Lumifer is right, and I think you are effectively confusing a law that gives you the right to work at the minimum wage with actual minimum wage laws which are instead laws forbidding you from working for less than the minimum wage.

I suspect that your model has been built to serve the hypothesis you started with.

First of all, I'm not sure what measure you're using for "rigorous thought". Is it a binary classification? Are there degrees of rigor? I can infer from some of your examples what kind of pattern you might be picking up on, but if we're going to try and say things like "there's a correlation between rigor and volume of publication", I'd like to at least see a rough operational definition of what you mean by rigor. It may seem obvious to you what you mean, and it may seem like a subject many people on this site devoted to refining human rationality will have opinions on. That makes it more important to define your terms rigorously, not less, because your results shouldn't explain variation in everyone's definition of rigor.

For the sake of argument, we could use something like "ratio of bits of information implied by factual claims to bits of information contained in presented evidence supporting factual claims" if we want something vaguely quantifiable. It seems your initial set of examples uses a more heuristic approach, with the rigorous group consisting mostly of well-known scientists, artists, and philosophers who are well-liked and whose findings/writings are considered well-founded/meaningful/influential in our current era, and your non-rigorous group consisting of mostly philosophers and some scientists who are at least partially discredited in our current era. I suspect that this might not be a very predictive heuristic, as I think it implicitly relies on some hindsight and also would be vulnerable to exactly the effect you claim if your claim turns out to be true.

Also, I suspect that academic publication and publication of e.g. novels, self-help books, poetry, philosophical treatises, etc. would follow very different rules with respect to rigor versus volume of publication; there are structures in place to make them do exactly that. While journal publication and peer review rules are obviously far from perfect, I suspect that producing a large volume of non-rigorous work is a much better strategy for a fiction writer, philosopher, or artist than it is for a scientist who, if unable to sufficiently hide their non-rigor, will not get their paper published at all, and might start becoming discredited and losing grant money to do further research. In particular, I think the use of a wide temporal range of publishers is going to confound you a lot, because standards have changed and publication rates in general have gone way up in the last ~150 years.

Actually, I'm not even sure how a definition of "rigorous thought" that applies to scientific literature could apply cleanly to fiction-writing, unless it's the "General Degree of Socially-Accepted Credibility" heuristic discussed earlier.

I am not sure where is this question coming from. I am not suggesting any particular studies or ways of conducting them.

Maybe it's worth going back to the post from which this subthread originated. Acty wrote:

If we set a benchmark that would satisfy our values ... then which policy is likely to better satisfy that benchmark...? But, of course, this is a factual question. We could resolve this by doing an experiment, maybe a survey of some kind.

First, Acty is mistaken in thinking that a survey will settle the question of which policy will actually satisfy the value benchmark. We're talking about real consequences of a policy and you don't find out what they are by conducting a public poll.

And second, if you do want to find the real consequences of a policy, you do need to run an intervention (aka an experiment) -- implement the policy in some limited fashion and see what happens.

What I had in mind was the difference between passive observation and actively influencing the lives of subjects. I would consider "surveys" to be observation and "experiments" to be or contain active interventions. Since the context of the discussion is kinda-sorta ethical, this difference is meaningful.

I'm talking common sense, not IRB legalese.

According to the US Federal code, a home-made pipe bomb is a weapon of mass destruction.

Because those vectors of argument are insufficiently patronizing, I'm guessing.

Right, it's only OK to be patronizing to people who aren't present to defend themselves.

You could have said something to the effect of "not all conservatives have such dumb opinions, they aren't representative of all conservatism, and there also are liberals with even dumber opinions, and anyway it's not a good idea to judge memeplexes from their worst members" -- but no, you chose to go for James A. Donald-level asshattery -- "if you say you know conservatives with dumb opinions, you're probably lying or confabulating". (And somehow even got seven upvotes for that.) What does make you think it's so unlikely that Acty actually knows conservatives with dumb opinions? Are you familiar with all groups of conservatives worldwide?

I won't even argue that, it is a fact it can be changed. Bicoastal America and NW Europe managed to make a fairly large young college-ed middle class that is surprisingly docile. The issue is simply the consequences of the change and its permanence.

