact value of the most recent experience:
NEW = 7 + 1(8 + 2 – 7)
10 = 7 + 1(3)
But this is probably a mistake. It erases all the past experience. Maybe this was an unusually good time in state S to take action A, and we shouldn’t take this good outcome as representative. So instead we can use a more modest learning rate like 0.2:
NEW = 7 + 0.2(8 + 2 – 7)
7.6 = 7 + 0.2(3)
The only change by adding back in the discount rate is that the mouse doesn’t count the full value of the best possible future rewards. They’re only possible — a cheese in the hand and all that. Here’s the same situation with a discount rate of 0.9:
NEW = 7 + 0.2(8 + 0.9(2) – 7)
NEW = 7 + 0.2(8 + 1.8 – 7)
7.56 = 7 + 0.2(2.8)
In summary, Q-learning works by adjusting the Q-value of each action based on immediate rewards and estimated future rewards, gradually refining an estimate for the likely value of each action in each state.
It takes only a simple tweak to adapt this style of algorithm for cybernetics.
Reinforcement learning assumes that each agent has a single value function that it tries to maximize, and that all kinds of value are the same. In this perspective, 2 points from hot chocolate is the same as 2 points worth of high fives.
The cybernetic paradigm rejects that — abstract “rewards” don’t exist. Instead, governors track specific changes. So in this case, an algorithm like Q-learning is running inside each of the governors. The keep-mouse-from-getting-too cold governor is keeping track of what different actions in different states do to its error signal. The keep-mouse-from-getting-too hot governor is keeping track of what different actions in different states do to its error signal.
Each of the governors has its own ledger of the effect of different possible actions, and is keeping track of how each of these actions influences the signal(s) it cares about. Then all the governors get together and vote for their favorite action(s).
2025-02-28 00:11:00
[PROLOGUE – EVERYBODY WANTS A ROCK]
[PART I – THERMOSTAT]
[PART II – MOTIVATION]
Human nature is not a machine to be built after a model, and set to do exactly the work prescribed for it, but a tree, which requires to grow and develop itself on all sides, according to the tendency of the inward forces which make it a living thing.
—John Stuart Mill
The cybernetic paradigm gives you a theory of personality for free.
There are lots of governors in your mind, and some governors are stronger than others. Other things being equal, a stronger governor has more influence over your actions than a weaker governor. It gets more votes and has more of a say when it comes time for your governors to decide what to do.
Someone with an unusually strong hunger governor will seek out food sooner and will spend more effort to get it than someone with an especially weak hunger governor.
Someone with an especially strong status governor will be especially sensitive to changes in their status, and will invest lots of time and effort into status games. Someone with an especially weak status governor will appear almost blind to status, and it will hardly ever influence their behavior.
This provides the cybernetic paradigm’s theory of personality. People differ in many ways, but a particularly important way they can differ is in the strength of each of their different governors/emotions. In the cybernetic paradigm, differences between people are differences between parameters like the setpoints, strength, and sensitivity of their different governors.
To say that one person is more extraverted than another is to say either that their setpoint for social interaction is higher, that they defend the setpoint more aggressively, or that they’re more sensitive to disturbances away from that setpoint. To say that someone is brazen is to suggest that their shame governor is weaker than normal. To say that they are humble says something about the governor that pays attention to status.
Let’s break this down a little further.
First: People can have different setpoints for the same governor. We don’t know what units danger is measured in, but if one person has a danger set point of 5 units and another person has a danger set point of 10 units, the first person will keep themselves much safer than the second person. They will avoid situations where they feel that danger is above 5 units, while the other person won’t be sensitive, won’t feel any fear, until the danger is much higher.
That said, we actually don’t think that most personality differences are differences in setpoints, because the setpoints we know about are pretty similar across different people. Most people defend very similar setpoints for body temperature (about 98.6 °F), very similar setpoints for plasma osmolality (about 280 mOsm/L), very similar setpoints for serum potassium (about 4 mmol/L).
But there are certainly some exceptions. People can defend very different body weights, making some people extremely lean and others extremely obese. And set points can change, so they’re sometimes different even within one person. A fever is a short-term change in the body temperature set point(s). Obesity is a long-term change in the body weight set point(s).
Finally, even if people do defend very similar setpoints across the board, there will always be small differences between their setpoints, which will lead to some differences in personality.
Second: People’s governors can be stronger or weaker when it comes time to negotiate with other governors. When two governors disagree, which one wins?
Mark’s anger governor is especially strong, and gets many more votes than the other governors. So when anger goes up against anything else, it almost always wins. Mark has anger-control issues.
Julie’s fatigue governor is especially weak, and gets many fewer votes than the other governors. So when fatigue goes up against anything else, it almost always loses. Julie often stays up until she is very tired, doing all sorts of activities until she practically collapses. She barely seems aware that she’s tired. Even when she lies down, she often has a very hard time falling asleep. If there’s anything else she has in mind, her fatigue is not strong enough to keep her from thinking of it, then getting up and doing it.
You can describe this in terms of each governor having a different weight, with a weight of 1 meaning average strength. If one of your governors has a weight of 1, then that drive is as strong for you as it is for the average person. Weights above 1 mean the governor is stronger than normal; weights below 1 mean it’s weaker.
If you are born with the weight on your fear governor set to 2, your experience of fear is twice as powerful as normal, it has something like twice the influence over your actions. This makes you very cowardly, since your fear becomes overpowering in situations that other people would find mildly concerning. After all, it has twice as many votes as usual!
If you are born with the weight on your fear governor set to 0.5, your experience of fear is half as powerful as normal, it gets half as many votes as it would normally. This makes you very brave. In situations that other people would find terrifying, your fear barely has enough votes to call a motion.
Third: People’s governors can be more or less sensitive to disturbances. By analogy, a thermostat might have a narrow or a wide acceptable range around the target temperature. Strict sensitivity would mean frequent corrections as soon as the temperature drifted even 0.1 °F away from the set point, while a looser control system would allow more drift before it reacts, with control not kicking in until it was 2-3 °F off target.
This is a natural tradeoff. Strict/aggressive control means you spend more energy, reacting even to small changes and adjusting constantly, but it also means you stay very close to the set point. Loose/sluggish control means you spend more time out of alignment but you also save a lot of energy on not making all these neurotic adjustments. Some things really do need to be kept right at the set point, but other things can be allowed to wander a bit.
We think these three kinds of differences are probably important. But just to show that this isn’t an exhaustive list, here are two more ways that people’s governors might be different.
For example, an important parameter in control systems is gain. A sluggish system applies weak corrections (low gain), meaning it takes longer to reach the target. An aggressive system cranks up corrections harder (high gain), leading to faster changes, but possibly overshooting.
So some governors respond to an error with a big correction all at once, while other governors respond to an error of the same size with many small, incremental corrections. This might look like a personality difference of overreacting or underreacting.
This isn’t the same as sensitivity to disturbances. For example, Julie has a cleanliness governor with low sensitivity and high gain. She lets her apartment get pretty dirty (because of the low sensitivity), but once it’s a certain level of mess, she cleans it all at once, back to a high level of cleanliness (high gain).
Mark also has a cleanliness governor with low sensitivity, but his has low gain. He also lets his apartment get pretty dirty (because of the low sensitivity), but once it’s a certain level of mess, he slowly cleans it bit by bit until it doesn’t bother him anymore (low gain).
A related idea is damping. Some thermostats have a built-in “wait time” after making a correction, which helps prevent the temperature from swinging wildly. If our governors have some kind of damping, this might also vary between people.
With a fear governor set to low damping, you would respond very quickly to danger, but might sometimes freak out over nothing. It might even look like an extreme flinch response. With a fear governor set to high damping, you would respond very slowly and deliberately to new threats — good in some situations, but very bad in others!
All these parameters can combine in some interesting ways. Consider two people who have unusual sugar-governors, but unusual in different ways. Alice has a normal sugar setpoint, but her sugar-governor is unusually strong. Bob has a normal weight on his sugar-governor, but an unusually high sugar setpoint.
Alice’s sugar-governor gets more votes than other people’s. Since it tends to have the votes it needs, from the outside this looks like making sweet foods a priority. She always eats her sweets first. But if you kept a close measure of how much sugar she’s eating, you’d see that it’s actually the same amount as the average person, because her set point is the same.
Bob’s sugar-governor gets the normal amount of votes, but aims for a higher setpoint. For a given level of desire, Bob doesn’t prioritize sugar more than other people. But if you keep track over the long term, he does consume more sugar to reach that higher set point.
The upshot is that there are at least as many personality dimensions as there are emotions, and each of these personality dimensions are linked to the “settings” of a particular emotion.
As of this writing, the most widely accepted theory of personality is the “Big Five” personality traits.
This theory comes from statistical analysis. When you have people rate themselves and others on a wide variety of adjectives, and then apply various statistical techniques, you usually end up with five clusters of adjectives. Over time people settled on a set of labels for those clusters: openness, conscientiousness, extraversion, agreeableness, and neuroticism.
It’s not hard to see how these might map on to various emotions. For example, extraversion is probably a rough measure of the strength of various social emotions.
But the Big Five has some problems as a theory. The first one is fundamental — the Big Five are an abstraction, not a model. We all have a casual sense of what it means to be neurotic, we know what kind of superficial behavior to expect from someone described with this word, but the theory doesn’t say anything about the mechanisms that cause someone to behave in a neurotic way. It caps out at being able to record that one measure is correlated with another measure. It can neither explain, nor in any meaningful way can it predict. (For more about these problems, see The Prologue.)
In addition, the method psychologists used to come up with these five factors is limited.
The Big Five were discovered through a method called factor analysis, a statistical approach that searches for clusters of correlated variables and hypothesizes factors that might account for the patterns it finds. Psychologists collected large sets of descriptive adjectives like “friendly” and “bashful” and had people rate how well the adjectives applied to themselves or others. Then they used factor analysis to estimate how these ratings co-occurred. This usually gave a solution of five factors — five clusters of adjectives that tended to be highly correlated within the clusters.
But language doesn’t capture all of the true personality differences, or at least doesn’t capture all of them to the same degree.
There are some terms, like “salt tooth” and “sweet tooth”, which hint at recognition of the fact that in some people the salt-hunger governor is unusually strong, and in other people the sugar-hunger governor is unusually strong. But these terms aren’t as much a part of our language as dimensions like “does this person spend lots of time around other people” or “is this person reliable”, which come out into the factors of “extraversion” and “conscientiousness”.
This is for social-historical reasons — at the moment, our culture cares a lot about communicating whether or not a person is sociable and/or reliable, and cares very little about their preferences for sweet or salty foods. Compare this to how Ancient Greek and Latin both had lots of different words for different kinds of shields. In their culture, the kind of shield you used said a lot about where you fit in society, so they had terms to make these distinctions. But in our culture no one cares what kind of shield you use, so modern English does not.
Different times and cultures will have different priorities, and will want sets of words that help them describe variation in the drives they care about the most. There’s still variation in the drives they don’t care about as much, but since they don’t care about that variation, they won’t talk about it, so they won’t need any words for it.
The fear governor is real, and martial cultures of the past had many ways to talk about differences in how someone responds to fear. How you responded to fear was very relevant in these cultures, it came up a lot. But today we are safe most of the time and these differences rarely matter, so the words we’ve inherited from such times, like brave and cowardly, are too few to pull their own group in a factor analysis. (You could get more by adding archaic terms like dauntless, plucky, valiant, doughty, aweless, and orped, but these probably don’t go in the surveys.)
The Icelandic language, on the other hand, which has changed much less than English over the centuries, still retains several words for these concepts — huglaus, óframur, ragur, blauður, deigur, all these mean something like “fearful” or “cowardly”. And on the opposite side, Icelandic has about a dozen words for “brave”.
But even though English doesn’t give them dozens of adjectives apiece, emotions like cold, tiredness, needing to pee, etc. all have personality dimensions just the same. Some people are driven more by the need to keep warm, and some barely notice the cold. Some people are driven by their bed. For some people, when nature calls, you must answer.
The seven deadly sins are a bit judgy as a personality measure, but they had it a little better. Gluttony and sloth are clearly ways to talk about individual differences in things like hunger and tiredness. And lust is, if anything, one of the most notable personality dimensions. How could you possibly explain Aella’s personality without mentioning that she is much, much hornier than average? On the opposite side, having a weight on this governor near zero would lead to asexuality, so being asexual should also be understood as part of personality.
There are also some differences that are not linked to the emotions and drives, that don’t reflect the settings on different governors.
For example, people can also be different in the parameters of motivation we described in Part II; like the gate threshold, i.e. the minimum number of votes to make an action happen. If you have a higher gate threshold, you are more likely to just sit there and less likely to do anything, every action needs a larger number of votes just to activate. If you have a lower gate threshold, you are constantly jumping around, every time an action gets any votes, you do it. Similarly, to say that someone is decisive is to imply something about the parameters of their selector, not their governors.
One underrated individual difference is being a night owl versus being a morning lark (sometimes called your chronotype). The dimension is related to sleep, but doesn’t seem like a parameter of the drive for sleep (probably?). Instead it’s a tendency or preference for when sleep will occur.
Some people are certainly more curious than others. But curiosity may not be an emotion, because it doesn’t seem to be satisfying a drive to send a signal to some specific target.
Another difference is taste preference. Certainly some tastes, like those for salt or fat, are nutritive, necessary for survival, and therefore probably controlled by a governor. But some taste preferences may not come from the drives, they may just be variation. Chunky and creamy peanut butter have almost exactly the same nutritional profile, but some people prefer one to the other. The same goes for preferences for smells — there is probably not a lavender-smell governor, but some people still like the smell of lavender more than others.
If these preferences really are preferences, and aren’t attached to drives, we’ll be able to tell because they will not be exhausted like drives are. Even someone who likes salt very much will eventually eat enough salty food and will stop eating it for a while. Their salt drive will send its error signal to zero and then be satisfied. But someone who likes the smell of lavender shouldn’t get satisfied by it in the same way, their preference should be mostly constant.
The reason for these differences is the same as for any kind of differences: diversity. It’s not just random chance; it is by design, because: bees.
How do the bees decide how many of them should be fanning? … There’s no communication, but as the ventilation gets worse in the hive, more and more bees start fanning their wings. How would you design bees to solve this problem? You don’t want every bee fanning their wings 24/7 or they’re wasting time, but a nice ratio of ‘bees fanning’ to ‘bees not fanning’ that adapts in order to hit your ventilation criteria.
When Huber examined the fanning problem, he came up with an elegant theory. He suggested that bees are differentially sensitive to noxious smells. So as the noxious smells get worse, the sensitivity threshold of more and more bees is reached, and more of them begin fanning until ultimately the entire hive is fanning.
If everyone in your village has the same set point for danger, then as danger increases, for a long time no one takes any precautions, and then at some point everyone flips over and starts fortifying the town all at once. This is kind of a nuts way to do things.
It’s better to have some diversity. If there’s only a little danger, a small number of villagers are stockpiling food and reinforcing the town walls. As the danger increases, more and more villagers attend to the safety of the town. This is actually its own form of control system.
The same thing goes for preferences. If everyone in your band of hunter-gatherers falls asleep exactly at dusk and rises at dawn, then you are all defenseless at the same time. But if some of you are morning larks and some of you are night owls, then someone is always awake to tend the fire and watch for saber-toothed tigers.
Now apply the same reasoning to taste and smell. If everyone in your town has identical tastes, then they will all eat pretty much the same food; if that food becomes rotten, everyone gets sick at once. Better to have variation in food preferences so you’re eating different things. Then if some food goes bad, only some of you get sick. Avoid a single point of failure.
To sum up, differences in the strength of different governors are a major part of personality, though not the only part. There are also various other individual differences, including simple preferences.
Academic psychologists claim they can’t find any clear mental differences between the sexes (mostly; for the nuanced version of things, see here). But here’s one: the huge and obvious differences in the desire to play certain kinds of video games.
