<p><strong>Short answer</strong><br>
In psychophysical tests, often %correct rates are determined. Hence, training effects are often measured by determining correct rates. The ultimate outcome measures can be wildly variable, as they are dependent on the physical characteristics of the stimulus (visual, auditory, tactile, gustatory <em>etc</em>).</p>

<p><strong>Background</strong><br>
Learning curves can be measured by measuring the performance on a certain task.</p>

<p>From what I understand of your question you are:</p>

<blockquote>
  <p>...trying to quantify trends in learning for <em>certain mediums</em> of audio-visual communication...  </p>
</blockquote>

<p>and you are looking for</p>

<blockquote>
  <p>What one measure...success rate over time and that's it? Or what?</p>
</blockquote>

<p>Taking a personal vantage point here, I have measured learning effects using various auditory, tactile and visual <strong><a href="http://psychology.jrank.org/pages/514/Psychophysics.html" rel="nofollow noreferrer">psychophysical</a></strong> tests (though not a combination of them like an audio-visual test as you are planning). I will provide a few tests I have done so far to look at training effects and I will provide some basic background information on psychophysics. Please following the links if you wish to learn more on specific subjects. I have measured the following, among others:</p>

<ul>
<li><strong>Speech understanding</strong> by measuring the <strong><a href="http://emedicine.medscape.com/article/1822962-overview" rel="nofollow noreferrer">speech-recognition threshold (SRT)</a></strong> in noise using the Dutch Matrix test <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4871208/" rel="nofollow noreferrer">(Houben &amp; Dreschler, 2015)</a>. The SRT basically shows you the signal-to-noise ratio where speech understanding is 50% correct. In other words, it shows how much noise a listener can handle to still understand 50% of the words in the sentences heard. I've performed this test for 12 times over four several sessions and <strong>within</strong> as well as <strong>between-session</strong> learning effects were observed, and also within-run training effects (unpublished observations); </li>
<li>Vibro-tactile <strong><a href="http://www.indiana.edu/~p1013447/dictionary/thresh.htm" rel="nofollow noreferrer">detection threshold</a></strong>. Basically we asked the subject to answer in a <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/11293457" rel="nofollow noreferrer">yes/no task</a></strong> if they felt a stimulus and the outcome measure was that <strong>stimulus level where the correct rate was 50%</strong>. There was no learning effect observed within and between sessions. A within-run training effect was observed, which may have been due to <strong><a href="https://en.wikipedia.org/wiki/Procedural_memory" rel="nofollow noreferrer">procedural training effects</a></strong> (unpublished observations);</li>
<li>Tactile spatial acuity using (<strong><a href="https://en.wikipedia.org/wiki/Two-point_discrimination" rel="nofollow noreferrer">2-point discrimination</a></strong>); here a person was asked to answer whether one or two stimuli were felt and then, again, a <strong>percent-correct rate</strong> (here: 62.5%) score was determined ultimately expressed as that <strong>distance where correct rate was 62.5%</strong>. No training effects other than procedural learning were observed <a href="https://www.ncbi.nlm.nih.gov/pubmed/28569046" rel="nofollow noreferrer">(Stronks <em>et al</em>, 2017)</a>;</li>
<li>A vibrotactile <strong><a href="http://apps.usd.edu/coglab/WebersLaw.html" rel="nofollow noreferrer">intensity-difference (JND) task</a></strong>, where the subject was asked to indicate whether they could feel the difference in intensity of two stimuli. Again the correct rates were measured and expressed as that intensity where the %correct scores equaled a certain threshold <a href="https://www.ncbi.nlm.nih.gov/pubmed/28569046" rel="nofollow noreferrer">(Stronks <em>et al</em>, 2017)</a>.</li>
<li><strong>Visual acuity</strong> was measured with a <strong><a href="http://www.michaelbach.de/fract/index.html" rel="nofollow noreferrer">grating task</a></strong> - again percent correct is measured, but there the outcome is visual acuity, namely an <strong>angle of resolution</strong> where the %correct rate exceeds a certain threshold. There was procedural learning observed (unpublished observations). </li>
</ul>

<p>Note that most of the above tasks were <strong><a href="http://www.cis.rit.edu/people/faculty/montag/vandplite/pages/chap_4/ch4p5.html" rel="nofollow noreferrer">alternative forced choice (AFC) tasks</a></strong>, where the threshold (%correct) is dependent on the number of choices.</p>

<p><sub><strong>References</strong><br>
<strong>-</strong> <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4871208/" rel="nofollow noreferrer">Houben &amp; Dreschler, <em>Trends Hear</em> (2015); <strong>11</strong>(19): 1-10</a><br>
<strong>-</strong> <a href="https://www.ncbi.nlm.nih.gov/pubmed/28569046" rel="nofollow noreferrer">Stronks <em>et al</em>, <em>Artif Organs</em> (2017); in press</a></sub>  </p>

CC BY-SA 3.0
2017-07-09T21:02:44.930Z
2017-08-03T05:59:31.047Z
2017-08-03T05:59:31.047Z
AliceD
<p>Specifically, I am trying to quantify trends in learning for certain mediums of audio-visual communication, and what I've gathered so far suggests that there are 4 distinguishable types, being linear, logarithmic (or possibly asymptotic), exponential and ogive. How can I go from peoples qualitative observations to an actual mathematical function to show these patterns for a specific medium? Would one measure...success rate over time and that's it? Or what?</p>

<experimental-psychology><statistics><experiment-design>
What variables allow one to empirically and scientifically quantify trends for learning curves?
RayOfHope
# What variables allow one to empirically and scientifically quantify trends for learning curves?

