Our Brain Uses Statistics to Calculate Confidence, Make Decisions

The article suggests that humans have an innate means of determining confidence in a particular hypothesis based on evidence. Why does this imply they act rationally?

The way to reconcile those claims is to say that many of our statistical/estimation patterns are very bad. Mostly, they were good once, but are completely unequipped to deal with the modern world. I know "we're optimized for the savannah" is a laugh-line at this point, but it doesn't seem absurd to claim that we're using statistical techniques that are easy but inflexible. Flawless Bayesian updating is basically unachievable, and a lot of our biases seem like once-useful heuristics that screw us a lot in the modern world.

Tetlock's Superforcasting seems like a good reference here: it's a study of people who are uncommonly good at estimating future probabilities. These are people who estimate near-future events to better than half a percent (as in, rounding from .1% to .5% reliably lowers their accuracy). Most of them claim to use a lot of numerical probability estimates, which suggests that they might be employing an existing system consciously to get the bugs out.

None of this means I buy the study, but I'm not willing to reject the hypothesis, either. Given that we're running on neural nets, I wouldn't be surprised if we deal in actually probabilities and are just exceedingly bad at it.

That's actually a really good example of statistics deciding a course of action.

Ask yourself this? Why did they believe it? It's most likely that group of people were repeatedly told stories that belongs to a larger framework of belief. What you might not understand is good literature has a tendency to fire parts of the brain related to sight, sound and touch in an fMRI. For so many people, a good story creates a parallel quasi-reality, and I wouldn't be surprised their brain is basically chalking up those stories as real experiences.

I was just listening to a story on This American Life about some kids whose prom was interrupted by a tornado. What shocked me was how many of these kids created internal narratives that they somehow caused the storm, or the storm was manifested as a personal lesson for them. If you think about the garbage plot lines and religious upbringing people absorb, this sort of narcissistic outlook makes a tremendous amount of sense.

I'll add this one point. Modern public relations and politics feeds on the principle that you can manufacture reality by constantly bombarding people with the same shallow message. It doesn't matter what you say as much as how loud and frequent you're saying it.

Statistics and rationality merely work by measuring current events against your previous experiences and inferring the most probable outcome of your possible actions. That means that your previous life experiences and how you have interpreted and classified them are key in what your brain is going to expect from future events and in consequence how you are going to react to them.

In your example you are assuming these people are acting "irrationally" because they aren't taking the "correct" decision by your own judgment. The reality is that likely they are acting based on very different premises from your own, extracted from their own life experiences and those of their ancestors. Given the outcome, they were clearly wrong on their conclusions, but that only means that rationality and statistics are useless if you are basing your reasonings on wrong premises and there is no mechanism to stop you from feeding them the wrong data, not that these seemly absurd actions didn't have a somewhat rational justification.

About cognitive biases, well, your brain makes decisions based on the data it has available, and whenever it is insufficient it can't simply delay decision making until its confidence level is acceptable, hence it needs some heuristics to guarantee it gets a result whenever enough previous experience is lacking.

Isn't that more or less what human brains do?

A neural network is just a curve fitting algorithm optimized to fit a bunch of data, that is, a statistical method. NNs are, to the best of my knowledge, a fairly good analogy for what the physical computation process of the brain.

NNs are also very easy to fool. Mislabel the humans in your robot training data as 'kill', and your network will make bad decisions. Garbage in, garbage out.

If you make a probabilistic model and attach a very high confidence to the things that spirits tell little girls down by the river, then sometimes your model will suggest some terrible ideas. Humans are also notoriously bad at sampling data, but that doesn't mean it isn't what they're doing.

Your edit raises a good question, though! I can't say I have a good answer, but would be interested to hear a good idea for such an experiment.

I want to use the phenomenon the inner monologue and the act of convincing oneself of something as a counter example, but on the other hand, language is very easily generated by statistical methods, and the brain apparently will make decisions and then come up with justifications afterward. http://pss.sagepub.com/content/early/2016/04/27/095679761664...

Luckily the conjunction fallacy qualifies: https://en.wikipedia.org/wiki/Conjunction_fallacy

I do not agree with TFAs assertion that "The feeling ultimately relies on the same statistical computations a computer would make".

I would agree, however, that one factor (of an undetermined number of factors) of decision making is something that roughly resembles statistical computation. The key difference is that our input data for these "computations" is absolute crap, and heavily influenced by any number of things, including confirmation bias, small sample size, selective memory, etc.