About half of gamers are women. But a few genres are overwhelmingly played by men. In particular, men are much more interested in tactical shooters like ARMA 3, and in grand strategy paint-the-map games like Europa Universalis. These games are about violent competition and domination, so this pattern may point to the existence of something like a “need to dominate” emotion.
Looking closer, the experience of shooters and strategy games are quite different, suggesting that there might actually be two separate dominance-related emotions that tend to be much stronger in men than in women. Let’s consider these drives one at a time.
The experience of a tactical shooter is shooting people in the head; it’s about as close as you can get these days to crushing your enemies, seeing them driven before you, and hearing the lamentations of their women. You may be wondering whether people really have a drive for such a thing, especially if you don’t play tactical shooters. But there’s good evidence that many people do. As one example, the subreddit r/CombatFootage (TAKE CARE IN CLICKING, CONTAINS DISTURBING COMBAT FOOTAGE) has 1.7 million members. Top videos on the subreddit get thousands of likes and hundreds of comments. For comparison, r/vegan also has 1.7 million members. Some people really want to see this stuff.
In contrast, grand strategy games are abstract and bloodless, lovingly referred to as spreadsheet simulators. These don’t seem like they could be about personal, physical domination, since they don’t even simulate that. But they’re not pacifistic — they do a very good job simulating the experience of forcing other societies to make concessions, become your vassals, and so on.
Between the two genres, there’s plausibly one dominance emotion about personally thrashing your enemies, and another dominance emotion about being in charge of organizing the logistics of thrashing — something like social domination, or having your group dominate other groups.
We see something similar in the list of words known better by males than by females, and vice versa. Men are much more likely to know words like howitzer, katana, and bushido (not just military terms, but historical military terms) while women are much more likely to know words like peplum, chignon, and damask (fabric and hairdressing terms). The authors of this paper characterize the result as, “gender differences in interests (games, weapons, and technical matters for males; food, clothing, and flowers for females)”.
The list suggests that on average men tend to have stronger dominance emotions and women tend to have stronger decorative emotions, or perhaps hygienic emotions (in the sense that being properly dressed is hygiene).
We are of course talking about average differences. There are plenty of women with strong dominance emotions, and plenty of men with strong decorative emotions. (And women may in fact have higher tuning on a different set of dominance emotions.) But on average there seems to be some difference.
We don’t care about the cause — differences could be the result of socialization, of nature, or both. Or something else. But there do seem to be average personality differences between the sexes, which make perfect sense when you think of personality as differences in the strength of different governors.
It’s also worth considering if sex differences we think of as physiological might actually be psychological. Women typically feel colder than men — this might be biological, something to do with their body size or metabolic rate. But it could also be psychological, something to do with the set point or strength of their cold governor.
Like most biological attributes, the strength of our governors probably falls on a normal distribution. The majority of people will have a fairly usual weight on each governor. But in rare cases, weights will be set incredibly high or incredibly low.
Since we have no idea what the units are for “strength of a governor”, as before we will just say that 1 is the population average. Having a weight of 0.5 on a drive means it is half the strength of the population average, and having a weight of 2 on a drive means it is twice the strength of the population average.
If you set the weight on a governor to 0, we call this a “knockout”. It’s functionally equivalent to not having that drive at all, because when the weight on a governor is 0, the governor gets no votes.
For example, take Alex Honnold, sometimes called “the World’s Greatest Solo Climber”. Alex enjoys climbing sheer cliffs without a rope, an experience so terrifying that many people can’t even stand to watch the videos. When neuroscientists put Honnold through an fMRI and showed him terrifying and gruesome pictures, they found that his brain is intact — he does have an amygdala — but he has almost no fear response.
Whatever the exact biological issue might be — whether he was born that way, or if he’s somehow turned down the fear governor through training and exposure — Honnold appears to be someone with a fear knockout. The weight on his fear governor is set very close to zero.
In cybernetic psychology, a lot of psychiatric conditions look, in a literal sense, like personality disorders. Personality is largely made up of differences in the weights on a person’s various governors. Personality disorders occur when some of those weights are not merely different, but set extremely low or extremely high.
Consider fear. Most people are somewhat concerned about things some of the time. They have a weight on their fear governor around 1. If you set the weight on “fear” to 10, they will instead be very concerned about things lots of the time. That looks a lot like paranoia.
This is a good spot to point out that a cybernetic system has multiple parts and can be broken in many ways. Let’s take the fear governor as an example.
You can break the input function, so it perceives danger as being higher than it otherwise would. This will cause paranoia. You can change the fear governor’s set point to a very low level of danger, so it reacts to even very small amounts of danger. This will cause paranoia. You can damage the output function, so that it thinks that large interventions are appropriate for small amounts of danger. This will cause paranoia. Or you can change how many votes the fear governor gets in the parliament of the mind. Again, this will cause paranoia.
These changes may present slightly differently, but notice how even though these are four different problems with the fear governor, you end up seeing basically the same behavior in every case. Among other things, this makes diagnosis and treatment quite tricky. You have at least four disorders, with categorically different causes, yet nearly identical presentation.
This also offers a plausible model for conditions like autism and psychopathy. Both appear to be congenital abnormalities in various emotions — conditions that happen when you are born with a couple of your emotions unusually strong or weak.
“Autism” seems to be a label that we apply to people who have very low weights, or complete knockouts, on some of their social emotions.
“Psychopathy” seems to be a label that we apply to people who have very low weights or knockouts on a different set of social emotions, especially when combined with high weights on emotions like anger or need for dominance.
As you can tell from our hedging, we suspect these categories are poorly-formed. There probably isn’t “a disorder” that can be identified with autism. It’s just a word, an abstraction that we use to refer to various personality types that are similar in the sense that they have low weights on certain social emotions. (See the Prologue for more on this.)
Autism and psychopathy are often framed as deficiencies, but you can also see them as deficiencies in some things combined with superabundances in other things.
We tend to call people “psychopaths” not when they merely lack in fear or compassion, but when a lack of fear or compassion are combined with unusually strong drives for status and dominance.
People tend to be considered autistic not when they merely lack a drive for status, but when this is combined with unusually strong interest in social rules and an unusually strong drive for compassion. People get confused about this. You often hear things like, “people who are autistic don’t understand social conventions”. But actual people who are autistic seem to believe things like, “if you eat a non-prime number of chicken nuggets you’re breaking the rules”.
It’s not clear if these are specific “disorders”, or just the extremes of normal personality variation. Some people have stronger social emotions than others. When the weights on your social emotions are 0.7, nobody cares, you just seem kind of introverted. But when some of your weights are 0.5 or lower, maybe they start calling you autistic.
Same thing for psychopathy. The lower your social weights are, and the higher your aggression and dominance weights, the more likely people are to call you a psychopath. But there’s not a bright line. It’s more like height than blood type. Type O and type AB blood are categorically different, but there’s no objective point at which you become “tall” or “short”, those are relative.
2025-02-21 00:11:00
[PROLOGUE – EVERYBODY WANTS A ROCK]
[PART I – THERMOSTAT]
Inland Empire: What if *you* only appear as a large singular body, but are actually a congregation of tiny organisms working in unison?
Physical Instrument: Get out of here, dreamer! Don’t you think we’d know about it?
— Disco Elysium
When you’re hungry, you eat a sandwich. When you feel kind of gross, you take a shower. When you’re lonely, you hang out with friends.
But what about when you want to do all these things and more? Well, you have to pick. You have many different drives, but only one body. If you try to eat a hamburger, kiss a pretty girl, and sing a comic opera at the same time, there will be a traffic jam in the mouth. You will suffocate, or at least you will greatly embarrass yourself. Only a true libertine can eat a sandwich in the shower while hanging out with friends.
To handle this, you need some kind of system for motivation.
For starters, consider this passage from Stephan Guyenet’s The Hungry Brain:
How does the lamprey decide what to do? Within the lamprey basal ganglia lies a key structure called the striatum, which is the portion of the basal ganglia that receives most of the incoming signals from other parts of the brain. The striatum receives “bids” from other brain regions, each of which represents a specific action. A little piece of the lamprey’s brain is whispering “mate” to the striatum, while another piece is shouting “flee the predator” and so on. It would be a very bad idea for these movements to occur simultaneously – because a lamprey can’t do all of them at the same time – so to prevent simultaneous activation of many different movements, all these regions are held in check by powerful inhibitory connections from the basal ganglia. This means that the basal ganglia keep all behaviors in “off” mode by default. Only once a specific action’s bid has been selected do the basal ganglia turn off this inhibitory control, allowing the behavior to occur. You can think of the basal ganglia as a bouncer that chooses which behavior gets access to the muscles and turns away the rest. This fulfills the first key property of a selector: it must be able to pick one option and allow it access to the muscles.
The human mind, and the minds of most vertebrates, operates in essentially the same way.
Motivation and action are determined by the collective deliberation of multiple governors. Each governor is one of the control systems described in Part I — some governors for thirst, some for pain, some for fear, and so on. They come together and submit bids for different actions and vote on which action to take next.
Inside Out, Disco Elysium, Internal Family Systems, The Sims, etc. — we have a deep intuition that behavior is the result of a negotiation between inner forces that want different things. This keeps manifesting in pop culture, but academic psychology has mostly missed it.
The technical term for this problem is selection, so we’ll refer to this system as the selector. In a physical sense this process probably happens in the basal ganglia, but we’ll let someone else worry about the neuroscience. For now we just want to talk about the psychology.
We can’t say exactly how the selector works, there are too many mysteries, lots more work to be done, a lot of possible lines of research. But here’s some speculation about how we think it might work, which will sketch out some of the open questions.
Governors cast votes based on the strength of their error signal. The stronger the error, the more votes it gets. When you’re not at all thirsty, the thirst governor gets basically no votes, because it doesn’t need them. Other priorities are more important. But if you are very thirsty, the thirst governor gets lots of votes (or if you prefer, one very strong vote). If you are starving, your hunger governor gets plenty of votes so it can drive you to eat and become less hungry.
Governors vote for behaviors that they expect will decrease their errors. The thirst governor votes for actions like “find water” and “drink water”. Later, the have-to-pee governor votes for actions like, “find a bathroom”. The pain governor votes for things like “stop picking a fight with the lions, get the hell out of the lion enclosure.”
Governors can also vote against behaviors that would increase their errors. It’s clear that the pain governor can vote against touching a hot stove, even if pain is currently at zero. You don’t have to wait until you burn your hand for your pain governor to realize this will be a bad idea.
This is because governors are predictive. If something is hurting you, the pain governor will vote for you to stop doing that, to avoid the thing that is causing you pain, to withdraw. But you don’t have to be in pain for the pain governor to influence your actions. As behaviors come up for a vote, the pain governor looks at each of them and tries to predict if they will increase its error, that is, if they will cause you pain. If it thinks some behavior will increase its error, the pain governor votes against that behavior.
So we see that governors don’t only get votes based on their current error signals — they also have the power to vote against behaviors they anticipate will increase their error. Maybe governors cast votes not based on the current strength of their error signal, but based on the predicted change in their error if the action were to be carried out. In this way when hunger is high, the hunger governor gets votes for “eat ham sandwich” because this is predicted to correct the error. And even when pain is zero, the pain governor still gets votes against “touch the electric fence” because touching the fence is predicted to increase its error. This would also fit most observed behavior.
Wherever votes come from, the governors need to allocate their votes, so there’s some procedure for this as well. One simple way to do things is for governors to propose behaviors and submit bids on those behaviors to the selector, and the strongest bid wins. If this is how it works, then each governor is supporting only one behavior at a given time.
This seems unlikely. We think it’s more likely that governors support many possible behaviors at once — just like how legislators in a real congress support many possible policies at once.
Actions that happen all the time are so common because they are popular with lots of governors. For example, the “eat a hamburger” action captures the votes of basically the whole hunger voting bloc — salt-hunger, fat-hunger, calorie-hunger, et cetera. Many different hungers will vote for this hamburger. No one dares to vote against the hamburger policy, except maybe the shame governor, if you’ve been taught that hamburgers are sinful or something.
It’s also not clear whether votes are conserved. If the hunger governor has 100 votes and you give it 50 options, can it only give each option 2 votes? Is this why no one can agree what they want for dinner? Or can it put all 100 votes towards every option that it likes?
Some governors may get more votes than others. You can imagine why the governor in charge of keeping you breathing might get extra votes — it has a very important job and it can’t wait to build a coalition. The same thing goes for governors like fear and pain. When you’re in serious danger, they always have the votes they need.
Our assumption so far is that the relationship between error signal and votes is linear. But certain governors, controlling things that are critical to your survival, may get more votes for the same amount of error signal — there may be different curves. This is how The Sims did it. If this is the case, it should be possible to discover the formula for votes as a function of error for each governor.
On the other hand, maybe the more critical governors just have stronger error signals than less-important governors. In any case, we should notice that things like suffocation and pain tend to get the votes they need, however that works out under the hood.
However votes are determined, the outcome is simple. Whatever action gets the most votes is the action you take next, assuming the action wins by a large enough margin.
This is not exactly a winner-take-all system. You can sometimes do more than one thing at once, the selector does try to account for multitasking — you can chew and drive at the same time, since your mouth and hands are not deadlocked. But you cannot e.g. both pee and stay in your clean, dry bed. Someone is going to have to win that vote.
An organism that can’t sit still and keeps doing stuff, even when it doesn’t need to, is wasting resources for no reason and putting itself in danger. Sometimes organisms do nothing at all, so our model of the selector needs to account for that.
We think it does that through a mechanism that recognizes votes below a certain threshold and reduces them to zero. In audio engineering, this is called a gate. An audio gate stops sounds below a certain volume from passing through, which is good for cutting out background noise and static. For more information, watch this Vox explainer or listen to some Phil Collins.
In the mental selector, the gate stops votes that are below some minimum threshold. If you are a tiny bit hungry, you shouldn’t bother leaving the house to get a meal, even if there is nothing better to do. Don’t go out and see people if you are only a tiny bit lonely.
An organism without a gate, or with a broken gate, will eat as soon as it is a tiny bit hungry, leave the house as soon as it is even a tiny bit lonely. It will constantly put on and take off its sweater to try to maintain a precise target temperature. But this is clearly not a good use of time or energy. Better to wait until you’re actually some minimum amount of hungry or lonely, before taking steps to correct things.
The gate may act on governors directly, preventing governors with very small error signals from voting at all. When you’re not in any danger, who cares what the fear governor thinks?
Or it could be that the gate acts on behaviors, and behaviors that get below some fixed number of votes are treated like they got zero votes instead. If no action gets a number of votes above the threshold, then no behavior occurs.
Also, it seems like an action only happens as long as it beats the next-highest action by a certain number of votes. It’s not clear whether it needs to win by a certain number of votes (“action with the most votes happens as long as it has more than 20 more votes than the action with the second-most votes”) or by some kind of fraction (“action with the most votes happens as long as the action with the second-most votes has no more than 90% its count”), or if this is even a meaningful question given how our motivation system is designed. The important thing is that if “drink coffee” gets 151 votes and “run to catch the bus” gets 152 votes, you will stand there looking like an idiot and miss your bus. (cf. Buridan’s ass)
We designed this model of motivation without concerning ourselves at all with neuroscience, so one reason for optimism is that it is largely convergent with a model of the function of the basal ganglia developed in 1999, also inspired by cybernetics. This was “The Basal Ganglia: A Vertebrate Solution to the Selection Problem?” by Redgrave, Prescott, and Gurney.
So far we’ve been assuming that governors are the only things that drive behavior, the only things that ever get votes in the selector. But there may be exceptions.
Curiosity is an unusual case, kind of an enigma. It might be an emotion, but it’s a bit strange. It might be something else, some other kind of signal.
