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Psy 260 Announcements. All late CogLab Assignment #1’s due today CogLab #2 (Attention) is due Thurs. 9/21 at the beginning of class Coglab booklets and disks--along with a printer that usually works--are available for use in the Psychology Resource Room (enter through Psych B 120) Quiz alert!.

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psy 260 announcements
Psy 260 Announcements
  • All late CogLab Assignment #1’s due today
  • CogLab #2 (Attention) is due Thurs. 9/21 at the beginning of class
  • Coglab booklets and disks--along with a printer that usually works--are available for use in the Psychology Resource Room (enter through Psych B 120)
  • Quiz alert!
neural network models
Neural network models
  • Nodes - processing units used to abstractly represent elements such as features, letters, and words
  • Links, or connections between nodes
  • Activation - excitation or inhibition that spreads from one node to another
slide4

Word superiority effect, revisited

Cond. 1: Cond. 2: Cond. 3:

WORD ORWD D

XXXXX XXXXX XXXXX

Test: Which one did you see?

K K K

D D D

word superiority effect revisited1
Word superiority effect, revisited

Word level

Letter level

Feature level

Input

See Reed, p. 36

word superiority effect an interactive activation model
Word superiority effect: An interactive activation model

WORK

K

| / \

Input: K or WORK or ORWD

See Reed, p. 36

interactive activation model of the word superiority effect mcclelland rumelhart 1981
Interactive Activation Model of the word superiority effect(McClelland & Rumelhart, 1981)
interactive activation model of the word superiority effect mcclelland rumelhart 19811
Interactive Activation Model of the word superiority effect(McClelland & Rumelhart, 1981)
james cattell 1886 word superiority effect reicher 1969 cattell 1886
James Cattell, 1886: Word superiority effect(Reicher, 1969; Cattell, 1886)

Subjects recognized flashed words more accurately

than flashed letters.

He proposed a word shape model.

evidence for word shape model
Evidence for word shape model:
  • Word superiority effect
  • Lowercase text is read faster than uppercase.
  • Proofreading errors tend to be consistent with word shape.
evidence for word shape model1
Evidence for word shape model:
  • Word superiority effect
  • Lowercase text is read faster than uppercase.
  • Proofreading errors tend to be consistent with word shape.
  • It’S dIfFiCuLt To ReAd WoRdS iN aLtErNaTiNg CaSe.
how do people recognize faces consider these types of theories
How do people recognize faces? Consider these types of theories:
  • Template theories
  • Feature theories
  • Structure theories
  • Prototype theories
feature theories
Feature theories
  • Patterns are represented in memory by their parts.
  • In perception, the parts are first recognized and then assembled into a meaningful pattern.
  • Piecemeal (as opposed to holistic)
what are the distinctive features for faces1
What are the distinctive features for faces ?

Eyes, nose, mouth - NOT!

Revisit Eleanor Gibson’s criteria:

  • Each feature should be present in some patterns and absent in others
  • A feature should be invariant (unchanged) for all instances of a particular pattern
  • Each pattern has a unique combination of features
  • The number of features should be fairly small

A set of features is evaluated by how well it can predict perceptual confusions.

inspiration caricatures
Inspiration: Caricatures
  • “More like the face than the face itself”
  • What are the distinctive features of a face - say, Richard Nixon’s???
    • Ski jump nose
    • Jowly face
    • Curly-textured hair
    • Receding bays in hairline
    • Boxy chin (David Perkins, 1975)
revisit problems w feature theories
Revisit: Problems w/ feature theories
  • How to determine the right set of features?
  • What about the relationships between features?
  • What if all the features are present in the pattern, but scrambled?

Features theories predict: No problem!

(and that’s the problem.)

face recognition is holistic
Face recognition is holistic

(Tanaka & Farah, 1993)

structure theories
Structure theories
  • Build on feature theories
  • Patterns are represented in memory by features AND by the relations between them.
  • Holistic
  • The context of the pattern plays an important role in pattern recognition.
a structure theory rbc biederman
A structure theory: RBC (Biederman)
  • Recognition by Components
  • Geons: simple volumes (~35 of them)
  • Construct objects by combining geons
rbc theory
RBC Theory
  • Analyze an object into geons
  • Determine relations among the geons
  • The relation among geons is critical!
rbc theory1
RBC Theory
  • It’s hard to recognize an object without the information about relations among geons.

Hard!

rbc theory2
RBC Theory
  • It’s hard to recognize an object without the information about relations among geons.

Easier!

rbc theory3
RBC Theory
  • Basic properties of Geons
    • View invariance
    • Discriminability
    • Resistance to visual noise
rbc theory problems
RBC Theory - Problems
  • Explains how people distinguish categories of objects (types) - like cups vs. briefcases. But how do people distinguish individual objects (tokens) that come from the same category (like faces)??
  • Neurons are to tuned respond to much smaller elements than those represented by geons!
recap so far
Recap so far:

Theory: What it explains:

Template Bar codes (by machines)

Feature Letter learning & confusions

Structural Biederman’s data (geons)

