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Back to [[Psych221-Projects-2013 |Psych 221 Projects 2013]]
Back to [[Psych221-Projects-2013 |Psych 221 Projects 2013]]


 
For my project, I chose to create a Wikipedia page on the von Kries coefficient law.


<br>
<br>
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= Background =
= Background =


You can use subsections if you like.
I chose this project because I thought that this would give me the opportunity to go very in-depth into a topic, become more well-versed in literature reviews, and learn how to write academically to a wide audience. I decided to write on the von Kries coefficient law because I had found the chromatic adaptation section the most interesting in class, and wanted to expand on my knowledge in this part of the class.
Below is an example of a retinotopic map.  Or, to be precise, below ''will'' be an example of a retinotopic map once the image is uploaded. To add an image, simply put text like this inside double brackets 'MyFile.jpg | My figure caption'. When you save this text and click on the link, the wiki will ask you for the figure.
<br>
[[File:Example.jpg | Figure 1]]
 
Below is another example of a reinotopic map in a different subject.
<br>
[[File:Example2.jpg | Figure 2]]
 
Once you upload the images, they look like this. Note that you can control many features of the images, like whether to show a thumbnail, and the display resolution.
[[File:Example3.jpg |thumb|300px|center| Figure 3]]
<br>
 
== MNI space ==


MNI is an abbreviation for [http://en.wikipedia.org/wiki/Montreal_Neurological_Institute Montreal Neurological Institute].


= Methods =
= Methods =
== Measuring retinotopic maps ==
== Literature Review ==
Retinotopic maps were obtained in 5 subjects using Population Receptive Field mapping methods [http://white.stanford.edu/~brian/papers/mri/2007-Dumoulin-NI.pdf Dumoulin and Wandell (2008)]. These data were collected for another [http://www.journalofvision.org/9/8/768/ research project] in the Wandell lab. We re-analyzed the data for this project, as described below. 
This was one of the most extensive parts of the process. I read several books, papers, and journals on or related to the von Kries coefficient law, its history, applications, and arguments for/against it. I sifted through all of the sources to find the ones that I felt would be most relevant to the general population, that added new and useful information, and that would fit in well with the overall vision of the Wikipedia page.
 
=== Subjects ===
Subjects were 5 healthy volunteers.
 
=== MR acquisition ===
Data were obtained on a GE scanner. Et cetera.
 
=== MR Analysis ===
The MR data was analyzed using [http://white.stanford.edu/newlm/index.php/MrVista mrVista] software tools.  


==== Pre-processing ====
=== Structure ===
All data were slice-time corrected, motion corrected, and repeated scans were averaged together to create a single average scan for each subject. Et cetera.
I then decided how to structure the page so that it flowed nicely and looked appealing to the reader. I decided that it would be important and relevant to include the prior history of the law so that people could see how it came about; I also included an equation section so that the reader could visually see what the law was explaining. I also felt that the evaluation and application section was important to include; this is where much of the information from journals and papers showed up. I felt this part was most interesting, because it shows the continual learning process of academia; even if a law has been around and used for more than 100 years, it does not mean that it is flawless; people will have differing opinions based on how they conduct their research and what they choose is relevant.  
 
==== PRF model fits ====
PRF models were fit with a 2-gaussian model.
 
==== MNI space ====
After a pRF model was solved for each subject, the model was trasnformed into MNI template space. This was done by first aligning the high resolution t1-weighted anatomical scan from each subject to an MNI template. Since the pRF model was coregistered to the t1-anatomical scan, the same alignment matrix could then be applied to the pRF model. <br>
Once each pRF model was aligned to MNI space, 4 model parameters - x, y, sigma, and r^2 - were averaged across each of the 6 subjects  in each voxel.
 
Et cetera.


== Sourcing ==
I included the sources as I wrote the paper; the majority of my Wikipedia article is an assembly of different pieces of information found from different papers. I tried to find a balance between including a lot of sources and having my paper too bogged down by citations.


= Results - What you found =
= Results - What you found =


== Retinotopic models in native space ==
The wikipedia article is currently under review:
Some text. Some analysis. Some figures.


== Retinotopic models in individual subjects transformed into MNI space ==
http://en.wikipedia.org/wiki/Wikipedia_talk:Articles_for_creation/von_Kries_Coefficient_Law
Some text. Some analysis. Some figures.
 
== Retinotopic models in group-averaged data on the MNI template brain ==
Some text. Some analysis. Some figures. Maybe some equations.
 
 
=== Equations===
If you want to use equations, you can use the same formats that are use on wikipedia. <br>
''See wikimedia help on  [http://meta.wikimedia.org/wiki/Help:Displaying_a_formula formulas] for help.'' <br>
This example of equation use is copied and pasted from [http://en.wikipedia.org/wiki/Discrete_Fourier_transform wikipedia's article on the DFT].
 