Like an emotion, curiosity seems to be able to drive behavior. We’ve all done things simply because we were curious. This suggests it might, like the other emotions, be the error signal of some kind of governor. And it seems to be able to compete with the other governors, because curiosity often wins out over concerns like sleep or even sex.
But in other ways, curiosity does not look like the other emotions. Unlike hunger or fear, it’s not obviously an error signal from a drive that keeps us alive. It’s not obviously connected to immediate survival in the way the other emotions are. A person who doesn’t sleep or breathe dies. A person who doesn’t feel shame is ostracized, and (in nature) soon dies. But a person who doesn’t act on their curiosity is just frustrated.
And unlike the other emotions, curiosity doesn’t seem to be easily satisfied. Acting on your fear should make you less afraid, acting on your thirst should make you less thirsty, but acting on your curiosity often seems to make you more curious.
We do have one suggestion of how curiosity might work. Let’s return to the idea that emotions are predictive. The fear governor not only knows that escaping the basement will reduce its error, it can also predict beforehand that entering the basement will increase its error. In general, governors have a model of the world which they use to predict how different behaviors will influence their errors.
Unlike the governors, which vote for behaviors that they predict will correct their errors, curiosity is a special drive that votes for behaviors the emotions have a hard time predicting. Actions can be ranked by how certain the governors are about their consequences. Curiosity, the most perverse, votes for actions that the other governors rate as having the greatest uncertainty.
This helps us learn about actions that the governors might otherwise ignore. It’s another way to encourage exploration. If you only act in response to emotions, then you lose the opportunity to learn about things that might be really important later. It’s a better long-term strategy to use your extra energy to try things that are probably safe, but where you aren’t sure what will happen. (See this paper for more on this kind of model.)
You know who loves doing this? Toddlers. Toddlers love doing this. It may not be that children are more curious than adults, but simply that adults have learned more about the consequences of their actions and have fewer of these very uncertain behaviors to explore.
One of the mysteries of motivation is that sometimes, you want to do something and it’s super easy to do. Why is it sometimes easy to do things?
The answer is simple. When a behavior gets votes from a governor, it’s easy to do. Outside of clinical depression, you don’t have to drag yourself to a delicious meal, or to hear the new hot gossip. Popular emotions are throwing all their votes behind these actions, they are going to become policy.
Behaviors that don’t have a governor behind them are hard to do. Evolution didn’t include a governor for “write your term paper”, so this project tends to go pretty slowly, especially if it’s in competition with behaviors that do have governors voting for them, like “hang out with your friends”. Sometimes the term paper never happens.
The same thing goes for the big-picture aspirations people so often struggle with. Intellectually you might want to become a famous author, or learn Japanese, or memorize pi to 100 digits. But the sad truth is that no governor is willing to support these ideas. You just don’t have the votes.
Things that can’t get votes from a governor only get votes from your executive function. Executive function must not have many votes to spend, because these actions tend to be very difficult.
Even if you can temporarily scrape together the votes for one of these actions, you have to hold your coalition together. This usually fails. You will inevitably get distracted once any of the other governors gets a large enough error signal to vote for something else, like getting a snack. This is why you are always looking in the fridge instead of studying.
One workaround is to convince a governor to vote for these actions. If you get a lot of praise and status at school for doing well on your math test, social governors that are concerned with status will be willing to vote for math-related activities in the future, because they realize that it’s good for their bottom line. Or if there’s a pretty girl in your Japanese class, you may find that it becomes easier for you to work on your presentation, in an effort to impress her. No points for guessing which governor is voting for this!
This is probably why people seem to find over and over again that money is not very motivating.
Money is motivating when it can directly address your needs. If you are starving, the connection between $5 and a block of cheese is pretty clear. As a result, the hunger governor will vote for things that get you $5.
But in a modern economy, most people’s remaining needs cannot be easily met by more money. They already have enough money to get all the food, warmth, sleep, and so on that they need. The only drives they have problems satisfying are the drives where, for one reason, there isn’t or can’t be a normal market.
Social factors like friendship or a feeling of importance are often left unsatisfied, but these are hard to trade directly for money. You can’t buy these things for any amount, or at least, there are no effective markets in these “goods”. So money is no longer very motivating for people who need these things. Their active governors, the ones with big errors, the ones that get the votes, understand that more money won’t solve their problems, so they don’t vote for actions that would get you more money.
As we hinted at above, we might assume that there is also an executive function that gets some votes. Executive function is why you can make yourself do dumb things that are in no way related to your survival, why you can plan for the very-long-term, and also why you have self-control in the face of things like cold and pain.
Eventually we may discover that what appears to be “self-control” is actually just the combined action of social emotions like shame. It may be that there is no such thing as an executive function, and what feels like self-control is really the result of different social emotions, the drives to do things like maintain our status or avoid shame, voting for things that are in their interest. But for now let’s keep the assumption that there is someone driving this thing.
Even so, executive function doesn’t have very many votes, which is why most people cannot starve themselves to death or hold their breath until they suffocate. At some point, the suffocation governor ends up with so many votes that it can make you do whatever it wants, and it always votes for the same thing: breathe.
Here’s another thing people find surprising: why don’t we maximize happiness?
People often complain about not being as happy as they would like. But their revealed preferences are clear: they don’t always do things that make them happy, even when they know what those things are, even when it’s easy. People often choose to do things that are painful, difficult, even pointless.
This is because there is no governor voting for happiness. Happiness is more like a side-effect, something that happens whenever you successfully correct any governor’s error signal. People who live challenging lives end up happy, assuming they are able to meet those challenges, but there is no force inside you that is voting for you to go and become more happy per se.
Remember that happiness isn’t an emotion. All emotions are error signals generated by a governor dedicated to controlling some signal related to survival. Governors have a simple relationship with the error signals they generate: they vote for behaviors that will drive their error signal towards zero. So if happiness were some kind of emotion, the governor that generated it would vote, whenever possible, to drive happiness towards zero!
Clearly people don’t behave in a way that tries to drive happiness to zero. While we aren’t happiness-maximizers either, many of our actions do make us happier, and when we take an action that makes us less happy, we’re less likely to take that action in the future. This is clear evidence that happiness isn’t an emotion.
The paradoxes of motivation are a lot like the paradoxes of democracy. A democracy does not institute the policies that are the best for its citizens. It doesn’t even institute the policies that are most popular. Democracies institute the policies that get enough votes.
Similarly, a person does not take the actions that make them happiest. They do not take the actions that are best for them, or even the actions that are most likely to lead to their survival. No, people take the actions that get the most votes.
Like with democracy, the system still mostly works, because “what gets the most votes” is close enough to “what’s good for you”, enough of the time. But there are all kinds of situations that lead to behavior that can appear mystifying, until you learn to see things through the lens of parliamentary procedure.
There’s nothing wrong with not being happy. You can not be happy and still be doing perfectly fine. So why do people find this startling, and ruminate about their lack of happiness? Isn’t it strange that people obsess so much over happiness, but don’t actually change their actions to become more happy?
The explanation may be purely social. In modern American culture, we are expected to be happy. Not being happy is seen as a sign of failure and weakness. Being unhappy, or even just feeling neutral, is enough to make us lose status in the eyes of others, it can be the source of ridicule and shame. Being anything less than perfectly happy can be enough to make you a subject of pity. So even though happiness is not directly controlled, if you exist in a culture with these norms, some of your social governors (associated with emotions like shame and drives for status) will vote for you to do things that will make you happy, just so you can get one over on the Joneses.
But our social emotions are not voting to make us happy per se — they are actually concerned with making sure we avoid the social consequences that would come from appearing unhappy. They want to make sure that we don’t lose status for being seen as gloomy, and keep us from feeling shame for our melancholy. One way to do this is to vote for actions that will make you happier. But equally good, better even, is to vote for actions that make you seem happy!
So other things being equal, the social emotions tend to drive us towards the appearance of happiness, rather than actual happiness. Actual happiness may or may not make us appear happy in a way that will increase our status or reduce our shame. But the appearance of happiness always appears happy. So that’s what gets the votes.
This is what makes people neurotic about not being as happy as they should be. When they’re feeling reflective, it makes some people worry that they are fake, since they feel consistently driven towards the appearance of happiness, even at the expense of what would actually make them happy.
This is a well-known problem in contemporary American culture, and for cultures that have borrowed American standards for happiness. But most other cultures don’t expect people to be happy all the time. Without this expectation, people from these cultures don’t have the problem of feeling like they must both seek happiness and perform it, and don’t run into this weird vicious cycle. (Though of course, other cultures have problems of their own.)
For a similar example, consider the problem of self-sabotage. In some cultures and contexts it’s not appropriate to perform better than your peers, or to get too much better too quickly (cf. tall poppy syndrome). In this case, some of the social governors will vote against performing your best, to avoid the social disapproval that might come from performing better than you “should”.
This suggests that the treatment for self-sabotage is to surround yourself with people who think that failure is shameful and success is impressive, rather than the other way around. And it suggests that something you can do for the people around you is to express polite disappointment when they accomplish less than they hope for and genuine enthusiasm when they accomplish more. Even an expression of envy can be a supportive thing to do for your friends, as long as it’s clear that it comes from a place of admiration rather than competition.
Of course, if you go too far in this direction, you can end up with a culture that is neurotic about success rather than about conformity. Decide your own point on the tradeoff, but we’d argue that self-sabotage is worse than pushing yourself too hard.
Why do people sometimes seek out extreme experiences? Why do we subject ourselves to things like roller coasters, saunas, horror movies, extreme sports, and even outright suffering?
Psychologist Paul Bloom explains these decisions in terms of chosen suffering versus unchosen suffering. For example, in this interview he says, “You should avoid being assaulted… there’s no bright side to the death of a loved one… there’s no happiness in watching your house burn down… nor is there happiness to be found in getting a horrible disease. Unchosen suffering is awful.”
In contrast he says, “chosen suffering, the sort of suffering we seek-out can be a source of pleasure … You choose to have kids, you choose to run a marathon, you choose to eat spicy food. You choose these things because there’s a payoff later in future pleasure.”
We think this is close. He’s picked the right examples, but getting assaulted, losing a loved one, or getting a horrible disease, are just bad. Choosing them wouldn’t make them any better. So it can’t be the chosen versus unchosen nature of these examples that makes the difference.
A better way to think about this is whether the suffering is under your control. If suffering is under your control, it can be corrected at any time. Since happiness is generated when errors are corrected, then controlled suffering is a neat hack — it’s a free way to generate happiness at no risk to actual life and limb.
Controlled suffering is like a sauna or a horror movie. You’re sweating or you’re scared, but you can stop at any time, and stopping feels pretty great, it’s a relief. The uncontrolled version would be more like being trapped in a sauna, or locked inside a haunted house — not so pleasurable, and not the sort of thing people go looking for. A really uncontrolled version would be the experience of being trapped inside a burning building, or being chased by an actual serial killer, where the stakes are not only real, they have permanent consequences.
When given a choice, people only tend to choose controlled suffering, and tend to suffer uncontrolled suffering only against their choosing. So almost all chosen suffering is controlled, and all uncontrolled suffering is unchosen. This should come as no surprise. But this has led Bloom to mistake the choosing for the active ingredient, rather than the controlled nature of the suffering.
Choosing uncontrolled suffering doesn’t make it good for you. Choosing to get assaulted is about as bad as getting assaulted by accident. Unchosen but controlled suffering isn’t usually that bad. Taking a wrong turn and ending up in the sauna by mistake is not that much of a bummer.
If you do want to become happier, the solution is simple — make yourself hungry, thirsty, cold, hot, tired, lonely, scared, etc. And then correct these errors promptly. It will feel amazing. If it doesn’t feel amazing, you are probably depressed in some more serious way. (See upcoming sections for more speculation about what this means for you.)
2025-02-14 00:11:00
[PROLOGUE – EVERYBODY WANTS A ROCK]
When the hands that operate the motor lose control of the lever;
When the mind of its own in the wheel puts two and two together…— Thermostat, They Might Be Giants
There are lots of ways to die.
To avoid biting the dust, lots of things need to be juuuust right. If you get too hot or too cold, you die. If you don’t eat enough food, you die. But if you eat too much food, you also die. If you produce too much blood, or too little blood, if you [other thing], if you [third thing], dead dead dead.
It’s a miracle that organisms pull this off. How do they do it? Easy: they make thermostats.
A thermostat is a simple control system.
Thermostats are designed to keep your house at a certain temperature. You don’t want the house to get much hotter than the target temperature, and you don’t want it to get much colder.
To make this happen, the thermostat is designed to drive the temperature of the house towards the target. If you’re not too allergic to anthropomorphism, we can say that the goal of the thermostat is to keep the house at that temperature. Or we can describe it as a control system, and say that the thermostat is designed to control the temperature of the house, keeping it as close to the target as possible.
The basic idea is simple. We divide the world into the inside of the thermostat and the outside of the thermostat, like so:
To begin with, we need some kind of sensor (sometimes called an input function) that can read the temperature of the house and communicate that information to the inside of the thermostat.
Some sensors are better than others, but it doesn’t really matter. As long as the sensor can get a rough sense of the temperature of the house and transport that information to the guts of the device, the thermostat should be able to do its job.
The sensor is a part of the thermostat, so we color-code it white, but it interacts with the outside world, so the box sticks a little bit out into the house.
The sensor creates a signal that we call a perception. In this case, the sensor perceives that the house is 68 degrees Fahrenheit.
The sensor can be very simple, like a thermometer that measures the temperature at one spot in the house. Or it can be very complicated — for example, a network of different kinds of sensors all throughout the house, feeding into a complex algorithm that references and weighs each one, providing some kind of statistical average.
The important thing is that the sensor generates a perception of the thing it’s trying to measure, the signal the control system is aiming to control. In this case, the sensor is trying to get an estimate of the temperature in the house, and it has sensed that the temperature is about 68 ºF.
The thermostat also needs a part that can interpret the signal coming in from the sensor. This part of the thermostat is usually called the comparator.
We call this part the comparator because its main job is to compare the temperature perception coming from the sensor to the target temperature for the house. To compare these two things, the thermostat needs to know the target temperature. So let’s add a set point.
The target is set by a human, and in this case we can see that they set it to 72 °F. So the set point for the thermostat is 72 °F.
If the set point is 72 °F and the sensor detects a temperature of 72 °F, the thermostat doesn’t need to do anything. Everything is all good. When the perception from the sensor is the same as the set point, then assuming the sensor is working correctly, the house is the correct temperature. There is a difference of 0 °F.
But sometimes everything is not all good. Sometimes the set point is 72 °F but the sensor is only reading 68 °F, like it is here.
In this case, the comparator compares the set point (72 °F) to the perception (68 °F) and finds that there is a difference of -4 °F. The perception of the house’s temperature is four degrees colder than the target, so the house itself is about four degrees colder than we want it to be.
Having done this math, the comparator creates an error signal, which is simply the difference between the perception and the set point. If there’s no difference between the perception and the set point, then the error signal will be zero, i.e. no difference at all. If the error is zero, the thermostat doesn’t need to do anything. But in this case, the difference between the perception and the set point is -4 °F, so the error signal is -4 °F too.
For the thermostat to do its job, we need to close the loop. The final thing the thermostat needs is some way of influencing the outside world. This is often called the output function or the control element, which is the name we will use here:
Like the sensor, the control element sticks out into the exterior world, to indicate that it can interact with things outside the thermostat.
But you’ll notice that the loop is still not closed. The control element needs ways to influence the outside world.
A really simple thermostat might have only one way to influence things — it might only be able to turn on the furnace, which will raise the temperature:
But this is a pretty basic thermostat. It can’t control how hot the furnace is running, it can only turn it on or off.
It will do better if we give it more options. We can improve this thermostat by installing three settings for the furnace, like so:
This is much better. If the house is just a little cold, the control element can turn on the lowest furnace setting. This will keep the thermostat from overshooting the set point and sending the temperature above 72 °F. But if the house is freezing, it can turn on the turbo setting, and drive it to the set point much more quickly.
