Are Successful People Just Putting in More Effort?

As someone who writes advice for a living, I’m always interested in the ways advice works, how it gets distorted and what the typical advice-receiver can do about it.

Recently, I came across some research that suggests a new way advice-givers aren’t being totally honest with you: paternalistic advice bias.

Here’s the abstract, from the journal article:

“Despite the near universality of the maxim that one should treat others as one ought to be treated, even well-intended advisers often advise others to act differently than they choose for themselves. We review several psychological factors that contribute to biased advice. Absent pecuniary motives to the contrary, advice tends to be paternalistically biased in favor of caution. Policies that would intuitively promote quality advice — such as making advisers accountable, taking advice from advisers who value the relationship, or having advisers disclose potential conflicts of interest — can perversely lower the quality of advice.”

Biased Advice Leads to Excess Caution

The idea here makes intuitive sense. An advice-giver, whether its someone giving advice in the form of a blog article, or a friend or mentor suggesting a course of action, is not merely transmitting what they know from their own experience.

Instead, there’s a subtle cost-benefit calculation that has to be done when giving advice. And here, there’s a big problem: the flaw of bad advice. If you give someone advice that ends up going disastrously wrong, you might get blamed for that. And the blame, reputationally speaking, might be worse than the benefit of helping you win big.

To use a purely hypothetical example, imagine you have to ask a trusted friend for advice about quitting your job to start a business. Suppose this person knows, privately, that there’s a 30% chance you’ll be a big success and 10% chance you’ll lose quite a bit of money, with the remaining 60% of the time the change is relatively neutral.

Now, if this trusted friend were doing the calculation themselves, they might value a 30% chance of big success more than a 10% chance of a financial blunder. So, privately, that person might go ahead with it anyways.

However, as an advice-giver, they may recognize that in the chance you’re successful you might only thank them mildly for encouragement, a small benefit, but if they go bankrupt they may blame you for goading them on, causing deep resentment or worse. As such, you may advise caution if this person is really on the fence about the decision.

Is There an Anti-Effort Bias in Some Advice?

This finding about advice was risk-aversion, but I can see how it could possibly extend to effort-aversion as well. If the effort itself forms a type of risk, you might caution a middle-route of reasonable effort as opposed to the high-intensity path you might personally take.

I may be over-generalizing the findings here, but this also makes intuitive sense to me. Expending a lot of effort is itself a kind of cost. Just as I could imagine being cautious advising someone to invest hundreds of thousands of dollars in an uncertain investment, I might also imagine being cautious telling someone to invest thousands of hours of effort in a project which isn’t guaranteed to work out.

This suggests to me the possibility that increased effort may be dissuaded against if it constitutes a greater risk.

Alternatively, if effort mostly guarantees success, then more effort would reduce risk and be (perhaps) overly recommended. How this affects advice might depend on the reliability of results after effort. People may recommend working hard at a new exercise plan to get in shape, which is a low-risk move, but not recommend working hard at becoming an actor or actress, which is high-risk.

Anti-Effort Posturing

It’s not related to the study mentioned earlier, but another plausible way advice can seem to reduce the emphasis on effort is when the person who is successful has an incentive to downplay their own effort invested.

This bias doesn’t seem to be one-sided. I think we can all think of situations where people have exaggerated the contribution of their personal efforts (such as the son of a U.S. president boldly claiming to be a self-made man). Others are humble and dismiss the idea that their (very obvious) hard work had anything to do with their successes.

Most people tend to attribute this bias to personality. Some people are braggarts who like to champion their hard work and effort, when they really don’t deserve the credit. Some people are modest and gracious and would rather have circumstances or other people get the acclaim.

While personality may be a factor, I prefer to see it another way. People try to maximize their appearance. Sometimes, claiming to have invested a lot of effort makes sense, especially if it can distract from less praiseworthy causal factors in success such as connections, wealth or inheritance.

However, sometimes claiming to have invested a lot of effort makes you look worse. You would appear more magnanimous by suggesting luck and feigning humility. In some endeavors, suggesting raw talent or intelligence is seen more highly than investing a lot of effort. Presumably the latter implies you made different choices, and therefore have different values, which can sometimes be a source of distrust or resentment itself.

My own experience has shown that in my personal life (less so professionally) there’s a strong social incentive to downplay effort. Being busy and overworked is okay (that’s circumstances), but one must be more careful advertising a self-inflicted ambition that requires immense effort (that’s different values).

Many people I’ve talked to who have succeeded at ambitious projects speak similarly, saying that, while working on such projects, they’re often given a rather uncomfortable reception from people who don’t exactly look up to their intense efforts. It seems weird, forced or unnatural, and they often politely question why this person would bother putting so much effort in.

