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PSY280 > PSY280 - 5. Object Recognition > Flashcards

PSY280 - 5. Object Recognition Flashcards

1
Q

Spatial Rule

A

stimuli presented close together in space tend to be integrated
hear a sound + see something at same time - link it to single event
if at diff locations, don’t see it as single event

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2
Q

Temporal Rule

A

stimuli presented close together in time tend to be integrated
no integration when stimuli presented at vastly diff times
hear creak + door opens, doesn’t make sense unless simultaneous

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3
Q

Inverse effectiveness

A

effect of integration is more pronounced with weaker
stimuli
bigger enhancement of perception when dealing with degraded intensity

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4
Q

Multisensory Integration improves

A

detection
response times: faster to respond when presented in 2 modalities
accuracy: various judgements of stimulus when more than 1 presented

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5
Q

fundamental attributes

A

defer to vision for judgments about space, to hearing for judgments about time
to identify 2 objects - have to be separated in space
tend to assume auditory stimulus at the location of visual event

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6
Q

Middle vision

A

stage of processing that comes after basic features & before object recognition or scene
house recognition: mix of complex processes that allows us to recognize it
this is the object, other is the background

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7
Q

Where are the edges?

A

How can we tell that car & house separate objects?
How can we tell that snowman & house are separate objects?
How can we tell that the windows & the house are part of
the same object?

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8
Q

Where are the edges?

A

decide where 1 object ends + another 1 behind cell identifies lines
we identify objects because continuous + connected
connectedness can’t answer snowman because they’re connected
we know windows + house are same objects, yet they’re connected
some objects overlap

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9
Q

Connectedness won’t work

Connectedness isn’t even necessary

A

continuous edges aren’t necessary for our system to perceive edges
visual system fills in gap
we perceive more contrast than actually there

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10
Q

Even if connectedness did
work, what are the
important edges?

A

we tend to see objects in contexts

our visual system not challenge to find boundaries even if there are multiple edges

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11
Q

illusory contours

A

don’t even need edges to perceive segregation
contour that is perceived even though nothing changes from 1 side of the contour to the other
illusory contours perception without sensation

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12
Q

Structuralism

A

perceptions result from summation of many elementary sensations
Gestalt
wilhelm wudnt - complex objects/perceptions can be understood by analyzing components

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13
Q

Structuralism

A

components = elementary sensations
couldn’t explain illusory contours or apparent motion
elementary sensations of apparent motion that structuralism couldn’t explain

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14
Q

Structuralism

A

flashing stationary light moving on + off giving illusion of movement
more + more perceptual phenomenon that structuralism couldn’t explain

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15
Q

Gestalt

A

whole is different than sum of it’s parts
direct response to structuralism
heuristics used to guide interpretation of retinal image
sometimes >1 principle at work - difficult to predict which one is going to win all else being equal

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16
Q

Gestalt

A

Gestalt principles are like individuals with opinions
about how the information should be organized
under diff conditions, diff principles/rules might have diff opinions

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17
Q

Gestalt

A

discuss how to organize sensory input into coherent sensory experience
perception by committee - a consensus does quickly emerge that allows us to settle on a single interpretation of a visual scene

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18
Q

gestalt principles

A
  1. simplicity: assume simplest structure - simpler to see it as
  2. similarity: tend to group together stars + circles
  3. proximity: 3 groups of 2 lines
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19
Q

gestalt principles

A
  1. common fate: movement in the same direction

5. closure: things close shaped tend to be grouped together/close objects that are incomplete

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20
Q

gestalt principles

A
  1. familiarity: 45 means something to us - then we see us
  2. good continuation
  3. similarity: all orange dots, proximity: close together
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21
Q

gestalt principles: good continuation vs. similarity

A

When 2 principles operating, it’s hard to predict which one takes precedence.
general assumption that smooth lines belong together + lines tend to follow smoothest path
not strict rules, just heuristics
but if good continuation vs similarity pitted against each other, similarity wins

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22
Q

similarity has it’s limits

A

similarity has limits: works for colour, shape, size, orientation
but combo of these features doesn’t work too well

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23
Q

perception by committee

A

diff rules can come together + our perceptual experience isn’t always clear about which one wins
middle vision = specialists, diff in opinion about what sensory input might mean
how to organize info through committee

24
Q

Pandemonium Model

A

Despite the potential for disagreement, there are some basic committee rules - example of how to implement computer vision model
Image demons like scanners: get sensory info
passes to first level of analysis: feature demons like specific features - cheer when preferred feature is present

25
Q

Pandemonium Model

A

cognitive demons like specific letters: listen to feature demons
R cognitive demons making lots of noise, D demon + P demon is also making some noise
Decision demon listen to cognitive demons to see which one is cheesing the loudest

26
Q

Every image is, in theory, ambiguous

A

multiple interpretations
every image is ambiguous
but committee comes to consensus quickly
using rules we can come to solution easily + rapidly
house rules: honour physics + avoid accidents

27
Q

RULE 1: honour physics

A

come to implicitly understand that objects occlude light
assumption white square is occluding 4 discs behind it
visual system know about this + other physical properties + use them to organize visual info

28
Q

RULE 2: avoid accidents

A

could be generated by quadrilaterals at diff depths + angles, just standing at one place that sees this
discards accidental viewpoints in consensus meeting
so unlikely that perceptual system doesn’t even consider it

29
Q

An accidental viewpoint

A

viewpoint relative to an object that results in

perception of an accidental or rarely encountered property

30
Q

What is the figure & what is the (back)ground?