But there’s still a problem: our poor thermostat still has no way to lower the temperature. If the house goes above 72 °F, it can’t do a thing. The temperature will go above the set point and stay there until it comes down on its own; the thermostat is powerless.
This is unacceptable. But we can fix this problem by giving the thermostat access to air conditioning:
The control element can have many different possible outputs. Its job is to measure the error signal and decide what to do about it, and its goal is to drive the error signal to zero, or as close to zero as it can manage.
Similar to the sensor, the control element can be very simple or very complex. A simple control element might just turn on the heat any time the error signal is negative, or when the error signal is below some threshold. A more complicated control element might look at the derivative of the change in temperature over time and try to control the temperature predictively.
A very smart control element might use machine learning, or might have access to information about the weather, time of day, or day of the week, and might learn to use different strategies in different situations. You could give it a bunch of output options and just let it mess around with them, learning how different outputs influence the error signal in different ways.
More sophisticated techniques will give you a more effective control system. But as long as the control element has some way to influence the temperature, the thermostat should work ok.
Back in our example thermostat, the temperature in this house is too low, so the control element turns on the furnace. This raises the temperature, driving the error signal towards zero:
Once the error signal is zero, the control element turns off the furnace:
But even with this success, it’s important for the loop to remain closed. Even when the thermostat has driven the house’s temperature to the set point, and driven the error signal to zero, the house is still subject to disturbances. People open the door, they turn on the oven, they spill ice cream on the floor. Some heat escapes through the windows, the sun beats down on the roof. Let’s add disturbances to the diagram:
Because of these outside disturbances, the temperature of the house is always changing. To control the house’s temperature, to keep it near the set point in the face of all these disturbances, the control system needs to remain active.
This makes it easy to tell whether or not the thermostat is working like it should. Successful behavior drives the temperature (or at least the perception of that temperature) to the set point, and drives the error signal to zero. In the face of disturbances, it keeps the error signal close to zero, or quickly corrects it there.
In many older thermostats, the sensor is a bimetallic coil of brass and steel. Because of differences in the two metals, this coil expands when it gets warmer and contracts when it gets cooler. If this is all set up properly, the coil gives a decent measure of the temperature and helps the rest of the mechanism drive the house’s temperature to a given target.
But if you were to hold this coil closed, or tie a string around it and pull it tight enough to give a reading of 60 °F, the system will behave as though the temperature is always 60 °F. If the set point is 72 °F, the system will experience a large error signal, just as though the real temperature of the house was 60 °F, and will make a futile attempt to raise the house temperature, pushing as hard as it can, forever, until the thermostat breaks or the coil is released.
The thing to hold on to here is that every control system produces multiple signals.
Dividing a control system into individual parts helps us understand what happens when a control system breaks in different ways:
The thermostat is just an example; control systems are everywhere. The technical term used to describe control systems like these is “cybernetic”, and the study of these systems is called cybernetics.
Both words come from the Ancient Greek κυβερνήτης (kubernḗtēs, “steersman”), from κυβερνάω (kubernáō, “I steer, drive, guide, act as a pilot”). Norbert Wiener and Arturo Rosenblueth, who invented the word, chose this term because control systems steer or guide their targets, and because a steersman or pilot acts as a control system by keeping the ship pointed in the right direction.
The English word “governor” comes from the same root (kubernetes -> gubernetes -> Latin gubernator -> Old French gouvreneur -> Middle English governour), so control systems are sometimes called cybernetic governors, or just governors.
Most famous of these is the centrifugal governor used to regulate the speed of steam engines. Look closely at any steam engine, and you should see one of these:
The engine’s output is connected to the governor by a belt or chain, so the governor spins along with the engine. As the engine starts to speed up, the governor spins faster, and its spinning balls gain kinetic energy and move outward, like they’re trying to escape.
This outward movement isn’t just for show; if the motion goes far enough, it causes the lever arms to pull down on a thrust bearing, which moves a beam linkage, which reduces the aperture of a throttle valve, which controls how much steam is getting into the engine. So, the faster the engine goes, the more the governor closes the valve. This keeps the engine from going too fast — it controls the engine’s speed.
Control systems maintain homeostasis, driving a system to some kind of equilibrium. A thermostat controls the temperature of a house, a centrifugal governor controls the speed of a steam engine, but you can control just about anything if you put your mind to it. As long as you can measure a variable in some way, influence it in some way, and you can put a comparator between these two components to create an error signal, you can make a control system to drive that variable towards whatever set point you like.
Every organism needs to make sure it doesn’t get too dry, too hot, too cold, etc. If it gets any of these wrong, it dies.
As a result, a lot of biology is made up of control systems. Every organ is devoted to maintaining homeostasis in one way or another. Your kidneys control electrolyte concentrations in your blood, the pancreas controls blood sugar, and the thyroid controls all kinds of crap.
The brain is a homeostatic organ too. But the brain does homeostasis with a twist. Unlike the other organs, which mostly drive homeostasis by changing things inside the body, the brain controls things with external behavior.
Thirst
One of the first behavioral control systems to evolve must have been thirst. All animals need water; without water, they die. So the brain has a control system that aims to keep the body hydrated.
This is a control system, just like a thermostat. Hydration is the goal, but that goal needs to be measured in some way. In this case the input function seems to be a measure of plasma osmolality, as detected by the brain. This perception is then compared to a reference value (in humans this is around 280-295 mOsm/kg), which generates an error signal that can drive behaviors like finding and consuming water.
As we see in the diagram, in this control system the error signal is thirst. We can tell this must be the case because successful behavior drives thirst to zero. The perception of osmolality can’t be the error signal, because osmolality is driven to 280-295 mOsm/kg, which is how we know that number is the target or set point. Whatever value is driven towards zero must be the error signal.
Just like with a thermostat, the output function can be very simple or very complex. Organisms with simple nervous systems may have only one output; they drink water when it happens to be right in front of them. Animals that live in freshwater streams and ponds may execute a program as simple as “open your mouth”, since they are always immersed in water that is perfectly good for them to drink.
Organisms with complex nervous systems, or that are adapted to environments where water is more scarce, will have more complex responses. A cat can go to its water bowl. In the dry season, elephants search out good locations and actively dig wells. Humans get in the car and drive to the store and make small talk while exchanging currency to purchase Vitamin Water®, a very complex response. But it’s all to control plasma osmolality by reducing the error signal of thirst to zero.
Hot and Cold
Organisms need to maintain a constant temperature, so the brain also includes systems for controlling heat and cold.
This adaptation actually consists of two different control systems — one that keeps the body from getting too warm, and another that keeps the body from getting too cold. We have two separate systems, rather than one system that handles both, because of the limits of how neurons can be wired up. “Neural comparators work only for one sense of error, so two comparators, one working with inverted signals, would be required to detect both too much and too little of the sensor output level.” (Powers, 1973)
We can also appeal to intuition — it feels entirely different to be hot or to be cold, they are totally different sensations. And when you are sick, sometimes you feel both too hot and too cold, something that would be impossible if this were a single system.
As usual, the output function can be simple or complex. Some responses are relatively automatic, like sweating, and might not usually be considered “behavior”. But other responses, like putting on a cardigan, are definitely behavior.
This is a chance to notice something interesting. A human can shiver, sweat, put on a coat, or open a window to control their temperature. But they can also… adjust the set point on the thermostat for their house! One way a control system can act on the world is by changing the set point of a different control system, and letting that “lower” system carry out the control for it.
Pain
Organisms need to keep from getting injured, so they have ways to measure damage to their bodies, and a system to control that damage.
Again, we see that pain is the error signal that’s generated in response to some kind of measure of damage or physical harm.
A very simple control system will respond to pain, and nothing else. This might be good enough for a shellfish. But a more complex approach (not pictured in this diagram) is for the control system to predict how much pain might be coming, and drive behavior to avoid that pain, instead of merely responding. Compare this to a thermostat which can tell a blizzard is incoming, and turns on the furnace in anticipation, before the cold snap actually hits.
Hunger
Most organisms need to eat in order to live. Once the food is inside your body there are other control systems that put it to the right use, but you need to express some behavior to get it there. So there’s another control system in charge of locating nutritious objects and putting them inside your gob.
Obviously in this case, the error signal is hunger — successful eating behavior tends to drive hunger to zero.
More realistically, there is not one eating control system, and not one kind of hunger, but several. There might even be dozens.
One control system probably controls something like your blood sugar level, and drives behavior that makes you put things with calories inside your mouth.
But man cannot live on calories alone, and neither can any other organism. For one thing, you definitely need salt. So there must be another control system that drives behavior to make you put salty things (hopefully salty foods, though perhaps not always) inside your mouth. This is confirmed by the fact that people sometimes crave salty foods. If you’ve ever had a moose lick the salt off your car, you’ll know that we’re right.
It’s hard to tell exactly how many kinds of hunger there are, but humans need several different nutrients to survive, and we clearly can have cravings for many different kinds of foods, so there must be several kinds of hunger. The same goes for other animals.
Fear
Organisms also need to avoid getting eaten themselves. This is somewhat more tricky than controlling things like heat and fluid levels, but evolution has found a way.
To accomplish this, organisms have been given a very complicated input function that estimates the threats in our immediate area, by weighing information like “is there anything nearby that looks or sounds like a tiger?” This input function creates a complicated perception that we might call “danger”.
This danger estimate is then compared to some reference level of acceptable danger, creating the error signal of fear. If you are in more danger than is considered acceptable, you RUN AWAY (or local equivalent).
Disgust
Getting eaten is not the only danger we face. Organisms also need to avoid eating poisonous things that will kill them, and avoid contact with things that will expose them to disease.
Like fear, the input function here is very complicated. It’s not as simple as checking the organism’s blood osmolality or some other internal signal. Trying to figure out what things out there in the world might be poisonous or diseased is a difficult problem.
But smelling spoiled milk or looking at a rotting carcass clearly creates some kind of signal, which is compared with some reference level, and creates an error signal that drives behavior. That’s why, if you drink too much Southern Comfort and later puke it up, you’ll never want Southern Comfort again.
In this case, the error signal is disgust.
Shame
Every organism needs to maintain internal homeostasis in order to survive. Organisms that can perceive the world and flop around a bit also tend to develop the ability to control things about the outside world, things like how close they get to predators. This improves their ability to survive even further.
Social organisms like humans also control social variables. It’s hard to know exactly what variables are being controlled, since they are not as simple as body temperature. They are at least as complicated as an abstract concept like “danger” — something we certainly perceive and can control, but that must be very complicated.
However, we can make reasonable guesses. For one, humans control things like status. You want to make sure that your status in your social group is reasonably high, that it doesn’t go down, that it maybe sometimes even goes up.
In this case, the error signal when status is too low is probably something like what we call shame. Sadness, loneliness, anger, and guilt all seem to be error signals for similar control systems that attempt to control other social variables.
Every sense you possess is an instrument for reacting to change. Does that tell you nothing?
― Frank Herbert, God Emperor of Dune
Control of blood osmolality leads to an error signal we call thirst. This drives behavior to keep you hydrated.
Control of body temperature leads to error signals we know as the experiences “hot” and “cold”. These drive behavior to keep you comfortable, even cozy.
Control of various nutritional values leads to error signals that we collectively call hunger. These drive behavior that involves “chowing down”.
While they can be harder to characterize, control of social values like status and face lead to error signals we identify with words like “shame” and “guilt”. These drive social behavior like trying to impress people and prove our value to our group.
All of these things are of the same type. They’re all error signals coming from the same kinds of biological control systems, error signals that drive external behavior which, when successful, pushes that error signal towards zero.
All of these things are of the same type, and the word for this type is “emotion”. An emotion is the error signal in a behavioral biological control system.
We say “behavioral” because your body also regulates things like the amount of copper in your blood, but there’s no emotion associated with serum copper regulation. It’s regulated internally, by processes you are unaware of, processes that may not even involve the brain. In contrast, emotions are the biological control errors that drive external behavior.
Thirst, hot, cold, shame, disgust, fear, and all the different kinds of hunger are all emotions. Other emotions include anger, pain, sleepy, need to pee, suffocation, and horny. There are probably some emotions we don’t have words for. All biological error signals that are in conscious awareness and that drive behavior are emotions.
Some emotions come in pairs that control two ends of one variable. The emotions of hot and cold are a good example. You try to keep your body temperature in a certain range, so it needs one control system (and one emotion) to keep you from getting too cold, and another control system (and another emotion) to keep you from getting too hot.
Feeling hot and feeling cold are clearly opposites, two emotions that keep your body temperature in the right range. There’s also an opposite of hunger — the emotion you feel when you have eaten too much and shouldn’t eat any more. We don’t have a common word for it in English, but “fullness” or “satiety” are close.
But for many goals, there’s only a limit in one direction. You just want to make sure some variable doesn’t get too high or too low. You’ll notice that “need to pee” is an emotion, but it doesn’t have an opposite. While your bladder can be too full, it can’t be too empty, so there’s no emotion for that.
This is counterintuitive to modern psychology because academic psychologists act as though nothing of interest happens below the neck. They couldn’t possibly imagine that “hungry” or “needs to pee” could be important to the study of psychology — even though most human time and energy is spent on eating, sleeping, peeing, fuckin’, etc.
In contrast, when the goal is to model believable human behavior, and not just to produce longwinded journal articles, these basic drives and emotions come about naturally. Even The Sims knew that “bladder” is one of the eight basic human motivations.
This is a joke; there are more than eight motivations. But frankly, the list they came up with for The Sims is pretty good. It’s clear that there are drives to keep your body and your living space clean, and it seems plausible that these might be different emotions. We don’t have words for the associated emotions in English, but The Sims calls these motivations “Hygiene” and “Environment” (originally called “Room”).
Emotions are easy to identify because they are errors in a control system. Like any error in a control system, successful behavior drives the error to zero. This means that happiness is not an emotion.
After all, it’s clearly not an error signal. Behavior doesn’t try to drive happiness to zero. That means it’s not the same kind of thing as the rest of these signals, which are all clearly error signals. And that means happiness isn’t an emotion. Happiness is some kind of signal, but it’s not an emotion.
Now you may be thinking, “Hold on a minute there, SMTM. I was on board with you about the biological control systems. I understand how hunger and cold and whatnot are all the error signals of various control systems, that’s very interesting. But you can’t just go around saying that pain and thirst are emotions, and that happiness isn’t an emotion. You can’t just go around using accepted words in totally made-up new ways. That’s not what science is all about.”
We disagree; we think that this IS what science is all about. Adapting old words to new forms is like half of the project.
For starters, language always changes over time. The word “meteor” comes from the Greek metéōron, which literally meant “thing high up”. For a long time it referred to anything that happened high up, like rainbows, auroras, shooting stars, and unusual clouds. This sense is preserved in meteorology, the study of the weather, i.e. the study of things high up. But in common use, “meteor” is now restricted to space debris burning up as it enters the atmosphere. And there’s nothing wrong with that.
Second, changing the way we use words is a very normal part of any scientific revolution.
Take this passage from Thomas Kuhn’s essay, What Are Scientific Revolutions?:
Revolutionary changes are different and … problematic. They involve discoveries that cannot be accommodated within the concepts in use before they were made. In order to make or to assimilate such a discovery one must alter the way one thinks about and describes some range of natural phenomena. … [Consider] the transition from Ptolemaic to Copernican astronomy. Before it occurred, the sun and moon were planets, the earth was not. After it, the earth was a planet, like Mars and Jupiter; the sun was a star; and the moon was a new sort of body, a satellite. Changes of that sort were not simply corrections of individual mistakes embedded in the Ptolemaic system. Like the transition to Newton’s laws of motion, they involved not only changes in laws of nature but also changes in the criteria by which some terms in those laws attached to nature.
