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Which part would you like data on?

The bit about relationships not scaling is well documented. The studies most interesting to me are the ones that suggest it is an evolved limit. If you'd like a general introduction, pretty much any introductory cultural anthropology text would cover that. Or you could dive right in with Co-evolution of Neocortex Size, Group Size, and Language in Humans and the references from there.

Antti Revonsuo's original paper

The Reinterpretation of Dreams: An evolutionary hypothesis of the function of dreaming

Abstract: Several theories claim that dreaming is a random by-product of REM sleep physiology and that it does not serve any natural function. Phenomenal dream content, however, is not as disorganized as such views imply. The form and content of dreams is not random but organized and selective: during dreaming, the brain constructs a complex model of the world in which certain types of elements, when compared to waking life, are underrepresented whereas others are overrepresented. Furthermore, dream content is consistently and powerfully modulated by certain types of waking experiences. On the basis of this evidence, I put forward the hypothesis that the biological function of dreaming is to simulate threatening events, and to rehearse threat perception and threat avoidance. To evaluate this hypothesis, we need to consider the original evolutionary context of dreaming and the possible traces it has left in the dream content of the present human population. In the ancestral environment human life was short and full of threats. Any behavioral advantage in dealing with highly dangerous events would have increased the probability of reproductive success. A dream production mechanism that tends to select threatening waking events and simulate them over and over again in various combinations would have been valuable for the development and maintenance of threat avoidance skills. Empirical evidence from normative dream content, children's dreams, recurrent dreams, nightmares, post-traumatic dreams and the dreams of hunter-gatherers indicates that our dream production mechanisms are in fact specialized in the simulation of threatening events, and thus provides support to the threat simulation hypothesis of the function of dreaming.

A lot of people think that talent is something innate that you are born with and if you don't have it, then you can never develop it to the same level as those 'naturals.' I'm not really sure where this idea comes from, I think it might be because people look at those talented ones, and don't see how they can acheive the same level of excellence, so they come up with explanations why it is impossible.

In any case, to the contrary, there is a Scientific American article that addresses the topic, and even goes a bit into how to teach your kid to become a chess genius (as the Pulgar family did, for example).

Michael Howe at Cambridge has spent the last 20 years or so studying child geniuses such as Mozart. He argues convincingly that talents are not something innate, but rather something that can be learned.

Finally there was a slashdot discussion which is also rather interesting.

In the end, all the hard evidence is on the side that 'natural ability' is something that can be developed, and most evidence to the contrary is just 'gut instinct' or a conclusion people arrive at from their own personal observation. If you disagree (and have read the evidence) then I would love to hear your viewpoint.

The one exception being the Journal of Brain and Behavioral Sciences, at BBS, which follows a model of open review commentary and publishes reviews, the author's answer to the review and third-party peer commentaries alongside the original paper. The journal goes as far as to publish papers which question the peer review system such as the famous D.P. Peters and S. J. Ceci 1982 paper titled "Peer-review practices of psychological journals: the fate of published articles, submitted again." The paper shows how resubmitting papers that had already been published, under false names and institutions, resulted in almost all cases in the paper being rejected. The explanation being, that the academic status of the author and host institution greatly affects the reviewers bias. That BBS published such a paper (an many other similar ones having been published there and elsewhere) is at least a glimpse of hope.

On what evidence do you base your conclusion that this is an absurd question?

Considering that sex hormones affect neural growth in humans and other higher-order animals - link1 link2 link3 link4 link5 - your insistence that examining male/female neural differences is "arbitrary" is ill-informed at best, and deceptive at worst.

The brains of men and women are - in general - different; that much is (to the best of current knowledge) simple fact. What is not known is what cognitive differences those structural differences create, both qualitatively and quantitatively.

What is also not known is the level of sheer stupidity that would drive someone to over-ride information about an individual with information about a population. If 90% of women are better at math than 90% of men, that's only useful information if I'm I'm hiring someone at random. If I have aptitude scores for each candidate in front of me, it doesn't really matter whether the man is in the 98th percentile of all men but only the 91st percentile of all people; if he's the best candidate, he gets the job.

