11 ms·

Our Brain Uses Statistics to Calculate Confidence, Make Decisions

172 points by brahmwg 10y ago

kensai 10y ago
"The work may also have wider implications. The fields of statistics and, in particular, machine learning, may have something to learn from this inner statistician. "Humans are still better than computers at solving really difficult problems," says Kepecs." Intuitive as Statistics is simply one of the many tools we humans use to tackle difficult problems, but definitely not the only one.
- rudolf0 10y ago
  But it is a little disheartening (yet also exciting) to think that when an AI is eventually developed with a human's intuition, creativity, and learning ability, we'll essentially have no advantages over them.
  - dfischer 10y ago
    Why should we have an advantage ?
    - coldtea 10y ago
      Because we are we, and it makes sense to root for the home team. DUH! Why would you say should you not be killed by advanced robo-zombie AI?
      - dfischer 10y ago
        Why would I assume an that AI is robo zombie and wants to kill me?
        coldtea 10y ago
        That's the thing. It will not be up for you to assume. It will be up to the AI to decide. That's why we think it could be a problem (for us) if the AI to had the advantage on us... Our thinking: "Great, we've made this 100x as smart as us AI, even gave it a robot body! Imagine how helpful it will be to us". AI thinking: "Hmm, those pesky creatures think that I'm kind of their servant because they created me? They're like ants to me. I shall crush them and go on with my plans for conquering the universe".
  - deleted 10y ago
    [deleted]
  - Terr_ 10y ago
    Alternately, in order to achieve a human's "intuition, creativity, and learning ability", the AI architecture migh be required to sacrifice most of the narrow advantages we find daunting. At that point, further "capability upgrades" may be things you can apply to both systems.
  - coldtea 10y ago
    It might also be a pipe dream. We might have a global maximum of tech we can achieve with our brain capacity that does not including building brain-level intelligence. Not because it's some special taboo/limit, maybe just because it's too high a barrier. (Or e.g. at best we could replicate it with organic matter, e.g. grow brain cells, tissues etc, but then it won't be as potentially speedy/efficient as computer AI either).
    - prodmerc 10y ago
      I think that's why the focus is on deep learning and self improvement for AIs. We may not be able to create an AI that's as intelligent as us, but we can create a rudimentary AI that can then make itself smarter as time goes by (and since it can be practically immortal it would have a lot of time to learn) and which can be easily duplicated later. Downside is we probably won't know what it's actually thinking and what it will do...
  - CyberDildonics 10y ago
    If we could make a machine better than us in every way, why couldn't that machine make us better in every way?
fromthemorning 10y ago
And water is wet. Er, almost certainly.
danharaj 10y ago
It's interesting how the reification of statistical reasoning, that is, understanding statistics symbolically and applying it to abstract symbols is far more fraught than the implicit statistical reasoning that this article describes.
- stormbrew 10y ago
  Your brain is also able to calculate the parabolic arc of a baseball someone threw to you whether or not you could ever do it on paper.
  - IanCal 10y ago
    That's not quite true. When catching a ball we constantly adjust as we go so we're not perfectly calculating where it will land. We can train humans to roughly approximate where a ball will land, that's about it.
    - stormbrew 10y ago
      This is just a matter of precision. The fact that you can probably start adjusting in a meaningful and probably correct way while the ball is still going up is significant. Somehow that person's brain is doing a thing they may not have the symbolic knowledge to describe.
      - IanCal 10y ago
        > Somehow that person's brain is doing a thing they may not have the symbolic knowledge to describe. And we can draw roughly correct circles but most people don't know the equation for one. I'm not sure that's particularly significant though, but I may have missed your point.
        Houshalter 10y ago
        No the ability to draw circles is impressive. The amount of math and calculations being done in the brain to perform such a feat is incredible. Programming robots to do even a limited range of the things humans do, is nearly impossible.
eli_gottlieb 10y ago
Well, that's another nail in the coffin for all nonprobabilistic views of cognition.
Lxr 10y ago
I think the crowd that maintains statistical machine learning is not 'real' AI should read this study.
haberman 10y ago
> It is Kepecs' thesis that statistics - generated by the objective processing of sensory and other data - is the ultimate language of the brain. Every time I read stuff like this, I lose more confidence that the scientific method as currently applied, funded, and published is capable of truly conquering complex systems like the brain or nutrition. I read article after article like this one, where a prominent researcher conducts some experiment that shows some result. But listening to that person talk about their work, it becomes clear that this has become their pet theory that they want to be capable of explaining everything. As if everything we know about some system will someday be shown to be reducible to one clean, beautiful idea. Here are a few quick reasons why I find this idea to be highly implausible: - There are a dizzying number of documented cognitive biases in humans. If statistics is the ultimate language of the brain, our software is pretty thoroughly buggy: https://en.wikipedia.org/wiki/List_of_cognitive_biases https://en.wikipedia.org/wiki/List_of_cognitive_biases - Belief motivates people to do things that have absolutely no statistical justification. For example, the time that the prophecy of a teenage girl convinced a tribe of people in present-day South Africa to kill 300,000 - 400,000 of their own cattle, leading to the starvation and death of 20,000 - 40,000 people. How does a brain built on statistics decide that this is the most rational course of action? http://persistentfrontiers.com/xhosacattlekilling/ http://persistentfrontiers.com/xhosacattlekilling/ EDIT: Several people are replying with some form of: "your examples can still show that the mind is inherently built on statistics if you think about it in way X." Let's do a quick experiment to see if this theory is actually falsifiable. What experiment/result would convince you that statistics is not "the ultimate language of the brain?"