What This Means for Advice

This all suggests to me, at least, that there’s a large possibility of a fairly invisible layer of people around you putting in a lot more effort than is deemed socially reasonable for the pursuit they are after, and often succeeding at it too.

Advice, especially when passed from person-to-person, may be overly risk-adverse, and encourage low-effort strategies for pursuits where success is uncertain.

The combination of these effects, if one doesn’t see through it, can be to fatally underestimate the intensity and effort successful people invest in their goals which inevitably leads to them doing well.

Ultimately, I think the magnitude of this effect will depend on how big a role these underlying factors play. More certain goals will probably show less anti-effort bias. Goals which are more conventionally appropriate to show extreme ambition in (athletics, academics in many settings, high-effort/high-status career paths such as medicine, etc.) will likely show less of this bias. In these cases, the bias might even reverse with successful people actually working *less* hard than you think.

However weird, risky goals like starting a new business, ultralearning projects and high-variance career paths, might have enough of the opposite bias that success for many people is inhibited simply because they don’t realize how hard successful people are actually working at those goals.

How Much Can You Possibly Learn?

How much can the brain store?

We all know how much our computers and phones can store, if only because we occasionally get the pings of messages telling us we’ve taken too many photos or downloaded too many apps or movies and something has to be deleted to store more.

The brain doesn’t seem to be like this. While we do forget things, this seems to be more a matter of decay from disuse than being actively “pushed out” by new knowledge.

On the other hand, the brain is still subject to the same laws of the universe that govern everything else. It can’t possibly store infinite amounts of data, as that would be physically impossible.

So how much can you actually learn?

Some Upper-Bounds on Memory

A good first attack on this kind of problem would be to look at the potential upper bounds of human memory, based on the laws of physics. These will be wildly too high, mostly because the brain is a living organism and not an idealized information storage medium, but they should give some starting points for thinking about it.

The brain is typically 350-450 cubic centimeters. The maximum possible information you can cram into a volume that size is defined by the Bousso bound, which ends up calculating to roughly 10^70 bits of information. However, in order to get this amount of information, your brain would become a black hole… so let’s try to reduce this bound further.

If we look, somewhat more modestly at the amount of information content possible in a volume of water at room temperature equivalent to the brain, we end up with a somewhat more modest 10^25 bits of information. This is one yottabyte of data or 7-8 orders of magnitude more data than the entire Internet.

This, of course is still way too high, since most of the matter in the brain isn’t encoding data but keeping the brain alive and functioning.

We’re still a long way from understanding the exact information carrying capacity of the neurons and synapses in the brain. As such, an estimate of brain information capacity has to use a simplified approximation of how much information these connections can possibly store.

If we ballpark the amount of data that can be stored in the brain as roughly the same order of magnitude as the amount of synapses in the brain, that leaves us with 100 trillion bits or ten terabytes of data—similar to the size of a large hard drive. Even if we imagine that synapses are storing more than one byte of data each (through multiple connections or non-binary connection strengths), we might be able to bump that up to a petabyte, but probably not much more.

By this look, the brain definitely can’t be storing more than a yottabyte of data, and it’s quite unlikely it’s storing more than a petabyte.

Of course, this is still an upper-bound. We know from people who suffer brain injuries, that the knowledge stored in the brain likely has some measure of redundancy. Those synaptic connections are not exclusively used for storing memories either—many are being used for processing, relaying information or may even be spurious, not doing anything at all. This suggests the memory storage of the brain might indeed be a fair bit smaller, but the certainty of this estimate is a lot less than the harder upper bounds mentioned before.

Why Don’t People Ever “Fill Up” Their Brain’s Capacity?

One explanation for why we don’t seem to run out of space to learn new things is that learning may be a lot slower than our mnemonic capacity. That is to say, the actual learning rate of information may be slow enough that we never reach that capacity.

Here’s an analogy: imagine you have a latest-generation harddrive which can store 10TBs of data, but you have to fill it up using a dial-up connection which only downloads at 3 kilobytes per second. It would take over a decade to fill it up, if you were downloading constantly, without rest, and without removing anything you previously downloaded.

Given that it’s likely only a fraction of our waking type is filling up our mental harddrive, and that we suffer from forgetting, it may simply be that we never experience the upper bound of our mental capacity because we learn too slowly.

However, a different explanation might be that, unlike a computer, we don’t store memories that way. Because we store them differently, when we run out of “space” the impact is different.

Vector Encoding, Learning and Forgetting

One of the most popular accounts for how the brain stores information comes from connectionism. This says that the brain uses vector encoding, which means each memory is distributed over many thousands of individual synapses and neurons, rather than there being a neuron which individually stores each atomic concept or memory.