That is why "but I know lots of women who are good at math!" anecdotes are completely useless; each person is an individual, and population-level statistics like "men are better at math" do no more than tell you about the distribution of those individuals. When you've actually got one of those individuals in hand, distributional information is meaningless.

There are population-level differences; that's not the point. The point is that population-level differences are meaningless when talking about a single person; that, I believe, is where you'll get the most effective combatting of sexism. Think of someone as an individual and suddenly they're not a stereotype anymore, regardless of what the stereotype in question was; cut the problem off at the root.

Modularization: Great for OO programming, crappy for the human brain.

IAWMUHTIPORI (I am writing my undergraduate honors thesis in philosophy on related issues) What sort of "modularization" are you referring to? Modularization of peripheral systems (input/output systems, i.e., the senses)? If so, you must realize that you would be in the extreme minority in opposing a modular architecture for these systems (see Jerry Fodor's Modularity of Mind, the standard treatment on peripheral systems modularity with which the vast majority of cognitive scientists agree).

If you are talking about central cognitive systems (belief formation, inference to the best explanation, theory of mind, etc.) things get a bit more complicated. Recent empirical evidence seems to indicate that anatomical modularization of central systems is probably not thoroughgoing in the human brain. However, a lack of any real anatomical modularity does not mean that the human brain is not ultimately modular, in some sense of the word.

The best evidence for conceptual modularity (that is easy for the non-expert to understand) is implicit in the arguments against the other major alternative for cognitive architecture: distributed connectionism (e.g., Parallel Distributed Processing). Specifically, distributed connectionist networks may be able to do certain specialized tasks -- such as optical character recognition -- rather well. But it is next to impossible to get a distributed connectionist network to do more than one thing well without the system eventually grinding to a halt. This is, in part, the result of the inability of a truly distributed connectionist network to maintain a manageable search space when serving multiple purposes.

A modular central architecture, in contrast, can do any number of distinct tasks without the sort of combinatorial explosion that a distributed connectionist architecture is apt to run up against. This is because the modules within a modular central architecture are thought to be highly specialized to handle specific tasks. This feature of modular systems also allows us to see how the brain develops and might have evolved -- one specialized system at a time (for the most part). It is extremely difficult to even imagine how a general problem solver, such as a distributed connectionist network, develops or could have evolved.

The most significant problem for modular cognitive (central) systems, then, doesn't involve a lack of thoroughgoing anatomical modularization, since we are often not talking anatomical modules when we talk about modularity. The main problem for the type of modularity that is popular these days has to do with the lack of a good way to tie all of the modules together to make a flexible system that has the surface appearance of being a general problem solver (as the anti-adaptationist Fodor points-out in his most recent book, The Mind Doesn't Work that Way , which is primarily a criticism of Steven Pinker's popular How the Mind Works ).

In the past couple of years, several theories have been put forth to explain modular integration. Perhaps the most notable among these is that the natural language module serves as the modular integrator. The original article in which this theory was articulated in detail has been made available by the author on his website. The article with criticisms and the author's response to the criticisms is available only in the print edition of the journal Behavioral and Brain Sciences ("The Cognitive Functions of Language" in Volume 25, Issue 6).

Again, then, the issue is a good bit more complex than the parent post indicated. In fact, if the cur

Their research indicates that the true number is more like 4 +/- 1.

Its still controversial (as I guess).

See ...

Cowan, N.(2001) The Magical Number 4 in Short-term Memory: A Reconsideration of Mental Storage Capacity.
Behavioral and Brain Sciences 24 (1)

CC.

Hey, don't give us intellectual blue balls like that... what are these experiments?

Sorry about the blue balls :-)

Try this paper of Dennett's as a starting point. He talks about four pieces of experimental evidence that throw the traditional model of consciousness into doubt. I think the fourth one is the most interesting. Libet ran an experiment where subjects were asked to flex their hand when they saw a rotating wheel reach a particular position. Their neural activity was monitored and they were asked to say when they made the decision to move their hand. The nerve impulse to make their hand move was consistently seen to occur before the subject was aware that they had made the "decision" to move the hand.

