Create an auditory discrimination task where the user discriminates between tones of different frequencies in matlab.

• The program should first ask for the id of the user.
• 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. [If you are working on the auditory experiment and you are running out of time, don't spend too much time trying to get the details of the sounds exactly right. We will deduct points for sound stimuli that are coded incorrectly, but it is more important that you program the overall structure of the experiment correctly].
• The user should receive feedback after each trial (correct or incorrect)
• After n trials are completed, calculate the following
  1. The probability of a hit (i.e. probability that a user reports a signal when the stimulus was sampled from the signal distribution)
  2. 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).
  3. Sensitivity (d) and the bias (beta) index from Signal Detection Theory 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 on the command line (the video clips above didn't show the prob of hit and false alarm but please do include those)
• 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. 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.