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Lecture 1

PSY 3108 Lecture Notes - Lecture 1: Statistical Inference, Jargon, Human Factors And ErgonomicsPremium

10 pages66 viewsFall 2017

Department
Psychology
Course Code
PSY 3108
Professor
Charles Collin
Lecture
1

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Chapter 1: fundamentals of Sensory Perception
Methods in Perception
Qualitative: Getting the big picture
o Whole phenomena in their entirety
Quantitative: Understanding the details
o Breaking things down; putting numbers
o Quantitative values; understanding details
Threshold-seeking methods
Looking at the Limits of your perceptual abilities
i.e at the optometrist: whats is the smallest letter you can see
Magnitude estimation
Everything else; 99% is not at the edge of perception
And how we map them out
Many others, often similar to those used in other areas of psych
o Advantages to understanding the mathematical relationship between the physical and
perceptual worlds
Provides an estimate of the perceptual quality of a stimulus in numerical terms and thus
allows comparisons with other stimuli
Allow comparisons among individuals and even species
Qualitative Observation
Thatcher illusion
o When you're looking at a picture upside it looks odd but can still be made out, but when its
flipped right side up, the face looks completely different
Qualitative observation uncovered the phenomenon
Much quantitative work now trying to find out why it happens and what it means
o This does’t happe ith egula ojets, petty uh oly ith faces
Qualitative Methods in Perception
Also called phenomenological or naturalistic observation methods
o Not trying to control or measure things precisely
Relatively non-systematic observation of a given perceptual phenomenon or environment (eg. An
illusion)
o Generally qualitative is not systematic
Yields a verbal description of ones observations
o Possibly with some simple numerical assessment
First step in any study of any perceptual phenomenon
o Gives the "big picture"
Example: famous perception research Jan p. Noticed that his flower bed looked light
red/dark green during the day but dark red and light green at twilight
This phenomenological observation led to the hypothesis of 2 visual systems and
ultimately to an understanding of the functions of rods and cones
Quantitative Methods in Perception
Threshold seeking methods measure a physical quantity representing a limit of perceptual ability
o I.e a threshold
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Applied examples include visual acuity tests and hearing tests used in medical and professional
qualification settings
o Eg. Airline pilots
o Tactile activity too
Thresholds are measured in physical units
o Decibels of sound
o Parts-per-million of chemical concentration
o Candelas/m^2 of light intensity
o Pascals of pressure on skin
Absolute threshold
o Smallest detectable physical quantity
o Point from which to begin plotting a function
Difference threshold
o Smallest detectable difference between two physical quantities
Thresholds are defined for a given level of response accuracy
Most often, we speak of the "50% threshold" , meaning the physical quantity detectable (absolute
threshold) or the physical difference detectable (difference threshold) 50% of the time
But a threshold ca be defined for any level of accuracy
Threshold Seeking Methods
Classic Methods (Fechner, 1850's)
o Method of adjustment: quick and dirty
What is the dimmest light you can see, and keep adjusting the light till you can just make it
out
Problem: some subjectivity
Generally self administered
o Method of limits: easy on observer, fairly fast and accurate
Used in medical tests
Show person a level (i.e eye test) and see when they cross a threshold
The osee does’t hae to do aythig
o Method of constant stimuli: very slow but very accurate
"gold standard for good threshold"
Adaptive methods: fast, very accurate, but can be difficult for untrained observers
o More recently developed methods
o Start of at some level; if you can see it then you keep making it harder
People think they can see but not really sure
Method of Adjustment
Stimulus intensity is adjusted (usually by the observer) continuously until the observer says they can
just detect it
Threshold is point to which observer adjust the intensity
Repeated trials (about 10) averaged for threshold
Fast, but not always accurate, due to inherently subject nature of adjustment
Points of Subjective Equality (PSEs)
Threshold-seeking Methods can also be used to find PSEs
The PSE is the level of a physical characteristic of a stimulus at which it appears similar to another
stimulus
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PSEs can be used to (among other things quantify the strength of an illusion
Example: if the lower figure has to 1.2 Ike's the length of the upper one to appear equal the new can
say that under these circumstances the illusion has a 20% effect
Method of Limits
Stimuli of different intensities presented in ascending and descending order
Observer responds to whether she perceived the stimulus
Cross over point (between "Yes I see it" ad "o, I do’t") is the theshold fo a seuee
Average of cross- over points from several ascending and descending sequences is taken to obtain
final threshold
o Data example
Why do we have ascending /descending sequin est
Due to basis: we need to change the way the tests are being given
This creates differences between tests and take out the basis
Why different starting points
Same as the above, getting rid of the bias
Changing the starting points eliminates the ability to remember the same answers
Methods of Constant Stimuli
5 to 9 stimuli for different intensities are presented many times each, in random order
The intensities must span the threshold, so must know approx. where it is a priori
Multiple trials often 100's of each intensity are presented
Threshold is the intensity that results in detection in 50% of trials
o Question
Where is the 50% threshold
Between .5 and .6
Psychometric Function
To calculate the thresholds we used curve fitting techniques to fit a sigmoidal (s shaped) function to
the data
This called a psychometric function (grey line) it links physical stimulus intensity to performance
Debate exists over which kind of function- cumulative normal (Weibull) logistic etc. Is theoretically
best but in a practice differences are minor
The Weibull function
o X is stimulus intensity (in some positive physical unit)
o Y is predicated probability of stimuli detection (from 0-1)
o A is the "offset" and b is the "slope"
o A sand b are free parameters whose values re chosen so that curve best fits the data. These
values are determined by curve-fitting algorithms whose details are beyond the scope of the
course
Calculating 50% Threshold Intensity Via Inverse Weibull
o To figure out the threshold, we need to figure out what the right values of a and b are for the
Weibull that best fits our data
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