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The program should ask for the number of trials n in the discrimination task. The number n should be an even number.
The program should randomly allocate n/2 signal events and n/2 noise events to the n trials
The randomization should be the same for each user id. In other words, if we restart the program with the same user id, we should get the same sequence of trials (i.e., make sure you are setting the random seed)
On each trial, a signal or noise event should be presented (sound or image) and the user has to respond with a signal or noise decision using letters or numbers as responses. You do not have to implement a graphical user interface for the task. Simply use the command window to present feedback and receive user input from the keyboard.
The user should receive feedback after each trial (correct or incorrect)
After n trials are completed, calculate the following
The probability of a hit (i.e. probability that a user reports a signal when the stimulus was sampled from the signal distribution)
The probability of a false alarm (i.e., probability that a user reports a signal when the stimulus was sampled from the noise/standard distribution).
Sensitivity (d’) and the bias (beta) index from Signal Detection Theory as described on p. 183 (Ch. 7). There is a problem calculating d’ when the observed hit or false alarm rates equal 0 or 1. There are several elegant solutions to this problem (including Bayesian analysis) but we will not pursue those here. One ad-hoc solution you can use is to replace the empirical hit or false alarm rates of 0 or 1 with fixed values of 0.01 and .99 respectively.
Show these performance numbers to the user
The data should be saved to a .mat Matlab file. The filename should be constructed in such a way that it is unique. The filename should have the user id in the file. Optionally, you can also put some timestamp in the name. You should save all the data that is need to reconstruct what happened in the discrimination task including all individual decisions. Also save the signal detection indices calculated at the end of the task.
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