Lab Session II:
Forced-Choice Methods and Magnitude Estimation
Background:
Purpose and Goals
To illustrate a different way of thinking about
human sensitivity: Signal Detection Theory
To expand your computational horizons.
Classical Psychophysical Methods
Method of Limits
Method of Constant Stimuli
Method of Adjustment
Limitations of Classical Methods
Can you think of Any?
consider ways that the data may be not
accurate or
ways that the subjects behavior might lead to
an incorrect measure of threshold?
Forced Choice Methods
There are several versions. In this class,
we will use forced choice with only two
alternatives.
During a single trial, there are two stimuli
presented. In an absolute threshold
experiment, one of the stimuli is a stimulus
intensity of 0 (or no stimulus) and the other is the
stimulus at some intensity.
The two stimuli can be presented at the same
time but in different locations or at different
times, one after the other.
If presenting the stimuli at the same time,
but in different locations, which stimulus is in
each location is randomly decided.
If presenting the stimuli at different times,
which stimulus is presented first is chosen
randomly.
The participant's task is different than in the
classic methods.
If the both stimuli are presented at the same
time, the task is to indicate where the target
(more intense) stimulus is.
If the stimuli are presented one after the
other, the task is to indicate if the target
stimulus is presented first or second.
In both cases, if the participant cannot be
sure when or where the target stimulus is, the
participant must guess.
This task is considered to be a pure measure of a
participants sensitivity without any input from a
cognitive bias.
Threshold is the stimulus intensity that can be
correctly identified 75% of the time.
Magnitude Estimation
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.
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?
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).
Simple basic idea. Present a stimulus, have
participants give the stimulus a number that they they
indicates the sensory strength of the that stimulus.
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.
More in Chapter 2 of the text.
Tasks Due for Next Week:
Do the following experiments:
Forced-Choice Method : Dot Threshold
Leave Stimulus Settings Alone
Adjust the following Method Settings
Type of Method of Constant Stimuli:
Forced-Choice
Number of Levels of Relative Dot
Luminance: 7
Number of Repetitions: 7
Magnitude Estimation: Tone Loudness &
Magnitude Estimation: Line Length from Chapter
2 Media
Number of Levels to Test: 10
Number of Repetitions: 7
Use the modulus (leave checked which is the
default). Note the value.
Problems:
Download Data from all Experiments: Forced Choice
and both sets for Magnitude estimation
For Forced-Choice: Show work calculating Threshold
with the linear interpolation equation
For Magnitude Estimation: create graph in Excel
with both data sets on one graph as separate
lines.
The Physical Dimension goes on the x-axis and
the Magnitude Estimate (Psychological) goes on
the y-axis.
Give axis titles but in this case you do not
need units because the physical dimensions are
relative, so the units cancel out, and the
Magnitude Estimates do not have units.
Add Legend since two lines.
Edit the graph to have black axis lines (both
y and x), remove the grid lines and the graph
title.
Enter all information into the Moodle Quiz.
Answer the following question:
Ok, you have uploaded your Magnitude Estimation
graph. Now, look at the graph. Really look at it
and look at the shape of the two sets of data
(line length and loudness). Do the two data
sets look the same. They have, because I
designed it that way, the same x values. But do
they have the same shape. Again, the correctness
of your answer depends on your data, not some
general conclusion. Look at it and see if the two
data sets have the same shape or not.
Explain your answer and refer to your graph to
support your answer.
Worth 25 points
Do figures in Excel and place in a word file and
then type the answers to the questions on the same
page. These may not be hand written.
Point: Learn about making graphs and reading them.