Sound Threshold Experiment

Introduction

One of the most basic measurements in perception is the measurement of threshold. The threshold measures how strong a stimulus must be before it can be detected or how different two stimuli must be before they can be discriminated from one another. Thresholds are of interest for several reasons. Threshold measurements are often used to reason about the nature of sensory processing. The goal of our third experiment is to measure thresholds for detecting tones. We will use the method of adjustment and/or a forced choiced procedure. In the method of adjustment, the subject adjusts the strength of the test stimulus until he or she is just able to detect it. In this sense, the method is subjective. In the forced choice procedure, the subject must identify which of two intervals contains the test stimulus on each trial. We will go through the material in this handout in class, but the information below should serve as a reminder.

Setting Up

You will need to get the materials onto your hard disk. You do so in the usual way, copying the SoundThresh folder from the 0044 folder to your U: disk partition. Before running the experiment, check to be sure the computer’s sound volume and settings are set properly. The default setting for the sound sampling rate on these computes has sometimes caused extraneous clicks to be audible during the stimuli. If this happens, get to the sound setting (e.g., by right-clicking the little speaker icon) and set the sampling rate to 48000 Hz/DVD quality or some such. Next, left-click on the speaker icon in the lower right part of the screen. The following window (or one something like it) will appear:

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The left-most slider controls the overall system volume, adjust it about 1/3 of the way up from the bottom (i.e. lower than shown above) and make sure the mute box at the bottom is not checked.

Volume and Safety

Once the computer’s sound volume control has been set, there are still two ways to control the volume level: while the program is running, you can adjust the volume of the test tone by using the up and down arrows; if the masker is too loud, you can adjust its volume by exiting the program (type q), then changing the “maskerAmpDB” parameter in the text of the program; now re-run the program by typing selecting the RUN option from the TOOLS menu. I do not think it is possible to produce unsafe sound levels using our program and the earphones we provide. Still, it is always wise to check the volume before putting the earphones on. Simply put them near your ear (about 2” away) and play a few tones. If it seems too loud, turn the volume down. The experiment will work just as well or better with moderate sound volumes. What I suggest is that you run the program first, and it will tell you the maximum masker amplitude you can use. Then, quit the program, and set that maximum masker amplitude. Run the program again, and adjust the volume so that the maximum masker is reasonably loud, but neither painful nor potentially damaging. Copy that volume setting down, and each time your return to class, make sure the volume is reset to the same value.

Running the experiment

1) Once you have copied the materials and set the path (using the path browser), you can run the experiment by typing SoundThresh in the command window. The following message should appear.

Creating masker Creating test Ready to go, type ? for help

If you type ?, you will see a menu of other options:

<space>: play a trial 2: decrease test intensity 8: increase test intensity 4: coarser adjustments 6: finer adjustments f: forced choice experiment d: guess first interval k: guess second interval c: see current intensity t: play test alone m: play masker alone s: set test intensity q: quit ?: display this menu

Each option is available to you by typing the appropriate key.

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2) For setting thresholds by adjustment, you will be playing trials and deciding whether to increase or decrease the intensity of the test tone. You play a trial by typing the space bar. The program then plays two separate sounds in two separate intervals. One interval contains the test tone plus some noise. The other interval contains just the noise. Which interval contains the target is chosen randomly on each trial. Sometimes it is the first and sometimes it is the second. We call the noise the masker. The masker is played in both intervals. If you can tell the two intervals apart, then you are able to detect the test tone and we say that it is above threshold. If you cannot tell the two intervals apart, then you are not able to detect the test tone and we say that it is below threshold. Your task is to adjust the intensity of the target tone until it is just at threshold. 3) Sometimes, it is difficult to be sure whether you correctly identified the interval with the target. You can use the program to double check. If you think the target was in the first interval on the previous trial, type d. The program will print Correct or Wrong accordingly. Similarly, if you think the target was in the second interval, type k. 4) You use the number keys to adjust the intensity of the target tone. The 8 key increases the intensity of the target on the next trial, the 2 key decreases the intensity. You may play multiple trials before adjusting the intensity. You may adjust the initial intensity by changing the parameter “initialTestAmpDB” in the SoundThresh.m program and then re-running the script. If the target tone is clearly audible over the masking noise at the initial intensity, you may find that decreasing the test intensity by typing the down arrow a few times doesn’t seem to change the intensity much each time you hit the key. You can adjust the step size of the decrease by using the 4 and 6 arrows. Use the 6 key to make larger changes, so that you get in the vicinity of your threshold faster, and then when the target tone starts being hard to hear over the masking noise, use the 4 key to decrease the step size again to help you find your threshold more precisely. 5) If you wish to hear the target or masker alone, you may type t or m to do so. 6) When you have adjusted the intensity of the target to the threshold, type q and the program will print your threshold intensity in dB.

