Assignment 1: Introduction to Matlab
*** Due date: Start of class in Week 2 ***
*** IMPORTANT *** : Installing Matlab can take a while and be somewhat tricky. Start EARLY and speak to the TA (Jack Morgan Mizell firstname.lastname@example.org) if you have any problems.
Part 1: Download and install Matlab (0 points, but essential for the class!)
Instructions and links are here. Note that you do NOT have to pay for Matlab if you are enrolled at the University of Arizona! If you are asked for credit card information don't give it! Instead reach out to one of us for help.
Part 2: Setup Dropbox on your computer (0 points, but also essential for the class!)
If you don't have it already, download and install from
- Make a folder called NSCS344_YOURNAME
- Share it with Jack Morgan (email@example.com)
- Share it with me (firstname.lastname@example.org, note my dropbox account uses my Gmail account not Arizona account).
Part 3: Implement the model (8 points)
Note: The code for this is all there in this week's notes (on D2L). In addition, the videos on D2L walk you through all the first two parts of the Assignment in detail.
- Make a folder inside your Dropbox folder called "Assignment_01"
- Make a script in Matlab (call it, for example, Script_01) that makes one decision in the dots task and save it into the Assignment_01 folder
Part 4: Go beyond! (2 points)
- Go beyond what's covered in the course material for this week and change the parameters of the model. Copy and paste your code into a second script (save it in your Dropbox folder as Script_02) and change the values of D and f and T.
- Add comments in your code to describe what happens when you change these variables. How do you think accuracy changes on average as D increases? As f increases? As T increases?
Part 5: Go way beyond! (2 extra credit points)
This is a totally optional extra credit item intended to stretch people with prior coding knowledge. But there's nothing here that requires knowledge beyond what we've covered this week so coding novices can do it just as well.
- Make a new script (call it Script_03) and change the model in some way. For example, how would you model a "forgetting" process in which people forget about dots in the past? Or how would you add randomness to the decision? Or try to imagine a different mechanism for making the decision altogether, maybe a "jump to conclusions" model that bases its choice on the first dot it sees ... Only one of these things is enough to get the extra credit.