<p>You've described a very general type of study design which has probably been employed in hundreds of studies or more. Your question might profit from a more specific description of the type of study you're looking for.</p>

<p>A few examples that fit the bill just off the top of my head are:</p>

<p>Goldstone (1996) Isolated and interrelated concepts</p>

<p>Zaki and Homa (1999) Concepts and transformational knowledge</p>

<p>Kornell and Bjork (2008) Learning concepts and categories: is spacing the "enemy of induction"?</p>

CC BY-SA 3.0
2013-01-18T15:51:07.947Z
thank you baixiwei, i have edited my question, making it more specific. i will follow up on those references, but please let me know if the new format of the question brings different papers to mind.
vdv
CC BY-SA 3.0
2013-01-18T04:37:30.227Z
2013-01-18T04:37:30.227Z
baixiwei
<p>Of course, the type of data you use depends  lot on what your model purports to explain. The way i take your question, you are looking for straight up artificial classification learning experiments. Is this correct?</p>

<p>If so, John Kruschke has published several of his most important datasets <a href="http://www.indiana.edu/~kruschke/publications.html" rel="nofollow">on his website</a>.  I have often used these in my model testing. His '93 and '96 papers in connection science are especially notable.</p>

<p>Unfortunately, Nosofsky has not done the same thing, but you can find PDFs <a href="http://www.cogs.indiana.edu/nosofsky/" rel="nofollow">on his website</a>, many of which contain reports of data (i.e., tables) detailed enough to simulate. I'd especially look at his '86 paper in JEP General, but there is tons on his publications page and that's just the start...</p>

<p>Finally, one of my favorite, under-simulated, datasets was published in the '92 cogsci proceedings by Aha &amp; Goldstone. <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.44.2943&amp;rep=rep1&amp;type=pdf" rel="nofollow">PDF</a> here</p>

CC BY-SA 3.0
2016-05-28T16:08:25.410Z
2016-05-28T16:08:25.410Z
Nolan Conaway
<p>Bob Rehder's work may be of interest, for instance: </p>

<p>Rehder, B., &amp; Hoffman, A. B. (2005). Thirty-something categorization results explained: Selective attention, eyetracking, and models of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 811-829.</p>

<p>You will see some eye-tracking data on some classic results, but if you are after the so called <em>performance</em> aspects of the data, this will be reported also. </p>

<p>You might also find the following data interesting, they are working with discrete category dimensions and corrective feedback:</p>

<p>Blair, Mark R., et al. "Extremely selective attention: Eye-tracking studies of the dynamic allocation of attention to stimulus features in categorization." Journal of Experimental Psychology: Learning, Memory, and Cognition 35.5 (2009): 1196.</p>

CC BY-SA 3.0
2013-01-30T00:18:51.863Z
2013-01-30T00:18:51.863Z
skleene
<p>I am looking for <em>performance</em> results from <strong>categorization</strong> studies with discrete stimulus features and reinforcement feedback. I need something like this:</p>

<pre><code>stimulus  - correct response - performance after X trials
(1,1,1,0) -         0        -   20.1% correct
(1,1,0,1) -         1        -   23.3% correct
(1,0,0,0) -         1        -   82.0% correct
...
</code></pre>

<p>(e.g. <a href="http://dx.doi.org/10.1037/h0093825" rel="nofollow">Shepard, Hovland &amp; Jenkins, 1961</a>; <a href="http://dx.doi.org/10.1037/0033-295X.85.3.207" rel="nofollow">Medin &amp; Schaffer, 1978</a>)</p>

<p><sub>
Medin, D. L., &amp; Schaffer, M. M. (1978). <a href="http://dx.doi.org/10.1037/0033-295X.85.3.207" rel="nofollow">Context theory of classification learning</a>. Psychological Review; Psychological Review, 85(3), 207.<br>
Shepard, R. N., Hovland, C. I., &amp; Jenkins, H. M. (1961). <a href="http://dx.doi.org/10.1037/h0093825" rel="nofollow">Learning and memorization of classifications.</a> Psychological Monographs: General and Applied, 75(13), 1-42.
</sub></p>

