<p>My colleagues have applied the COVIS model of category learning to the WCST. COVIS isn't a model of WCST performance per se, but can account for several known phenomena. See this Google Scholar search: <a href="http://scholar.google.com/scholar?hl=en&amp;q=Helie+Paul+ashby&amp;btnG=&amp;as_sdt=1%2C5&amp;as_sdtp=" rel="nofollow">http://scholar.google.com/scholar?hl=en&amp;q=Helie+Paul+ashby&amp;btnG=&amp;as_sdt=1%2C5&amp;as_sdtp=</a></p>

CC BY-SA 3.0
2012-07-05T04:24:14.173Z
Welcome to the site. Are there any articles, in particular, you would recommend? Could you elaborate on how it accounts for known phenomena?
Jeromy Anglim
CC BY-SA 3.0
2012-07-04T16:45:33.343Z
2012-07-04T16:45:33.343Z
neurobot
<p><a href="https://academic.oup.com/cercor/article-abstract/1/1/62/408917" rel="nofollow noreferrer">Dehaene &amp; Changeux (1991)</a> made a neural-network model:</p>
<blockquote>
<p>The coding units are clusters of neurons organized in layers, or assemblies. A sensonmotor loop enables the network to sort the input cards according to several criteria (color, form, etc.). A higher-level assembly of rule-coding clusters codes for the currently tested rule, which shifts when negative reward is received. Internal testing of the possible rules, analogous to a reasoning process, also occurs, by means of an endogenous autoevaluation loop. When lesioned, the model reproduces the behavior of frontal lobe patients.</p>
</blockquote>
<p><a href="https://doi.org/10.1007/BF01108843" rel="nofollow noreferrer">Parks et al. (1992)</a> extended the previous neural models of WCST to take into account verbal-fluency.</p>
<p><a href="https://direct.mit.edu/jocn/article-abstract/12/3/505/3441/A-Computational-Model-of-Information-Processing-in" rel="nofollow noreferrer">Amos (2000)</a> built a neural-network model that helps distinguish between the sort of errors made by patients with schizophrenia, Parkinson's disease, and Huntington's disease. He relates the model to neuroanatomy and says:</p>
<blockquote>
<p>The model also made specific, empirically falsifiable predictions that can be used to explore the utility of these putative mechanisms of information processing in the frontal cortex and basal ganglia.</p>
</blockquote>
<p><a href="https://www.sciencedirect.com/science/article/abs/pii/S0893608000000587" rel="nofollow noreferrer">Monchi et al. (2000)</a> followed a similar approach as Amos. Their model suggested different impairments in Parkinson’s disease and schizophrenic patients, and made specific predictions of what would be observed in fMRI scans. They tested this prediction in their paper.</p>
<p>By 2005, the WCST had become a benchmark for more general models. <a href="https://www.pnas.org/doi/abs/10.1073/pnas.0502455102" rel="nofollow noreferrer">Rougier et al. (2005)</a> made a general model of the prefrontal cortex based on general neurobiological principles as opposed to symbolic approaches. They model learnt from experience and generalized to novel tasks. They tested it on WCST and the <a href="https://en.wikipedia.org/wiki/Stroop_effect" rel="nofollow noreferrer">Stroop task</a> data for typical and frontally damaged subjects.</p>
<p><a href="https://www.sciencedirect.com/science/article/abs/pii/S0893608005002285" rel="nofollow noreferrer">Kaplan et al. (2006)</a> use a neural-network approach with two parts: a Hopfield net serves as the working memory and a Hamming block as a hypothesis generator.</p>
<p><a href="https://www.sciencedirect.com/science/article/abs/pii/S0022249608001004" rel="nofollow noreferrer">Bishara et al. (2010)</a> use a more ACT-R-like (although not actual ACT-R) model with probabilistic loading on symbolic rules. The resulting sequential learning model is used to identify specific process that subjects might be struggling with. It is suppose to help diagnosis in a clinical setting, and they test it on substance dependent individuals.</p>
<p><a href="https://www.frontiersin.org/articles/10.3389/fncom.2010.00024/full" rel="nofollow noreferrer">Rigotti et al. (2010)</a> created a network of randomly connected neurons that could solve the task.  The key point was that the random connections induced mixed selectivity, which would solve the task with high probability.</p>

CC BY-SA 4.0
2012-06-18T14:31:28.187Z
2022-06-02T05:55:18.750Z
2022-06-02T05:55:18.750Z
Artem Kaznatcheev
<p>The <a href="http://en.wikipedia.org/wiki/Wisconsin_card_sort" rel="nofollow">Wisconsin Card Sorting</a> task is rather famous but appears to be quite difficult to model computationally.</p>

