1. Pattern Recognition
I. What is pattern recognition?
II. Template Models
III. Feature Models
IV. Top-Down & Bottom-Up processing
V. Neural Network Models
VI. Prototype Models
VII. Facial Recognition
VI. Conclusions
I. What is Pattern Recognition
A. Definition: A process of identifying a
stimulus. Recognizing a correspondence
between a stimulus and information in
permanent (LTS) memory.
I. What is Pattern Recognition
B. In the context of the Atkinson and Shiffrin Model
Input Sensory Short- Long-
Store Term Term
Store Store
Control Processes
rehearsal
coding
retrieval strategies
Response Output
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2. I. What is Pattern Recognition
C. This process is often accomplished with
incomplete or ambiguous information.
D. Many variations on a pattern may be
recognized as the same object or class of
objects.
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3. Turing test (used by Yahoo,
Hotmail, and ebay)
F. Pattern recognition that is difficult for
machines is easy for people.
fi yuo cna raed tihs, yuo hvae a
sgtrane mnid too.
I cdnuolt blveiee taht I cluod aulaclty
uesdnatnrd waht I was rdanieg. The
phaonmneal pweor of the hmuan mind!
Aoccdrnig to a rscheearch at Cmabrigde
Uinervtisy, it dseno t mtaetr in waht oerdr the
ltteres in a wrod are, the olny iproamtnt tihng
is taht the frsit and lsat ltteer be in the rghit
pclae.
The rset can be a taotl mses and you can sitll
raed it whotuit a pboerlm.
Tihs is bcuseae the huamn mnid deos not raed
ervey lteter by istlef, but the wrod as a wlohe.
Azanmig huh?
[This demonstration is food for thought. The psychological
principles it espouses are only partly correct. See Reicher
(1969)]
II. Template Model
A. Basic Assumptions
1) Memory representation is a holistic
unanalyzed entity (a template).
2) An input pattern is compared to the stored
representation.
3) Identity is determined by selection of the
template with the greatest amount of overlap.
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4. II. Template Model
B. Schematic of a Template System
Stimulus
Brightness A
Detector
Templates Light Source
II. Template Model (cont)
C. Template systems in action
Template Model (cont)
D. Problems with template models
1. Intolerance to deviations
2. Large number of templates required
3. Cannot support similarity-difference
judgments
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5. III. Feature Theories
A. Basic Assumptions
1. The stored representation is a description
of past inputs in terms of list of attributes or
features.
2. Inputs are broken down into a small list of
constituent features.
3. Identity is determined by selecting the
feature list most similar to the input.
III. Feature Theories
B. Schematic of a Feature Model
Stimulus
III. Feature Theories (cont)
C. Supporting Evidence
1. Hubel & Wiesel (1962): Recorded
electrical activity in the visual cortex of the
cat.
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6. Hubel & Wiesel (1962) Results: specific cells
respond to specific visual features.
III. Feature Theories (cont)
B. Supporting Evidence (cont)
2. Letter recognition times
Gibson, Shapiro, & Yonas (1968)
Step 1: Analyze letters in terms of a small set of features.
Step 2: Give subjects a reaction test two determine if two letters are the
same or different.
e.g. G vs.. W RT = 458 msec
P vs.. R RT = 571 msec
Step 3: Compare the clustering of letters in the reaction time task to the
similarities in features.
Step 1: Feature Analysis of Letters
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7. Step 2: Letter Groupings based on RT
III. Feature Theories (cont)
D. Criticisms of Feature Theories
1. Importance of Context
2. Importance of Arrangement
IV. Top-Down vs. Bottom-up Processing
comprehension
Bottom Up phrase processing Top Down
(data driven) word processing (conceptually
letter processing driven)
feature processing
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8. IV. Top-Down vs. Bottom-up Processing
In Control of Attention (Bushman & Miller, 2007)
implanted electrodes in monkeys
the monkeys were trained to search for a target in a
visual display
the researchers measured reaction time and recorded
firing rates in parietal cortex (25 electrodes) (visual-
sensory information) and the prefrontal cortex (25
electrodes).
IV. Top-Down vs. Bottom-up Processing
Bushman & Miller (continued)
Bottom up: visual pop-out
Sensory neurons (parietal) responded first
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9. IV. Top-Down vs. Bottom-up Processing
Bushman & Miller (continued)
Top down (visual search)
prefrontal cortex responded first
IV. Top-Down vs. Bottom-up Processing
Conclusion:
Button up processing signals arise from the
sensory cortex.