A

needs to parse everything to separate objects + background

earlier gestalt rules can help us separate, but can’t help us differentiate which is background + object

31
Q

What is the figure & what is the (back)ground?

A

some specific Gestalt rules that just apply to figure-ground
assignment (figure-ground segregation).
process of determining that some regions of an image belong to figure (object) + other regions are part of the background

32
Q

Border ownership

A

associated with the figure

face/vase figure - border ownership changes based on how you interpret the image

33
Q

figure & ground

A

surroundedness: likely surrounded region is figure
parallelism: contours parallel groups into a figure
size: smaller figure as object, larger object as background

34
Q

figure & ground

A

relative location: lower region is object

symmetry: identify objects or features based on symmetry
meaningfulness: objects that are meaningful to us tend to be identified as objects

35
Q

naive template theory

A

visual system recognizes objects by matching neural

representation of image with a stored representation of same “shape” in the brain

36
Q

naive template theory

A

matching pixels - templates involve an array of spot detectors
if stimulus falls on filled spot detectors, match template
too many templates to be involved

37
Q

recognition by components

A

structural description of an object in terms of nature of its constituent
parts & the relationships between those parts

38
Q

recognition by components

A

parallel contours: lines always parallel to each other

exploits the concept of non-accidental properties: an edge feature that is not dependent on exact viewpoint.

39
Q

recognition by components

A

always able to detect edge feature no matter what orientation except for 1 viewpoint: coin curves except when dead on

40
Q

recognition by components

A

t-junctions (occlusion): occur when 1 surface occludes another
y- & arrow junctions (corners): signal corners

41
Q

Geons

A

geometric ions - basic units of objects
36 geons, each composed of unique set of NAPs
elements that involve structure descriptions
combined non accidental properties in novel ways to represent 37 unique geons recognizable from any viewpoint

42
Q

Geons

A 
viewpoint-invariance: can be identified when viewed from most viewpoints
nonaccidental properties (edges)
axis straight or curved: skewer on
constant/expanded: parallel?
43
Q

Principle of componential recovery

A

If we can see the geons, we can identify the object
too many of nonaccidental properties are obscured meaning you can’t identify geons
if you move a little bit, you can identify it
need access to nonaccidental properties, to get geons, to get object

44
Q

Geons

A

only need to see a few geons to identify an object
give them 3/9 78%, 6/9 90%
flexible templates

45
Q

recognition is sometimes holistic

A

recognition is not completely viewpoint invariant.
experts on identifying greebles would do poorly if upside down
but rbc does get us far in object recognition

46
Q

contextual modulation

A

evidence of Gestalt rules in the brain, both through neuroimaging & at the level of the neuron
contextual modulation: change based on context

47
Q

contextual modulation

A

neuron’s response is influenced by stimulation outside it’s
receptive field
same stimulus in diff contexts
providing context of good continuation - neuron likes it more + is more active

48
Q

contextual modulation: good continuation

A

likes a continuous line outside its receptive field

changing response based on stimulation outside receptive field

49
Q

contextual modulation: figure-ground segmentation

A

neuron recognizes figure ground,

top: bar is in object, more response
bottom: bar is in background, less response

50
Q

neural activity correlates with sensory experience

A

different sensory input
different neural activity
hard to tell if cat sees them as separate stimulus

51
Q

does neural activity correlates with perceptual experience?

A

You have to be able to keep the sensory information identical, but somehow generate different perceptual
experiences.

52
Q

does neural activity correlates with perceptual experience?

A

have to make task really difficult
show them image in peripheral vision
perception outlasts sensory stimulation
once removed still capable of receiving info unless you mask it by presenting another stimulus

53
Q

does neural activity correlates with perceptual experience?

A

yes: All of these curves represent responses to
the presentation of Harrison Ford’s face.
had to identify what they saw: harrison ford, a diff face, or diff picture

54
Q

does neural activity correlates with perceptual experience?

A

correct identification, face but incorrect identification
face but did not see face: all cases harrison ford was stimulus
fmri signal is huge when correct identification
identification is baseline when saw pattern
middle when saw face

55
Q

binocular rivalry

A

You can also force 2 experiences
with the same stimuli
1 image to 1 eye, other image to another eye: back + forth because of rivalry
at fairly regular intervals - without permission

56
Q

does neural activity correlates with perceptual experience?

A

same sensory input diff perceptual experience + diff neural signature
green face over red house
face comes in left eye, house on right eye
switches back + forth

57
Q

does neural activity correlates with perceptual experience?

A

identify when they see house + when they see face by pressing button
fmri activity greater activation in ffa when seeing face, when house greater ppa activation than ffa
ppa when house + ffa when face

PSY280 (11 decks)

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