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Sensation and Perception > Midterm 2 Review > Flashcards

Midterm 2 Review Flashcards

1
Q

How do we see colour?

A

Prism decomposes white light into the colour spectrum.

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2
Q

Equation for light from surface

A

Illumination x reflectance

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3
Q

Differences in colour vision example

A

Train painted “improved engine green” but the colour is red

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4
Q

Colour vision deficiency

A

Red-green “colour-blindness”

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5
Q

Why is red-green colour blindness more common in men?

A

It’s an x-linked trait so it’s on the X chromosome

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6
Q

What are the three types of cone photoreceptors?

A

Short, medium, long

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7
Q

Univariance

A

For one receptor, different combinations of wavelengths and intensity will produce the same response

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8
Q

Why do we see in black and white at night?

A

You cannot perceive colour with only one receptor

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9
Q

Why three cones?

A

Each cone by itself is colourblind, the combination of cones gives us colour perception.

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10
Q

Does every species have three cones?

A

No, the number of cone types varies across species

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11
Q

Tetrachromats

A

Birds and bees have four or more cone types - they have extra UV photoreceptors and can see more wavelengths.

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12
Q

Trichromats

A

Such as humans - have three cones

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13
Q

Dichromats

A

Many mammals such as dogs - have two cone types so can distinguish yellow from blue but not red from yellow.

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14
Q

Missing cones

A

Most often missing medium or long cone types which causes red-green colour deficiency

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15
Q

Red-green colour deficiency

A

Red and green are difficult to distinguish- when colour blind, colours are still perceived but difficult to distinguish.

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16
Q

Cortical achromatopsia

A

Colour vision loss at a cortical level despite normal cone function - true colour loss

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17
Q

Low pressure sodium lamp

A

Colour groups look different under a sodium lamp and are hard to distinguish - displays cortical achromatopsia

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18
Q

Subtractive colour mixing

A

Light is subtracted by adding pigment because pigment absorbs light.
Start with white light such as in paintings
Red + green = brown

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19
Q

Additive colour mixing

A

Add wavelengths of light to a surface that had no light
Start without light such as TVs and iPads
Combination of lights add together to produce colour
Green + red = yellow

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20
Q

Thomas Young

A

Our eyes aren’t big enough to receive all wavelengths so maybe we have receptors which combine them (primary colours)

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21
Q

Colour matching experiment

A

2 primaries aren’t enough but 4 is too many.

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22
Q

Ewald Hering

A

Made the opponent colour theory to oppose the trichromatic theory

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23
Q

Trichromacy in the eye

A

Wavelength is compressed into 3 dimensions (3 types of cone photoreceptor)
Perceived colour depends on relative strength of activation

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24
Q

Evidence for trichromacy

A

Colour matching experiment -
People have to adjust the lights to match the colour provided.

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25
Q

Consequences of trichromacy

A

Multiple spectra can elicit same ratio of cones - thus appearing identical.
. Some colours are the same wavelength so they appear the same.

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26
Q

Opponent colour theory

A

Says that there’s 4 primary colours
- red, green, yellow and blue
These are organised into opponent pairs
- red-green and yellow-blue

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27
Q

Unique hues

A

Certain colour combinations don’t exist
- we can have reddish-orange and bluish-green but not a reddish-green or a bluish-yellow

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28
Q

Hue cancellation experiments

A

Adjust red light to cancel out green
- red-green combination seen as yellow
Adjust blue light to cancel out yellow
- blue-yellow perceived as white

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29
Q

Negative afterimages

A

Lilac chaser - see a green dot filling in the space but it’s not actually green.

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30
Q

Trichromacy vs opponency

A

Both can be correct

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31
Q

Two-stage model of colour coding

A

Different cones receive wavelengths that create different colours

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32
Q

What is the visible light spectrum?

A

400 - 700 nm

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33
Q

what are the three receptor types?

A

Short, medium, long

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34
Q

short cones

A

have peak sensitivity to short wavelengths

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35
Q

medium cones

A

have peak sensitivity to medium wavelengths

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36
Q

long cones

A

Peak sensitivity to long wavelength

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37
Q

principe of univariance

A

having a single cone type means you can’t discriminate between different wavelengths or intensities.