It seems that the subconscious makes the decision to move the hand, then the consciousness rationalizes this into a "decision" afterwards.

This is just one example. If this is the underlying model of the way the mind makes decisions, then it has significant implications for the perceived free will we have.

the question of whether computers use intelligence the same way as humans use intelligence has long been determined through the 'chineese room'.

the point of John Searle's Chinese Room being is to see if 'understanding' is involved in the process of computation. if you can 'process' the symbols of the cards without understanding them (since you're using a wordbook and a programme to do it) - by putting yourself in the place of the computer, you yourself can ask yourself if you required understanding to do it:

Minds Brains and Programmes (The Original Chineese Room):
http://www.bbsonline.org/documents/a/00/00/04/84 /b bs00000484-00/bbs.searle2.html

the complementary question - 'is the human brain
a digital computer' is answered by the same author:

Is the Human Brain a Digital Computer (John Searle):
http://www.ecs.soton.ac.uk/~harnad/Papers/Py104/ se arle.comp.html

Summary of the Argument:

1. On the standard textbook definition, computation is defined syntactically in terms of symbol manipulation.

2. But syntax and symbols are not defined in terms of physics. Though symbol tokens are always physical tokens, "symbol" and "same symbol" are not defined in terms of physical features. Syntax, in short, is not intrinsic to physics.

3. This has the consequence that computation is not discovered in the physics, it is assigned to it. Certain physical phenomena are assigned or used or programmed or interpreted syntactically. Syntax and symbols are observer relative.

4. It follows that you could not discover that the brain or anything else was intrinsically a digital computer, although you could assign a computational interpretation to it as you could to anything else. The point is not that the claim "The brain is a digital computer" is false. Rather it does not get up to the level of falsehood. It does not have a clear sense. You will have misunderstood my account if you think that I am arguing that it is simply false that the brain is a digital computer. The question "Is the brain a digital computer?" is as ill defined as the questions "Is it an abacus?", "Is it a book?", or "Is it a set of symbols?", "Is it a set of mathematical formulae?"

5. Some physical systems facilitate the computational use much better than others. That is why we build, program, and use them. In such cases we are the homunculus in the system interpreting the physics in both syntactical and semantic terms.

6. But the causal explanations we then give do not cite causal properties different from the physics of the implementation and the intentionality of the homunculus.

7. The standard, though tacit, way out of this is to commit the homunculus fallacy. The humunculus fallacy is endemic to computational models of cognition and cannot be removed by the standard recursive decomposition arguments. They are addressed to a different question.

8. We cannot avoid the foregoing results by supposing that the brain is doing "information processing". The brain, as far as its intrinsic operations are concerned, does no information processing. It is a specific biological organ and its specific neurobiological processes cause specific forms of intentionality. In the brain, intrinsically, there are neurobiological processes and sometimes they cause consciousness. But that is the end of the story.

--

best regards,

john

it was curious that i found the inclusion of the Turing Test on your web-site, but i found no corresponding counter-balancing link to Searle's Chineese Room (Minds Brains and Programs).

however:

The Turing test enshrines the temptation to think that if something
behaves as if it had certain mental processes, then it must actually
have those mental processes. And this is part of the behaviourist's
mistaken assumption that in order to be scientific, psychology must
confine its study to externally observable behaviour. Paradoxically,
this residual behaviourism is tied to a residual dualism. .... The
mind, they suppose, is something formal and abstract, not a part of
the wet slimy stuff in our heads. ...unless one accepts the idea that
the mind is completely independent of the brain or of any other
physically specific system, one could not possibly hope to create
minds just by designing programs. (Searle 1990a, p. 31)

the point of searle's chinese room is to see if 'understanding'
is involved in the process of computation. if you can 'process'
the symbols of the cards without understanding them (since you're
using a wordbook and a programme to do it) - by putting yourself
in the place of the computer, you yourself can ask yourself if
you required understanding to do it.

since Searle has generally debunked the Turing Test with the
Chineese Room -- and you post only the
Turing Test -- i'd like to ask you personally:

What is your own response to the Chineese
Room argument (or do you just ignore it)?

best regards,
john penner