Biases: An Example Non-accidental properties: Properties that appear in an image that are very...

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Biases: An Example ccidental properties: Properties that appear in an are very unlikely to have been produced by chance, fore are likely to reflect properties of the 3-D wo straight lines parallel lines view-point invariant properties

Transcript of Biases: An Example Non-accidental properties: Properties that appear in an image that are very...

Page 1: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Biases: An Example

Non-accidental properties: Properties that appear in an imagethat are very unlikely to have been produced by chance, andtherefore are likely to reflect properties of the 3-D world.

straight lines parallel lines

view-point invariant properties

Page 2: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

mental representation of objects

view-point invariantcat

book

tree

key

etc..

mental representation of bars of light view-point dependent

?

Page 3: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

mental representation of objects

view-point invariantcat

book

tree

key

etc..

mental representation of bars of light view-point dependent

1) Direct models

Page 4: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

mental representation of objects

view-point invariantcat

book

tree

key

etc..

mental representation of bars of light view-point dependent

1) Direct models:

Lades et al Model

Poggio & Edelman Model

•Input layer like representation in V1 (called “Gabor Jets”)•Inputs on that layer are matched to inputs in a memory layer•The object is identified based on the match with least distortion

•Input layer like representation in V1•3-layer network is trained to rotate all views of an image to one view.•The hidden units are seen as a way of rotating images to match memory images (“radial basis functions”)

Page 5: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

mental representation of objects

view-point invariantcat

book

tree

key

etc..

mental representation of bars of light view-point dependent

1) Direct models:

Lades et al Model

Poggio & Edelman Model

•Input layer like representation in V1 (called “Gabor Jets”)•Inputs on that layer are matched to inputs in a memory layer•The object is identified based on the match with least distortion

•Input layer like representation in V1•3-layer network is trained to rotate all views of an image to one view.•The hidden units are seen as a way of rotating images to match memory images (“radial basis functions”)

Page 6: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

mental representation of objects

view-point invariantcat

book

tree

key

etc..

mental representation of bars of light view-point dependent

mental representation of non-accidental

properties of an image

view-point invariant

2) View-point invariant

Lowe’s SCERPO Model

Ullman’s Model

•Input layer takes information represented as it is in V1•view-point invarient information is extracted•this allows the input image to be rotated in order to fit an image stored in memory

Page 7: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

The problem with all of these theories:

The representation of objects in memory is stored asa two-dimensional image, which visual images are rotated, distorted, and matched to.

But in actuality, objects are three dimensional thingsin the world.

So lets make a model which has the basic units thatmake up mental representations of objects beingthree-dimensional solids, rather than lines and edges.

Page 8: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

mental representation of objects

view-point invariantcat

book

tree

key

etc..

mental representation of bars of light view-point dependent

mental representation of non-accidental

properties of an image

view-point invariant

mental representation of geons

view-point invariant

Page 9: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Geonsstraight, parallel

curved, parallel

Y intersections

Y intersections

corner

Page 10: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

mental representation of objects

view-point invariantcat

book

tree

key

etc..

mental representation of bars of light view-point dependent

mental representation of non-accidental

properties of an image

view-point invariant

mental representation of geons

view-point invariant

Page 11: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

what processing

rectangle unitcylinder unit

cone unittube unit

mental representation of objects

view-point invariantcat

book

briefcase

key

etc..

mental representation of geons

view-point invariant

where processing

above unitbelow unitleft of unit

righ of unittemporal binding

Page 12: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Evidence for geons?

Experiment 1: Visual Priming

(response)

time

(response)

Reaction Time: 900 ms.

Page 13: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Evidence for geons?

Experiment 1: Visual Priming

time

(response)

Reaction Time: 700 ms.

(response)

Page 14: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Evidence for geons?

Experiment 1: Visual Priming

time

(response)

Reaction Time: 800 ms.

(response)

Page 15: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Evidence For Geons: Priming StudiesFirst Second

Shared?Lines Geons Basic Response TimeEdges Cat.

Yes Yes Yes Yes

No Yes Yes Yes

No No No No

700 ms

700 ms

900 ms

Page 16: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Evidence For Geons: Priming StudiesShared?

Lines Geons Spec. Basic Resp. TimeEdges Cat. Cat.

Yes Yes Yes Yes Yes

No No Yes Yes Yes

No No No Yes Yes

700 ms

800 ms

800 ms

First Second

Page 17: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Objects, Faces and Rotation:Objects

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Page 18: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Face Recognition

Recognizing faces is an entirely different problem(computationally) from recognizing objects?

Objects

Faces

• “M” shaped function

• Shape of rotation function?

Page 19: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Face Recognition Experiment

Page 20: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Objects, Faces and Rotation:Faces

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Page 21: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Face Recognition

Recognizing faces is an entirely different problem(computationally) from recognizing objects?

Objects Faces

• “M” shaped function• Less affected by illumination

Recognition by components(Geon Theory)

• Shape of rotation function?• More affected by illumination

Recognition by coordinates(Templates)

different processes?

Page 22: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Double Dissociation

Patient Studies

• Prosopagnosia: Objects can be recognized, Faces can not.

Processes responsible for object recognition:

Processes responsible for face recognition:

Page 23: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Double Dissociation

Patient Studies

• Prosopagnosia: Objects can be recognized, Faces can not.• (Patient CK): Faces can be recognized, Objects can not

Processes responsible for object recognition:

Processes responsible for face recognition:

Page 24: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Double Dissociation

Patient Studies

• Prosopagnosia: Objects can be recognized, Faces can not.• (Patient CK): Faces can be recognized, Objects can not

Neuroimaging Studies

• Faces activate the FFA area of the temporal lobe• Objects activate the PPA area of the temporal lobe

Page 25: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

Double Dissociation

General Form

IF:The ability to perform some task X is affected by or correlatedwith some factor A (damage to an area, activity in a part of the brain, or performing some other task) but not factor B (damageto a different area, activity in a different part, or some other task),AND:The ability to perform some task Y is affected by or correlatedwith some factor B but not factor A,THEN:There is a double dissociation between X and Y, and themechanisms required to perform them are functionally independent.

Page 26: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

DissociationWhat and Where Pathways

Some patients

Can identify objects by shape

CannotSay where objects areCannot navigate the world

DamageParietal lobeDorsal visual stream

X

Page 27: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

DissociationWhat and Where Pathways

X

Some patients

Cannot identify objects by shape

CanSay where objects areCannot navigate the world

DamageTemporal lobeVentral visual stream

Page 28: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

DissociationWhat and Where PathwaysPatient D.F.

Cannot Explicitly line up a line with a lineTask: Take an envelope and line it up with this

lineCan

Use motor system (part of “where” pathway)to do same task just failed atTask: Take this envelope and “mail the letter” pretending that this line is a mailbox

Page 29: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

• Recognize faces normal way “ Hey I know you”

• Recognize by change in skin conductance for familiar faces

DissociationTwo face recognition systems

Page 30: Biases: An Example Non-accidental properties: Properties that appear in an image that are very unlikely to have been produced by chance, and therefore.

• Person A can recognize normal way but no change in skin conductance

• Person B cannot recognize in normal way but does have change in skin conductance

DissociationTwo face recognition systems