motion illusions as optimal percepts
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Motion Illusions As Optimal Percepts. What’s Special About Perception?. Arguably, visual perception is better optimized by evolution than other cognitive abilities. Human visual perception outperforms all modern computer vision systems. - PowerPoint PPT PresentationTRANSCRIPT
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Motion Illusions As Optimal Percepts
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What’s Special About Perception?
Visual perception important for survivalLikely optimized by evolution
at least more so than other cognitive abilitiesHuman visual perception outperforms all modern computer vision systems.
Understanding human vision should be helpful for building AI systems
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Ambiguity of Perception
One-to-many mapping of retinal image to objects in the world
Same issue with 2D retina and 3D images
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Hermann von Helmholtz(1821-1894)
German physician/physicist who madesignificant contributions to theories ofvision
Perception as unconscious inference
Recover the most likely objects in the world based on the ambiguous visual evidence
Percept is a hypothesis about what the brain thinks is out there in the world.
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Additional KnowledgeIs Required To Perceive
• Innate knowledge
– E.g., any point in the image has only one interpretation
– E.g., surfaces of an object tend tobe a homogeneous color
– Gestalt grouping principles
• Specific experience
– E.g., SQT is an unlikely lettercombination in English
– E.g., bananas are yellow orgreen, not purple
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Illusions
• Most of the time, knowledge helps constrain perception to produce the correct interpretation of perceptual data.
• Illusions are the rare cases where knowledge misleads
– E.g., hollow face illusion
– http://www.michaelbach.de/ot/fcs_hollow-face/
– Constraints: light source, shading cues, knowledge of faces
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The Aperture Problem
Some slides adapted from Alex Pouget, Rochester
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The Aperture Problem
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The Aperture Problem
Horizontal velocity (deg/s)Ve
rtica
l vel
ocity
(deg
/s)
horizontal velocityve
rtica
l vel
ocity
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The Aperture Problem: Plaid
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The Aperture Problem: Plaid
Horizontal velocity (deg/s)Ve
rtica
l vel
ocity
(deg
/s)
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The Aperture Problem: Rhombus
Horizontal velocity (deg/s)Ve
rtica
l vel
ocity
(deg
/s)
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The Aperture Problem
Horizontal velocity (deg/s)Ve
rtica
l vel
ocity
(deg
/s)
Actual motion in blue
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Standard Models of Motion Perception
Feature tracking
focus on distinguishing features
IOC
intercept of constraints
VA
vector average
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Standard Models of Motion Perception
Horizontal velocity (deg/s)Ve
rtica
l vel
ocity
(deg
/s)
IOCVA
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Standard Models of Motion Perception
Horizontal velocity (deg/s)Ve
rtica
l vel
ocity
(deg
/s)
IOCVA
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Standard Models of Motion Perception
Problem
Perceived motion is close to either IOC or VA depending on stimulus duration, retinal eccentricity, contrast, speed, and other factors.
Maybe perception is an ad hoc combination of models, but that’s neither elegant nor parsimonious.
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Standard Models of Motion Perception
Example: Rhombus With Corners Occluded
Horizontal velocity (deg/s)
Verti
cal v
eloc
ity (d
eg/s
) IOCVA
Horizontal velocity (deg/s)
Verti
cal v
eloc
ity (d
eg/s
) IOCVA
Percept: VAPercept: IOCActual motion Actual motion
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Rhombus Thickness Influences Perception
rhombus demo
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Bayesian Model of Motion Perception
Perceived motion correspond to the Maximum a Posteriori (MAP) estimate
v: velocity vector
I: snapshot of image at 2 consecutive moments in time
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* Digression * Maximum a posteriori
Maximum likelihood
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Bayesian Model of Motion Perception
Perceived motion corresponds to the Maximum a Posteriori (MAP) estimate
Conditional independenceof observations
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Prior
Weiss and Adelson:Human observers favor slow motions
-50 0 50
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Horizontal Velocity
Verti
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eloc
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Likelihood
Weiss and Adelson
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Horizontal Velocity
Verti
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Likelihood
First-orderTaylor seriesexpansion
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Likelihood
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Posterior
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Bayesian Model of Motion Perception
Perceived motion corresponds to the MAP estimate
Only one free parameter
Gaussian prior, Gaussian likelihood→ Gaussian posterior→ MAP is mean of Gaussian
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Likelihood
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-50 0 50
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Horizontal Velocity
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Motion Through An Aperture
ML
MAP
Prior Posterior
Likelihood
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Driving In The Fog
Drivers in the fog tend to speed up
underestimation of velocity
Explanation
Fog results in low contrast visual information
In low contrast situations, poor quality visual information about speed
Priors biased toward slow speeds
Prior dominates
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Horizontal Velocity
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Horizontal Velocity
Verti
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Horizontal Velocity
Verti
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Influence Of Contrast On Perceived Velocity
ML
MAP
Prior Posterior
HighContrast
Likelihood
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Horizontal Velocity
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Horizontal Velocity
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Horizontal Velocity
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Influence Of Contrast On Perceived Velocity
ML
MAP
Prior Posterior
LowContrast
Likelihood
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Influence Of Contrast On Perceived Direction
high vs. low contrast rhombus
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Influence Of Contrast On Perceived Direction
Low contrast -> greater uncertainty in motion direction Blurred information from two edges can combine if edges have similar angles
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Influence Of Contrast On Perceived Direction
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Horizontal Velocity
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Verti
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IOC
MAP
Prior Posterior
HighContrast
Likelihood
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Influence Of Contrast On Perceived Direction
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IOC
MAP
Prior Posterior
LowContrast
Likelihood
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Influence Of Edge AnglesOn Perceived Direction Of Motion
Example: Rhombus
Horizontal velocity (deg/s)
Verti
cal v
eloc
ity (d
eg/s
) IOCVA
Horizontal velocity (deg/s)
Verti
cal v
eloc
ity (d
eg/s
) IOCVA
Percept: VAPercept: IOCActual motion
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Greater alignment of edges -> less benefit of combining information from the two edges
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Barberpole Illusion (Weiss thesis)
Actual motion
Perceived motion
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Motion Illusions As Optimal PerceptsMistakes of perception are the result of a rational system designed to operate in the presence of uncertainty.
A proper rational model incorporates actual statistics of the environment
Here, authors assume without direct evidence:(1) preference for slow speeds(2) noisy local image measurements(3) velocity estimate is the mean/mode of posterior distribution
“Optimal Bayesian estimator” or “ideal observer” is relative to these assumptions
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Motion And Constrast
Individuals tend to underestimate velocity in low contrast situations
perceived speed of lower-contrast grating relative to higher-contrast grating
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Influence Of Edge AnglesOn Perceived Direction Of Motion
Type II plaids True velocity is not between the two surface normals
Vary angle between plaid components Analogous to varying shape of rhombus
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Interaction of Edge Angle With Contrast
More uncertainty with low contrastMore alignment with acute angle-> Union vs. intersection of edge information at low contrast with acute angle
Horizontal velocity (deg/s)
Verti
cal v
eloc
ity (d
eg/s
) IOCVA
Horizontal velocity (deg/s)
Verti
cal v
eloc
ity (d
eg/s
) IOCVA
Actual motion
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Plaid Motion: Type I and II
Type I: true velocitylies between twonormals
Type II: truevelocity lies outsidetwo normals
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Plaids and Relative Contrast
Lower contrast
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Plaids and Speed
Perceived direction of type II plaids depends on relative speed of components
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Plaids and Time
Viewing time reduces uncertainty