The [[sequence]] of ''N'' [[complex number]]s ''x''<sub>0</sub>, ..., ''x''<sub>''N''−1</sub> is transformed into the  sequence of ''N'' complex numbers ''X''<sub>0</sub>, ..., ''X''<sub>''N''−1</sub> by the DFT according to the formula:
 
:<math>X_k = \sum_{n=0}^{N-1} x_n e^{-\frac{2 \pi i}{N} k n} \quad \quad k = 0, \dots, N-1</math> 
           
where i is the imaginary unit and <math>e^{\frac{2 \pi i}{N}}</math>  is a primitive N'th [[root of unity]]. (This expression can also be written in terms of a [[DFT matrix]]; when scaled appropriately it becomes a [[unitary matrix]] and the ''X''<sub>''k''</sub> can thus be viewed as coefficients of ''x'' in an [[orthonormal basis]].)
 
The transform is sometimes denoted by the symbol <math>\mathcal{F}</math>, as in <math>\mathbf{X} = \mathcal{F} \left \{ \mathbf{x} \right \} </math> or <math>\mathcal{F} \left ( \mathbf{x} \right )</math> or <math>\mathcal{F} \mathbf{x}</math>. 
 
The '''inverse discrete Fourier transform (IDFT)''' is given by
 
:<math>x_n = \frac{1}{N} \sum_{k=0}^{N-1} X_k e^{\frac{2\pi i}{N} k n} \quad \quad n = 0,\dots,N-1.</math>
 
== Retinotopic models in group-averaged data projected back into native space ==
Some text. Some analysis. Some figures.




= Conclusions =
= Conclusions =


Here is where you say what your results mean.
In this project, I learned an extensive amount of the von Kries coefficient law and, especially, on how to read journals and papers. I learned how to select relevant information from the papers and assemble them into a format that is open to the general public. I also learned how to write in a dense, consise manner, and got extensive experience in citing sources. This was a very enlightening experience, and I hope that I wrote a Wikipedia article that can be useful to someone in the future.  


= References - Resources and related work =
= References - Resources and related work =


References
(included in Wikipedia page)
 
Software
 
= Appendix I - Code and Data =
 
==Code==
[[File:CodeFile.zip]]
 
==Data==
[[File:DataFile.zip | zip file with my data]]
 
= Appendix II - Work partition (if a group project) =
Brian and Bob gave the lectures. Jon mucked around on the wiki.

Revision as of 09:48, 20 March 2013

Back to Psych 221 Projects 2013

For my project, I chose to create a Wikipedia page on the von Kries coefficient law.



Background

I chose this project because I thought that this would give me the opportunity to go very in-depth into a topic, become more well-versed in literature reviews, and learn how to write academically to a wide audience. I decided to write on the von Kries coefficient law because I had found the chromatic adaptation section the most interesting in class, and wanted to expand on my knowledge in this part of the class.


Methods

Literature Review

This was one of the most extensive parts of the process. I read several books, papers, and journals on or related to the von Kries coefficient law, its history, applications, and arguments for/against it. I sifted through all of the sources to find the ones that I felt would be most relevant to the general population, that added new and useful information, and that would fit in well with the overall vision of the Wikipedia page.

Structure

I then decided how to structure the page so that it flowed nicely and looked appealing to the reader. I decided that it would be important and relevant to include the prior history of the law so that people could see how it came about; I also included an equation section so that the reader could visually see what the law was explaining. I also felt that the evaluation and application section was important to include; this is where much of the information from journals and papers showed up. I felt this part was most interesting, because it shows the continual learning process of academia; even if a law has been around and used for more than 100 years, it does not mean that it is flawless; people will have differing opinions based on how they conduct their research and what they choose is relevant.

Sourcing

I included the sources as I wrote the paper; the majority of my Wikipedia article is an assembly of different pieces of information found from different papers. I tried to find a balance between including a lot of sources and having my paper too bogged down by citations.

Results - What you found

The wikipedia article is currently under review:

http://en.wikipedia.org/wiki/Wikipedia_talk:Articles_for_creation/von_Kries_Coefficient_Law


Conclusions

In this project, I learned an extensive amount of the von Kries coefficient law and, especially, on how to read journals and papers. I learned how to select relevant information from the papers and assemble them into a format that is open to the general public. I also learned how to write in a dense, consise manner, and got extensive experience in citing sources. This was a very enlightening experience, and I hope that I wrote a Wikipedia article that can be useful to someone in the future.

References - Resources and related work

(included in Wikipedia page)

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