Object perception Flashcards

1
Q

Describe Picasso image

A

Very challenging for visual system
But brain recognizes ppl and table and stuff
Must compare to internal mdoels

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Why do need to recognize objects

A

Ability to recognize and categorize objects is fudnamatemally for survival and interaction with envrieomnt
Allows us to navigate world, dangers, food and plays a crucial role in social interactions
Perception to action loop
Technological context = replicating these abilities = can enhance our safety, health and well being, handling tasks ranging from autonomous driving to early detection of disease in medical images

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

What are challenges of creating effective object recognition systems

A

Reflect complexities of visual processing in brain = variability of objects - brain deals with variance, context, lighting conditions = influences what it looks like
Requires systems capable fo abstraction and generalization from limited exs, similar to human ability to learn and recognize new or unfamiliar objects - brains and as

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Describe 5 blind monks

A

None of them have same conclusion - diff based on what they perceiving - touching
Need to increase info to get full pic

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Describe template theory

A

Proposition that visual system recognizes objects by matching the neural representation of the image with an internal representation of s she shape in brain
Drawback = hard to store unique template for ever occurrence of object ever seen

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Describe exemplar theory

A

Brain reorganizes objects by comparing them to multiple stored examples rather than single template
Not relying on idealizes template - recognition occurs based on what you already experienced in the past, more flexible than template

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Describe generalized context model

A

Diff metrics
Marie formal and mathematical
Formalized exemplar theory in generalized context model

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Describe prototype model

A

Ppl form average of category of objects store - abstract prototype that represents best ex of category
Like best ex of dog - compared to this
Cognitive categories organized around prototypes
View as typical ex or an average over several examples than form category

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Storage - template theory

A

Fixed templates for each object

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Recognition process template tehory

A

Direct matching to a single internal representation

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Flexibility template theory

A

Limited -sensitive to variations
Cannot store infinite templates, grandmother cells

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Scalability template theory

A

Requires many templates for diff views

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Dog ex template theory

A

Matches input to a stored dog template
Like side view of Labrador

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Storage exemplar theory

A

Mueller stores examples - exemplars

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Recognition process exemplar theory

A

Cora prison with multiple previously seen instances

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Flexibility exemplar theory

A

High - handles variability well

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

Scalability exemplar theory

A

Stores many exemplars but generalizes well

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

Dog ex exemplar theory

A

Compares input to multiple stored dog examples - various breeds, angles and contexts

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Storage prototype theory

A

Single abstracted prototype per category

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

Recognition process prototype tehory

A

Matching to the most representative prototype

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

Flexibility prototype theory

A

Moderate - allows for some variation but relies on an average

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

Scalability prototype theory

A

Requires storing only one prototype per category

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

Dog ex prototype theory

A

Compares input to an idealized average dog that represents the category

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

Describe general recognition theory - categorizing based on

A

Multivariate
Extension of signal detection tehory
Focusing on how perceptual distributions influenced decision making