In this view, there is no “grandmother neuron” which specifically points out a memory of your grandmother, but that memory of your grandmother is stored across many different neurons.

Each neuron may be involved in tens of thousands of memories, each contributing a tiny, but necessary, part to the processing that results in thinking of your grandmother, calculus or recognizing the words in this sentence.

What happens when we learn, therefore, is quite unlike storing a file on a computer, where each memory address exclusively and completely stores one piece of data. Instead, we “pile on” data on top of old data. As more and more memories are stored, older memories may get weaker and weaker, as they become harder to activate, since their contribution to the total network of weights is relatively small.

Intuitively, this seems to jive with our own experience of memory. Unlike a computer which remembers things all-or-nothing, human memory seems to fade, and needs to be refreshed or it will become harder and harder to summon up. It may even still exist in our brains, but become unretrievable, until the exact pattern of triggers can summon it up again.

If this is how our memory fills its capacity, it may be that we encounter the limits of our brain’s storage ability all the time.

What Kind of Memories Get Overwritten?

One idea here is also that the memory fading process, where accumulated new memories make older memories harder to find, may depend on the kind of information learned. Information that must be finely discriminated from similar, but different, possibilities, might require more storage than memories which are easy to tell apart.

This might explain the effect of interference on learning new languages. If you learn Spanish, and then learn Portuguese, for instance, you may have difficulty recalling Spanish words when they’re different from Portuguese. Because the languages are quite similar, but sometimes vocabulary is different, you may have difficulty remembering.

A more extreme example of this comes from mnemonics. People who train themselves to memorize vast swaths of information with techniques like the memory palace, have to be careful not to reuse the same areas for mapping information for two sets of things they want to retain in memory. Without care, the connections can get mixed up and old memories may become unretrievable.

By this account, you should be more careful when learning information which is quite similar to something you already know, but has distinct differences in use (say similar languages), rather than fields which are either completely different (chemistry and art history) or complementary (physics and math).

My own experience from learning multiple languages suggests that, if you want to go down that route, you often need to invest time switching between the languages, so the cues for distinguishing vocabulary get reinforced and they don’t mix together.

Should You Worry About Running Out of Space?

Probably not. Even if the brain does have a more limited capacity than an ideal physical medium for storage, and even if we occasionally run into the limits of memory from ideas and concepts that get pushed down, my sense is that running out of memory isn’t a concern for almost anyone.

One reason might be that memory decay happens naturally, interference or not. This might mean that trying to “save” space in your mind, by avoiding learning unnecessary things, may not stop forgetting any less than learning constantly.

Another reason is that many memories are supportive of each other. Learning one thing often connects to another thing. If retrieval, not storage, is the major flaw in our memory hardware, then overinvesting in memory cues more than makes up for trying to save extra space.

For extremely memory-intensive subjects or tasks, it may be possible to reach a saturation point, where new memories can only be created at the expense of old ones. I could imagine, for instance, that there’s an upper bound on how many languages one can learn to mastery, since each may require remembering hundreds of thousands of pieces of linguistic information.

However, it may be that those bounds are reached because natural decay processes, and thus the need to practice previously learned information in order to keep it active, eventually overwhelmns the ability to learn new things. This way, your memory capacity would be hit, not because you suddenly run out of space one day, but because maintaining everything else you’ve learned requires 100% of your time.

If this is the case, though, it is likely far greater than what most of us will ever experience. Polyglots like Alexander Arguelles have proficiency in 50+ languages, albeit through lifelong devotion. If there is a threshold for theoretically-maximal linguistic fluency, it might be well over a hundred, given that Arguelles is still a human being and needs to eat, sleep and do things other than learn languages.

We also can’t discount the possibility of the brain itself to expand its capacity under the pressure to learn more things. London taxicab drivers, who must memorize the city’s infamously complex road system, sights and stops, have larger hippocampuses (a part of the brain involved in forming long-term memories). What’s more, this seems to be caused by their intensive study, rather than being the result of those with larger hippocampuses becoming taxi drivers.

Albert Einstein’s brain supposedly had larger sections related to spatial reasoning and visualization. That could have been a genetic endowment, but it’s also possible it was an outcome of years of strenous thought experiments trying to imagine the warps and curvatures in spacetime. If the learning capacity of the brain is itself plastic, this provides extra weight on the idea that one shouldn’t “save” brain capacity for other things.

As a practical issue, it’s probably unlikely that the storage limits of the human brain should be a concern for everyday learning. However, by understanding how your memory works better, you maximize what you’re able to learn.

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