<p>I think that humans initially understand all of life as a "oneness" and learn to differentiate and mentally "un-integrate" things according to what each individual considers pertinent characteristics.</p>

<p>I think another question that will have the same answer as the answer you seek for your question is "How do humans so effectively reduce complicated processes to their constituent tasks?"</p>

CC BY-SA 3.0
2014-08-27T20:14:55.087Z
Welcome to cogsci.stackexchange.com! Although your answer does come from an interesting viewpoint, it could use a little work. For a guide on how to write a good answer, check [here](https://cogsci.stackexchange.com/help/how-to-answer).
Seanny123
CC BY-SA 3.0
2014-08-27T23:43:05.107Z
This mostly follows those minimal guidelines already, but doesn't satisfy a higher standard that isn't exactly required, but is still worth encouraging users to aim for. This answer is essentially one direct sentence and one less-than-specific implication. Both are opinion statements. Generally speaking, answers should seek to be direct, more than a few sentences, and based on objective information like facts or references to whatever extent this is feasible. See also "[What level of citing references or sources should be required for answers?](http://meta.cogsci.stackexchange.com/q/201/4086)"
Nick Stauner
CC BY-SA 3.0
2014-08-27T23:46:09.160Z
Indeed I come the land of Quora, and I am happy to learn of your ways. Thank you for the introduction -- I will certainly take a look!
ProductionValues
CC BY-SA 3.0
2014-08-27T19:57:39.950Z
2014-08-27T19:57:39.950Z
ProductionValues
<p>The term I was looking for is "concurrent activities". Some research in the domain of hierarchical learning has been done in this domain by <a href="http://arxiv.org/pdf/1301.2307.pdf" rel="nofollow">Rohanimanesh and Mahadevan</a>.</p>

<p>According to <a href="http://people.cs.umass.edu/~mahadeva/papers/hrl.pdf" rel="nofollow">this literature review on hierarchical learning</a>, basically what they did was determine how multiple tasks can be managed without interfering with one another and how they should be scheduled.</p>

<p>I couldn't find any cognitive models of this concurrency, so there appears to be quite a bit of work done on the idea of <a href="https://www.cs.drexel.edu/~salvucci/publications/Salvucci-PR08.pdf" rel="nofollow">multi-tasking</a>, which might be synonymous. Unfortunately, most of this seems to be quite firmly in the camp of the Classical Cognitive Science Paradigm (such as the work by <a href="http://www.ai.rug.nl/~niels/procedural.html" rel="nofollow">Niels Taatgen which interestingly rejects hierarchy</a>) with not a lot of thought put into neurobiological plausibility. Additional answers pointing to fMRI experimental data similar to the one found <a href="http://www.sciencedirect.com/science/article/pii/S0896627310001960" rel="nofollow">here</a> for hierarchical learning would be greatly appreciated.</p>

CC BY-SA 3.0
2014-08-28T19:18:12.473Z
2015-04-22T02:22:46.680Z
2015-04-22T02:22:46.680Z
Seanny123
<p>I've been reading about <a href="https://mindmodeling.org/cogsci2014/papers/221/paper221.pdf" rel="nofollow">hierarchical learning</a> (a variant of reinforcement learning from what I understand) and how it is shown to allow learning of a higher-level task (the main example is assembly). I made the mistake of assuming that since the paper shows how a set of sequential actions are learned more quickly when the individual tasks are learned beforehand, that the same could be accomplished with a set of tasks to be combined.</p>

<p>For example, imagine learning to play hockey. First, you have to learn how to skate effectively. Secondly, you learn how to handle the puck with a stick. Thirdly, you have to learn to be aware of the other players and their movement. Finally, you combine all of these and sometimes have to spend time refining the lower end skills to become an effective hockey player.</p>

<p>Is there a name for this type of learning and a model associated to it? Is this type of learning encompassed in hierarchical learning?</p>

<learning><cognitive-modeling><theoretical-neuroscience><reinforcement-learning>
How do humans learn to combine tasks?
Seanny123
# How do humans learn to combine tasks?

(Asked by: username_0 on 2014-08-25)

I've been reading about [hierarchical learning](https://mindmodeling.org/cogsci2014/papers/221/paper221.pdf) (a variant of reinforcement learning from what I understand) and how it is shown to allow learning of a higher-level task (the main example is assembly). I made the mistake of assuming that since the paper shows how a set of sequential actions are learned more quickly when the individual tasks are learned beforehand, that the same could be accomplished with a set of tasks to be combined.

For example, imagine learning to play hockey. First, you have to learn how to skate effectively. Secondly, you learn how to handle the puck with a stick. Thirdly, you have to learn to be aware of the other players and their movement. Finally, you combine all of these and sometimes have to spend time refining the lower end skills to become an effective hockey player.

Is there a name for this type of learning and a model associated to it? Is this type of learning encompassed in hierarchical learning?

**Tags:** learning, cognitive-modeling, theoretical-neuroscience, reinforcement-learning


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## 2 answers

### Answer by username_0 on 2014-08-28. Score: 4 | Accepted answer

The term I was looking for is "concurrent activities". Some research in the domain of hierarchical learning has been done in this domain by [Rohanimanesh and Mahadevan](http://arxiv.org/pdf/1301.2307.pdf).

According to [this literature review on hierarchical learning](http://people.cs.umass.edu/~mahadeva/papers/hrl.pdf), basically what they did was determine how multiple tasks can be managed without interfering with one another and how they should be scheduled.

I couldn't find any cognitive models of this concurrency, so there appears to be quite a bit of work done on the idea of [multi-tasking](https://www.cs.drexel.edu/~salvucci/publications/Salvucci-PR08.pdf), which might be synonymous. Unfortunately, most of this seems to be quite firmly in the camp of the Classical Cognitive Science Paradigm (such as the work by [Niels Taatgen which interestingly rejects hierarchy](http://www.ai.rug.nl/~niels/procedural.html)) with not a lot of thought put into neurobiological plausibility. Additional answers pointing to fMRI experimental data similar to the one found [here](http://www.sciencedirect.com/science/article/pii/S0896627310001960) for hierarchical learning would be greatly appreciated.


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### Answer by username_1 on 2014-08-27. Score: 0

I think that humans initially understand all of life as a "oneness" and learn to differentiate and mentally "un-integrate" things according to what each individual considers pertinent characteristics.

I think another question that will have the same answer as the answer you seek for your question is "How do humans so effectively reduce complicated processes to their constituent tasks?"

> **Comments:**
> **username_0**: Welcome to cogsci.stackexchange.com! Although your answer does come from an interesting viewpoint, it could use a little work. For a guide on how to write a good answer, check [here](https://cogsci.stackexchange.com/help/how-to-answer).  
> **username_2**: This mostly follows those minimal guidelines already, but doesn't satisfy a higher standard that isn't exactly required, but is still worth encouraging users to aim for. This answer is essentially one direct sentence and one less-than-specific implication. Both are opinion statements. Generally speaking, answers should seek to be direct, more than a few sentences, and based on objective information like facts or references to whatever extent this is feasible. See also "[What level of citing references or sources should be required for answers?](http://meta.cogsci.stackexchange.com/q/201/4086)"  
> **username_1**: Indeed I come the land of Quora, and I am happy to learn of your ways. Thank you for the introduction -- I will certainly take a look!  

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