Lab Session III:  Psychophysical Laws

 

Background:

  1. Purpose and Goals
    1. To illustrate how different people have described the relationship between the physical world and our experience, i.e., illustrate different proposed psychophysical laws
    2. To collect some data to allow us to test some of these laws
    3. To build on the foundations of the last two labs
  2. Magnitude Estimation
    1. All of the methods so far have measured something about perception at or near our limits to either detect a stimulus or a change in the stimulus.
    2. There was a need for a method to try to learn something about stimuli that are easily detectable or the difference between two stimuli that are easily told apart, i.e., is one stimulus twice as bright as another stimulus?
    3. Harvard psychologist, S.S. Stevens pondered this question and basically developed magnitude estimation out of an elevator conversation with another Harvard professor (not a psychologist).
    4. Simple basic idea.  Present a stimulus, have participants give the stimulus a number that they they indicates the sensory strength of the that stimulus.
    5. Modulus:  In some versions, a standard stimulus is used, call the modulus.  This stimulus is given a standard number, whatever the researcher wants, say 50.  Then the participant assigns numbers to the other stimulus that takes the modulus into account.  For example, if the participant thinks the stimulus just presented is twice as strong as the modulus and the modulus is 50 then the subject should give the stimulus a 100.
    6. More in Chapter 2 of the text.
  3. What is a scientific law?
    1. A description of a regularity observed in the data
    2. They do not imply an explanation for the regularity
  4. Weber's Law
    1. Developed to describe how JND's change as the background intensity changes
    2. This is an attempt to describe how our sensitivity to change relates to the intensity of the background stimulus
    3. Questions:
      1. Are all difference thresholds the same size regardless of the intensity of the background stimulus?
      2. If not, is there any systematic way that the size of these difference thresholds change?
    4. Weber answered these questions as: 
      1. No, thresholds get larger as the background intensity gets larger
      2. More than that, the JND increase at the same proportional rate as the background
      3. He summarized his statements with the following equation (it says exactly the same thing as above):

    k

       ΔI    
         I

k is a constant.

I is the background intensity

ΔI is the difference between the intensity of the JND stimulus and background intensity

  1. Fechner's Law (Not in text)
    1. Review logarithms for yourself
    2. Observations:
      1. Fechner starts with Weber's Law
      2. Weber's Law suggests that we get progressively less sensitive to stimulus change as intensity increases
    3. Conjecture:
      1. What is Weber's Law refers not just to our ability to detect change but our overall sensitivity to stimuli
      2. In essence, threshold perception and suprathreshold perception are the same
    4. Leads to the following Law

      S=klog(I)
      S is the strength of our sensory experience
      k is a constant and the same k as in Weber's Law
      I is the intensity of the stimulus

  2. Stevens' Power Law
    1. Starts with his development of Magnitude Estimation
    2. This is a direct measure of suprathreshold perception which is the object of Fechner's Law
    3. In many cases Fechner's Law is a reasonable fit for magnitude estimation data, say brightness or loudness
    4. In some cases, Fechner's Law does not fit at all, e.g., pain
    5. Stevens wanted a general law to cover all of the situations - that is one equation not a set of several
      • Science likes to use as general a description as is possible
    6. Stevens developed the following law, also called just the Power Law

      S=cIb
      S and I are the same as for Fechner's Law
      c is a constant and can be anything
      b is the exponent that changes the shape of the function.  See the text for more.

Tasks Due for Next Week:

  1. Do the following experiments from Chapter 2 on the media website:
    1. Weber's Law Experiment: Use Forced Choice: Frequency Discrimination: ISLE 2.8b
      1. Will Run 6 times
      2. On the Stimulus Settings Tab Change (once per run):
        1. Standard Frequency:
          • Lab 1 (AM): 100, 200, 400, 800, 1600, & 3200 Hz (This will be your I)
          • Lab 2 (PM): 200, 400, 800 1600, 3200, & 6400 Hz (This will be your I)
        2. Change no other settings
      3. On the Method Settings:
        1. Leave all the settings except:
        2. Maximum value of Frequency Difference (Hz): 75
      4. This is a Forced Choice method (Remember how to calculate these thresholds)
    2. Magnitude Estimation: Tone Loudness & Magnitude Estimation: Line Length from Chapter 2 Media
      1. Number of Levels to Test: 10
      2. Number of Repetitions: 7
      3. Use the modulus (leave checked which is the default).  Note the value.
  2. Problems:
    1. For Weber's Law Experiment : Method of Constant Stimuli: Frequency Discrimination
      1. Put data into the report.
      2. Calculate the JND for each standard frequency
        • Use the Forced Choice threshold equation
      3. Your JND will already be ΔI
      4. Convert to ΔI/I
      5. Plot on a bar graph (What Excel calls a column graph)
        • X axis = I (Standard Frequency (Hz))
        • Y axis = ΔI/I
      6. So 6 bars
      7. Answer the following question: Does the data fit Weber's Law or not?  Why or why not?
    2. Using the Magnitude Estimation Data from Week 2
      1. Paragraph describing procedure
      2. Plot the result from both Magnitude Estimation experiments on the same graph.
      3. Does the graph fit better with Fechner's or Stevens' predictions?  Look at the shape.  Consider all of the data together.  Do not answer for each data set separately but the answer must apply to both results together.
    3. Worth 25 points
    4. Point: Develop your skills at interpreting graphs, in particular, in how data relate to theoretical ideas.