<p>ACT-R is a complete cognitive model that incorporates Working Memory,  Declarative Memory and Procedural Memory, but also incorporates input (visual and auditory) and output (manual) buffers. It is a really interesting model about the human in its entirety but, it is at a high level of abstraction. </p>

<p>The paper about memory you want is "<a href="http://act-r.psy.cmu.edu/?post_type=publications&amp;p=13920" rel="nofollow">REFLECTIONS OF THE
ENVIRONMENT IN MEMORY</a>" by Schooler and Anderson. It describes how and why memory is like Reïnforcement learning. In this <a href="http://onlinelibrary.wiley.com/doi/10.1111/tops.12183/abstract;jsessionid=1EE00B01462ACBBBDA6E467198B56DE8.f03t03" rel="nofollow">paper</a> you can read how memory is used for studying. About learning to leverage the working memory, I don't exactly know what you mean, but I think these papers give you a nice headstart. </p>

<p>If you want to learn more about ACT-R, I would recommend this <a href="http://act-r.psy.cmu.edu/?post_type=publications&amp;p=14305" rel="nofollow">book</a>, or just read the many papers they have published over the years. They have great examples that explain <a href="http://act-r.psy.cmu.edu/wordpress/wp-content/uploads/2012/12/755Salvucci-PR08.pdf" rel="nofollow">multitasking</a> and <a href="http://link.springer.com/article/10.3758/s13421-011-0113-0/fulltext.html" rel="nofollow">time perception</a> among others. </p>

CC BY-SA 3.0
2016-05-04T10:01:29.570Z
2016-05-04T10:01:29.570Z
Robin Kramer-ten Have
<p>I could only find a single instance of learning to leverage a memory. <a href="http://adb.sagepub.com/content/15/3/329.abstract" rel="nofollow">"The Origin of Epistemic Structures and Proto-Representations" by Chandrasekharan and Stewart</a> shows how to include the option to save the current state (by training a neural network to output '1' given the current state input) and input this memory into the state representation of a reinforcement learning task. In the paper, this method allows an agent to switch between foraging and returning home.</p>

<p>Frustratingly, checking the citations of this paper leads to a dead end, so further references would be greatly appreciated.</p>

CC BY-SA 3.0
2016-02-29T23:52:56.993Z
2016-02-29T23:52:56.993Z
Seanny123
<p>Recently in deep learning, there's been a surge in learning how to use memories as part of the optimisation process (i.e. <a href="https://en.wikipedia.org/wiki/Long_short-term_memory" rel="nofollow">LSTM</a>'s and <a href="https://research.facebook.com/blog/inferring-algorithmic-patterns-with-stack/" rel="nofollow">Stacks</a>). However, these aren't really analogous to how a cognitive systems learns how to use it's working memory.</p>

<p>Are there models of how working memory modules (where a saved value decays over time) can be learned to be optimally leveraged? Either via reinforcement learning or supervised learning?</p>

CC BY-SA 3.0
2016-01-21T01:39:35.240Z
From memory, there are a few simple cognitive models which can predict optimal memory search patterns. However, I don't know if it is exactly what you are after.
Doctor David Anderson
CC BY-SA 3.0
2016-01-21T02:18:56.740Z
@MichaelAnderson I'm more after learning how to use memory modules that may or may not need to be searched
Seanny123
<memory><reference-request><cognitive-modeling><reinforcement-learning><short-term-memory>
Cognitive Models of learning Working Memory usage
Seanny123
# Cognitive Models of learning Working Memory usage

(Asked by: username_0 on 2016-01-19)

Recently in deep learning, there's been a surge in learning how to use memories as part of the optimisation process (i.e. [LSTM](https://en.wikipedia.org/wiki/Long_short-term_memory)'s and [Stacks](https://research.facebook.com/blog/inferring-algorithmic-patterns-with-stack/)). However, these aren't really analogous to how a cognitive systems learns how to use it's working memory.

Are there models of how working memory modules (where a saved value decays over time) can be learned to be optimally leveraged? Either via reinforcement learning or supervised learning?

**Tags:** memory, reference-request, cognitive-modeling, reinforcement-learning, short-term-memory

> **Comments:**
> **username_1**: From memory, there are a few simple cognitive models which can predict optimal memory search patterns. However, I don't know if it is exactly what you are after.  
> **username_0**: @MichaelAnderson I'm more after learning how to use memory modules that may or may not need to be searched  

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## 2 answers

### Answer by username_2 on 2016-05-04. Score: 2

ACT-R is a complete cognitive model that incorporates Working Memory, Declarative Memory and Procedural Memory, but also incorporates input (visual and auditory) and output (manual) buffers. It is a really interesting model about the human in its entirety but, it is at a high level of abstraction.

The paper about memory you want is "[REFLECTIONS OF THE
ENVIRONMENT IN MEMORY](http://act-r.psy.cmu.edu/?post_type=publications&p=13920)" by Schooler and Anderson. It describes how and why memory is like Reïnforcement learning. In this [paper](http://onlinelibrary.wiley.com/doi/10.1111/tops.12183/abstract;jsessionid=1EE00B01462ACBBBDA6E467198B56DE8.f03t03) you can read how memory is used for studying. About learning to leverage the working memory, I don't exactly know what you mean, but I think these papers give you a nice headstart.

If you want to learn more about ACT-R, I would recommend this [book](http://act-r.psy.cmu.edu/?post_type=publications&p=14305), or just read the many papers they have published over the years. They have great examples that explain [multitasking](http://act-r.psy.cmu.edu/wordpress/wp-content/uploads/2012/12/755Salvucci-PR08.pdf) and [time perception](http://link.springer.com/article/10.3758/s13421-011-0113-0/fulltext.html) among others.


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### Answer by username_0 on 2016-02-29. Score: 1

I could only find a single instance of learning to leverage a memory. ["The Origin of Epistemic Structures and Proto-Representations" by Chandrasekharan and Stewart](http://adb.sagepub.com/content/15/3/329.abstract) shows how to include the option to save the current state (by training a neural network to output '1' given the current state input) and input this memory into the state representation of a reinforcement learning task. In the paper, this method allows an agent to switch between foraging and returning home.

Frustratingly, checking the citations of this paper leads to a dead end, so further references would be greatly appreciated.


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