1/29/2002psyc202-005, term 2, copyright 2002 jason harrison1 perception of motion and movement
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1/29/2002 PSYC202-005, Term 2, Copyright 2002 Jason Harrison
1
Perception of Motion and Movement
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Quiz: List five ways to make a spot of light appear to move:1. Move the light2. Apparent Motion
• Turn off the light, turn on another3. Induced Motion
• Move a large framing object4. Auto kinetic effect
• View a dim light in a very dark room5. Movement aftereffect
• Move something for a long time - then look at the light
6. (Change the intensity of the light)
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Say at V1 the neurons detected this pattern
Positions across retina
Ori
enta
tion
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and a moment later the neurons detected this pattern
Positions across retina
Ori
enta
tion
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What could be the source of this motion?
Positions across retina
Ori
enta
tion
Positions across retina
Ori
en
tati
on
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Sources of movement for the eye-brain system • motion across the retina
– object moving or eye or head or body moving?
• eye movement– tracking an object or just looking around?
• head movement• body movement
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World in motion:
Eyes inmotion:
Categorize into four possibilities
No Yes
NoStatic Vision
Retinal Motion
YesEye/Body moveme
ntTracking
Gregory: Inflow/Outflow
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Retinal (image) motion• movement of light across retina• sensitivity found in all animals
– not all animals see static images– all animals see motion
• motion detected by some animals in retina– house fly, frog
• motion detected by some animals in brain– cats, humans
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Uses of motion information1. Relative velocity of observer and
environment• direction of heading, time to contact
2. Segmentation of figure from ground• disruption of camouflage techniques
3. Recovery of 3D parameters• motion parallax (3D depth)• kinetic depth (3D shape)
4. Object identification• humans, friend/foe
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Centre of expansion ------> = direction of heading
1. Relative velocity of observer and environment
• speed and direction of objects/observer• optic flow - pattern of motion in image
when observer moves
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Opticflowdemo
Optic flow• used to determine
– direction of heading– time to collision– approach/avoidance
• types of optic flow caused by global motion– expansion/contraction (collision?)– translation– rotation
• Gibsonian approach uses optic flow a lot
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2. Segmenting figure from ground
• Figure: object that draws our attention• Ground: non-moving dots/contours/blobs• Gestalt law of Common Fate
– items that move together belong together
From http://www.human.pefri.hr/~bsremec/figure_motion.html
Commonfatedemo
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3. Recovering three-dimensional shape
• Retinas can only record 2D projection of world• depth dimension is lost
• Use motion to recover some 3D information• different information from different views• combine into a single 3D model (hypothesis)
• Two methods:a. motion parallaxb. kinetic depth
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Position 1
Observer sees cube to the right of sphere:
(Overhead view)
• a. Motion parallax:• different viewpoints recover depth
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Position 2
Observer sees cube to the left of sphere:
(Overhead view)
• a. Motion parallax:• different viewpoints recover depth
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b. Kinetic depth• different views recover shape
QuickTime™ and aAnimation decompressor
are needed to see this picture.
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4. Object identification• motion pattern identifies object• visual form masked by “stuff”, such as
• trees, leaves• smoke• bad vision• distance• low lighting
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From http://www-white.media.mit.edu/~jdavis/MotionTemplates/motiontemplates.html
QuickTime™ and aVideo decompressor
are needed to see this picture.
Davis
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QuickTime™ and aPNG decompressor
are needed to see this picture.
From my research!
PLD
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Point light displays (Johansson)• separate visual form and motion pattern• robust detection even with distractors
– separation of figure and ground• accurate determination
– sex, friends, animals, emotional affect, action• humans only really good at recognizing
humans– relies on human motor control system– “if I could move like that then it could be like me”
• but humans are pretty good at recognizing everything
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Finally• What was the purpose of this
presentation?
• Which question remains unanswered?
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Detecting image motion
It moved! It moved!
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Motion detection versus perception• detection: low level process
– low level, like edge detection
• perception: higher level process– high level, like object identification
• how do we know that we have specific neurons for motion detection?
• how do we know that we have specific centers for motion perception?
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Specific neurons for motion detection• motion after effect
– similar to colour after effect (“negative” after starring at image)
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Bang! The Canadian Flag!
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Motion aftereffect
• shape not affected• adaptation to
motion independent of shape
• separate systems for motion and shape!(and colour!)
QuickTime™ and aAnimation decompressor
are needed to see this picture.
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Neurons specialized for motion detection• 1960-1970s
– frogs (“bug”, “predator” detectors)– house fly (optic flow)– rabbit– cat (string, yarn -- just kidding)– monkey
• model proposed: Reichardt detector
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Input 1 Input 2
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Motiondetected
Reichardt detector• correlator + two different inputs
– one input has a delay
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Input 1 Input 2
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won't fire: only 1 signal arrives
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Directionally sensitive• fires for change in one direction, not in other
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Temporally sensitive• fires for change over particular interval• change too fast: first spike arrives too late• change too slow: first spike arrive too early
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Take home message
only a particular speed and directionsets off any particular Reichardt detector
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Apparent Motion (NOT phi motion)1. A visible item suddenly disappears2. A new item appears soon afterwards at
neighboring location• Perception:
the original item “moves” to a new location
• Where have you seen this?
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Apparent Motion (NOT phi motion)1. A visible item suddenly disappears2. A new item appears soon afterwards at
neighboring location• Perception:
the original item “moves” to a new location
• Where have you seen this?
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Apparent motion constraints1. not influenced by cognition - pops out2. short spatial range (< 0.25°)3. short temporal range (< 80ms)4. many objects at once5. very much like “real” motion detection
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MotiondetectedInput 1 Input 2
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Motiondetected
Explanation of apparent motion• Reichardt detector
– can detect continuous motion
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Input 1 Input 2
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Motiondetected
Explanation of apparent motion• Reichardt detector
– can detect sudden “jumps”– cannot distinguish them
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Applications of apparent motion• Movies and Television
– Reichardt detectors cannot distinguish “real” from “artificial” motion
• Present a sequence of frames/static images– seen the same as real motion
• Requires proper timing– separation between frames < 80ms
• Movies: 24 FPS = 1/24 seconds = 41ms• TV: 30 FPS = 1/30 = 33ms
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Importance of context• Reichardt detectors do not completely
account for motion perception• Just as lateral inhibition does not
completely account for brightness perception
• Context counts!– Apparent motion of the human body– Perception of motion can occur (or be
misperceived) -> induced motion
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Apparent motion of the human bodyShiffrar and Freyd (1990)1. image of human in one pose2. delay3. image of human in second pose4. chose from one of four motion pathsresults: physically plausible paths require
delays approximately equal to real world motion
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Shiffrar and Freyd: Implications• brain takes into account typical
movement patterns• brain “knows” how long a movement
should take• brain uses motor control and perception
centers to form perceived motion path
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Downing et al (2001)• EBA: extrastriate body area• active when viewing human bodies
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Is object/world moving or eye??• see Gregory pp 99-105
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Finally• What was the purpose of this
presentation?
• Which question remains unanswered?