If you talked to any random Roman or Ancient Greek author about it, he would basically say you guys are actively trying to get decadent and expect it will work out well? To give you the most simple potential consequence: doesn't it lead to reducing courage or motivation as well? Since this is what we precisely see in the above mentioned group: a decrease of aggressivity correlates with an increase of social anxiety, timidity, shyness i..e. low courage and with the kind of attitudes where playing videogames can be primary hobby, nay, even an identity.

Of a personal experience, as my aggression levels fluctuated, so fluctuated motivation, courage, happines, self-respect and similar things with it. Not in the sense of fluctuating between aggressive and docile behavior of course, but in the sense of needing to exercise a lot of self restraint to always stay civil vs. not needing to.

You can raise the same things about its permanence. The worst outcome is a lower-aggresion group just being taken over by a higher one. Another potential impermance comes from the fluctuation of generations. My father was a rebel (beatnik), so I had only rebellion to rebel against, and my own counter-revolutionary rebellion was approved by my grandfather :)

Finally a visual type of explanation, maybe it comes accross better. You can understand human aggressivity as riding a high energy engine towards a bad, unethical direction. Having a lot of drive to do bad things. We can do two things, steer it away into a good one or just brake and turn off the engine. Everything we seem to do in this direction seems to more like braking than steering away. For example, if we were steering, we would encourage people to put a lot of drive into creative hobbies instead of hurting each other. Therefore, we would shame the living fsck out of people who don't build something. Yet we don't do this: we praise people who build, but we neglect to shame the lazy gamers. Putting it differently, we "brake" kids when they do bad stuff, but we don't kick their butts in order to do good stuff, so they end up doing nothing mostly. Every time a child or a youth would do something useful with a competitive motivation like "I'll show those lazy fscks" we immediately apply the brake. This leads to demotivation.

So in short, negative motivation can be surpressed. The issue is, it has consequences, it is probably not permanent, and really hard to replace it with a positive one. Of course I am not talking about people like us but more like the average.

For e.g. the ferret rewiring experiments, tongue based vision, etc., is a plausible alternative hypothesis that there are more general subtypes of regions that aren't fully specialized but are more interoperable than others?

It's far more likely that different brain modules implement different learning rules, but all learn, than that they encode innate mental functionality which is not subject to learning at all.

Thanks, I was waiting for at least one somewhat critical reply :)

Specifically, I think you fail to address the evidence for evolved modularity: * The brain uses spatially specialized regions for different cognitive tasks. * This specialization pattern is mostly consistent across different humans and even across different species.

The ferret rewiring experiments, the tongue based vision stuff, the visual regions learning to perform echolocation computations in the blind, this evidence together is decisive against the evolved modularity hypothesis as I've defined that hypothesis, at least for the cortex. The EMH posits that the specific cortical regions rely on complex innate circuitry specialized for specific tasks. The evidence disproves that hypothesis.

Damage to or malformation of some brain regions can cause specific forms of disability (e.g. face blindness). Sometimes the disability can be overcome but often not completely.

Sure. Once you have software loaded/learned into hardware, damage to the hardware is damage to the software. This doesn't differentiate the two hypotheses.

In various mammals, infants are capable of complex behavior straight out of the womb. Human infants are only exhibit very simple behaviors and require many years to reach full cognitive maturity therefore the human brain relies more on learning than the brain of other mammals, but the basic architecture is the same, thus this is a difference of degree, not kind.

Yes - and I described what is known about that basic architecture. The extent to which a particular brain relies on learning vs innate behaviour depends on various tradeoffs such as organism lifetime and brain size. Small brained and short-living animals have much less to gain from learning (less time to acquire data, less hardware power), so they rely more on innate circuitry, much of which is encoded in the oldbrain and the brainstem. This is all very much evidence for the ULH. The generic learning structures - the cortex and cerbellum, generally grow in size with larger organisms and longer lifespans.

This has also been tested via decortication experiments and confirms the general ULH - rabbits rely much less on their cortex for motor behavior, larger primates rely on it almost exclusively, cats and dogs are somewhere in between, etc.

This evidence shows that the cortex is general purpose, and acquires complex circuitry through learning. Recent machine learning systems provide further evidence in the form of - this is how it could work.