<p>I work in RL and I am interested in how people learn the optimal strategy. I'm interested in the task because it would allow a number of experimental manipulations. I want to capture the role of <em>memory</em> in the optimal strategy. People can keep track of some, but not very much, history of trials when doing the task. (I.e the mathematically optimal strategy seems too computationally heavy.)</p>

<ul>
<li>How could I approach modelling the Wisconsin Card Sorting task?</li>
<li>What other research has modelled the task?</li>
</ul>

CC BY-SA 3.0
2015-04-09T14:31:28.173Z
@JeromyAnglim Have these edits addressed the issue raised by Artem? If so, I believe this comment thread can be removed.
Steven Jeuris
CC BY-SA 3.0
2015-04-10T03:15:23.927Z
@StevenJeuris I assume if Artem edited it subsequently that it is fine now and these comments can be deleted.
Jeromy Anglim
CC BY-SA 3.0
2012-06-18T13:30:50.620Z
What paradigm are you working in, and why does the first result in Google Scholar ([Dehaene & Changeux, 1991](http://cercor.oxfordjournals.org/content/1/1/62.short)) not answer your question? Also, what is your reason for believing it is hard to model?
Artem Kaznatcheev
CC BY-SA 3.0
2012-06-18T16:14:56.263Z
For this, I work in RL and interested how people learn the optimal strategy. That is, I havent worked with this task before, but interested in it as it would allow a number of experimental manipulations. I find it hard, and didnt like Dehaene & Changeux (1991), because it doesnt seem to capture the role of _memory_ in the optimal strategy. People can keep track of some, but not very much, history of trials when doing the task. I.e the mathematically optimal strategy seems too computationally heavy, and I couldnt find anyone who developed a better model. Thank you so much for your response
user865
CC BY-SA 3.0
2012-06-18T18:50:43.893Z
next time you should include such details in your question (although editing it in now would be a pretty big change to the question). However, take a close look at Kaplan et al. (2006). Hopfield nets are pretty bad at remembering things, and so this approach might be what you are looking for.
Artem Kaznatcheev
CC BY-SA 3.0
2012-06-18T23:59:01.040Z
edited question to incorporate comments
Jeromy Anglim
CC BY-SA 3.0
2012-06-19T00:04:02.817Z
@JeromyAnglim the edits you made make part of my answer look silly, and are a significant change to the original question.
Artem Kaznatcheev
CC BY-SA 3.0
2012-06-19T00:10:00.987Z
Also @user865 I just skimmed through D&C91. How does it not capture the role of memory? That seems to be the exact thing they study, and they model memory as clusters of activation in a neural network and not as an arbitrary database that remembers everything.
Artem Kaznatcheev
CC BY-SA 3.0
2012-06-19T00:13:25.407Z
@ArtemKaznatcheev Sorry about that. I was trying to make the question as useful as possible for future readers. I edited it in line with the exchange in comments that you had with OP. I don't think future readers should have to scour through the comments to understand the question. I think in general where someone can be bothered, insight gained through comments should generally be edited into the question. The question was also looking like it was going to be closed. I have a preference to salvage. I've tweaked the edit a little bit to try to show the changes more clearly.
Jeromy Anglim
CC BY-SA 3.0
2012-06-19T00:15:29.123Z
@JeromyAnglim no problem, but I made some more edits, because I don't understand where the OP gets the idea that D&C don't model memory.
Artem Kaznatcheev
<reference-request><cognitive-modeling><computational-modeling>
How to computationally model the Wisconsin Card Sorting task?
user865
# How to computationally model the Wisconsin Card Sorting task?

(Asked by: username_0 on 2012-06-18)

The [Wisconsin Card Sorting](http://en.wikipedia.org/wiki/Wisconsin_card_sort) task is rather famous but appears to be quite difficult to model computationally.

I work in RL and I am interested in how people learn the optimal strategy. I'm interested in the task because it would allow a number of experimental manipulations. I want to capture the role of *memory* in the optimal strategy. People can keep track of some, but not very much, history of trials when doing the task. (I.e the mathematically optimal strategy seems too computationally heavy.)

* How could I approach modelling the Wisconsin Card Sorting task?
* What other research has modelled the task?