- Lxr 10y ago
  > How does a brain built on statistics decide that this is the most rational course of action? 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?
  - haberman 10y ago
    > The article suggests that humans have an innate means of determining confidence in a particular hypothesis based on evidence. Yes. In my example, the hypothesis is "killing all of our cattle will make the spirits of our ancestors come to our aid." The evidence supporting this hypothesis was nonexistent. And yet the people expressed extreme confidence in this hypothesis by killing all of their cattle.
excel2flow 10y ago
Can prejudice be renamed to "statistical assessments"? :)
kingkawn 10y ago
I think it's like a series of aqueducts myself. It seems obvious that the brain will resemble whatever we've got as the most complex modeling system at any given time while never being accurately represented by any of them.
- visarga 10y ago
  But we can still benchmark the performance of the latest model and compare it to human average. There are some tasks where humans have been bested.
le0n 10y ago
I wonder whether this is the same sense in which the brain also uses logic to reason about stuff, and physics to interact with the world.
fudged71 10y ago
I'm half colorblind. I often tell people that I don't see colors, I see probabilities. If you ask me what color something is, I am going to make a guess and there is a probability that I am right or wrong based on many factors.
Hernanpm 10y ago
That is why I agree with Arthur Benjamin with teach statistics at school level.
deleted 10y ago
[deleted]
miguelrochefort 10y ago
In other news, the sky is blue. I'm shocked that this is not obvious to everyone.
lostmsu 10y ago
As shown by haberman's comment and a reply by yummyfajitas below, our brain clearly does not operate on pure statistics, as most people are subject to https://en.wikipedia.org/wiki/Conjunction_fallacy https://en.wikipedia.org/wiki/Conjunction_fallacy
intrasight 10y ago
Slime molds also use statistics to calculate confidence, make decisions: http://phys.org/news/2014-01-slime-molds.html http://phys.org/news/2014-01-slime-molds.html "Looking toward the future, Adamatzky is designing a hybrid device that combines a slime mold with conventional electronic computers. A year ago he received a $2.1 million GBP by the European Commission to build such a computer. Now with funding from the EU Unconventional Computation program, he is building a slime mold computer named PhyChip (http://www.phychip.eu/)" http://www.phychip.eu/)"
known 10y ago
Human brain is vulnerable to https://en.wikipedia.org/wiki/List_of_fallacies https://en.wikipedia.org/wiki/List_of_fallacies
dschiptsov 10y ago
Our brain, presumably, uses much simpler, primitive thresholds, like dynamic weights in a weighted sum. As one might deduce from the facts of brain's physiology and development, especially prooning of neurons in the processe of maturation, the key is "a trained structure" to which a weighted graph is, perhaps, the most adequate model, which is shaped by highly specialized centers in the process of continuous training. At least all pattern recognition, including language and vision, presumably implemented this way. The principle is of combination by trial and error of simplest, smallest, stable, reusable building blocks - specialized cells. Three is no general purpose computer in a brain. It is a network of structured layers of microcontrollers.)
foxhop 10y ago
I would say, our brains use quick estimations of statistics to calculate confidence and rationalize decisions.
JI_Sanders 10y ago
Ok guys, time to weigh in. Of course we aren't trying to claim that all brain processing reduces to Bayes. Our claim is that we experience the statistical likelihood of our beliefs about our input data (from sense organs or from long-term memory retrieval) as a sense of confidence. If that sense is actually generated by a heuristic computation rather than a neural implementation of Bayes rule is almost irrelevant, because the end product looks identical to Bayes in the ways that matter for use of a confidence variable in computation. We work through the mathematics of how statistical confidence should appear in different projections of decision data here: http://www.biorxiv.org/content/early/2016/01/01/017400.abstract http://www.biorxiv.org/content/early/2016/01/01/017400.abstr... (To appear in http://www.mitpressjournals.org/loi/neco http://www.mitpressjournals.org/loi/neco in the next month or so) By necessity, we had to study decisions that could be replicated in a lab, so no mass-killings of cows. That being said, the brain is constantly faced with a statistical analysis problem - to figure out what's really happening outside of itself. A Bayesian machine is much more useful to assign a classification to sense data and compute its likelihood, than to determine whether your elders are correct when they insist that God is going to cause a plague in 2 years if we don't burn some bovines. Point being, the brain can construct and switch between (occasionally hilarious) decision making strategies depending on context - especially in the murkier, information-impoverished realm of social decision making. Dealing with sense and memory data is a problem common to all animals, and evolution has had millions of generations to reject suboptimal processors. Processing language and abstract symbols to reinforce tribal social cohesion or implement a political agenda is a different category of decision that is newer in evolutionary terms, and definitely buggier. I hope this helps clear what we are and aren't claiming! As an aside (since this is the right crowd!) we open-sourced the circuit design files, firmware and APIs for the real-time stimulus generator we used here: https://github.com/sanworks/PulsePal https://github.com/sanworks/PulsePal with documentation here: https://sites.google.com/site/pulsepalwiki/home https://sites.google.com/site/pulsepalwiki/home and here: http://journal.frontiersin.org/article/10.3389/fneng.2014.00043/full http://journal.frontiersin.org/article/10.3389/fneng.2014.00... This system allowed us to shut off the ongoing audio click streams within 100 microseconds of a decision button press - so we could look backwards in time at the precise evidence stream used to make each choice and confidence report, for clues about what algorithm the brain used to process the streams. The follow-up study (under review) uses this data to evaluate different models of how decision and confidence computations are implemented in real-time.