CC BY-SA 3.0
2013-01-18T16:43:14.643Z
can you tell us what you're trying to do? as it is, the information you're requesting is very broad (who knows how each author defines 'performance'?) and the numbers in one study most likely are not comparable to any other.
Jeff
CC BY-SA 3.0
2013-01-19T00:22:08.787Z
Have you already done a forward search and looked at some of the articles that cite these two?
Artem Kaznatcheev
CC BY-SA 3.0
2013-01-21T22:32:08.857Z
Jeff: i have a computational model i'm trying to validate across as many studies as possible. by 'performance' i simply mean that i want to know how often correct/incorrect responses are given (rather than neural-responses, eye-data, etc).
vdv
CC BY-SA 3.0
2013-01-21T22:36:17.803Z
Artem: yes, i've done a forward search. the problem is that i get too many studies that do not follow the format described above. the signal-noise ratio is too low to be useful. any help here would be appreciated.
vdv
CC BY-SA 3.0
2013-01-28T10:49:34.507Z
@vdv, please incorporate any relevant extra information in your question as well. Comments should only be seen as 'temporary'. The feedback you gave can easily be edited into your question.
Steven Jeuris
<cognitive-psychology><memory><learning><experimental-psychology><data>
Categorization studies with a procedure similar to Shepard et al (1961) and Medin & Schaffer (1978)
vdv
# Categorization studies with a procedure similar to Shepard et al (1961) and Medin & Schaffer (1978)

(Asked by: username_0 on 2013-01-18)

I am looking for performance results from categorization studies with discrete stimulus features and reinforcement feedback. I need something like this:
stimulus  - correct response - performance after X trials
(1,1,1,0) -         0        -   20.1% correct
(1,1,0,1) -         1        -   23.3% correct
(1,0,0,0) -         1        -   82.0% correct
...

(e.g. Shepard, Hovland & Jenkins, 1961; Medin & Schaffer, 1978)

Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review; Psychological Review, 85(3), 207.
Shepard, R. N., Hovland, C. I., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75(13), 1-42.

**Tags:** cognitive-psychology, memory, learning, experimental-psychology, data

> **Comments:**
> **username_1**: can you tell us what you're trying to do? as it is, the information you're requesting is very broad (who knows how each author defines 'performance'?) and the numbers in one study most likely are not comparable to any other.  
> **username_2**: Have you already done a forward search and looked at some of the articles that cite these two?  
> **username_0**: username_1: i have a computational model i'm trying to validate across as many studies as possible. by 'performance' i simply mean that i want to know how often correct/incorrect responses are given (rather than neural-responses, eye-data, etc).  
> **username_0**: Artem: yes, i've done a forward search. the problem is that i get too many studies that do not follow the format described above. the signal-noise ratio is too low to be useful. any help here would be appreciated.  
> **username_3**: @username_0, please incorporate any relevant extra information in your question as well. Comments should only be seen as 'temporary'. The feedback you gave can easily be edited into your question.  

---

## 3 answers

### Answer by username_4 on 2013-01-18. Score: 5

You've described a very general type of study design which has probably been employed in hundreds of studies or more. Your question might profit from a more specific description of the type of study you're looking for.

A few examples that fit the bill just off the top of my head are:

Goldstone (1996) Isolated and interrelated concepts

Zaki and Homa (1999) Concepts and transformational knowledge

Kornell and Bjork (2008) Learning concepts and categories: is spacing the "enemy of induction"?

> **Comments:**
> **username_0**: thank you username_4, i have edited my question, making it more specific. i will follow up on those references, but please let me know if the new format of the question brings different papers to mind.  

---

### Answer by username_5 on 2016-05-28. Score: 3

Of course, the type of data you use depends lot on what your model purports to explain. The way i take your question, you are looking for straight up artificial classification learning experiments. Is this correct?

If so, John Kruschke has published several of his most important datasets [on his website](http://www.indiana.edu/~kruschke/publications.html). I have often used these in my model testing. His '93 and '96 papers in connection science are especially notable.

Unfortunately, Nosofsky has not done the same thing, but you can find PDFs [on his website](http://www.cogs.indiana.edu/nosofsky/), many of which contain reports of data (i.e., tables) detailed enough to simulate. I'd especially look at his '86 paper in JEP General, but there is tons on his publications page and that's just the start...

Finally, one of my favorite, under-simulated, datasets was published in the '92 cogsci proceedings by Aha & Goldstone. [PDF](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.44.2943&rep=rep1&type=pdf) here


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### Answer by username_6 on 2013-01-30. Score: 2

Bob Rehder's work may be of interest, for instance:

Rehder, B., & Hoffman, A. B. (2005). Thirty-something categorization results explained: Selective attention, eyetracking, and models of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 811-829.

You will see some eye-tracking data on some classic results, but if you are after the so called *performance* aspects of the data, this will be reported also.

You might also find the following data interesting, they are working with discrete category dimensions and corrective feedback:

Blair, Mark R., et al. "Extremely selective attention: Eye-tracking studies of the dynamic allocation of attention to stimulus features in categorization." Journal of Experimental Psychology: Learning, Memory, and Cognition 35.5 (2009): 1196.


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