Prototype

we see faces everywhere
We see faces everywhere.
  • Image from

Mars’ surface

by Viking Orbiter 1

(Mcneill, 1998, p. 5)

are faces special
Are faces “special”?
  • How many faces can you recognize?
are faces special1
Are faces “special”?
  • How many faces can you recognize?
  • Gibson: Patterns are easier to encode as faces than as writing
are faces special2
Are faces “special”?
  • How many faces can you recognize?
  • Gibson: Patterns are easier to encode as faces than as writing
are faces special3
Are faces “special”?
  • How many faces can you recognize?
  • Gibson: Patterns are easier to encode as faces than as writing
  • Prosopagnosia
why is face recognition so interesting
Why is face recognition so interesting?
  • It’s important!
  • Faces are highly similar to one another.
  • Yet we’re really good at it: we can tell an astounding number of faces apart.
  • Not all facial information is created equal.
  • Could machines ever do as well as people? Or even better?
  • Are faces somehow “special”?
why is face recognition so interesting1
Why is face recognition so interesting?
  • It’s important!
  • Faces are highly similar to one another.
  • Yet we’re really good at it: we can tell an astounding number of faces apart.
  • Not all facial information is created equal.
  • Could machines ever do as well as people? Or even better?
  • Are faces somehow “special”?
faces are hard to recognize upside down yin 19691
Faces are hard to recognize upside down (Yin, 1969)

“Early processing in the recognition of faces”

http://www.diss.fu-berlin.de/2003/35/Kap4.pdf

faces are hard to recognize upside down yin 19692
Faces are hard to recognize upside down (Yin, 1969)

“Early processing in the recognition of faces”

http://www.diss.fu-berlin.de/2003/35/Kap4.pdf

slide49
Why?
  • The configural processing hypothesis:

When faces are inverted, the relationships among features are disturbed.

So we don’t notice the odd configuration in the Thatcher illusion.

(Bartlett & Searcy, 1993)

faces are hard to recognize upside down yin 19693
Faces are hard to recognize upside down (Yin, 1969)

“Early processing in the recognition of faces”

http://www.diss.fu-berlin.de/2003/35/Kap4.pdf

what kind of theory accounts for face recognition
What kind of theory accounts for face recognition?

Theory: Objection:

Template Different lighting, orientation,

motion, hair, glasses, age

Feature What is a facial “feature”?

Invariant vs. transient features

Structural

Prototype

familiar vs unfamiliar faces
Familiar vs. unfamiliar faces
  • “Attribute Checking Theory”
    • A feature theory
    • For familiar faces, internal features seem to be more important than outside features.
    • For new faces, we pay more attention to outside features (hair, face shape, etc.)

(Bradshaw & Wallace)

familiar vs unfamiliar faces1
Familiar vs. unfamiliar faces

“Early processing in the recognition of faces”

http://www.diss.fu-berlin.de/2003/35/Kap3.pdf

children recognize faces differently than adults do
Children recognize faces differently than adults do.
  • Children under 10 use transient features to distinguish unfamiliar faces.
    • Strangers wearing the same hat seem similar, and are confusable.

(Susan Carey)

problem
Problem:
  • Identikit: piecemeal, featural
  • Photo methods: Introduce interference, bias
  • Lineup: when the perpetrator is not present, 20-40% of witnesses select someone anyway.
  • With photos and lineups, witnesses compare the suspects and choose the most similar one
  • False convictions often have eyewitness testimony as the strongest evidence in the
the right way to do a lineup
The right way to do a lineup:
  • “Showup” - view suspects or pictures one at a time, ideally only once
  • If multiple viewings, then view each one the same number of times, always in random order (avoid between-suspect comparisons)
  • The one showing the faces must be blind to whom law enforcement believes suspect is

(Otherwise, impossible to avoid bias)

  • Then false IDs drop to 10%.
what about a structural theory of face recognition
What about a structural theory of face recognition?
  • Pro: The relationships between features are very important.
  • Pro: We often fail to recognize a familiar face when we see it out of context.
  • Con: A structural theory doesn’t explain how we can distinguish so many highly similar, individual tokens.

(Moving right along: A prototype theory

what is a caricature
What is a caricature?
  • An exaggerated representation of a face
  • More like a face than the face itself!

The Caricature Generator (Brennan, 1982)

a prototype theory of face recognition
A prototype theory of face recognition

When drawings were recognized, caricatures were faster than veridical drawings, which were faster than “anti-caricatures.”

Average face 0 distortion Caricature

(Rhodes, Brennan, & Carey, 1987)

slide70
Caricatures

&

Anti-Caricatures

For a face,

maybe we encode

the difference from

a prototype.

what kind of theory accounts for face recognition1
What kind of theory accounts for face recognition?

Theory: Objection:

Template Different lighting, orientation, motion, hair, glasses, age

Feature What is a facial “feature”? Invariant vs. transient features

Structural Faces are highly similar tokens with the same structure!

Prototype This works! (but maybe not for unfamiliar faces and not for kids)

is face recognition special
Is face recognition “special”?

No!

  • There are other classes of patterns for which people can distinguish huge numbers of individuals (tokens).
    • Ornithologists recognize individual birds
    • New England Kennel Club judges recognize individual dogs
  • There is even prosopagnosia for things other than faces!
some sources
Some sources
  • George Lovell’s slides from Roth & Bruce

http://www.face-rec.org/interesting-papers/Other/FaceRecognition.pdf

  • “Early processing in the recognition of faces”

http://www.diss.fu-berlin.de/2003/35/Kap3.pdf

  • Harmon, L. D. (1973). The recognition of faces. Scientific American, 229(5), 71-82.