**Tags:** reference-request, cognitive-modeling, computational-modeling

> **Comments:**
> **username_3**: What paradigm are you working in, and why does the first result in Google Scholar ([Dehaene & Changeux, 1991](http://cercor.oxfordjournals.org/content/1/1/62.short)) not answer your question? Also, what is your reason for believing it is hard to model?  
> **username_0**: For this, I work in RL and interested how people learn the optimal strategy. That is, I havent worked with this task before, but interested in it as it would allow a number of experimental manipulations. I find it hard, and didnt like Dehaene & Changeux (1991), because it doesnt seem to capture the role of _memory_ in the optimal strategy. People can keep track of some, but not very much, history of trials when doing the task. I.e the mathematically optimal strategy seems too computationally heavy, and I couldnt find anyone who developed a better model. Thank you so much for your response  
> **username_3**: next time you should include such details in your question (although editing it in now would be a pretty big change to the question). However, take a close look at Kaplan et al. (2006). Hopfield nets are pretty bad at remembering things, and so this approach might be what you are looking for.  
> **username_2**: edited question to incorporate comments  
> **username_3**: @JeromyAnglim the edits you made make part of my answer look silly, and are a significant change to the original question.  
> **username_3**: Also @username_0 I just skimmed through D&C91. How does it not capture the role of memory? That seems to be the exact thing they study, and they model memory as clusters of activation in a neural network and not as an arbitrary database that remembers everything.  
> **username_2**: @ArtemKaznatcheev Sorry about that. I was trying to make the question as useful as possible for future readers. I edited it in line with the exchange in comments that you had with OP. I don't think future readers should have to scour through the comments to understand the question. I think in general where someone can be bothered, insight gained through comments should generally be edited into the question. The question was also looking like it was going to be closed. I have a preference to salvage. I've tweaked the edit a little bit to try to show the changes more clearly.  
> **username_3**: @JeromyAnglim no problem, but I made some more edits, because I don't understand where the OP gets the idea that D&C don't model memory.  
> **username_1**: @JeromyAnglim Have these edits addressed the issue raised by Artem? If so, I believe this comment thread can be removed.  
> **username_2**: @StevenJeuris I assume if Artem edited it subsequently that it is fine now and these comments can be deleted.  

---

## 2 answers

### Answer by username_3 on 2012-06-18. Score: 10 | Accepted answer

[Dehaene & Changeux (1991)](https://academic.oup.com/cercor/article-abstract/1/1/62/408917) made a neural-network model:

> The coding units are clusters of neurons organized in layers, or assemblies. A sensonmotor loop enables the network to sort the input cards according to several criteria (color, form, etc.). A higher-level assembly of rule-coding clusters codes for the currently tested rule, which shifts when negative reward is received. Internal testing of the possible rules, analogous to a reasoning process, also occurs, by means of an endogenous autoevaluation loop. When lesioned, the model reproduces the behavior of frontal lobe patients.

[Parks et al. (1992)](https://doi.org/10.1007/BF01108843) extended the previous neural models of WCST to take into account verbal-fluency.

[Amos (2000)](https://direct.mit.edu/jocn/article-abstract/12/3/505/3441/A-Computational-Model-of-Information-Processing-in) built a neural-network model that helps distinguish between the sort of errors made by patients with schizophrenia, Parkinson's disease, and Huntington's disease. He relates the model to neuroanatomy and says:

> The model also made specific, empirically falsifiable predictions that can be used to explore the utility of these putative mechanisms of information processing in the frontal cortex and basal ganglia.

[Monchi et al. (2000)](https://www.sciencedirect.com/science/article/abs/pii/S0893608000000587) followed a similar approach as Amos. Their model suggested different impairments in Parkinson’s disease and schizophrenic patients, and made specific predictions of what would be observed in fMRI scans. They tested this prediction in their paper.

By 2005, the WCST had become a benchmark for more general models. [Rougier et al. (2005)](https://www.pnas.org/doi/abs/10.1073/pnas.0502455102) made a general model of the prefrontal cortex based on general neurobiological principles as opposed to symbolic approaches. They model learnt from experience and generalized to novel tasks. They tested it on WCST and the [Stroop task](https://en.wikipedia.org/wiki/Stroop_effect) data for typical and frontally damaged subjects.

[Kaplan et al. (2006)](https://www.sciencedirect.com/science/article/abs/pii/S0893608005002285) use a neural-network approach with two parts: a Hopfield net serves as the working memory and a Hamming block as a hypothesis generator.

[Bishara et al. (2010)](https://www.sciencedirect.com/science/article/abs/pii/S0022249608001004) use a more ACT-R-like (although not actual ACT-R) model with probabilistic loading on symbolic rules. The resulting sequential learning model is used to identify specific process that subjects might be struggling with. It is suppose to help diagnosis in a clinical setting, and they test it on substance dependent individuals.

[Rigotti et al. (2010)](https://www.frontiersin.org/articles/10.3389/fncom.2010.00024/full) created a network of randomly connected neurons that could solve the task. The key point was that the random connections induced mixed selectivity, which would solve the task with high probability.


---

### Answer by username_4 on 2012-07-04. Score: 2

My colleagues have applied the COVIS model of category learning to the WCST. COVIS isn't a model of WCST performance per se, but can account for several known phenomena. See this Google Scholar search: <http://scholar.google.com/scholar?hl=en&q=Helie+Paul+ashby&btnG=&as_sdt=1%2C5&as_sdtp=>

> **Comments:**
> **username_2**: Welcome to the site. Are there any articles, in particular, you would recommend? Could you elaborate on how it accounts for known phenomena?  

---