7) You can use the f key to run a set of forced-choice trials. These will find your 82% correct threshold by using an adaptive method, which we will talk about in class. The forced-choice method usually gives more reliable thresholds than the adjustment method, but it takes a little getting used to.

Saving your data

To get your threshold after adjustments, just type c. It will print the current test intensity. The forced-choice procedure types out your threshold at the end of a run. Type it into Excel. Or just write it down on a piece of paper. It is not copied to the clipboard, but the raw data and fit psychometric function are saved into a file.

Finding the threshold

Adjustment You will want to take the average of several adjustment runs to find the threshold. Compute the standard error of the mean in the usual manner. The method of adjustment turns out to be quite sensitive to whether or not the observer obtains feedback about error during the experiment (in our case, by using the d and k keys to find out whether your guesses are right or

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wrong). For example, you might return to a particular condition for which you have already determined your threshold, and find that you can now near the target tone at a lower dB setting than last time. You might also find that you can’t hear the target at all at the threshold sound level you determined last time. This might be due to small, moment-to-moment fluctuations in the state of your nervous system, but it’s more likely that you have simply shifted your criterion for when you are willing to say that you detected the tone. Feedback about error often has the effect of decreasing the value of the threshold, because it can induce the observer to try harder. In order to obtain stable estimates of your threshold, then, it is quite important that you maintain a stable criterion for when you are willing to say you heard the target tone. Be forewarned, however! You may be surprised how hard this is to do. Because we don’t care too much about obtaining the lowest possible thresholds, you may want to emphasize consistency instead, and not make use of the d and k keys to see whether your guesses are right or wrong. Set a criterion for yourself something like: “I’m about 90% positive I heard the tone”, and stick with it.

Forced choice

The key to the forced choice procedure is concentration. If you make a careless mistake, it will produce some inaccuracy in the data. The other thing you need to concentrate on is which of the two tones is the target. Sometimes you can tell them apart but not identify the target. But if you pay attention, you will find you get good at this. It is a good idea to start the forced choice procedure at a level that is a bit above threshold. This will give you a few easy trials before things get hard.

As noted, you start a forced-choice experiment by typing f. This runs a fancy staircase procedure called “Quest” that chooses which levels to test on each trial based on how well the observer did on previous trials in this block. It starts at the level initalTestAmpDB (see code below). The setting of this value is quite important, because the program will only test levels within 50 dB of this level (more or less intense). Thus, if your initial level is more than 50 dB away from threshold, the procedure will never reach threshold. The way you can tell is to look at the graph that appears after the block of trials. It shows the levels that were tested on each trial. It should get near threshold and then randomly bounce up and down as the subject gets correct and incorrect answers. But, if all levels tested are too easy, for example, the observer will always be correct, the level will drop over and over until it has dropped 50 dB, and then it will stick at that level, and you won’t learn what threshold is (the same issue arises if you start at a level that is far too low). So, each time you run a block of trials, you should check that graph to make sure the run went reasonably well and that you have a good estimate of threshold.