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38
Q

opponency

A

4 colour primaries organised in opponent pairs
- red green
- blue yellow

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39
Q

unique hues

A

Evidence for opponency:
- no such thing as a reddish green or a bluish yellow

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40
Q

colour cancellation

A

add green to “cancel” out red
- you get a yellowish brown colour instead of reddish green

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41
Q

negative afterimages

A

lilac chaser - see a green dot where the purple is not, but there is no green

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42
Q

retinal ganglion cells

A

excitatory and inhibitory receptive fields which react to different wavelengths.

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43
Q

white light has what wavelengths?

A

has short, medium and long wavelengths

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44
Q

neurons

A

react differently to different colours with different wavelengths.

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45
Q

chromatic edge detection

A

differentiate red and green etc as well as light and dark
- important for colour constancy

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46
Q

colour constancy

A

if the lights change (natural to fluorescent to dim to candle light etc) the colour of an object stays the same once you have seen it.

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47
Q

assumptions about colour

A

“Paint” versus “light”
Is it actually that colour or is shadow falling on it changing the way it looks?

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48
Q

cues for shadows

A

fuzzy edges and darker
The same shade could be perceived as darker if the brain thinks it’s a shadow

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49
Q

correspondence problem

A

when objects are moving, which goes with which?
- population coding helps to solve the correspondence problem (cues such as distance etc)
- are dots moving side to side or up and down?

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50
Q

local motion detectors

A

in the V1
- aperture problem: looking at movement through a small peephole means that the motion could be going any direction.

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51
Q

Global motion detectors

A

in MT
- get more motion information
-reicard detection (add more apertures to see flow of motion)
- MST optic flow

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52
Q

depth

A

2D retinal image to 3D layout of space

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53
Q

monocular depth cues - pictorial

A

make assumptions about the world

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54
Q

Occlusion

A

Nearer objects block further objects but can’t tell distance apart

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55
Q

Relative size and height

A

Objects that are larger and lower in picture are closer to/ in front
Objects that are smaller and higher in picture are further away

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56
Q

Texture gradient

A

Lots of objects the same size such as bricks or blades of grass gives a stronger sense of depth

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57
Q

Familiar size

A

Use the known size of an object/ person to infer size and distance of an unknown object

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58
Q

Linear perspective

A

Parallel lines in real life converge in 2D pictures
Ponzo illusion - 2 bars the same size look different when placed above converging lines.

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59
Q

Name the 4 pictorial depth cues

A
  1. Occlusion
  2. Relative size and height (texture gradient)
  3. Familiar size
  4. Linear perspective
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60
Q

Motion parallax

A

Closer objects move a bigger distance on retina and further objects move slower in background

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61
Q

Vergence and accommodation

A

As focus adjusts and eyes move

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62
Q

Binocular disparity - stereopsis

A

Our eyes are offset in space
- focus on one thing which lands on the fovea of the eyes

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63
Q

Crossed disparity

A

Closer than fixation points on eye (outside the fovea)

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64
Q

Uncrossed disparity

A

Farther than fixation points on eye (falls inside fovea)

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65
Q

Gestalt grouping

A
  1. Proximity
  2. Similarity
  3. Good continuation
  4. Closure
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66
Q

Kanizsa triangle

A

3 Pac-Man line up and we see a triangle even though there’s no triangle
- modal completion from amodal completion.

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67
Q

T junctions

A

Signal occlusion
- amodal completion

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68
Q

Generic view principle

A

Not likely things would line up by accident - must be intentional and so the brain amodally completes the object.

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69
Q

6 different classes of opponent cells

A

R+ and G-
G+ and R-
B+ and Y-
Y+ and B-
White+ and Black -
Black+ and White -

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70
Q

Spatial antagonism

A

On and off cells cancel eachother out in specific wavelengths.
Single colour opponency is found in retina and LGN.