(Asked by: username_0 on 2017-07-09)

Specifically, I am trying to quantify trends in learning for certain mediums of audio-visual communication, and what I've gathered so far suggests that there are 4 distinguishable types, being linear, logarithmic (or possibly asymptotic), exponential and ogive. How can I go from peoples qualitative observations to an actual mathematical function to show these patterns for a specific medium? Would one measure...success rate over time and that's it? Or what?

**Tags:** experimental-psychology, statistics, experiment-design


---

## 1 answers

### Answer by username_1 on 2017-07-09. Score: 5 | Accepted answer

**Short answer**  
In psychophysical tests, often %correct rates are determined. Hence, training effects are often measured by determining correct rates. The ultimate outcome measures can be wildly variable, as they are dependent on the physical characteristics of the stimulus (visual, auditory, tactile, gustatory *etc*).

**Background**  
Learning curves can be measured by measuring the performance on a certain task.

From what I understand of your question you are:

> ...trying to quantify trends in learning for *certain mediums* of audio-visual communication...

and you are looking for

> What one measure...success rate over time and that's it? Or what?

Taking a personal vantage point here, I have measured learning effects using various auditory, tactile and visual **[psychophysical](http://psychology.jrank.org/pages/514/Psychophysics.html)** tests (though not a combination of them like an audio-visual test as you are planning). I will provide a few tests I have done so far to look at training effects and I will provide some basic background information on psychophysics. Please following the links if you wish to learn more on specific subjects. I have measured the following, among others:

* **Speech understanding** by measuring the **[speech-recognition threshold (SRT)](http://emedicine.medscape.com/article/1822962-overview)** in noise using the Dutch Matrix test [(Houben & Dreschler, 2015)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4871208/). The SRT basically shows you the signal-to-noise ratio where speech understanding is 50% correct. In other words, it shows how much noise a listener can handle to still understand 50% of the words in the sentences heard. I've performed this test for 12 times over four several sessions and **within** as well as **between-session** learning effects were observed, and also within-run training effects (unpublished observations);
* Vibro-tactile **[detection threshold](http://www.indiana.edu/~p1013447/dictionary/thresh.htm)**. Basically we asked the subject to answer in a **[yes/no task](https://www.ncbi.nlm.nih.gov/pubmed/11293457)** if they felt a stimulus and the outcome measure was that **stimulus level where the correct rate was 50%**. There was no learning effect observed within and between sessions. A within-run training effect was observed, which may have been due to **[procedural training effects](https://en.wikipedia.org/wiki/Procedural_memory)** (unpublished observations);
* Tactile spatial acuity using (**[2-point discrimination](https://en.wikipedia.org/wiki/Two-point_discrimination)**); here a person was asked to answer whether one or two stimuli were felt and then, again, a **percent-correct rate** (here: 62.5%) score was determined ultimately expressed as that **distance where correct rate was 62.5%**. No training effects other than procedural learning were observed [(Stronks *et al*, 2017)](https://www.ncbi.nlm.nih.gov/pubmed/28569046);
* A vibrotactile **[intensity-difference (JND) task](http://apps.usd.edu/coglab/WebersLaw.html)**, where the subject was asked to indicate whether they could feel the difference in intensity of two stimuli. Again the correct rates were measured and expressed as that intensity where the %correct scores equaled a certain threshold [(Stronks *et al*, 2017)](https://www.ncbi.nlm.nih.gov/pubmed/28569046).
* **Visual acuity** was measured with a **[grating task](http://www.michaelbach.de/fract/index.html)** - again percent correct is measured, but there the outcome is visual acuity, namely an **angle of resolution** where the %correct rate exceeds a certain threshold. There was procedural learning observed (unpublished observations).

Note that most of the above tasks were **[alternative forced choice (AFC) tasks](http://www.cis.rit.edu/people/faculty/montag/vandplite/pages/chap_4/ch4p5.html)**, where the threshold (%correct) is dependent on the number of choices.

**References**  
**-** [Houben & Dreschler, *Trends Hear* (2015); **11**(19): 1-10](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4871208/)  
**-** [Stronks *et al*, *Artif Organs* (2017); in press](https://www.ncbi.nlm.nih.gov/pubmed/28569046)


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