The parameters

On the next page are the experimental parameters you may adjust. We went over most of these in class. I will use the words “test” and “target” interchangeably. In the parameter set, amplitudes (both masker and test) are measurements of total noise power. In other words, if you double the bandwidth, and leave maskerAmpDB unchanged, the amplitude per frequency will be halved (in technical terms: the noise spectral density will be halved). On a graph of frequency versus noise intensity, the parameter maskerAmpDB describes the area of the noise, not the height, in a log scale. Thus, for example, if you want to keep the height constant and double the bandwidth, you'll need to increase maskerAmpDB by 6 dB. Also note that both the test and mask are constructed by adding a bunch of discrete frequency components

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together. These frequencies are integer multiples of 2 Hz (given the setting of nBaseSamples in CreateWaveFormST.m). Thus, if you ask for a frequency of 100.5 Hz, you'll actually get 100 Hz. And, if you ask for a 100 Hz noise with a bandwidth of 7 Hz. you'll only get components with frequencies of 98, 100 and 102 Hz.

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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % EXPERIMENTAL PARAMETERS % % Masker parameters. The masker is defined by its % center frequency, bandwidth, intensity, and duration. maskerFreq = 1000; % Masker frequency (Hz) maskerBandWidth = 100; % Masker bandwidth (Hz) maskerAmpDB = 72; % Masker intensity in dB maskerDuration = 1000; % Masker duration (millisecs) % Test parameters. The test is defined by its center % frequency, bandwidth, and duration. testFreq = 1000; % Test frequency (Hz) testBandWidth = 1; % Test bandwidth (Hz) initialTestAmpDB = 76; % Initial intensity for test in dB testDuration = 1000; % Test duration (millisecs) % Experimental parameters. ISI = 250; % Interstimulus interval (millisecs) nTrials = 40; % Number of forced choice trials per run adjFixedOrder = 1; % Set to 1 to always play masker first % for adjustment trials. questPlot = 1; % Set to 0 to eliminate Quest plotting. reverseRole = 0; % Set to 1 to reverse role of test and masker. beepindic = 1; % Set to 1 to begin each trial with a beep beeptime = 100; % During in msec of beep and following silence beepDB = 76; % Beep amplitude filename = ‘text’; % base of data filename %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Note: the last few parameters are used to insert a beep before each of the two intervals. This is useful if you plan on running an unmasked detection experiment (with maskerAmpDB set to zero). In such an experiment, one interval has the test and the other has silence, so it’s hard to figure out which interval is which without a signal to tell you when the two intervals happened! More details and important settings The value of initialTestAmpDB is quite important if you plan on running a forced-choice experiment (with key f). That value should be pretty close to threshold. If it’s way too high relative to threshold, you will get the psychophysical procedure will give you all easy trials, fail to determine threshold and behave erratically. Because of this, for each condition you should make an initial threshold determination by hand using the menu (e.g., s to try various test amplitude settings and <space> to see how difficult the discrimination is at that test amplitude). Then set initialTestAmpDB based on the result. You will need to do this separately for each condition of your experiment. The option reverseRole is a bit strange. Rather than playing mask-alone and mask-plus-test, where a correct answer is to indicate the interval containing the test, instead it plays test-alone and test-plus-mask, and a correct answer is to indicate the interval containing the test alone.

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Your assignment

Your assignment is to measure auditory thresholds as a function of something. There are lots of options. I’ll go over a few in class and review those here as well, but they are not the only options. I personally think the critical bandwidth experiments are more interesting, but you may exercise your own judgment. Another possibility is to compare thresholds measured with adjustment and forced choice. Bloch’s Law

Bloch’s Law states that the total signal energy required to detect a sound is constant for very short durations (so an intense, brief sound is equally detectable as a sound that has half the power but double the duration, for short-enough durations). Energy summation is less than perfect for longer durations. Here’s an illustration of the method:

Audiogram

A simple experiment is to collect an audiogram (threshold as a function of frequency), much as you might have done in the nurse’s office when you were in elementary school. Another variation is to also collect the same data on a white-noise masker. The white noise should, if its amplitude is high enough, control detection threshold (rather than factors internal to the listener), thus flattening out the audibility curve:

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Weber’s Law

Weber’s law relates the threshold to detect one tone in the presence of a masker to the intensity of the masker. Strictly speaking, the test and masker should have the same frequency and bandwidth, and I recommend you set these parameters to match each other if you decide to test Weber’s Law. Weber’s Law states that the threshold ΔI of the test is related to the intensity I of the masker by the equation ΔI = kI . In this equation, I and ΔI are in pressure units. But, the program specifies and outputs these values in decibels. So, taking logs of both sides and multiplying by 20 (to convert from pressure to dB), Weber’s law becomes 20 logΔI = 20 log I + 20 log k or, more simply, ΔIdB = IdB + ′k . To test Weber’s law, you would measure the threshold for a series of masker intensities and plot the results (ΔI in dB as a function of I in dB). The results should fall along a straight line with slope equal to one. It is easy to use Excel to fit a line to the data—see the “Graphing in Excel” handout for details. For a good report, you should check Weber’s Law at a number of frequencies. Also be sure to see the calibration section below. Again, here’s an illustration. Note that the flat section at the bottom is because Weber’s Law can’t be true at absolute threshold. Instead, at very low pedestal amplitudes (low values of I) you will likely find ΔI to be constant.

Critical Bandwidths

The idea behind critical bands is that sounds at different frequencies are processed by different auditory channels or mechanisms. If this is so, then a masker at one frequency will not mask a test at another frequency that is sufficiently different, since if the two differ in frequency they will be processed by different channels. There are a lot of ways to test this. For example, you could measure the threshold to detect a tone in the presence of a masker at different frequencies. For this experiment, it is a good idea to set the test bandwidth to zero and the masker bandwidth to about 200 or so. You then measure the threshold T as a function of the masker frequency F. There should be a lot of masking (a high threshold) when the masker has the same frequency as the test. But as the frequency of the masker increases or decreases from the test, we expect the threshold for the test to get smaller, producing an inverted U-shaped curve when T is plotted against F. The width of the U-shape curve tells us the range of frequencies that the mechanism detecting the test is sensitive to. This critical bandwidth is thought to increase with test frequency, and a really nice experiment would be to try to measure this increase in bandwidth. Here’s an illustration of this method both for a noise masker and a tone masker (zero bandwidth):

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Frequency

Am

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Masker Center Frequency

Thre

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itude

Critical Band Masking Expected Results

Frequency

Am

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Masker Frequency

Thre

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Expected Results

Version 3

Version 4

An alternative is to use a masker with center frequency equal to the frequency of the test tone. As you widen the bandwidth, threshold increases, but stops increasing when the bandwidth is larger than the critical band. Another version increases bandwidth but keeps total energy of the masker constant. Here are illustrations of these methods:

Note that the specification of parameters for these experiments is a little complicated, because the specification of the amplitude of a noise in these programs does not correspond to the height (the y-value) on the graphs above, but rather to the area of the rectangle describing the noise. So, if you use a constant value for the masker, that corresponds to Version 2 above. Speak with the instructor about how to specify Version 1 instead, if that is your choice. Finally, detection of a noise stimulus itself also depends on whether the noise bandwidth fits within a critical band or is wider than that. Within a critical band, noise energy sums perfectly, but less so across critical bands. So, one can measure the bandwidth of the critical band by doing a detection experiment for noises

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with varying bandwidth (note: the noise is the signal you are trying to detect in these experiments; there is no masker here). Here’s an illustration:

Compare adjustment and forced choice

The idea here is to compare thresholds measured by adjustment and forced choice. Which is lower? Which is more reliable? You’d want to think carefully about counterbalancing the order of your measurements to avoid confounding methodological differences with practice effects.