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71
Q

Double opponent centre surround

A

Combines different single opponent inputs such as red-green and blue -yellow

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72
Q

Assumptions about shadows

A

Shadows darken surfaces without changing the colour.
“Checker shadow illusion” shows squares are the same colour just lighter or darker so they look different.

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73
Q

Context affects colour perception

A

We assume the colour or brightness of an object based on what we know (is it in light or in shadow?)

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74
Q

Motion

A

A change in spatial position over time.

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75
Q

Kinematogram

A

Motion can be perceived independent of object recognition

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76
Q

Motion can be perceived separate of object recognition

A

Kinematogram

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77
Q

Random dot Kinematogram

A

Motion perception can come before shape recognition
When the dots are static they do not represent a shape, but when they appear to spin they create a cylindrical shape.

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78
Q

Waterfall illusion

A

Variant of the motion after effect (MAE)
After looking at a waterfall then looking away, things still appear to be moving.

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79
Q

The motion after effect (MAE)

A

After adaptation, perceived motion of stationary pattern in opposite direction occurs.

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80
Q

Repulsive after effect

A

Pushes perception in the opposite direction.
The shrinking Buddha example.

81
Q

Reichardt detector

A

Neuron has a delay so that the motion of an object is perceived at the same time by both eyes.
- if the object crosses the path of one eye first, then there is a delay in time after that position in space until it crosses the path of the other eye so they reach the motion detector unit at the same time.

82
Q

Motion detectors in V1

A

Combined simple cell receptive fields
- offset in space and time

83
Q

What can the visual system not distinguish between?

A

Continuous motion and a discrete jump

84
Q

Apparent motion

A

Perceived smooth motion from alternating stationary targets.

85
Q

Lilac chaser experiment

A

Green dot is a negative after image.
Can change how smoothly the dots appear to move with different speeds.

86
Q

The correspondence problem

A

Example: bistable quartet
- unsure whether the dots are moving side to side or up and down based on the distance apart from each other.

87
Q

Local motion

A

Ambiguous
V1 neurons are tuned to different directions.

88
Q

The aperture problem

A

Viewing through a small peephole makes it more difficult to see which direction the motion is actually travelling.

89
Q

MT area (V5)

A

MoTion
Middle Temporal area
Solves the aperture problem because it integrates global motion

90
Q

Global motion

A

Larger receptive fields
Independent of local direction
Opponent coding of motion direction

91
Q

Motion blindness

A

Akinetopsia

92
Q

Akinetopsia

A

Motion blindness

93
Q

Patient LM example of motion blindness

A

Had a stroke
Damage to MT
Cannot detect motion
Can see water in a glass but cannot see it filling up so it overflows
Her world is made up of disjointed still images - like a slow camera flashing every 3 seconds.

94
Q

Motion perception in daily life

A

Real world consequences of low-level motion perception
Carries info about objects:
- form
- depth
- biological entities

95
Q

The “deer in headlights” look

A

Freeze response in many animals
Part of a camouflage strategy
- camouflage is broken by motion
- avoiding predators and approaching prey
Freezing response reinforces the camouflage effect which is conserved across many mamal species.

96
Q

Depth from motion

A

Motion information gives us clues about form.
-Stereokinesis = strong sense of 3D object when it moves.
-Kinetic depth effect = looks 2D until you rotate it.
-Motion parallax

97
Q

Stereokinesis

A

Strong sense of 3D object when it moves

98
Q

Kinetic depth effect

A

Looks 2D until you rotate it

99
Q

Form from motion example

A

“Point - light displays”
- strong sense of form even from sparce display
Appears to be a bunch of dots until it moves, then it represents a dog playing or a human walking etc…

100
Q

Motion from action

A

Motion signals arise from interactions with environment

101
Q

Environmental motion signals

A

“Optical flow”
- as we move around, we gain useful information about how to interact with the environment.

102
Q

Optic flow and heading

A

Pattern of motion in visual field
Information about direction of motion, distances, direction of movement (heading).

103
Q

Focus of expansion (FOE)

A

Central point where there’s no movement - tells you direction of heading

104
Q

Heading

A

Focus of expansion is where we are looking.
Direction of movement is where the body is facing.
- head direction is forward but heading slightly right

105
Q

MT versus MST

A

MT receptive fields cover only one hemisfield at most.
MST has very large receptive fields which covers both hemisfields.