Let me draw an example of my many years and thousands of hours of play in a game that is dominated by statistics, pool (billiards):

Beginners at the game develop all sorts of incorrect assumptions about how likely a given shot is to succeed, informed by their insufficient sample size. "I made that shot three times in a row last night, why would I miss it this time?" This is, of course, the equivalent of "I hit 13 on the roulette wheel 3 times in a row last night, why would I miss it this time".

As you build experience, you start to get a more correct intuitive understanding of the actual statistical likelihood of a given shot going in, but it is still affected by selection bias, and a number of other factors.

The way (well, some) professional and semi-professional pool players have attempted to eliminate this bias is by actually taking notes on the types of shots they take and actually collecting the true statistics. Even then, after collecting this data for months/years, when confronted with a shot that, statistically, they are more likely to miss than to make, they will still assign an incorrect confidence level to their decision, particularly when they are in, say, the finals of a tournament with a big payday (adrenaline is bad for rational thought, though it can occasionally help you operate at the right edge of the bell curve of your "normal" ability, hence, poorly computed bias).

I don't think that we can boil the decision making process of the brain down to something as simple as a NN, or bayesian inference or whatever. I do, however, think that the brain does attempt a flawed statistical inference as one of many factors in making decisions.

(Note: i'm not anywhere near a professional pool player, nor will I ever be or desire to be, but I've brushed shoulders with some of the best in the world, so I believe I've got a modicum of observational authority on the matter. I also think it's a pretty good example of our brain trying and failing to properly infer the statistical likelihood of a given outcome; chance based gambling is another good one)

Sometimes, you can make the correct decision and still have a bad outcome.

If someone offers you even money that they can roll double sixes on a pair of dice, the rational and logical decision is to take the bet. The rationality of the decision doesn't change if they happen to roll double sixes, it just means you got unlucky.

We already have this problem with artificial neural networks, which are still much simpler than the networks in the brain. ANN's have recently achieved significant successes and approach or exceed human performance on some tasks. They still however make mistakes and no one is claiming that they represent the optimal Bayesian solution, they are just the best solutions that we have so far been able to find.

I think the same thing happens in the human brain but can lead to behavior that is much more extremely erroneous due to the greater complexity of the brain as well as that of the domains to which it finds itself applied.

But that doesn't mean that the brain isn't based on statistical models anymore than the fact that ANN's aren't perfect means that they are not statistical models. They are statistical models but they are heuristic models, not perfect models.

People get concerned about hypnosis precisely because they believe it may access a lower level of their brain that they can't control very well. People want to believe that their thinking is in fact rooted in statistical modeling rather than things they can't control.

I tend to believe our thinking is influenced by many things, including things we aren't in control of, but when we discover elements of our thoughts that we don't control, we can slowly take control of them. For example, we can slowly overcome habits and addictions.

Reflexes, like in your knee. Your spinal cord is very much part of your brain and the pateller tendon reflex is nearly 100% with 'healthy' people. Perhaps the statistical nature of the brain comes from measuring many people to get the right sampling power for the experiment. Genetics and the stochastic behavior of life then influence these brains to behave in statistical ways compared to each other. Twins studies should bear that out as true or false.

Unfortunately the links to the source article don't work, but from careful reading it appears that the real theory is that the brain tracks confidence in its measurement of data, and uses this confidence when making decisions.

This is a Bayesian approach, and it has been widely speculated that the brain does this.

This theory is falsifiable, because you can ask people their confidence in the original measurements, their confidence in a decision depending on that, and then use Bayes' rule to see if those confidences have propagated.

Quote:

In experiments with human subjects, Kepecs and colleagues therefore tried to control for different factors that can vary from person to person. The aim was to establish what evidence contributed to each decision. In this way they could compare people's reports of confidence with the optimal statistical answer. "If we can quantify the evidence that informs a person's decision, then we can ask how well a statistical algorithm performs on the same evidence"

They go on to describe a video game experiment. I suspect that the description is incomplete, and there is a decision making component to the game that depends on how frequently the person thinks they are hearing the clicks. If that is the case then that answers your question about how to falsify it.

It's not all the prefrontal cortex detecting patterns.

The kind of signal the amygdala fires because of a change in body temperature is different than a "pixel" changing in your visual cortex.

An example experiment would be if you gave someone a rewards with different probabilities after some random event like clicking, and the brain would learn to estimate the probability that a sequence of clicks would lead to a reward.

On a high level this obviously breaks down. The brain doesn't have thousands of examples of slaughtering cows leading to starvation. Cognitive biases all operate at a high level of reasoning, and involve verbal arguments and stuff. But on a low level, the brain is absolutely statistically based.