The same is true of this revolution. Before this transition, happiness and fear were emotions, while hunger was not. After it, hunger is an emotion, like shame and loneliness; happiness is some other kind of signal; and other signals like stress may be new sorts of signals as well.
As in any revolution, we happen to be using the same word, but the meaning has changed. This kind of change has been a part of science from the beginning.
Kuhn can be a little hard to follow, so here’s the same idea in language that’s slightly more plain:
Ontologically, where “planet” had meant “lights that wander in the sky,” it now meant “things that go around the sun.” Empirically, the claim was that all the old planets go around the sun, except the moon and the sun itself, so those are not really planets after all. Most troublingly, the earth too goes around the sun, so it is a planet.
The earth does not wander in the sky; it does not glow like the planets; it is extremely large, whereas most planets are mere pinpoints. Why call the earth a planet? This made absolutely no sense in Copernicus’ time. The claim appeared not false, but absurd: a category error. But for Copernicus, the earth was a planet exactly in that it does wander around the universe, instead of sitting still at the center.
Maybe heliocentrism would have succeeded sooner if Copernicus used a different word for his remodeled category! This is a common pattern, though: an existing word is repurposed during remodeling. There is no fact-of-the-matter about whether “planet” denoted a new, different category, or if the category itself changed and kept its same name.
So just like Copernicus, our claims aren’t false, they’re absurd. In any case, it’s too cute to hold so closely onto the current boundaries for the word “emotion”, given that the term is not even that old. Before the 1830s, English-speakers would have said “passions” or “sentiments” instead of “emotions”. So to slightly change the meaning of “emotion” is not that big a deal.
In any case, we can use words however we want. So back to the question at hand: If happiness isn’t an emotion, or at least isn’t a cybernetic error signal, then what is it?
The answer is quite simple. People and animals have many different governors that try to maintain a signal at homeostasis, near some target or on one side of some threshold. When one of these signals is out of alignment, the governor creates an error signal, an emotion like fear or thirst. The governor then does its best to correct that error.
When a governor sends its signal back into alignment, correcting an error signal, this causes happiness. Happiness is what happens when a thirsty person drinks, when a tired person rests, when a frightened person reaches safety.
Consider the experiences that cause the greatest happiness. A quintessential happy experience might be finishing a long solitary hike in the February cold, arriving at the lodge freezing and battered, and throwing open the door to the sight of a roaring fire, soft couches, dry socks, good company, and an enormous feast.
The reason this kind of experience is so joyous is because a person who has just finished a long winter’s hike has driven many of their basic control systems far out of alignment, creating many large error signals. They are cold, thirsty, hungry, tired, perhaps they are in a bit of discomfort, or even pain. The opportunity to correct these error signals by stepping into a warm ski lodge leads to 1) many errors being corrected at once, and 2) the corrections being quite fast and quite large.
When errors are corrected by a large amount, or they are corrected very quickly, that creates more happiness than when they are corrected slowly and incrementally. A man who was lost in the desert will feel nothing short of bliss at his first sip of cool water — he is incredibly thirsty, and correcting that very large error creates a lot of happiness.
Imagine yourself on a hot summer day. To quaff a tall glass of ice water and eliminate your thirst all at once is immensely pleasurable. To sip the same amount over the course of an hour is not nearly so good. More happiness is created when a correction is fast than when it is slow.
Or consider:
Here we see some confirmation that “need to pee” is an emotion. We also see evidence of the laws of how error correction causes happiness. Since the error signal was so big, and since it was resolved all at once in that dirty little gas station bathroom, the correction was both large and sudden, which is why peeing made the author so happy. “Moans”, or happiness in general, “are connected with not getting what you want right away, with putting things off.” Or take Friedrich Nietzsche, who asked: “What is happiness? The feeling that power is growing, that resistance is overcome.”
Correcting any error signal creates happiness, and the happiness it creates persists for a while. But over time, happiness does fade. We don’t know the exact rate of decay, but if you create 100 points of happiness today, you might have only 50 points of happiness tomorrow. The next day you will have only 25, and eventually you will have no happiness at all.
But in practice, your drives are constantly getting pushed out of alignment and you are constantly correcting them, and in most cases this leads to a steady stream of happiness. You get hungry, thirsty, tired, and you correct these errors, generating more happiness each time. As long as you generate happiness faster than the happiness decays, you will be generally happy on net.
You can think of this as a personal happiness economy. Just like a business must have more money coming in than going out to stay in the black, you’ll feel happy on net as long as errors are being corrected faster than happiness decays.
In this model, there are as many ways to feel bad as there are things that are being controlled. But there’s only one way to feel good. Which would mean that all of our words for positive emotion — joy, excitement, pride — are really referring to the same thing, just in different contexts.
Happiness is also related to the concept of “agency”, the general ability to affect your world in ways of your choosing. A greater ability to affect your world means more ability to cause large changes in any context. If you have a lot of ability to make things change, you can make big corrections in your error signals — you can take the situation of being very hungry and correct that error decisively, leading to a burst of happiness.
(It may also be the case that even an arbitrary exercise of agency can make you somewhat happy, since people do seem to gain happiness from meeting some very arbitrary goals. But this is hard to distinguish from social drives — maybe you are just excited at how impressed you think everyone will be when they see how many digits of pi you have memorized.)
People are consistently surprised to find that living in posh comfort and having all your needs immediately met isn’t all that pleasurable. But with this model of happiness, it makes perfect sense. Pleasure and happiness are only generated when you are out of alignment in a profound way, a way that could legitimately threaten your very survival, and then you are brought back into alignment in a way that is literally life-affirming.
This is why people who are well-off, the idle rich in particular, often feel like their lives are pointless and empty. To have all your needs immediately met generates almost no happiness, so the persistently comfortable go through life in something of a gray fog.
Does this suggest that horrible experiences can, at least under the right circumstances, make you happy and functional? Yes.
See this section about the Blitz during World War Two, from the book Tribe (h/t @softminus):
On and on the horror went, people dying in their homes or neighborhoods while doing the most mundane things. Not only did these experiences fail to produce mass hysteria, they didn’t even trigger much individual psychosis. Before the war, projections for psychiatric breakdown in England ran as high as four million people, but as the Blitz progressed, psychiatric hospitals around the country saw admissions go down. Emergency services in London reported an average of only two cases of “bomb neuroses” a week. Psychiatrists watched in puzzlement as long-standing patients saw their symptoms subside during the period of intense air raids. Voluntary admissions to psychiatric wards noticeably declined, and even epileptics reported having fewer seizures. “Chronic neurotics of peacetime now drive ambulances,” one doctor remarked. Another ventured to suggest that some people actually did better during wartime.
The positive effects of war on mental health were first noticed by the great sociologist Emile Durkheim, who found that when European countries went to war, suicide rates dropped. Psychiatric wards in Paris were strangely empty during both world wars, and that remained true even as the German army rolled into the city in 1940. Researchers documented a similar phenomenon during civil wars in Spain, Algeria, Lebanon, and Northern Ireland. An Irish psychologist named H. A. Lyons found that suicide rates in Belfast dropped 50 percent during the riots of 1969 and 1970, and homicide and other violent crimes also went down. Depression rates for both men and women declined abruptly during that period, with men experiencing the most extreme drop in the most violent districts. County Derry, on the other hand—which suffered almost no violence at all —saw male depression rates rise rather than fall. Lyons hypothesized that men in the peaceful areas were depressed because they couldn’t help their society by participating in the struggle.
Horrible events can also traumatize people, of course. Being bombed by the Luftwaffe is dangerous to your health. But in other ways, being thrust into catastrophe can be very reassuring, even affirming. We were put together in an era of constant threat, it should be no surprise that we can be functional in that kind of environment.
So happiness isn’t an emotion, and doesn’t drive behavior. The natural question has to be, why does happiness exist at all? What function does it serve if it is not, like an emotion, helping to drive some important signal to homeostasis.
We think happiness is a signal used to calibrate explore versus exploit.
The exploration-exploitation dilemma is a fancy way of talking about a basic problem. Should you mostly stick to the options you know pretty well, and “exploit” them to the fullest extent, or should you go out and “explore” new options that might be even better?
For example, if you live in a city and have tried 10 out of the 100 restaurants in the area, when you decide where to go to lunch, should you go to the best restaurant you’ve found so far, for an experience that is guaranteed to be pretty good, or should you try a new restaurant and maybe discover a new favorite? And how much time should you spend with your best friend, versus making new friends?
It’s a tradeoff. If you spend all your time exploring, you never get the opportunity to enjoy the best options you’ve found. But if you exploit the first good thing you find and never leave, you’re likely to miss out on better opportunities somewhere else. You have to find a balance.
This dilemma makes explore versus exploit one of the core issues of decision-making, and finding the right balance is a fundamental problem in machine learning approaches like reinforcement learning. So it’s not at all surprising that psychology would have a signal that helps to tune this tradeoff.
Remember that in this model of happiness, behavior is successful when it corrects some error, and creates some amount of happiness. This makes happiness a rough measure of how consistently you are correcting your errors.
If you are reliably generating happiness, that means you’re correcting your errors all the time, so your overall strategies for survival must be working pretty well. Keep doing what you’re doing. On the other hand, if you are not frequently generating happiness, that means you are almost never correcting your errors, and you must be doing rather poorly. Your strategies are not serving you well — in nature, you would probably be on the fast track to a painful death. In this situation, you should switch up your strategies and try something new. In a word, you should explore.
When you’re generating plenty of happiness, you are surviving, your strategies are working, and you should stick with them. When you’re not generating much happiness, your strategies are not working, you may not be surviving long, and you should change it up and try new things in an attempt to find new strategies that are better.
All this makes sense in a state of nature, where sometimes you have to change or die. But note that in the modern world, you can survive for a long time without generating much happiness at all. This is why modern people sometimes explore their way into very strange strategies.
(Tuning explore vs. exploit is just one theory. Another possibility is that your happiness is a signal for other people to control. For example, a parent might have a governor that tries to make sure their child has at least a certain level of happiness. There are reasons to suspect this might be the case — we are much more visibly happy and unhappy than we are visibly hungry or tired. If this is true, then our happiness might be more important for other people than for ourselves.)
Psychologists don’t usually think of happiness in these terms, but this perspective isn’t entirely original. See this Smithsonian Magazine interview with psychologist Dan Gilbert from 2007. The interviewer asks, “Why does it seem we’re hard-wired to want to feel happy, over all the other emotions?” Dan responds with the following:
That’s a $64 million question. But I think the answer is something like: Happiness is the gauge the mind uses to know if it’s doing what’s right. When I say what’s right, I mean in the evolutionary sense, not in the moral sense. Nature could have wired you up with knowing 10,000 rules about how to mate, when to eat, where to seek shelter and safety. Or it could simply have wired you with one prime directive: Be happy. You’ve got a needle that can go from happy to unhappy, and your job in life is to get it as close to H as possible. As you’re walking through woods, when that needle starts going towards U, for unhappy, turn around, do something else, see if you can get it to go toward H. As it turns out, all the things that push the needle toward H—salt, fat, sugar, sex, warmth, security—are just the things you need to survive. I think of happiness as a kind of fitness-o-meter. It’s the way the organism is constantly updated about whether its behavior is in support of, or opposition to, its own evolutionary fitness.
As for terms like “unhappiness”, we think they should be defined out of existence. When people use the word “unhappy”, we think they mean one of two things. Either their happiness levels are low, in which case they are not-happy rather than un-happy; or some error, like fear or shame, has just increased by a large amount. This is unpleasant, and there is a sense of being more out of alignment than before, but it’s always linked to specific emotions. It’s not some generic deficit of happiness, and happiness cannot go negative; there is no anti-happiness.
2025-02-07 00:11:00
We who have nothing to “wind string around” are lost in the wilderness. But those who deny this need are “burning our playhouse down.” If you put quotes around certain words it sounds more like a metaphor.
— John Linnell, 2009 interview with Rolling Stone
Take almost anything, heat it up, and it gets bigger. Heat it up enough, it melts and becomes a liquid. Heat it up even more, it becomes a gas, and takes up even more space. Or, cool it down, it contracts and becomes smaller again.
The year is 1789. Antoine Lavoisier has just published his Traité Élémentaire de Chimie. Robert Kerr will soon translate it into English under the title Elements of Chemistry in a New Systematic Order containing All the Modern Discoveries, usually known as just Elements of Chemistry.
The very first thing Lavoisier talks about in his book is this mystery about heat. “[It] was long ago fully established as a physical axiom, or universal proposition,” he begins, “that every body, whether solid or fluid, is augmented in all its dimensions by any increase of its sensible heat”. When things get hotter, they almost always get bigger. And when things get colder, they almost always shrink. “It is easy to perceive,” he says, “that the separation of particles by heat is a constant and general law of nature.”
Lavoisier is riding a wave. About two hundred years earlier, Descartes had suggested that we throw out Aristotle’s way of thinking, where each kind of thing is imbued with its own special purpose, and instead bring back a very old idea from Epicurus, that everything is made out of tiny particles.
The plan is to see if “let’s start by assuming it’s all particles” might be a better angle for learning about the world. So Lavoisier’s goal here is to try to describe heat in terms of some kind of interaction between different particles.
He makes the argument in two steps. First, Lavoisier says that there must be two forces: one force that pushes the particles of the object apart (which we see when the object heats up), and another force that pulls them together (which we see when the object cools down). “The particles of all bodies,” he says, “may be considered as subjected to the action of two opposite powers, the one repulsive, the other attractive, between which they remain in equilibrio.”
The force pushing the particles apart obviously has something to do with heat, but there must also be a force pushing the particles together. Otherwise, the separating power of heat would make the object fly entirely apart, and objects wouldn’t get smaller when heat was removed, things wouldn’t condense or freeze as they got cold.
“Since the particles of bodies are thus continually impelled by heat to separate from each other,” he says, “they would have no connection between themselves; … there could be no solidity in nature, unless they were held together by some other power which tends to unite them, and, so to speak, to chain them together; which power, whatever be its cause, or manner of operation, we name Attraction.” Therefore, there is also a force pulling them together.
Ok, that was step one. In step two, Lavoiser takes those observations and proposes a model:
It is difficult to comprehend these phenomena, without admitting them as the effects of a real and material substance, or very subtle fluid, which, insinuating itself between the particles of bodies, separates them from each other; and, even allowing the existence of this fluid to be hypothetical, we shall see in the sequel, that it explains the phenomena of nature in a very satisfactory manner.
Let’s step back and notice a few things about what he’s doing.
First: While he’s happy to speculate about an attractive force, Lavoisier is very careful. He doesn’t claim anything about the attractive force, does not even speculate about “its cause, or manner of operation”. He just notes that there appears to be some kind of force causing the “solidity in nature”, and discusses what we might call it.
He does the same thing with the force that separates. Since it seems to be closely related to heat, he says we can call this hypothetical fluid “caloric” — “but there remains a more difficult attempt, which is, to give a just conception of the manner in which caloric acts upon other bodies.”
We don’t know these fluids exist from seeing or touching them — we hypothesize them from making normal observations, and asking, what kind of thing could there be, invisible but out there in the world, that could cause these observations? “Since this subtle matter penetrates through the pores of all known substances,” he says, “since there are no vessels through which it cannot escape, and consequently, as there are none which are capable of retaining it, we can only come at the knowledge of its properties by effects which are fleeting, and difficultly ascertainable.”
And Lavoisier warns us against thinking we are doing anything more than speculating. “It is in these things which we neither see nor feel,” he says, “that it is especially necessary to guard against the extravagance of our imagination, which forever inclines to step beyond the bounds of truth, and is very difficulty restrained within the narrow line of facts.”
Second: In addition to speculating, Lavosier proposes a model.
But not just any model. Lavosier’s theory of heat is a physical model. He proposes that heat is a fluid with particles so small they can get in between the particles of any other body. And he proposes that these particles create a force that separates other particles from each other. The heat particles naturally seep inside the particles of other objects, because they are so small. And this leads to the expansion and contraction that was the observation we started with.