For all the speculation, there is still no clear evidence that the brain uses anything similar to backpropagation.

As I mentioned in the article, backprop is not really biologically plausible. Targetprop is, and there are good reasons to suspect the brain is using something like targetprop - as that theory is the latest result in a long line of work attempting to understand how the brain could be doing long range learning. Investigating and testing the targetprop theory and really confirming it could take a while - even decades. On the other hand, if targetprop or some variant is proven to work in a brain-like AGI, that is something of a working theory that could then help accelerate neuroscience confirmation.

There seems to be a trend in AI where for any technique that is currently hot there are people who say: "This is how the brain works. We don't know all the details, but studies X, Y and Z clearly point in this direction." After a few years and maybe an AI (mini)winter the brain seems to work in another way...

I did not say deep learning is "how the brain works". I said instead the brain is - roughly - a specific biological implementation of a ULH, which itself is a very general model which also will include any practical AGIs.

I said that DL helps indirectly confirm the ULH of the brain, specifically by showing how the complex task specific circuitry of the cortex could arise through a simple universal learning algorithm.

Computational modeling is key - if you can't build something, you don't understand it. To the extent that any AI model can functionally replicate specific brain circuits, it is useful to neuroscience. Period. Far more useful than psychological theorizing not grounded in circuit reality. So computational neuroscience and deep learning (which really is just the neuroscience inspired branch of machine learning) naturally have deep connections.

Some of the most successful deep learning approaches, such as modern convnets for computer vision, rely on quite un-biological features such as weight sharing and rectified linear units

Biological plausibility was one of the heavily discussed aspects of RELUs.

From the abstract:

"While logistic sigmoid neurons are more biologically plausible than hyperbolic tangent neurons, the latter work better for training multi-layer neural networks. This paper shows that rectifying neurons are an even better model of biological neurons and yield equal or better performance than hyperbolic tangent networks in spite of . . "

Weight sharing is unbiological: true. It is also an important advantage that von-neumman (time-multiplexed) systems have over biological (non-multiplexed). The neuromorphic hardware approaches largely cannot handle weight-sharing. Of course convnents still work without weight sharing - it just may require more data and or better training and regularization. It is interesting to speculate how the brain deals with that, as is comparing the details of convent learning capability vs bio-vision. I don't have time to get into that at the moment, but I did link to at least one article comparing convents to bio vision in the OP.

"Deep learning" is a quite vague term anyway,

Sure - so just taboo it then. When I use the term "deep learning", it means something like "the branch of machine learning which is more related to neuroscience" (while still focused on end results rather than emulation).

Perhaps most importantly, deep learning methods generally work in supervised learning settings and they have quite weak priors: they require a dataset as big as ImageNet to yield good image recognition performances

Comparing two learning systems trained on completely different datasets with very different objective functions is complicated.

In general though, CNNs are a good model of fast feedforward vision - the first 150ms of the ventral stream. In that domain they are comparable to biovision, with the important caveat that biovision computes a larger and richer output parameter map than most any CNNs. Most CNNs (there are many different types) are more narrowly focused, but also probably learn faster because of advantages like weight sharing. The amount of data required to train the CNN up to superhuman performance on narrow tasks is comparable or less than that required to train a human visual system up to high performance. (but again the cortex is doing something more like transfer learning, which is harder)

Past 150 ms or so and humans start making multiple saccades and also start to integrate information from a larger number of brain regions, including frontal and temporal cortical regions. At that point the two systems aren't even comparable, humans are using more complex 'mental programs' over multiple saccades to make visual judgements.

Of course, eventually we will have AGI systems that also integrate those capabilities.

days of continuous simulated gameplay on the ATARI 2600 emulator to obtain good scores

That's actually extremely impressive - superhuman learning speed.

Therefore I would say that deep learning methods, while certainly interesting from an engineering perspective, are probably not very much relevant to the understanding of the brain, at least given the current state of the evidence.

In that case, I would say you may want to read up more on the field. If you haven't yet, check out the original sparse coding paper (over 3000 citations), to get an idea of how crucial new computational models have been for advancing our understanding of cortex.