106
Q

Complex motion

A

MST neurons respond to complex motion:
- expansion
- contraction
- rotation

107
Q

Uses of optic flow

A

If you’re not moving, there’s no optic flow pattern.
- self motion cue
- posture control
- time to collision

108
Q

Self motion cue

A

Detect which way you’re moving

109
Q

Time to collision

A

Detect objects moving towards you

110
Q

Vection illusions

A

A lack of optic flow
- optic flow in periphery overrides vestibular input - dominance of vision over vestibular information.

111
Q

Vestibular input

A

Any motion, movement, tilt or change in direction of the head.

112
Q

Visual control of posture

A

Vestibular system helps with balance

113
Q

“Swinging room” experiment
- posture

A

Stationary floor but moving walls and ceiling
- when room moves forwards, gives visual perception that you’re falling backwards
- adjust posture to lean forwards which makes you fall.

114
Q

Time to collision

A

Object approaching observer - how much time before contact?
- optic flow works out the time to collision and symmetrical expansion predicts a direct hit

115
Q

Time to collision - patterns

A

When an object is coming towards you, if the visual system is looming or expanding, then it isn’t coming directly at you - if it is symmetrical as it expands then it is coming directly at you.

116
Q

Maintaining collision path

A

“Linear optical tracking”
- move so that the ball looks like its moving in a straight line, then you should end up in its path.
- fixed line of sight
- symmetrical looming

117
Q

Predicting the present - moving objects

A

Visual system predicts where the moving object will be.
- doesn’t work for flashing objects
- “flash-lag effect”

118
Q

Assumptions

A

Brain makes assumptions abut the world:
- paint vs shadows
- depth
This is the visual system’s best guess of what’s out there based on past visual experiences.

119
Q

2D retinal images become 3D

A

Cues to depth are learnt from experience

120
Q

Monocular depth cues

A

Monocular = one eye

121
Q

Binocular depth cues

A

Binocular = two eyes

122
Q

Monocular depth cues examples

A
  • pictorial depth cues
  • motion parallax
  • accommodation and convergence
123
Q

Accommodation and convergence

A

How our eyes focus and the movement of the eyes to adjust the lens

124
Q

Pictorial depth cues

A

-occlusion
- relative height and size
- linear perspective
- aerial perspective (haze)

125
Q

Occlusion

A

Closer objects block objects further away, but don’t know the distance between these objects from that.

126
Q

Relative size and height

A

Closer objects appear bigger and lower in picture.
Further objects appear smaller and higher.

127
Q

Familiar size

A

Use size of known object to infer size of unknown object.

128
Q

Linear perspective

A

Vanishing point of two parallel lines converging in 2D.

129
Q

Aerial perspective/ “haze”

A

Objects in distance appear hazy because there’s a lo of air particles between our eyes and that object.

130
Q

Motion parallax

A

Requires head motion
- closer objects move across field quicker
- further objects move across field slower

131
Q

Vergence

A

Both eyes move inwards or outwards (non-conjugate movement)

132
Q

Convergence

A

Near focused
- moves from far away to close
- pulls eyes inwards

133
Q

Divergence

A

Far focus
- moves from close to far away
- pulls eyes outwards

134
Q

Accommodation

A

Thickness of lens adjusted depending on focus
- near focus = fatter lens

135
Q

Size illusions

A
  • ponzo illusion
  • moon illusion
  • Ames rom and Beuchet chair
136
Q

Ponzo illusion

A

Bars are the same size but placed over converging parallel lines so they appear different sizes.

137
Q

Moon illusion

A

Relative size and distance cues on horizon
- makes the moon appear larger than usual

138
Q

Ames room illusion

A

Look through one peephole into a room which looks normal .
- room actually a trapezoid but looks normal from jut one angle.
- makes people of the same height (identical twins) look extremely different heights.