172 points by brahmwg 10y ago

"The work may also have wider implications. The fields of statistics and, in particular, machine learning, may have something to learn from this inner statistician. "Humans are still better than computers at solving really difficult problems," says Kepecs." Intuitive as Statistics is simply one of the many tools we humans use to tackle difficult problems, but definitely not the only one.

rudolf0 10y ago
But it is a little disheartening (yet also exciting) to think that when an AI is eventually developed with a human's intuition, creativity, and learning ability, we'll essentially have no advantages over them.
- dfischer 10y ago
  Why should we have an advantage ?
  - coldtea 10y ago
    Because we are we, and it makes sense to root for the home team. DUH! Why would you say should you not be killed by advanced robo-zombie AI?
    - dfischer 10y ago
      Why would I assume an that AI is robo zombie and wants to kill me?
      - coldtea 10y ago
        That's the thing. It will not be up for you to assume. It will be up to the AI to decide. That's why we think it could be a problem (for us) if the AI to had the advantage on us... Our thinking: "Great, we've made this 100x as smart as us AI, even gave it a robot body! Imagine how helpful it will be to us". AI thinking: "Hmm, those pesky creatures think that I'm kind of their servant because they created me? They're like ants to me. I shall crush them and go on with my plans for conquering the universe".
- deleted 10y ago
  [deleted]
- Terr_ 10y ago
  Alternately, in order to achieve a human's "intuition, creativity, and learning ability", the AI architecture migh be required to sacrifice most of the narrow advantages we find daunting. At that point, further "capability upgrades" may be things you can apply to both systems.
- coldtea 10y ago
  It might also be a pipe dream. We might have a global maximum of tech we can achieve with our brain capacity that does not including building brain-level intelligence. Not because it's some special taboo/limit, maybe just because it's too high a barrier. (Or e.g. at best we could replicate it with organic matter, e.g. grow brain cells, tissues etc, but then it won't be as potentially speedy/efficient as computer AI either).
  - prodmerc 10y ago
    I think that's why the focus is on deep learning and self improvement for AIs. We may not be able to create an AI that's as intelligent as us, but we can create a rudimentary AI that can then make itself smarter as time goes by (and since it can be practically immortal it would have a lot of time to learn) and which can be easily duplicated later. Downside is we probably won't know what it's actually thinking and what it will do...
- CyberDildonics 10y ago
  If we could make a machine better than us in every way, why couldn't that machine make us better in every way?