Lavoisier is proposing a model of entities and rules. In this case, the entities are particles. There are rules governing how the particles can interact: Heat particles emit a force that pushes apart other particles. Particles of the same body mutually attract. There may be more entities, and there will certainly be more rules, but that’s a start.
Third: Instead of something obscure, he starts by trying to explain existing, commonplace observations.
People often think that a theory should make new, testable predictions. This thought seems to come from falsificationism: if a theory gives us a prediction that has never been seen before, we can go out and try to falsify the theory. If the prediction stands, then the theory has some legs.
But this is putting the cart before the horse. The first thing you actually want is for a theory to make “testable” predictions about existing observations. If a new proposal cannot even account for the things we already know about, if the entities and rules don’t duplicate a single thing we see from the natural world, it is a poor theory indeed.
It’s good if your model can do the fancy stuff, but first it should do the basic shit. A theory of weather doesn’t need to do much at first, but it should at least anticipate that water vapor makes clouds and that clouds make rain. It’s nice if your theory of gravity can account for the precession of the perihelion of Mercury, but it should first anticipate that the moon won’t fall into the earth, and that the earth attracts apples rather than repels them.
Fourth: His proposal is wrong! This model is not much like our modern understanding of heat at all. However, Lavoisier is entirely unconcerned. He makes it very clear that he doesn’t care whether or not this model is at all accurate in the entities:
…strictly speaking, we are not obliged to suppose [caloric] to be a real substance; it being sufficient … that it be considered as the repulsive cause, whatever that may be, which separates the particles of matter from each other; so that we are still at liberty to investigate its effects in an abstract and mathematical manner.
People are sometimes very anxious about whether their models are right. But this anxiety is pointless. A scientific model doesn’t need to be right. It doesn’t even need to describe a real entity.
Lavoisier doesn’t care about whether the entities he describes are real; he cares about the fact that the entities he proposes 1) would create the phenomenon he’s trying to understand (things generally expand when they get hotter, and contract when they get colder) and 2) are specific enough that they can be investigated.
Lavoisier’s proposal involves entities that operate by simple rules. The rules give rise to phenomena about heat that match existing observations. That is all that is necessary, and Lavoisier is quite aware of this. “Even allowing the existence of this fluid to be hypothetical,” he says, “we shall see … that it explains the phenomena of nature in a very satisfactory manner.”
This is how scientific progress has always worked: Propose some entities and simple rules that govern them. See if they give rise to the things we see all the time. It’s hard to explain all of the things, so it’s unlikely that you’ll get this right on the first try. But does it explain any of the things?
If so, congratulations! You are on the right track. From here, you can tweak the rules and entities until they fit more and more of the commonly known phenomena. If you can do this, you are making progress. If at some point you can match most of the phenomena you see out in the world, you are golden.
If you can then go on to use the entities as a model to predict phenomena in an unknown set of circumstances, double congratulations. This is the hardest step of all, to make a called shot, to prove your model of rules and entities in unknown circumstances.
But first, you should prove it in known circumstances. If your theory of heat doesn’t even account for why things melt and evaporate, there’s no use in trying to make more exotic predictions. You need to start over.
Much of what passes for knowledge is superficial.
We mean “superficial” in the literal sense. When we call something superficial, we mean that it deals only with the surface appearances of a phenomenon, without making appeal or even speculating about what might be going on beneath the surface.
There are two kinds of superficial knowledge: predictions and abstractions.
1. Predictions
Predictions are superficial because they only involve anticipating what will happen, and not why.
If you ask an astronomer, “What is the sun?” and he replies, “I can tell you exactly when the sun will rise and set every day”… that’s cool, but this astronomer does not know what the sun is. That will still be true even if he can name all the stars, even if he can predict eclipses, even if he can prove his calculations are accurate to the sixth decimal point.
Most forms of statistics suffer from this kind of superficiality. Any time anyone talks about correlations, they are being superficial in this way. “The closer we get to winter, the less time the sun spends in the sky.” Uh huh. And what is the sun, again?
Sometimes it is ok to talk about things just in terms of their surface appearances. We didn’t say “don’t talk about correlations”. We said, “correlations are superficial”. But often we want to go deeper. When you want to go deeper, accept no substitutes!
Sometimes all you want to do is predict what will happen. If you’re an insurance company, you only care about getting your bets right — you need to have a good idea which homes will be destroyed by the flood, but you don’t need to understand why. You know that your business involves uncertainty, and these predictions are only estimates. If all you want to do is predict, that’s fine.
But in most cases, we want more than just prediction. If you’re a doctor choosing between two surgeries, you certainly would rather conduct the surgery with the 90% survival rate than the surgery with the 70% survival rate. But you’d ideally like to understand what’s actually going on. Even having chosen the surgery with better odds, what can you do to make sure your patient is in the 90% that survive, rather than the 10% that do not? What are the differences between these two groups? We aspire to do more than just rolling the dice.
Consider this for any other prediction. In the Asch conformity experiments, most participants conformed to the group. From this, we can predict that in similar situations, most people will also conform. But some people don’t conform. Why not? Prediction by itself can’t go any deeper.
Or education. Perhaps we can predict which students will do well in school. We predict that certain students will succeed. But some of these students don’t succeed, and some of the students we thought would be failures do succeed. Why? Prediction by itself can’t go any deeper.
There’s something a little easy to miss here, which is that having a really good model is one way to make really good predictions. However good your predictions are when you predict the future by benchmarking off the past, having a good model will make them even better. And, you will have some idea of what is actually going on.
But people often take this lesson in reverse — they think that good predictions are a sign of a good understanding of the processes behind the thing being predicted. It can be easy to just look for good predictions, and think that’s the final measure of a theory. But in reality, you can often make very good predictions despite having no idea of what is actually happening under the hood.
This is why you can operate a car or dishwasher, despite having no idea how they work. You know what will happen when you turn on your dishwasher, or shift your car into reverse. Your predictions are very good, nearly 100%. But you don’t know in a mechanical sense why your car moves backwards when you shift into reverse, or how your dishwasher knows how to shut off when it’s done.
If you want to fix a dishwasher that’s broken, or god forbid design a better one, you need to understand the inner guts of the beast, the mechanical nature of the machine that creates those superficial features that you know how to operate. You “know” how to operate the superficial nature of a TV, but how much do you understand of this:
Let’s take another different example. This Bosch dishwasher has only 6 buttons. Look how simple it is for any consumer to operate:
But look how many parts there are inside. Why are some of the parts such weird shapes? How much of this do you understand? How much of it does the average operator understand:
2. Abstractions
Successful models will always be expressed in terms of entities and rules. That might seem obvious — if you’re going to describe the world, of course you need to propose the units that populate it, and the rules that govern their behavior!
But in fact, people almost never do this. Instead, they come up with descriptions that involve neither entities nor rules. These are called abstractions.
Abstractions group similar observations together into the same category. But this is superficial, because the classification is based on the surface-level attributes of the observations, not their nature. All crabs look similar, but as we’ve learned more about their inner nature, what we call DNA, we learned that some of these crabs are only superficially similar, that they came to their crab-like design from entirely different places. The same thing is true of trees.
We certainly cannot do without abstractions like “heat”, “depression”, “democracy”, “airplane”, and so on. Sometimes you do want to group together things based on their outward appearance. But these groups are superficial at best. Airplanes have some things in common abstractly, but open them up, and under the hood you will find that each of them functions in its own way. Democracies have things in common, but each has its own specific and mechanical system of votes, representation, offices, checks and balances, and so on.
Imagine that your car breaks down and you bring it to a mechanic and he tells you, “Oh, your car has a case of broken-downness.” You’d know right away: this guy has no idea what he’s talking about. “Broken-downness” is an abstraction; it doesn’t refer to anything, and it’s not going to help you fix a car.
Instead, a good mechanic will describe your car’s problem in terms of entities and rules. “Your spark plugs are shot [ENTITIES], so they can’t make the pistons [ENTITIES] go up and down anymore [RULES].”
It’s easy to see how ridiculous abstractions are when we’re talking about cars, but it can be surprisingly hard to notice them when we’re talking about science.
For instance, if you feel sad all the time, a psychologist will probably tell you that you have “depression.” But depression is an abstraction — it involves no theory of the entities or rules that cause you to feel sad. It’s exactly like saying that your car has “broken-downness.” Abstractions like this are basically useless for solving problems, so it’s not surprising that we aren’t very good at treating “depression.”
Abstractions are often so disassociated from reality that over time they stop existing entirely. We still use words like “heat”, “water”, and “air”, but we mean very different things by these words than the alchemists did. Medieval physicians thought of medicine in terms of four fluids mixing inside your body: blood, phlegm, yellow bile, and black bile. We still use many of those words today, but the “blood” you look at is not the blood of the humorists.
It’s possible that one day we’ll stop using the word “depression” at all. Some people find that idea crazy — depression is so common, so baked into our culture, that surely it’s going to stick around. But stuff like this happens all the time. In the 19th and 20th centuries, “neurasthenia” was a common diagnosis for people who felt sad, tired, and anxious. It used to be included in the big books of mental disorders, the Diagnostic and Statistical Manual (DSM) and the International Statistical Classification of Diseases and Related Health Problems (ICD).
Now it isn’t. But that’s not because people stopped feeling sad, tired, and anxious — it’s because we stopped using “neurasthenia” as an abstraction to describe those experiences. Whatever people learned or wrote about neurasthenia is now useless except for historical study. That’s the thing about abstractions: they can hang around for a hundred years and then disappear, and we can be just as clueless about the true nature of the world as when we began. Don’t even get us started on Brain fag syndrome.
The DSM will never fully succeed because it’s stuck dealing with abstractions. One clue we’re still dealing with geocentric psychology here is that the DSM groups disorders by their symptoms rather than their causes, even though causes can vary widely for the same symptoms (e.g. insomnia can be biological, psychological, or your cat at 3 am).
Imagine doing this for physical diseases instead — if you get really good at measuring coughing, sneezing, aching, wheezing, etc. you may ultimately get pretty good at distinguishing between, say, colds and flus. But you’d have a pretty hard time distinguishing between flu and covid, and you’d have no chance of ever developing vaccines for them, because you have no concept of the systems that produce the symptoms.
Approaches like this, where you administer questionnaires and then try to squeeze statistics out of the responses, will always top out at that level. At best, you successfully group together certain clusters of people or behaviors on the basis of their superficial similarities. This can make us better at treating mental disorders, but not much better.
If you don’t understand problems, it’s very unlikely you will solve them.
Abstractions are dangerous because they seduce you into thinking you know something. Medicine is especially bad at this. Take an abstraction, give it a Latin name, then say “because”, and it sounds like an explanation. You’ve got bad breath? That’s because you have halitosis, which means “bad breath”. This isn’t an explanation; it’s a tautology.
Will the treatment for one case of halitosis work on another case? Impossible to say. It certainly could. One reason things sometimes have the same surface appearance is because they were caused in the same way. But some people have halitosis because they never brush their teeth, some people have it because they have cancer, and other people have it because they have a rotting piece of fish stuck in their nose. Those causes will require different treatments.
Abstractions are certainly useful. But by themselves, abstractions are a dead end, because they don’t make specific claims. This is exemplified by flowchart thinking. You can draw boxes “A” and “B” and draw an arrow between them, but what is the specific claim made by this diagram? At most it seems to be that measures of A will be correlated with measures of B, and if the arrow is in one direction only, that changing measures of A will also change measures of B.
That’s fine if this is the level of result you’re satisfied with, but it bears very little resemblance to the successes of the mature sciences. Chemistry’s successes don’t come from little flow charts going PROTON –> GOLD <—> MERCURY. If anything, that flowchart looks a lot more like alchemy.
Abstractions can be useful starting points, but they’re bad ending points. For example, people noticed that snow melts in the sunlight and gold melts in a furnace. They noticed that hot water boils and that hot skin burns. It seemed like the same force was at work in all of these cases, so they called it “heat”.
The sensation of warmth, the force of sunlight, the similarities between melting and evaporation, are abstracted: “these go together so well that maybe they are one thing”.
That’s only a starting point. Next you have to take the hypothesis seriously and try to build a model of the thing. What are the entities and rules behind all this warming, melting, and burning?
That’s what Lavoisier did: he came up with a model to try to account for these superficial similarities. Subsequent chemists proposed updates to the entities and the rules that did an even better job, and now we have a model that accounts for heat very well. We still call it “heat”, but because the model is a proposal about the underlying structure, it’s not superficial, so it’s not an abstraction.
This is Conway’s Game of Life:
The universe of this game is an infinite two-dimensional grid of square cells. This means each cell has eight neighbors, i.e. the cells that are horizontally, vertically, and diagonally adjacent.
The cells have only two properties — each cell is either alive or dead (indicated as black and white); and each cell has a location in the infinite two-dimensional grid. Time occurs in discrete steps and is also infinite. This is the full list of the entities in this world.
At each step in time, the following rules are applied:
This is the full list of the rules in this world.
All those parts, and no others, come together to create this world. You can try it for yourself here.
Despite being inspired by things like the growth of crystals, Conway’s Game of Life isn’t a model for any particular part of the natural world. However, it is an example of a set of simple entities, and simple rules about how those entities can interact, that gives rise to complex outcomes.
This is the kind of model that has served as the foundation for our most successful sciences: a proposal for a set of entities, their features, and the rules by which they interact, that gives rise to the phenomena we observe.
Instead of being a chain of abstractions, a flowchart that operates under vaguely implied rules, Conway’s Game of Life is a set of entities that interact in specific ways. And because it is so precise, it makes specific claims.
In principle, we can give you any starting state in the Game of Life, and you should be able to apply the rules to figure out what comes next. You can do that for as big of a starting state as you want, or for as many timesteps as you want. The only limit is the resources you are willing to invest. For example, see if you can figure out what happens to this figure in the next timestep:
Or if you want a more challenging example, try this one:
There are, of course, an infinite number of these exercises. Feel free to try it at home. Draw a grid, color in some cells at random, and churn through these rules. Specific claims get made.
In comparison, take a look at this diagram. Wikipedia assures us that the diagram depicts “mental state in terms of challenge level and skill level, according to Csikszentmihalyi’s flow model”:
You might wonder what exactly is being claimed here. Yes, if you are medium challenged and low skilled, you are “worried”. But it’s not clear what that means outside of the context of these words.
This diagram is just mapping abstractions to abstractions. There is no proposal about the entities underlying those abstractions. What, specifically, might be going on when a person is medium skilled, or low challenged? LOW SKILL + HIGH CHALLENGE —> ANXIETY sounds like a scientific statement, but it isn’t. It’s like saying LOW CAR ACTIVITY + HIGH AMOUNTS OF WEIRD NOISES —> CAR BROKEN-DOWNNESS. Forget about such questions, what matters is that HIGH SKILL + HIGH CHALLENGE —> FLOW.
The Big Five is considered one of the best theories in psychology, and provides five dimensions for describing personality, dimensions like extraversion and openness. But the dimensions are only abstractions. The theory doesn’t make any claim about what constitutes being “high openness”, literally constitutes in the sense of what that factor is made up of. The claims are totally superficial. At most, the big five is justified by showing that its measures are predictive. This so-called theory is not scientific.
Modern scientists often claim that they are building models. However, these are usually statistical models. They are based on historical data and can be used to guess what the future will look like, assuming the future looks like the past. Statistical models predict relationships between abstract variables, but don’t attempt to model the processes that created the data. A linear regression is a “model” of the data, but no one really thinks that the data entered the world through a linear model like the one being used to estimate it.