139
Q

Beuchet chair

A

Chair is in 2 parts but from the angle of the peephole it looks like one whole chair.
- makes people seem insanely different sizes when really they are just certain distances apart.

140
Q

Stereoscopic vision

A

Depth information from binocular disparity.

141
Q

Binocular vision geometry

A

Object being fixated on falls on the fovea in both eyes.

142
Q

Horopter

A

A locus of all the points in 3D space that fall on corresponding retinal points.

143
Q

Binocular disparity

A

Objects that aren’t on the horopter fall on different retinal points.

144
Q

Crossed disparity

A

Object is closer so falls on the outside of the fovea in each eye when fixated on.

145
Q

Uncrossed disparity

A

Object is further away so sits on inside of fovea in each eye when fixated on.

146
Q

Binocular receptive fields in V1

A

Show sensitivity to both position and phase
- many V1 neurons receive information input from both eyes.

147
Q

Disparity - selective neurons

A

Different neurons are tuned to different disparities
- receptive fields in each eye
- crossed or uncrossed

148
Q

Stereoblindness

A

Amblyopia (lazy eye)
- one of the eye’s ocular motor muscles don’t work so the eyes don’t track together.
- only one eye is fixating on what you want it to.
- causes blurred image in visual system.

149
Q

“Stereo Sue” example of stereoblindness

A

Learnt to see in 3D at age 48
- best treated before the age of 5 if born with it
- wear a patch on good eye to train the bad eye to take input into the visual system.

150
Q

Panum’s fusional area

A

Region where stereoscopic depth is perceived.
- range of disparities over which fusion of views occurs

151
Q

Outside Panum’s fusional area

A

Suppression
Double vision
Binocular rivalry

We can’t see our noses because the images don’t match an fall outside the fusional area.

152
Q

Binocular rivalry

A

When the input to two eyes is completely different
- visual system cycles back and forth between the two images because they’re so different.
- will see a face then a house then a face then a house instead of a face house merged.

153
Q

Seeing stereo

A

Free fusion
Stereoscope
Anaglyphic glasses

154
Q

Free fusion

A

Shift focus of eyes to see different images
Parallel or cross-eyed

155
Q

Parallel free fusion

A

Wide-eyed: focus behind the actual image

156
Q

Cross-eyed free fusion

A

Focus in front of the image

157
Q

Anaglyphic 3D

A

Range of disparity
Larger distances produce exaggerated effects
- cut out effect
- miniaturisation

158
Q

Mid-level vision

A

Image
Surfaces
Objects

159
Q

Image

A

Orientation
Spatial frequency
Colour
Disparity
Motion

160
Q

Surfaces

A

Grouping
Occlusion
Completion

161
Q

Surface perception

A

Surfaces are actively constructed by the brain - going beyond image data.

162
Q

Gestalt principles

A

Principles of grouping:
- proximity
- similarity
- good continuation
- closure
“Laws” reflect the probability that features go together

163
Q

Proximity

A

Features near each other are more likely to group together than those that are separated.

164
Q

Similarity

A

Features that are similar to each other are more likely to group together.

165
Q

Good continuation

A

Features forming the smoothest contour group together
- smooth contours appear more in the real world
- imagine following a string of

166
Q

Closure

A

Features that enclose space group more strongly than those that do not.
- dashes create a circle even though that circle’s outline isn’t fully complete.

167
Q

Consequences of closure

A

Surface completion
- modal and amodal completion
Tendency to complete figures even when not given all the information.
- a panda could have wings but we don’t see the panda with wings we just imagine it based off our experience of pandas.

168
Q

Modal completion

A

Surfaces appear to continue infront of other objects even though the border isn’t visible.
- relatively uncommon in real life.

169
Q

Amodal completion

A

Surfaces complete behind occluding surfaces
- not seen but registered
- we know people have faces but if they’re covered we do not know what their face looks like.
It is the visual systems best guess.

170
Q

Amodal completion at work - border occlusion

A

Allows fragments to be grouped when they otherwise wouldn’t be:
- a group of shapes aren’t connected until border occlusion occurs, ten we group the fragments together to see the shape they make behind the border.