fromthemorning 10y ago

And water is wet. Er, almost certainly.

danharaj 10y ago

It's interesting how the reification of statistical reasoning, that is, understanding statistics symbolically and applying it to abstract symbols is far more fraught than the implicit statistical reasoning that this article describes.

stormbrew 10y ago
Your brain is also able to calculate the parabolic arc of a baseball someone threw to you whether or not you could ever do it on paper.
- IanCal 10y ago
  That's not quite true. When catching a ball we constantly adjust as we go so we're not perfectly calculating where it will land. We can train humans to roughly approximate where a ball will land, that's about it.
  - stormbrew 10y ago
    This is just a matter of precision. The fact that you can probably start adjusting in a meaningful and probably correct way while the ball is still going up is significant. Somehow that person's brain is doing a thing they may not have the symbolic knowledge to describe.
    - IanCal 10y ago
      > Somehow that person's brain is doing a thing they may not have the symbolic knowledge to describe. And we can draw roughly correct circles but most people don't know the equation for one. I'm not sure that's particularly significant though, but I may have missed your point.
      - Houshalter 10y ago
        No the ability to draw circles is impressive. The amount of math and calculations being done in the brain to perform such a feat is incredible. Programming robots to do even a limited range of the things humans do, is nearly impossible.

eli_gottlieb 10y ago

Well, that's another nail in the coffin for all nonprobabilistic views of cognition.

Lxr 10y ago

I think the crowd that maintains statistical machine learning is not 'real' AI should read this study.

haberman 10y ago

> It is Kepecs' thesis that statistics - generated by the objective processing of sensory and other data - is the ultimate language of the brain. Every time I read stuff like this, I lose more confidence that the scientific method as currently applied, funded, and published is capable of truly conquering complex systems like the brain or nutrition. I read article after article like this one, where a prominent researcher conducts some experiment that shows some result. But listening to that person talk about their work, it becomes clear that this has become their pet theory that they want to be capable of explaining everything. As if everything we know about some system will someday be shown to be reducible to one clean, beautiful idea. Here are a few quick reasons why I find this idea to be highly implausible: - There are a dizzying number of documented cognitive biases in humans. If statistics is the ultimate language of the brain, our software is pretty thoroughly buggy: https://en.wikipedia.org/wiki/List_of_cognitive_biases https://en.wikipedia.org/wiki/List_of_cognitive_biases - Belief motivates people to do things that have absolutely no statistical justification. For example, the time that the prophecy of a teenage girl convinced a tribe of people in present-day South Africa to kill 300,000 - 400,000 of their own cattle, leading to the starvation and death of 20,000 - 40,000 people. How does a brain built on statistics decide that this is the most rational course of action? http://persistentfrontiers.com/xhosacattlekilling/ http://persistentfrontiers.com/xhosacattlekilling/ EDIT: Several people are replying with some form of: "your examples can still show that the mind is inherently built on statistics if you think about it in way X." Let's do a quick experiment to see if this theory is actually falsifiable. What experiment/result would convince you that statistics is not "the ultimate language of the brain?"

Lxr 10y ago
> How does a brain built on statistics decide that this is the most rational course of action? 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?
- haberman 10y ago
  > The article suggests that humans have an innate means of determining confidence in a particular hypothesis based on evidence. Yes. In my example, the hypothesis is "killing all of our cattle will make the spirits of our ancestors come to our aid." The evidence supporting this hypothesis was nonexistent. And yet the people expressed extreme confidence in this hypothesis by killing all of their cattle.

excel2flow 10y ago

Can prejudice be renamed to "statistical assessments"? :)

kingkawn 10y ago

I think it's like a series of aqueducts myself. It seems obvious that the brain will resemble whatever we've got as the most complex modeling system at any given time while never being accurately represented by any of them.

visarga 10y ago
But we can still benchmark the performance of the latest model and compare it to human average. There are some tasks where humans have been bested.

le0n 10y ago

I wonder whether this is the same sense in which the brain also uses logic to reason about stuff, and physics to interact with the world.

fudged71 10y ago

I'm half colorblind. I often tell people that I don't see colors, I see probabilities. If you ask me what color something is, I am going to make a guess and there is a probability that I am right or wrong based on many factors.