This is made even more confusing because there is another totally different kind of “statistical model” found in fields like statistical physics. These are models in the sense that we mean. Despite involving the word “statistical”, they are nothing like a linear regression. Instead of looking backwards at historical data of abstract variables, models in statistical physics take hypothetical particles and step them forward, in an attempt to describe the collective behavior of complex systems from microscopic principles about how each particle behaves. These models are “statistical” only in the sense that they use probability to attempt to describe collective behavior in systems with many particles.
We want a model that is a proposal for simple entities, their properties, and the rules that govern them, that can potentially give rise to the natural phenomena we’re interested in. The difference between the Game of Life and a genuine scientific model is simply that while the Game of Life is an artificial set of entities and rules that are true by fiat, answering to nothing at all about the real world, a scientific model is a proposal for a set of entities and rules that could be behind some natural phenomenon. All we have to do is see if they are a good match.
Physics first got its legs with a model that goes something like this. The world is made up of bodies that exist in three-dimensional space and one-dimensional time. The most important properties of bodies are their mass, velocity, and position. They interact according to Newton’s laws. There are also some forces, like gravity, though the idea of forces was very controversial at first.
If you read Newton’s laws, you’ll see that these are the only entities he mentions. Bodies that have mass, speed/velocity, and a location in time according to space. Also there is a brief mention of forces.
Since this model was invented, things have gotten much more complicated. We now have electrical forces, Einstein changed the nature of the entities for space/time/mass, and there is all sorts of additional nonsense going on at the subatomic level.
We were able to get to this complicated model by starting with a simpler model that was partially right, a model that made specific claims about the entities and rules underlying the physical world, and therefore made at least somewhat specific predictions. These predictions were wrong enough to be useful, because they could be tested. Claims about the rules and entities could be challenged, and the models could be refined. They did more than simply daisy-chain together a series of abstractions.
Coming up with the correct model on the first go is probably impossible. But coming up with a model that is specific enough to be wrong is our responsibility. Specific enough to be wrong means proposals about entities and rules, rather than superficial generalizations and claims about statistical relationships.
Like Lavoisier, we should be largely unconcerned as to whether these models are real or purely hypothetical. We should be more concerned about whether it “explains the phenomena of nature in a very satisfactory manner.” Remember that “we are not obliged to suppose this to be a real substance”!
As another example, consider different models of the atom.
Dalton was raised in a system where elements had been discovered by finding substances that could not be broken down into anything else. Hydrogen and oxygen were considered elements because water could be separated into both gases, but the gases themselves couldn’t be divided. So Dalton thought of atoms as indivisible.
When electrons were discovered, we got a plum pudding model. When Rutherford found that atoms were mostly empty space, we got a model with a small nucleus and electrons in orbit. Emission spectra and other observations led to electron shells rather than orbits. None of these models were right, but they were mechanical and accounted for many observations.
Anyways, what is science?
Most people these days claim that the legitimacy of science comes from the fact that it’s empirical, that you’re going out and collecting data. You see this in phrases like, “ideas are tested by experiment”. As a result, people who do any kind of empirical work often insist they are doing science.
Testing ideas by experiment is essential — what else are you going to rely on, authority figures? But what kind of ideas are tested by experiment? Science can’t answer normative ideas, like “how should I raise my child?” or “what kind of hat is best?” It also can’t answer semantic ideas like “is a hot dog a sandwich?”
Some things are empirical but don’t seem very much like science at all. For example, imagine a study where we ask the question, “are red cars faster than blue cars?” You can definitely go out and get a set of red cars and a set of blue cars, race them under controlled conditions, and get an empirical answer to this question. But something about this seems very wrong — it isn’t the kind of thing we imagine when we think about science, and doesn’t seem likely to be very useful.
Similarly, you could try to get an empirical answer to the question, “who is the most popular musician?” There are many different ways you could try to measure this — record sales, awards, name recognition, etc. — and any approach you chose would be perfectly empirical. But again, this doesn’t really feel like the same thing that Maxwell and Newton and Curie were doing.
You could object to these studies on the grounds that the questions are moving targets. Certain musicians are very popular today, but someday a different musician will be more popular. Even if right now, across all cars, red cars are faster than blue cars, that may not be true in the future, may not always be true in the past. If you go far enough back in time, there weren’t any cars at all.
You could also object that the results aren’t very stable, they can be easily altered. If we paint some of our red cars blue, if we spend some marketing dollars on one musician over another, the empirical answer to these questions could change.
Both of these complaints are correct. But they identify symptoms, not causes. They reflect why the questions are nonsensical, but they’re not the source of the nonsense.
Better to say, these studies are unscientific because they make no claim about the underlying entities.
We say that science is when metaphysical proposals about the nature of the entities that give rise to the world around us are tested empirically. In short, you propose entities and rules that can be tested, and then you test your proposal. Science does have to be empirical. But being empirical is not enough to make something science.
A good way to think of this is that we’re looking for a science that is not merely empirical, but mechanical, in the sense of getting at a mechanism. The ideal study tries to get a handle on proposals about the mechanics of some part of the natural world. And you can only get at the mechanics by making a proposal for entities and rules that might produce parts of the natural world that we observe.
This isn’t always possible at first. When you hear there’s some hot new mold that cures infections, your first question should be plain and empirical — does it actually cure infections or not? The practical reason to firmly establish empirical results is to avoid dying of infections. But the scientific reason is so that you can come around and say, “now that we have established that this happens, let’s try to figure out why it happens.” Now you are back to mechanism.
But you still have to be careful, because many things that people think are mechanisms are actually more abstractions. Psychology gets this wrong all the time. Let’s pick on the following diagram, which is theoretically a claim about mechanism, i.e. the mechanism by which your death/life IAT is correlated with some measure of depression. But “zest for life” isn’t a proposal for a mechanism, it’s just another abstraction. You need a specific proposal of what is happening mechanically for something to be a mechanism.
Incidentally, this suggests that having a background in game design may give you a serious leg up as a theoretical scientist.
Game designers can’t be satisfied with abstractions. Their job is to invent mechanisms, to fill a world with entities and laws that make the gameplay they want to make possible, possible; the gameplay they don’t want impossible; and that help players have the intended experience.
Compare this story from Richard Feynman:
[My Father] was happy with me, I believe. Once, though, when I came back from MIT (I’d been there a few years), he said to me, “Now that you’ve become educated about these things, there’s one question I’ve always had that I’ve never understood very well.”
I asked him what it was.
He said, “I understand that when an atom makes a transition from one state to another, it emits a particle of light called a photon.”
“That’s right,” I said.
He says, “Is the photon in the atom ahead of time?”
“No, there’s no photon beforehand.”
“Well,” he says, “where does it come from, then? How does it come out?”
I tried to explain it to him—that photon numbers aren’t conserved; they’re just created by the motion of the electron—but I couldn’t explain it very well. I said, “It’s like the sound that I’m making now: it wasn’t in me before.” (It’s not like my little boy, who suddenly announced one day, when he was very young, that he could no longer say a certain word—the word turned out to be “cat”—because his “word bag” had run out of the word. There’s no word bag that makes you use up words as they come out; in the same sense, there’s no “photon bag” in an atom.)
He was not satisfied with me in that respect. I was never able to explain any of the things that he didn’t understand. So he was unsuccessful: he sent me to all these universities in order to find out those things, and he never did find out.
You can see why Feynman’s father found this frustrating. But to a game designer, nothing could be more trivial than to think that God designed things so that atoms spawn photons whenever the rules call for it. Where were the photons before? The question isn’t meaningful: “photons” is just a number in the video game engine, and when the rules say there should be new photons, that number goes up.
This is also why abstractions don’t work for science. Listening to someone explain a new board game is already one of the most frustrating experiences of all time. But imagine someone explaining the rules to you in abstractions rather than in mechanics.
In Settlers of Catan, the universe is an island consisting of 19 hexagonal tiles. Settlements can be built at the intersections of tiles, and tiles generate resources depending on their type. The game could be described abstractly. But this is not as useful as describing it mechanically:
MR. ABSTRACTIO: You can make a new settlement with resources. Maritime trade creates value. The player with the best economy wins. Okay, let’s play!
MR. MECHANICO: Building a new settlement requires a Brick, Lumber, Wool, and Grain card. A settlement or a city on a harbor can trade the resource type shown at 3:1 or 2:1 as indicated. You win by being the first to reach 10 victory points, and you earn victory points from settlements (1 point each), cities (2 points each), certain development cards (1 point each), having the longest road (2 points), and having the largest army (2 points).
Another source of unappreciated mechanical thinking is video game speedrunners. Game designers have a god’s-eye view of science, as they make the rules of a world from scratch; speedrunners are more like scientists and engineers, using experiments to infer the underlying rules of the world, and then exploiting the hell out of them.
Many sciences like neuroscience and nutrition pretend to be model-building, but are actually just playing with abstractions. They appear to make claims about specific entities, but on closer inspection, the claims are just abstractions in a flowchart.
This can be hard to spot because many of these entities, like neurotransmitters or vitamins, really are specific entities in the chemical sense. But in neuroscience and nutrition these entities are often invoked only as abstractions, where they interact abstractly (e.g. more of X leads to more of Y) rather than mechanically. They tell you, “X upregulates Y.” How fascinating, what are the rules that lead to this as a consequence?
As neuroscientist Erik Hoel puts it:
If you ask me how a car works, and I say “well right here is the engine, and there are the wheels, and the steering wheel, that’s inside,” and so on, you’d quickly come to the conclusion that I have no idea how a car actually works.
Explanations are often given in terms of abstractions. “Please doc, why am I depressed?” “Easy, son: Not enough dopamine.” If you’re like us, you’ve always found these “explanations” unsatisfying. This is because abstractions can’t make sense of things. They just push the explanatory burden on an abstract noun, and hope that you don’t look any deeper.
Explanations need to be in terms of something, and scientific explanations need to be in terms of a set of entities and their relationships. Why do sodium and chlorine form a salt? Because one of them has one extra electron in its outer shell, leading to a negative charge, while one has one missing electron in its outer shell, leading to a positive charge, and they form an ionic bond. This is why chlorine also readily forms a salt with potassium, etc. etc. The observed behavior is explainable in terms of the entities and their properties we’ve inferred over several hundred years of chemistry, interacting according to the rules we’ve inferred from the same.
The fake version of this can be hard to spot. “Why am I depressed? Not enough dopamine” sounds a lot like “Why does my car not start? Not enough gasoline.” But the second one, at least implicitly, leads to a discussion of spark plugs, pistons, and fuel pumps acting according to simple rules, genuine mechanics’ mechanics. The first one promises such an implied mechanism but, in our understanding at least, does not deliver.
This also dissolves one of our least-favorite discussions about psychology, whether or not there are “real truths in the social sciences”. There may or may not be real truths in the social sciences. But human behavior, and psychology more generally, is definitely the result of some entities under the hood behaving in some way, and we can definitely do more to characterize those entities and how they interact.
There’s a common misunderstanding. We’ll use an example from our friend Dynomight, who says:
Would you live longer if you ate less salt? How much longer? We can guess, but we don’t really know. To really be sure, we’d need to take two groups of people, get them to eat different amounts of salt, and then see how long they live.
This way of thinking follows a particular strict standard, namely “randomized controlled experiments are the only way to infer causality”. But this isn’t really how things have ever worked. This is pure extrapolation, not model-building. In contrast to the impressionistic research of inventing abstractions, you might call this brute-force empiricism.
Experiments are useful, but we can’t let them distract from the real goal of science, which is building models that work towards a mechanistic understanding of the natural world.
To get to the moon, we didn’t build two groups of rockets and see which group made it to orbit. Instead, over centuries we painstakingly developed a mechanical understanding of physics, or at least a decent model of physics, that allowed us to make reasonable guesses about what kind(s) of rockets might work. There was a lot of testing involved, sure, but it didn’t look like a series of trials where we did head-to-head comparisons of hundreds of pairs of rocket designs, one pair at a time.
So to “get to the live longer”, we probably won’t build a low-salt and high-salt diet and fire them both at the moon. Instead we will, slowly, eventually, hopefully, develop a mechanical understanding of what salt does in the body, where things are likely to go well, and where they’re likely to go wrong. Then we will compare these models to observations over time, to confirm that the models are roughly correct and that things are going as anticipated, and we’ll correct the models as we learn more.
It won’t look like two groups of people eating broadly different diets in large groups. That is science done with mittens. There is a better way than losing all articulation and mashing together different conditions.
Astronomy may have forced us to do science the right way because it enforces a “look but don’t touch” approach. Newton didn’t run experiments where he tried the solar system one way and then tried it the other way. Instead he (and everyone else) looked, speculated, came up with models, and saw which models would naturally cause the action they had already seen in the heavens. None of the models were entirely right, but some of them were close, and some of them made interesting predictions. And in time, some of them got us to the moon.
These are the insights you need to make sense of the famously confusing but deeply insightful philosopher of science Thomas Kuhn.
One-paragraph background on Kuhn: Thomas Kuhn was a philosopher of science who introduced the concept of “paradigms”. According to Kuhn, each science (biology, chemistry, etc.) is built on a paradigm, and scientific progress is more than the slow accumulation of facts, it involves revolutions, where an old paradigm is tossed out and a new one installed as the new foundation.
But even though it’s his biggest concept, Kuhn can be kind of vague about what a “paradigm” involves, and this has led to a lot of confusion. So let’s try to pin it down.
A paradigm is not just a shared set of assumptions or tools and techniques. If it were, any tennis club would have a paradigm.
A paradigm is specifically a proposal (or rather, class of proposals) about the entities, properties, and relationships that give rise to some natural phenomenon.
Kuhn says:
Effective research scarcely begins before a scientific community thinks it has acquired firm answers to questions like the following: What are the fundamental entities of which the universe is composed? How do these interact with each other and with the senses? What questions may legitimately be asked about such entities and what techniques employed in seeking solutions? At least in the mature sciences, answers (or full substitutes for answers) to questions like these are firmly embedded in the educational initiation that prepares and licenses the student for professional practice.
(The Structure of Scientific Revolutions, Chapter 1)
Why “a class of proposals” and not “a proposal”? Well, because the specifics are always very much up for debate, or at least subject to empirical scrutiny. Any particular proposal, with exact values and all questions pinned down, cannot be a paradigm. A paradigm is a general direction that includes some flexibility.
For example, we may not know if the mass of a specific particle is 2 or 1 or 156 or 30,532 — but we do agree that things are made up of particles and that one of the things you can say about a particle is that it has some mass.
There may even be disagreement about the limits of the proposal itself — can the mass of a particle be any real number, say 1.56, or is mass limited to the positive integers, like 2, 4, and 10? Can the mass of a particle be negative? But in general we have a basic agreement on what kind of thing we are looking for, i.e. the types of entities, their features, and their interactions.
Kuhn gives an example based on Descartes’s corpuscularism. Descartes didn’t give a specific proposal about exactly what kinds of corpuscules there are, or exactly the rules by which they can interact. Instead, it was more of an open-ended suggestion: “hey guys, seems like a good model for physics would be something in the class of proposals where all things are made up of tiny particles”:
After the appearance of Descartes’s immensely influential scientific writings, most physical scientists assumed that the universe was composed of microscopic corpuscles and that all natural phenomena could be explained in terms of corpuscular shape, size, motion, and interaction. That nest of commitments proved to be both metaphysical and methodological. As metaphysical, it told scientists what sorts of entities the universe did and did not contain: there was only shaped matter in motion. As methodological, it told them what ultimate laws and fundamental explanations must be like: laws must specify corpuscular motion and interaction, and explanation must reduce any given natural phenomenon to corpuscular action under these laws. More important still, the corpuscular conception of the universe told scientists what many of their research problems should be. For example, a chemist who, like Boyle, embraced the new philosophy gave particular attention to reactions that could be viewed as transmutations.
