diff --git a/07_final_assignment/paper/main.tex b/07_final_assignment/paper/main.tex index 06d734f..e755ab4 100644 --- a/07_final_assignment/paper/main.tex +++ b/07_final_assignment/paper/main.tex @@ -10,7 +10,9 @@ \usepackage{color} \usepackage{todonotes} -\title{Simulation of Grainger et al. (2012) with Rescorla Wagner equations} + + +\title{Simulation of \cite{Grainger245} with Rescorla Wagner equations} \shorttitle{Grainger et al. (2012) simulation with RW equations} \author{Robert Geirhos (3827808), Klara Grethen (3899962), \\David-Elias Künstle (3822829), Felicia Saar (3818590)} \affiliation{Linguistics for Cognitive Science Course, University of Tübingen} @@ -60,7 +62,7 @@ It makes it possible to modulate saliency of a stimulus. A more salient stimulus could not only have higher learning rates but also a higher maximum activation. In the original experiment the stimulus were same colored words and nonwords with four letters on a equally colored background. We assume the single words and nonwords are equally salient and keep therefore $\lambda$ constant (1). \subsection{Running Parallelized Experiments} -Running an experiment with a single combination of $ \alpha $ and $ \beta $ on a normal desktop computer took about 75 minutes. Therefore, the parameter space one could explore within a reasonable amount of time was quite restricted. We decided to write a parallelized version of the code to reduce the overall runtime. Using the R packages foreach, parallel and doParallel \todo{(TODO: Cite them properly)}, we restructured the experiment. Since conflicts can easily occur when more than one core is trying to access a shared data structure at the same time, we implemented a parallelized version that is able to run without even containing critical sections. Instead, each thread has its own data structure, a .txt file, and in the end the results are harvested and combined. This version of the experiment ran on a cluster with 15 cores, each performing a total amount of eight experiments. Altogether, 120 combinations of $ \alpha $ and $ \beta $ were explored overnight, which would have taken about 150 hours in a non-parallelized version. +Running an experiment with a single combination of $ \alpha $ and $ \beta $ on a normal desktop computer took about 75 minutes. Therefore, the parameter space one could explore within a reasonable amount of time was quite restricted. We decided to write a parallelized version of the code to reduce the overall runtime. Using the R packages foreach \parencite{Rforeach}, parallel \parencite{Rparallel} and doParallel \parencite{RdoParallel}, restructured the experiment. Since conflicts can easily occur when more than one core is trying to access a shared data structure at the same time, we implemented a parallelized version that is able to run without even containing critical sections. Instead, each thread has its own data structure, a .txt file, and in the end the results are harvested and combined. This version of the experiment ran on a cluster with 15 cores, each performing a total amount of eight experiments. Altogether, 120 combinations of $ \alpha $ and $ \beta $ were explored overnight, which would have taken about 150 hours in a non-parallelized version. \section{Results} \todo{results} @@ -91,9 +93,14 @@ \section{Discussion} -\todo{"your conclusions about what is most likely to underlie the different success rates of the baboons"} +\todo{"your conclusions about what is most likely to underlie the different success rates of the baboons" In our simulation the influence of the random factor was a lot higher than of the learning rates. So the motivation of really trying to make a correct guess could result in different success rates.} + +\printbibliography{} \appendix + +\onecolumn + \section{Complete Results} Here are the complete results of our experiments. The abbreviations used are: \begin{APAitemize} @@ -105,15 +112,12 @@ \item NWAcc: Nonword accuracy \end{APAitemize} -\onecolumn \input{result_tables.tex} \lstinputlisting[language=R]{../baboonSimulation.R} -\printbibliography{} - \end{document} -%%% Local Variables: +%%% Local Variable: %%% mode: latex %%% TeX-master: t %%% End: diff --git a/07_final_assignment/paper/references.bib b/07_final_assignment/paper/references.bib index e69de29..c0108dc 100644 --- a/07_final_assignment/paper/references.bib +++ b/07_final_assignment/paper/references.bib @@ -0,0 +1,40 @@ +@article {Grainger245, + author = {Grainger, Jonathan and Dufau, St{\'e}phane and Montant, Marie and Ziegler, Johannes C. and Fagot, Jo{\"e}l}, + title = {Orthographic Processing in Baboons (Papio papio)}, + volume = {336}, + number = {6078}, + pages = {245--248}, + year = {2012}, + doi = {10.1126/science.1218152}, + publisher = {American Association for the Advancement of Science}, + abstract = {An orthographic object such as a set of letters, and the ability to recognize such sets as words, is a key component of reading. The ability to develop these skills has often been attributed to the prior acquisition of a complex language. For example, we learn how letters sound and thus recognize when a particular letter makes up part of a word. However, orthographic processing is also a visual process, because we learn to recognize words as discrete objects, and the ability to read may thus be related to an ability to recognize and classify objects. Grainger et al. (p. 245; see the Perspective by Platt and Adams) tested orthographic skills in baboons. Captive, but freely ranging, baboons were trained to distinguish real English words from combinations of similar letters that are not words, and they were able to distinguish real words with remarkable accuracy. Thus, a basic ability to recognize words as objects does not require complex linguistic understanding.Skilled readers use information about which letters are where in a word (orthographic information) in order to access the sounds and meanings of printed words. We asked whether efficient processing of orthographic information could be achieved in the absence of prior language knowledge. To do so, we trained baboons to discriminate English words from nonsense combinations of letters that resembled real words. The results revealed that the baboons were using orthographic information in order to efficiently discriminate words from letter strings that were not words. Our results demonstrate that basic orthographic processing skills can be acquired in the absence of preexisting linguistic representations.}, + issn = {0036-8075}, + URL = {http://science.sciencemag.org/content/336/6078/245}, + eprint = {http://science.sciencemag.org/content/336/6078/245.full.pdf}, + journal = {Science} +} + +@Manual{Rparallel, + title = {R: A Language and Environment for Statistical Computing}, + author = {{R Core Team}}, + organization = {R Foundation for Statistical Computing}, + address = {Vienna, Austria}, + year = {2015}, + url = {https://www.R-project.org/}, +} + +@Manual{Rforeach, + title = {foreach: Provides Foreach Looping Construct for R}, + author = {Revolution Analytics and Steve Weston}, + year = {2015}, + note = {R package version 1.4.3}, + url = {https://CRAN.R-project.org/package=foreach}, +} + +@Manual{RdoParallel, + title = {doParallel: Foreach Parallel Adaptor for the 'parallel' Package}, + author = {Revolution Analytics and Steve Weston}, + year = {2015}, + note = {R package version 1.0.10}, + url = {https://CRAN.R-project.org/package=doParallel}, +}