Responding Optimally with Unknown Sources of Evidence (ROUSE)

To run your own simulations, load these Matlab files onto your computer

• Files necessary for the Graphical User Interface (GUI) – these files are not well commented and should only be changed by an experienced Matlab user. Once all the files (including the ROUSE core routines below) are loaded onto any machine with Matlab and the appropriate folder is included in the Matlab path, then typing “sim_rouse” at a Matlab prompt will start the GUI.

• sim_rouse.mat (GUI layout file. Transfer in binary; right click on link and "save as")
• sim_rouse.m (GUI layout file)
• rouse_run.m (routine called when “Begin Simulation” is pressed – calls subsequent routines)
• load_rouse_params.m (loads parameter values from GUI)
• predicted_plot.m (displays a plot of the probability correct results)
• plot_log_odds.m (displays a plot of the log odds)

 

• ROUSE core routines – these three routines are all that is needed for a basic simulation of ROUSE. The main routine, “rouse.m” returns an array containing target (1’s) and foil (0’s) responses for the four basic priming conditions and the specified number of simulations given the parameter arguments. In addition, it returns a similar sized array of the separate target and foil log-likelihood values. This code is heavily commented. These routines are not “good Matlab code” in that they heavily rely on for loops. If rewritten using Matrix calculations (an example of such replacement code is give in “create_words.c”) they will run much faster. The use of for loops is intentional; this makes it easier for a user to rewrite these routines in similar languages such as Pascal, Fortran, and C/C++.

• rouse.m (main routine – initializes variables and loops through simulations)
• create_words.m (stochastically determines which features are shared)
• rouse_trial.m (stochastically determines feature activation and then tallies the log-likelihoods)

 

• 1000 times faster “mex” files – either loading “rouse.c” and compiling your own mex file or loading the mex file “rouse.dll” if you are running Matlab on a Windows PC will result in a replacement of the ROUSE core routines (there may be problems associated with just copying rouse.dll since this executable file is not properly registered; the mex command in Matlab does more than just compile). These C routines might result in a 1000 times speed up. I do not specify the process for compiling a mex file (it’s involved), although this information can be learned through the online manuals at http://www.mathworks.com/access/helpdesk/help/pdf_doc/matlab/api/apiguide.pdf. In particular chapter 2 will get you started. It will be necessary to change a couple of include commands at the start of "rouse.c" in order to specify the proper libraries (this will depend upon where exactly you have placed Matlab on your hard drive). In addition, it might be that “rouse.c” is not fully compatible with your particular C Compiler and needs some alteration. The code is not well commented, and contains several routines specific to passing variables back and forth between Matlab and C, and therefore users wishing to create their own versions of the core routines should start with the above Matlab routines, instead of these C routines.

• rouse.c (The passed and returned arguments are the same as “rouse.m”)
• rouse.dll (If you’re running Matlab on a Windows PC, this is a pre-compiled mex file. Instead of loading rouse.c or any of the three core routines, you can load this file. Transfer in binary; right click on link and "save as")

 

• Even faster analytic version of ROUSE ("mex" file) – see above about using mex files. "rouse_analytic.c" is well commented (but in general is harder to understand than the above stochastic versions of ROUSE). An analytic version of ROUSE is obtained by considering all possible combinations of ON, DISCOUNTED, and OFF features (i.e., a trinomial distribution). This routine represents a speed up of perhaps 100 times over even "rouse.c". In addition, this analytic version does not rely upon a random number generator and is therefore ideal for parameter estimation. A Matlab version of analytic ROUSE is not provided (and would be too slow because of Matlab's abhorrence of "for" loops). This routine returns different variables than "rouse.c"/"rouse.m", and therefore the calling function "rouse_run.m" is changed slightly. "rouse_analytic.c" returnrs probability distributions for the ON and DISCOUNTED features (but not the entire log-odds distributions). Therefore the "Plot Log Odds" button in the GUI will produce errors if pressed.

• rouse_analytic.c (analytic version of ROUSE with comments)
• rouse_analytic.dll (If you’re running Matlab on a Windows PC, this is a pre-compiled mex file)
• rouse_run_analytic.m (modified rouse_run function in order to accommodate the different passed and returned variables associated with rouse_analytic. The name of this file should be changed to "rouse_run.m" in order to integrate with the other GUI routines)