(The Structure of Scientific Revolutions, Chapter 4)
Kuhn’s arguments definitely line up with one proposal: a book by the cyberneticist William Powers, called Behavior: The Control Of Perception. And the two men must have recognized at least some of this in each other, judging from the blurb that Kuhn wrote for Powers’s book:
Powers’ manuscript, Behavior: The Control of Perception, is among the most exciting I have read in some time. The problems are of vast importance, and not only to psychologists; the achieved synthesis is thoroughly original; and the presentation is often convincing and almost invariably suggestive. I shall be watching with interest what happens to research in the directions to which Powers points.
And it’s worth considering what Powers says about models:
In physics both extrapolation and abstract generalization are used and misused, but the power of physical theories did not finally develop until physical models became central. A model in the sense I intend is a description of subsystems within the system being studied, each having its own properties and all—interacting together according to their individual properties—being responsible for observed appearances.
As you can see, this is another description of a model based on rules and entities.
The final concept to take away here is that these models are mechanistic. There’s a reason that Descartes was celebrated for his mechanical philosophy. When you assume the universe is akin to a gigantic clock, a real machine where the hands and numbers on the face are driven by the interaction of gears and levers below, your theories will be mechanical too. They will appeal to the interaction of gears and wires, rather than to abstract notions of what is happening on the clock face. (“The minute-hand has minute-force, and that’s why it moves faster than the hour-hand, which only has hour-force.”)
If a model is not mechanical in this way, if it does not speculate about the action of mechanisms beneath what is seen, it will be superficial. And it is not enough to speculate about things beneath. You can layer abstractions on abstractions (e.g. your anxiety is caused by low self-esteem). But you can’t design a watch without talking about individual pieces and how they will interact according to fixed rules.
Psychology is pre-paradigmatic. It’s not simply that we can’t agree on what entities make up the mind — it’s that there have been almost no proposals for these entities in the first place. There are almost no models, or even proposals for models, that could actually give rise to even a small fraction of the behavior we observe. A couple hundred years of psychology, and almost all we have to show for it are abstractions.
But there are a few exceptions, proposals that really did try to build a model.
The first major exception is Behaviorism. This was an attempt to explain all human and animal behavior in the terms of reward, punishment, stimulus, and muscle tension, according to the laws of association. If, after some stimulus, some muscle tension was followed by reward, there would be more of that muscle tension in the future following that stimulus; if followed by punishment, there would be less.
This ended up being a terrible way to do psychology, but it was admirable for being an attempt at describing the whole business in terms of a few simple entities and rules. It was precise enough to be wrong, rather than vague to the point of being unassailable, which has been the rule in most of psychology.
A more popular proposal is the idea of neural networks. While models based on this proposal can get pretty elaborate, at the most basic level the proposal is about a very small set of entities (neurons and connections) that function according to simple rules (e.g. backpropagation). And it’s hard to look at modern deep learning and large language models and not see that they create some behavior that resembles behaviors from humans and animals.
That said, it’s not clear how seriously to take neural networks as a model for the mind. Despite the claim of being “neural”, these models don’t resemble actual neurons all that much. And there’s a thornier problem, which is that neural networks are extremely good function approximators. You can train a neural network to approximate any function; which means that seeing a neural network approximate some function (even a human behavior like language) is not great evidence that the thing it is approximating is also the result of a neural network.
Finally, there is a proposal that the main entities of the mind are negative feedback loops, and that much or even all of psychology can be explained in terms of the action of these feedback loops when organized hierarchically. This proposal is known as cybernetics.
2025-02-03 05:56:25
Just over a year ago we launched the Potato Diet Riff Trial, the first of its kind.
The riff trial is a new type of study design. In most studies, all participants sign up for the same protocol, or for a small number of similar conditions. But in a riff trial, you start with a base protocol, and every participant follows their own variation. Everyone tests a different version of the original protocol, and you see what happens.
As the first test of this new design, we decided to riff on one of our previous studies: the potato diet. For many people, eating a diet of nothing but potatoes (or almost nothing but potatoes) causes quick, effortless weight loss, 10.6 lbs on average. It’s not a matter of white-knuckling through a boring diet — people eat as much (potato) as they want, and at the end of a month of spuds, they say things like, “I was quite surprised that I didn’t get tired of potatoes. I still love them, maybe even more so than usual?!”
Why the hell does this happen? Well, there are many theories. The hope was that running a riff trial would help get a sense of which theories are plausible, try to find some boundary conditions, or just more randomly explore the diet-space. We thought it might also help us figure out if there are factors that slow, stop, or perhaps even accelerate the rate of weight loss we saw on the full potato diet.
In the first two months after launching the riff trial, we heard back from ten riffs. Those results are described in the First Potato Riffs Report. Generally speaking, we learned that Potatoes + Dairy seems to work just fine, at least for some people, and we saw more evidence against the idea that the potato diet works because you are eating only one thing (people still lost weight eating more than one thing), or because the diet is very bland (it isn’t).
Between January 5th and March 18th, 2024, we heard back from an additional seventeen riffs. Those results are described in the Second Potato Riffs Report. Generally speaking, we learned that Potatoes + Dairy still seems to work just fine. Adding other vegetables may have slowed progress, and the protein results were mixed. However, the Potatoes + Skittles riff was an enormous success.
Between March 18th and October 9th, 2024, we heard back from an additional eleven riffs. Those results are described in the Third Potato Riffs Report. Generally speaking, we saw continued support for Potatoes + Dairy.
The trial is closed, but since the last report, we’ve heard back from an additional two riffs, which we will report in a moment. This gives us a total of 40 riffs in this riff trial. Note that this is not the same as 40 participants, since some people reported multiple riffs, and a few riffs were pairs of participants.
Raw data are available on the OSF.
Participant 87259648 did a Fried Potatoes riff, specifically, “mostly fried in a mix of coconut oil and tallow or lard” and continuing her “normal daily coffees with raw whole milk, heavy cream, honey and white sugar.”
Despite consuming only “around 30 percent potato on average”, she lost a small amount of weight and “found [the] diet to be easy and enjoyable, I never felt sick of potato although I did have a hard time getting myself to eat MORE potato each day.”
Participant 80826704 was formerly participant 41470698, but asked for a new number to do a new kind of riff. In Riff Trial Report Two, he had done Potatoes + Eggs as participant 41470698 and lost almost no weight. This time, he did a full potato diet and lost a lot of weight, more than 13 lbs:
This definitely fits with our suspicion that eggs may be related to weight gain, and the observation that eggs often contain high concentrations of lithium.
Let’s recap all the riffs. Here’s a handy table:
Mean weight change was 6.4 lbs lost, with the most gained being 5.2 lbs and the most lost being two people who both lost 19.8 lbs. One person gained weight, one person saw no change, one person reported no data, and the rest lost weight. One person also gained 6.3 lbs on “Whole Foods” + Chocolate, but this was not a potato diet (only about 10% of her diet was potatoes).
Here are all the completed riffs, plotted by the amount of weight change and sorted into very rough riff categories:
There are also a large number of people who signed up, but never reported closing their riff. We’re not going to analyze them at this point, but all signup data is available on the OSF if you want to take a look at the demographics.
The potato diet continues to be really robust. You can eat potatoes and ketchup, protein powder, or even skittles, and still lose more than 10 lbs in four weeks.
The main thing we learned is that Potatoes + Dairy works almost as well as the normal potato diet. There were many variations, but looking at the 10 cases that did exclusively potatoes and dairy, the average weight lost on these riffs was 9.2 lbs. This is pretty comparable to the 10.6 lbs lost on the standard potato diet, suggesting that Potatoes + Dairy is almost as good as potatoes by themselves (though probably not better).
We didn’t see much evidence that there might be a protocol more effective than the potato diet. This is sad, because it would have been really funny if Potatoes + Skittles turned out to be super effective.
That said, three riffs did do unusually well, and it’s still possible that there is some super-potato-diet that causes more weight loss than potatoes on their own, or that’s better in some other way.
There’s some evidence that meat, oil, vegetables, and especially eggs make the potato diet less effective. But with such a small sample, it’s hard to know for sure. This could be a productive direction for future research. You could organize it as an RCT, and compare a Just-Potato condition to a Potato + Other Thing condition. Or an individual could test this by first doing a potato diet with one of these extra ingredients for a few weeks, then removing the extra ingredient and doing a standard potato diet for a few weeks as comparison.
The strongest evidence is against eggs, because participant 41470698 / 80826704 did exactly that. First he did a Potatoes + Eggs riff and lost only 1.8 lbs. Then he did a standard potato diet and lost 13.2 lbs. That’s not proof positive, but it’s a pretty stark comparison. If that happens in general, it would be hard not to conclude that eggs stop potatoes from working their weight-loss wonders.
If you want to try the potato diet for weight loss, our current recommendation is this funnel:
If dairy doesn’t work for you for some reason (like you’re a vegan, or you just hate milk), consider replacing Step 2 with a different riff that showed good results, like Potatoes + Lentils or Potatoes + Skittles.
Remember to get vitamin A. Mixing in some sweet potatoes is a good idea for this reason.
Remember to get plenty of water. Thirst can feel different on the potato diet, you will need to drink more water than you expect.
Remember to eat! In potato mode, hunger signals often feel different. But if you don’t eat you will start to feel terrible, even if you don’t feel hungry. If anything, eating a good amount of potatoes each day may make you lose weight faster than you would skipping meals.
If the potato diet makes you miserable, try the three steps above. If you try those three steps and you’re still miserable, stop the diet.
This is the first-ever riff trial. But it won’t be the last. So for the next time someone does one of these, here’s what we’ve learned about how to do them right.
We hoped that riff trials would use the power of parallel search to quickly explore the boundary conditions of the base protocol, and discover what might make it work better or worse.
This works. We had suspected that dairy might stop the potato effect, but we quickly learned that we were wrong. We saw that the potato effect is also sometimes robust to lots of other foods, like skittles. And we saw that other foods, like eggs and meat, seem like they might interfere with the weight-loss effect.
That said, there was not as much diversity in the riffs as we might have hoped.
Most people signed up for some version of Potatoes + Dairy. This was great because it provided a lot of evidence that Potatoes + Dairy works, and works pretty damn well. But it was not great for the riff trial’s ability to explore the greater space of possible riffs.
In future riff trials, the organizers should think about what they can do to encourage people to sign up for different kinds of riffs. If you don’t, there’s a good chance you’ll find that most of your scouting parties went off in the same direction, and that’s not ideal if you want to really explore the landscape.
One way to do this would be to run a riff trial with multiple rounds. First, you have a small number of people sign up and complete their riffs. Then, you take some of the most interesting riffs from the first round and encourage people to sign up to riff off of those. You could even do three or four rounds.
In fact, this is kind of what we did. Since we reported the results in waves, and had rolling signups, some people were definitely inspired to try things like Potatoes + Dairy or Potatoes + Lentils because of what they saw from completed riffs. But we could have done this even more explicitly, and that might be a good idea in the future.
There’s no formal skincare riff trial. But it does kind of exist anyway. People get interested in skincare, and go look at other people’s routines. They copy the routines they like, but usually with some modifications. This is all it takes for skincare protocols to mutate, combine, and spread through the population, getting better and better over time.
The same is true of any protocol floating out there in the culture, including the potato diet itself. Even if we hadn’t run the riff trial, people would have experimented with potato diets for the next 10 or 20 years, trying new variations and learning new things about the diet-space. But this process would have been slow, and it would have been hard to tell what we were learning, because the results would have been spread out over time and space.
The fact that we planted our flag and ran this as a riff trial didn’t change the nature of this exploration. But making it one study, clearly marking out its existence, definitely sped things up, and helps make all the riffs easier to compare and interpret.
Potatoes, mostly fried in a mix of coconut oil and tallow or lard. I will continue with my normal daily coffees with raw whole milk, heavy cream, honey and white sugar. Maybe occasional fruit on cheat days but mostly just potatoes, dairy, coconut oil, tallow, coffee and honey/sugar. 28 days. My reasoning for choosing this is that fried potatoes are delicious, i really don’t want to give up my coffee routine, or waste the raw milk that i get through a cow share, and anecdotally, coconut oil and stearic acid have both been reported to help with weight loss.
So I didn’t lose a lot of weight, but I definitely lost somewhere between 3 – 6.5 lbs (hard to tell due to fluctuations in water weight) and an inch off my waist despite doing a pretty relaxed version of the diet.
What I ended up doing was a diet of around 30 percent potato on average (even though I only ate potatoes for dinner and “grazed” on smallish things throughout the rest of the day, it was hard for me to get past around 30 percent potato calorie-wise). The rest of my diet was mostly dairy (raw milk, heavy cream, sour cream, butter, cheese and occasional ice cream), fruit, sugar (and sugary drinks), honey, chocolate and saturated fats (coconut oil and beef tallow).
I rarely boiled the potatoes so the potato portion of the diet was mainly peeled yellow or red potatoes pan-fried in a mixture of tallow and coconut oil, baked russet potatoes with the skins, or roasted red and yellow baby potatoes with the skins.
I occasionally supplemented extra potassium, as well as other supplements. Around day 5 I started drinking coconut water in order to get extra potassium.
I found this diet to be easy and enjoyable, I never felt sick of potato although I did have a hard time getting myself to eat MORE potato each day. The skins didn’t seem to bother me. Something about the diet definitely seemed to have an appetite lowering effect, although my appetite did fluctuate from day to day. I never intentionally cut calories or deprived myself of anything I really wanted. So even on the very low calorie days I ate as much as I felt like eating that day. (i am used to doing extended fasts so this is not super unusual for me, but I DO think that the extra potassium or something DID result in more days than usual where I didn’t feel like eating as much).
I didn’t exercise any more or less than I usually do.
My husband and another male family member did even less strict versions of the diet along with me (potatoes for dinner, whatever else they wanted the rest of the day) and they both seemed to lose more weight than I did, but they didn’t keep track of any data. I’m a 49 year old female, the other two men are 49 and 66. In the last couple years it has gotten much harder for me to lose weight, and I have been pretty fatigued in general. I didn’t notice any extra energy on this diet, but appetite did often seem suppressed.
I didn’t observe any noteworthy reduction in pulse or body temperature over the course of the diet. Three weeks after finishing the diet I have not been able to keep the weight off and am back up to 190.
I kept track of everything in the Cronometer app, so if you have any questions I can access some data that’s even more specific from there, let me know!
Formerly participant 41470698, who asked for a new number: “I would like to try the full potato diet at some point during 2024. Could you prepare a new Google Sheet for me for this purpose?”
I completed the potato only version in August, but neglected to send you a report. Happy to report that I’ve completed it and filled the 4 week sheet.
In terms of feeling it was very similar to my riff experiment. In terms of results this has been completely different. One thing I am now throughly convinced about is the “ad libitum” part. I am hungry, I eat. It’s so simple it’s scandalous, but it’s been buried under years of well meant status quo advice.
From that point it simply matters which food types I eat. Even if the lithium hypothesis turns out wrong, this part I am thoroughly convinced about now.
Difficulty
In a way this was easier than potatoes + eggs. One reason I remember for this was the forced pre-planning. Because I knew I was going to eat only potatoes I generally tried to peel way more potatoes than I was hungry for. Because of this, for the next meal I would have potatoes already lying around. I could then eat those as-is, or more tasty, (re-)baking them in a frying pan.
Somehow I had less inclination to cheat.
I’ve also gone to McDonalds like 6 times, ordering only fries without sauce. And a lot of fries from a Snackbar (https://nl.wikipedia.org/wiki/Snackbar). It’s super convenient when going by train to just order a big portion of fries without sauce.
Fun stuff
Potatoes are fucking delicious by the way. I’ve taken to eating them without sauce, because now it just feels like potatoes with sauce taste like sauce. And then I’m missing the potato flavor. Maillard reaction for the win.
With a group of friends I did a “potato tasting”. I bought 8 breeds of potatoes and cooked them with the oven or boiled. So we tasted 16 different kinds. People were truly surprised by the amount of variation.
My surprise was mostly about how difficult the different breeds were to peel. Some potatoes are truly monsters.
