The Human Mind is a Bayes Logic Machine

lexxyz writes "Apparently the human mind can predict the distribution type for a given sample of results. A study found in The Economist has shown that a group of minds working on single pieces of data, can together generate the statistical model used to represent a given sample. Note that it takes a group of people to be able to accurately predict the behaviour of something, not a single individual"

Comments

the answer is statistically probably 42

[yagu]

From the fine article:

They suggest that the Bayesian capacity to draw strong inferences from sparse data could be crucial to the way the mind perceives the world, plans actions, comprehends and learns language, reasons from correlation to causation, and even understands the goals and beliefs of other minds.

Phew! Once I read that, I realized I didn't have to read the rest of the article having now taken a large enough "sparse" sample.

An added benefit, I already know what all of the posts are going to say, including this one!

Re:the answer is statistically probably 42

[The-Bus]

Phew! Once I read that, I realized I didn't have to read the rest of the article having now taken a large enough "sparse" sample.

Careful. That's the kind of attitude that could put you in charge of editing Slashdot!

Working "together"?

[GuyMannDude]

A study found in The Economist has shown that a group of minds working on single pieces of data, can together generate the statistical model used to represent a given sample. Note that it takes a group of people to be able to accurately predict the behaviour of something, not a single individual.

Well, that's a somehwat misleading summary. These people were not knowingly collaborating. Each person would have had to answer the questions independently (not knowing what the other respondants' answers were) in order for Bayes to be applicable. Each person's response counts as a piece of evidence or clue in inferring the underlying probability distribution. Their answers are combined using Bayes's rule by an external third party (the researchers). So, yes, this technically counts as a group of minds working together, but I think the way it this summary was worded might give people the wrong impression.

Think about it this way: if you lock a bunch of people in a room toegther and have them come up with an answer, the "strong" personalities in the room are likely to have a heavy influence on the "weaker" ones. People who aren't really firm in their opinions are going to influenced -- whether they realize it or not -- by people who sound confident. The article makes a big to-do about the fact that Bayesian techniques allow you to get good answers with a small number of people working on the problem. But the key is that those people have to be working independently because it's going to be damn difficult to identify and subtract out the cross-correlation of members influencing each other.

I'm making (what I hope to be) an important point. I think business people who read this article or even slashdotters who read the above summary may get the impression that small meetings are a great way to arrive at strikingly effective solutions. That's not what Bayes techinques are about. If you want to put a small group of people to work on a problem, you'd better separate them , otherwise Bayes's rule is not strictly applicable.

GMD

Re:Prediction

[Tomun]

Furthermore, there is actually no solid evidence that the future exists

There is now.

Re:Cumulative video game response

[stonecypher]

Occam's razor suggests that there was in fact no interplay of minds, but rather that the likelihood that any given person was off by +X was equivalent to that another person was off by -X. The experiment measures only the average of the player's skill; there is no mechanism interconnecting minds, as the people do not have any direct observation of one another's states, nor in fact any observation of their own.

If there was a private monitor on which they saw both the average and *just* their own path, then you'd start getting very, very different results.