171
Q

Local rules, not object knowledge

A

Completion is primitive and automatic
- see a naked man behind the dots rather than imagining him in a speedo
- all black squares are equal until you put a red to over some, then some appear to make a larger black square or a black cross.
- 2 donkeys next to each other look normal but cover the middle and it looks like one long donkey

172
Q

Modal from amodal completion

A

Amodal completion leads to modal completion
- kanizsa triangle
- see the edges of the triangle which makes it look brighter even though here’s no triangle there at all.

173
Q

Edge labelling

A

Focus on edges of simple block lines and corners

174
Q

Depth edge

A

Occluding edge
- whatever object is to the right of the arrow (>) is in the front.
- symbolised with > on each edge showing the direction.

175
Q

Convex edge

A

Edges that stick out towards you
- symbolised by + on the edge that sticks out

176
Q

Concave edge

A

Edges that retract and point away from you
- symbolised by - on the edge pointing away

177
Q

L vertices

A

Where 2 lines meet to make an L shape

178
Q

Arrow vertices

A

Where 2 surfaces meet (3 lines total forming an arrow)
- corner points away from me

179
Q

T vertices

A

Three lines meet to form a T shape (can be sideways)
- used to tell depth
- two of the lines have to be co-linear

180
Q

Y vertices

A

Three lines meet to bring together three surfaces
- corner points towards me
- lines make some similarity to a Y

181
Q

T junctions and modal completion

A

L-junction = an edge
T-junction = occlusion

182
Q

Border ownership

A

Borders between objects must belong to only one of them objects
- the object that owns the border must be the object in front.

183
Q

Unbounded regions

A

Regions that do not own the border - usually behind the object which owns the border.
- 2 unbounded surfaces can join together through amodal completion (our visual system may connect them even if they’re not connected)

184
Q

Area V2

A

Illusory contours
Border ownership

185
Q

Illusory contours

A

V2 neurons respond to orientation defined by real or illusory contour

186
Q

Neural coding of border ownership

A

V1 simple cell codes for light and dark but border ownership cell reacts when it’s in the border side rather than the light side.

187
Q

Pattern seeking

A

The brain is a pattern seeker
- looks for patterns in visual input
- certainty over ambiguity

188
Q

Building surfaces from images

A

“Quick and dirty” rules - our visual system invents structure from images.
- sometimes there’s more than one interpretation which the brain switches between.

189
Q

Ambiguous figures

A

Figure-ground reversal
Ambiguous depth
Conceptual ambiguity

190
Q

Figure-ground assignment

A

Figure = object in the front which owns the border
Ground = surfaces/ objects behind
- sometimes its ambiguous and you can switch between what’s in front and what’s behind.

191
Q

Figure-ground reversal

A

The face-vase example or silhouettes and columns
- can switch between both but cannot see both together - one always falls into the background.

192
Q

Ambiguous depth perception

A

Schroeder staircase - can be perceived from the top of stars or below staircase.

193
Q

Conceptually ambiguous figures

A

Edge labelling is the same but interpretation differs:
. Duck-rabbit
. Young-old woman
. Old man turns into two people getting married
. Mans face turns into two people kissing
Can switch between these images even when you know both.

194
Q

Cues to surface perception

A

“Quick and dirty” rules
- surfaces don’t line up by accident
- unconscious and automatic
Based on local information, not object knowledge

195
Q

Light and shading

A

Assume that light comes from above
- dimples and dots are seen due to shading in opposite directions.

196
Q

No accidental alignments

A

Two edges are unlikely to be accidentally aligned so visual system assumes that they are actually touching.
- man kicking leaning tower of pisa

197
Q

Impossible objects

A

Penrose triangle
- geometry isn’t physically possible but visual system perceives a 3D object
- even after we know it isn’t connected we still see it as being one shape
- can be edge labelled without problem

198
Q

Impossible objects example

A

Devils fork
- brain uses knowledge of the world and reality to make them look normal even though they cannot physically exist.

199
Q

Generic viewpoint

A

Assumes scene is viewed from a generic rather than an accidental viewpoint.

Sensation and Perception (2 decks)

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