Reasoning and brain mechanisms

[Ferno]

In one of our laboratory's coffee breaks, a debate arose but no one was able to give concrete answers. The main question is the following:

Which brain mechanisms makes one more able to perform complex reasoning than others? And I mean time and no time related reasoning... Or, maybe more simpler, why are some people able to solve a complex problems while others, even spending more time into it, simply can't? I know the intelligence limit isn't the same for all of us, so what I am asking is what defines this limit.

Can you give me some evidences of this? Thanks.

Cheers.

 

[robertm]

My current focus is not with psychology, however it is an ongoing interest of mine. I am not quite sure exactly what your question is: Mechanisms for different forms of rational cognition? Specific/General Intelligence mechanisms? Physiological differences in human brains and their specific effects on 'intelligence'? Maybe you should narrow your question a bit.

Anyway, here are some links to get you started with what I think you are asking:

http://www.apa.org/" The American Psychological Association

http://www.psychology.org/" A great psychology specific search engine

http://www.apa.org/journals/releases/rev1091116.pdf" A paper that might be relevant to your interests

http://en.wikipedia.org/wiki/Psychology" Quite a large wiki with a wealth of information and links/references.

I'm afraid that is the best I can do without a reformulated question

 

[Cincinnatus]

Well intelligence is really some kind of property of learning. Maybe people who learn faster or better or something like that could be said to be more intelligent. We should translate this question into one about learning since it will be much easier to get a handle on that way.

We can distinguish a few different parts of any "learning machine".

1). There has to be a finite amount of data coming in from the outside world.

2). There has to be a set of assumptions about the way that the data is generated. This could take the form of an a priori probability distribution or some other similar notion.

3). There has to be a learning algorithm which generalizes from the data so as to be able to predict new data that has not yet arrived.

We can think of this like fitting a curve to data. Consider a finite set of data (x_i,y_i). We can pick a learning algorithm like "pick the line that minimizes the sum of the squares of error on the observed data". We use the assumptions in order to restrict the class of curves we consider. That is, perhaps we've assumed the data should all fall on a line then we only need to minimize error on the data for lines.

Such learning machines can generalize from observed data. In a deep sense, this is what we mean when we speak of intelligence. We say that someone who is more intelligent is better at generalizing from observed data to new situations.

So this implies that there could be a few reasons that one person is more intelligent than another. Perhaps the more intelligent seeming person just has a better set of assumptions about the world. If you are trying to learn the rule as to how data was generated by a Gaussian distribution but you refuse to entertain the possibility that the data was not generated by a Poisson distribution then no matter how much data you get, you'll never be able to generalize well. Similarly, incorrect prior assumptions about the world can impede human learning.

Another reason that someone could seem more intelligent could be that this person simply has more data. Learning machines always perform better when given more training data to work with. This could translate into a sensory issue, maybe some people merely notice more things in the world and thus appear to learn more easily/accurately.

Lastly, a person might have a better learning algorithm than someone else. Maybe one that converges faster or takes outliers into account in different ways. This is speculative, but it seems likely that the learning algorithms we use are probably hardwired into us by genetics.

Also it is certainly true that no learning algorithm is ever superior to others in every domain of application. So it is likely that if we really do have a person who is doing better at some task than other people because she has a better learning algorithm then this is probably task dependent. We could probably find other tasks where she does not excel in the same way.

 

[Ferno]

Well intelligence is really some kind of property of learning. Maybe people who learn faster or better or something like that could be said to be more intelligent. We should translate this question into one about learning since it will be much easier to get a handle on that way.

We can distinguish a few different parts of any "learning machine".

1). There has to be a finite amount of data coming in from the outside world.

2). There has to be a set of assumptions about the way that the data is generated. This could take the form of an a priori probability distribution or some other similar notion.

3). There has to be a learning algorithm which generalizes from the data so as to be able to predict new data that has not yet arrived.

We can think of this like fitting a curve to data. Consider a finite set of data (x_i,y_i). We can pick a learning algorithm like "pick the line that minimizes the sum of the squares of error on the observed data". We use the assumptions in order to restrict the class of curves we consider. That is, perhaps we've assumed the data should all fall on a line then we only need to minimize error on the data for lines.

Such learning machines can generalize from observed data. In a deep sense, this is what we mean when we speak of intelligence. We say that someone who is more intelligent is better at generalizing from observed data to new situations.

So this implies that there could be a few reasons that one person is more intelligent than another. Perhaps the more intelligent seeming person just has a better set of assumptions about the world. If you are trying to learn the rule as to how data was generated by a Gaussian distribution but you refuse to entertain the possibility that the data was not generated by a Poisson distribution then no matter how much data you get, you'll never be able to generalize well. Similarly, incorrect prior assumptions about the world can impede human learning.

Another reason that someone could seem more intelligent could be that this person simply has more data. Learning machines always perform better when given more training data to work with. This could translate into a sensory issue, maybe some people merely notice more things in the world and thus appear to learn more easily/accurately.

Lastly, a person might have a better learning algorithm than someone else. Maybe one that converges faster or takes outliers into account in different ways. This is speculative, but it seems likely that the learning algorithms we use are probably hardwired into us by genetics.

Also it is certainly true that no learning algorithm is ever superior to others in every domain of application. So it is likely that if we really do have a person who is doing better at some task than other people because she has a better learning algorithm then this is probably task dependent. We could probably find other tasks where she does not excel in the same way.

Good answer mate.

Thank you all for the enlightenment. And I am sorry for my ignorance, but as I am an engineer, normally I don't have a detailed interest in this kind of sciences.

But I got another question for you all. One that came up today.

What happens to our brain as it becomes more and more fatigued, let's say in a non stop 12h study session? I know it decreases its productivity, but in what does this productivity translates? Is it an overall slowing (i.e., you can still solve all the complex problems that you did when you weren't fatigued but at a slower speed) or is it also a decrease in the maximum limit of complex reasoning one can achieve (i.e., you can spend all the time you want trying to solve a problem but you just won't be able to solve it without resting)?
What mechanisms are responsible for this (synaptic failure, etc.)?

 

[Cincinnatus]

Well I was hesitant to respond here since I don't really know the answer to this new question about fatigue. I see you've posted it elsewhere as well. I'm also not sure if anyone knows much about it.

One interesting fact though. There are drugs like Adderol which allow us to maintain concentration for longer periods of time without getting fatigued. Generally we know very little about how such psychopharmaceuticals are working though.

I know very little about this class of drugs but presumably we know which receptors that they bind to in the brain and such. It is possible that we could identify mechanisms involved in fatigue by better understanding the action of Adderol and related drugs in the nervous system.

However, this certainly isn't guaranteed. It might be that these drugs are activating a completely different system than the one involved in normal fatigue. Perhaps they are really tapping into more "fight or flight" type responses. These are experimental questions that can only be resolved by empirical work. It is possible that this work has been done. If so, I am unaware of it.