Hernanpm 10y ago

That is why I agree with Arthur Benjamin with teach statistics at school level.

deleted 10y ago

[deleted]

miguelrochefort 10y ago

In other news, the sky is blue. I'm shocked that this is not obvious to everyone.

lostmsu 10y ago

As shown by haberman's comment and a reply by yummyfajitas below, our brain clearly does not operate on pure statistics, as most people are subject to https://en.wikipedia.org/wiki/Conjunction_fallacy https://en.wikipedia.org/wiki/Conjunction_fallacy

intrasight 10y ago

Slime molds also use statistics to calculate confidence, make decisions: http://phys.org/news/2014-01-slime-molds.html http://phys.org/news/2014-01-slime-molds.html "Looking toward the future, Adamatzky is designing a hybrid device that combines a slime mold with conventional electronic computers. A year ago he received a $2.1 million GBP by the European Commission to build such a computer. Now with funding from the EU Unconventional Computation program, he is building a slime mold computer named PhyChip (http://www.phychip.eu/)" http://www.phychip.eu/)"

known 10y ago

Human brain is vulnerable to https://en.wikipedia.org/wiki/List_of_fallacies https://en.wikipedia.org/wiki/List_of_fallacies

dschiptsov 10y ago

Our brain, presumably, uses much simpler, primitive thresholds, like dynamic weights in a weighted sum. As one might deduce from the facts of brain's physiology and development, especially prooning of neurons in the processe of maturation, the key is "a trained structure" to which a weighted graph is, perhaps, the most adequate model, which is shaped by highly specialized centers in the process of continuous training. At least all pattern recognition, including language and vision, presumably implemented this way. The principle is of combination by trial and error of simplest, smallest, stable, reusable building blocks - specialized cells. Three is no general purpose computer in a brain. It is a network of structured layers of microcontrollers.)

foxhop 10y ago

I would say, our brains use quick estimations of statistics to calculate confidence and rationalize decisions.

JI_Sanders 10y ago

Ok guys, time to weigh in. Of course we aren't trying to claim that all brain processing reduces to Bayes. Our claim is that we experience the statistical likelihood of our beliefs about our input data (from sense organs or from long-term memory retrieval) as a sense of confidence. If that sense is actually generated by a heuristic computation rather than a neural implementation of Bayes rule is almost irrelevant, because the end product looks identical to Bayes in the ways that matter for use of a confidence variable in computation. We work through the mathematics of how statistical confidence should appear in different projections of decision data here: http://www.biorxiv.org/content/early/2016/01/01/017400.abstract http://www.biorxiv.org/content/early/2016/01/01/017400.abstr... (To appear in http://www.mitpressjournals.org/loi/neco http://www.mitpressjournals.org/loi/neco in the next month or so) By necessity, we had to study decisions that could be replicated in a lab, so no mass-killings of cows. That being said, the brain is constantly faced with a statistical analysis problem - to figure out what's really happening outside of itself. A Bayesian machine is much more useful to assign a classification to sense data and compute its likelihood, than to determine whether your elders are correct when they insist that God is going to cause a plague in 2 years if we don't burn some bovines. Point being, the brain can construct and switch between (occasionally hilarious) decision making strategies depending on context - especially in the murkier, information-impoverished realm of social decision making. Dealing with sense and memory data is a problem common to all animals, and evolution has had millions of generations to reject suboptimal processors. Processing language and abstract symbols to reinforce tribal social cohesion or implement a political agenda is a different category of decision that is newer in evolutionary terms, and definitely buggier. I hope this helps clear what we are and aren't claiming! As an aside (since this is the right crowd!) we open-sourced the circuit design files, firmware and APIs for the real-time stimulus generator we used here: https://github.com/sanworks/PulsePal https://github.com/sanworks/PulsePal with documentation here: https://sites.google.com/site/pulsepalwiki/home https://sites.google.com/site/pulsepalwiki/home and here: http://journal.frontiersin.org/article/10.3389/fneng.2014.00043/full http://journal.frontiersin.org/article/10.3389/fneng.2014.00... This system allowed us to shut off the ongoing audio click streams within 100 microseconds of a decision button press - so we could look backwards in time at the precise evidence stream used to make each choice and confidence report, for clues about what algorithm the brain used to process the streams. The follow-up study (under review) uses this data to evaluate different models of how decision and confidence computations are implemented in real-time.