Top down processing signals begin in the
frontal cortex.
V. Neural Network Model of Word
Pattern Recognition Analysis
A. Interactive Activation Model
(McClelland & Rumelhart, 1981)
Letter
Analysis
Incorporates top-down processing from the
word level to the letter level.
Excitatory connections:
Feature
Inhibitory connections:
Analysis
Visual Input
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10. Simplified view of the Network of Connections
Excitatory connections:
Inhibitory connections:
Word Level CAT CHAIR THE
Letter Level A C H T E
Feature Level
Input
More Complete view of the
Network of Connections:
B. Supporting Evidence:
The word/letter effect
Reicher (1969)
Stimulus Example Test Percent Correct
letter h h/t 78
series csah csah/csat 76
word cash cash/cast 89
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11. VI. Prototype Theory
A. Basic Assumptions
1. The stored representation is a Prototype: an
abstraction of the typical or best example of
an object.
examples: chairs, cars, and trucks
2. Inputs are broken down into feature lists.
3. Recognition is process of comparing the
features of the input to the features of
prototypes, and selecting the best fit.
VI. Prototype Theory (cont.) 75%
B. Evidence for Prototype Theory
Solso & McCarthy (1981)
50%
face recognition
25%
Prototype
100%
0%
VI. Prototype Theory (cont.)
Solso & McCarthy (1981): results
5
Old Items
4 New Items
Old 3
2
Confidence
1
0
-1
-2
-3
New
-4
-5
100 75 50 25
Percent Overlap with Prototype
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12. VI. Prototype Theory (cont.)
C. Prototype Theory and attractiveness
1) goodness of category membership can
be defined with respect to the prototype.
2) good category members may be seen as
more attractive, or desirable, than poor
category membership
C. Prototype Theory and attractiveness
(cont.)
Example: attractive faces are average
(Langlois & Roggman, 1990)
Stimulus set:
individual faces
composite faces containing 2 - 32 faces.
Examples of composite faces:
Number in composite
4
8
16
32
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13. Rated attractiveness
Number of faces average rating
1 2.51
2 2.87
4 2.84
8 3.03
16 3.06
32 3.25
VII. Facial Recognition:
Why Barack Obama is Black
(Halberstadt et al, 2011)
Hypodescent: association of mixed race
individuals as belonging to the minority race.
Hypothesis: individuals learn to minority
groups later than majority groups, so they
learn to focus attention on features that
distinguish the groups.
Increased attention to distinctive
(distinguishing) features leads to over-
classification in the “new” group.
Why Barack Obama is Black
(Halberstadt et al, 2011)
Evidence: Experiment 1
Participants:
½ Caucasians (New Zealanders)
½ of Chinese decent (raised in China or
Asian Pacific regions).
Individuals performed a speeded
classification of faces that were morphed
blends of Chinese and Caucasian faces:
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14. Why Barack Obama is Black
(Halberstadt et al, 2011)
Experiment 2
Participants: 75% Caucasian, 25 % other
Procedure: participants learned to classify
faces into different (arbitrary) groups.
“majority faces” classified 9 times
“minority faces” classified 3 times
Why Barack Obama is Black
(Halberstadt et al, 2011)
Results
Experiment 1
Percent of ambiguous faces rated as Chinese:
Chinese Participants: 44 %
Caucasian Participants: 49 %
Experiment 2
Percent of ambiguous faces rated as B’s
A faces “majority”: 40 %
B faces “majority”: 36 %
Conclusions:
Biracial classifications are based on learning
history.
Distinctive racial features receive greater
attention if they are learned later in life.
Why Barack Obama is Black
(Halberstadt et al, 2011)
Conclusions:
Biracial classifications are based on learning
history.
Distinctive racial features receive greater
attention if they are learned later in life.
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15. VII. Facial Recognition:
A special problem for theories of pattern
recognition:
A. Different set of rules? (Example: object vs.
facial recognition).
Yin (1970), and Rock (1974) demonstrated that
facial recognition is more easily impaired by
inversion than is object recognition.
Who is this?
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16. A B
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17. A B
VII. Facial Recognition (cont)
B. Different Neurological Structures?
Dissociation between loss of object recognition
(visual agnosia) and face recognition in
stroke victims.
(e.g., Msocovithc, Winocur, & Behrman, 1997)
VI. Conclusions on Pattern Recognition
A. Template and Feature Models are
inadequate
B. Context and top-down processing are very
important
C. Neural Networks can explain top down
processes.
D. Important role of prototypes
E. Challenge of explaining facial recognition
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