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Describe general recognition theory - categorizing defined by
Probabilistic distributions and categorization is based on decision boundaries that separate perceptual regions Ability to differentiate objects depends on how much their features overlap If do not overlap = easier to find decision boundary (hyperplanes)= decision easier and faster
26
Compare gcm vs grt
Gcm = store many specific faces you’ve seen before, when seeing new face = compare it to stored examples and assign the category based on similarity to past faces Grt = rely on perceptual dimensions - face shape, Jaw width, eye size - make a decision based on statistical boundaries between 2 categories
27
Describe recognition by components
Structuralist tehory Alphabet of shapes - geometric ions - goons Form objects = combined to create Limitation = doesn’t really handle variability we see in objects, just a crude characterization fo obejcts
28
Describe grandmother cell theory - gen
Kinda impossible bc some variability - one single neuron for every single concept in physical world —single neuron responsible for recognizing grandma?
29
Describe grandmother cell theory - specifics
Concept contributes to ongoing debate between localized vs distributed representation Extreme ex of localized representation in brain Also if cell dies = does that mean wont recognize grandma anymore
30
Describe grandmother cell theory - Jennifer Anniston
Ppl with electrodes in brain - epilepsy treatment - did exp Jennifer Anniston = cell fires when see her, other systems also fire if hear her voice - also for Harrison ford for some ppl Kinda supports grandmother cell theory
31
Describe computational models of object recognition
Deep neural network = Multiple layer neural networks capable fo being trained to recognize obejcts Numerous instances of an object shown to network with feedback provided Overtime = network learns to recognize new instances of object that is has never been explicitly trained on - need to generalize so can see object
32
Describe deep neural network ex
Alex net Stimulus —> layer 1 —> … layers 6-8 = huamn face So small area stimulus inputted and processes = spatial average passed on until can put label of huamn face on it
33
Describe deep learning in object recognition
Deep neural networks rival representational performance of inferior temporal cortex - it - in monkeys in object recognition task Representations of Dnn based object recognition model successfully predict the representations measured in inferior temporal cortex using fair Using dnn to mdoel visual properties of stimuli= demonstrate that intermediate and high level image features can predict visual awareness and provide mechanistic explanation for phenomenon of attentional blink - like if show image v quick
34
How do recognize objects
Detecting spots and edges and bars = use retinal ganglion cells, lateral geniculate nucleus and primary visual cortex -v1
35
What detects spots
Retinal ganglion cells and Lgn - localized contrast
36
What detects edges and bars
Primary visual cortex - orientation selectivity - combine spots
37
How do spots become objects and surfaces
Brain performs sophisaticated processing b beyond v1 Integrating visual features into structured representations of obejcts - intermediate level vision and high level vision
38
Describe intermediate level vision
V2,v33,v4 etc —> grouping features into contours, textures and surfaces
39
Describe high level vision
It cortex —> recognizing complex shapes, obejcts and categories - tolerance to variability
40
What is object recognition about
Not just about simple features but about hierarchical processing across multiple visual areas - feedback and feed forward features
41
Describe lines to border to textures - gen
Receptive fields of extrastriate cells respond to visual properties crucial for object perception Only respond if boundary belongs to object and not background
42
Describe lines to border to textures - ex of boundary ownership
For given edge or contour = neurons determine which side belongs to object and which belongs to background - a fundamental processs in figure ground segregation
43
Describe intermediate mid level vision = define
Loosely defined stage of visually processing that occurs after low level feature extraction - like edges, contrast and before high level object recognition and scene understanding
44
Describe intermediate mid level vision = key functions
Perception of edges and surfaces Determines which regions of an image should be grouped into obejcts Bridges low level feature detection and high level object recognition
45
Hypo do we detect object edges - intermediate mid level vision
Primary visual cortex v1 neurons have smaller respective fields that detect local edges and contrast Neurons are orientation selective - responding to edges at specific angels
46
How do we know which edges belong together
Complicated Computerized edge detectors are not as effective as humans in detecting meaningful edges As humans = can see it better
47
Can computers detect edges the same as humans
Locally contrast between background and foreground nto strong enough Computers miss edges that humans easily perceive bc they rely on local contrast and intensity differences
48
Describe illusory contour
Contour that is perceived even though no physical edge exists between one side and the other Edge detectors fail Minds fail gaps Problem with some of more structuralist theories - mind can solve problem
49
What is gestalts theory
Whole is greater than sum of parts Opposes structuralism - which emphasizes breaking perception into basic elements Suggests that perception is holistic = meaning we naturally organize elements into meaningful wholes rather than processing each part independently
50
Define gestalt grouping principles
Set of rules that describe when and how elements in an image appear grouped together
51
Define gestalt = similarity
Similar objects - colour, shape, size or texture = appear grouped together - perceived as group Segment animal from background
52
Define gestalt = Proximity
Elements close to each other tend to be grouped together in perception
53
Define gestalt = Good continuation
Lines and edges are perceived as following the smoothest past Doesn’t explain everything tho - group as x, if have context = beak = ex
54
Define gestalt = Closure
Mind fills in missing info to perceive complete shapes Illusory controls - segment arrow from background
55
Define gestalt =Common fate
Elements moving together are grouped Flock of birds - moving together in shaped directions
56
Define gestalt =Figure ground
Brain separates objects from background Vase segmented in foreground ex
57
Define gestalt =Common region
Elements located within a shared boundary or enclosed area are perceived as a group Stronger than proximity
58
Define gestalt =Connectedness
Elements visually connected by lines tend to be grouped Overrules proximity
59
Describe parallelism
Parallel contours are likely to belong to same group
60
Describe symmetry
Symmetrical regions are more likely to be perceived as a group
61
Describe camouflage
Animals take advantage of gestalt grouping principles to form groups in their environment Sometimes camouflage is used to confuse observed Like Tiger - most animals are dichromats so camo better
62
What are gestalt rules good for - gen
All together = can help figure out object Ambiguity and perceptual committes Metaphor for how perception operates Committees must integrate conflicting inputs and reach consensus Many diff and sometimes competing principles influence perception Perception emerges as result of dominant interpretation agreed upon by these processes Combined info from many regions = draw most likely conclusions
63
Describe bust of Voltaire image
Similarity, closure, good continuation, proximity, figure ground organization
64
Name and briefly describe the 5 principles of intermediate vision
1 = group what should be grouped together 2 = separate what should be separated 3 = use prior knowledge - brain stores experiences to Avoid mistakes/suprises 4 = avoid accidents, like leaning tower Pisa illusion 5 = seek consensus and minimize ambiguity = on most likely hypothesis, what’s nature of object in front of me
65
Describe theory of ventral and dorsal pathways
After processing in extrastriate cortex, object info divided into 2 distinct pathways = where and what pathways
66
Describe Where pathway
Dorsal stream Processes locations and shapes of obejcts Does not encode object names or functions Extends from occipital love to parietal lobe
67
Describe what pathway
Processes object identity - names and functions, independent of location Extends form occipital lobe to temporal lobe - infra temporal cortex Not unidirectional v1 = bigger V2 = complex, boundary ownership V4 = cells that respond to linear shapes
68
What does where and what pathway help
Supports spatial awareness = dorsal And object recognition = ventral In visual perception
69
Describe neural responses - to what in area v4 and explain
Neural response to polar, hyperbolic and Cartesian gratings in area v4 of monkey V4 = bridges early edge detection - v1 and object recognition in inf temporal cortex More responses to these specific patterns Not much activity for sinuosoidal gratings or patches with oriented linear edges
70
What happens after v4
V2 = bit more complex boundaries = fore and background V4 Posterior it = responds to object parts but nto whole objects - don’t need whole object there
71
Describe lateral occipital complex generally
Some areas show specificity = preferential responses to certain categories Results obtained by univariance analysis - functional mri = averaging - take average response and contrast it Shown pics and see if area responds more to one thing
72
Describe lateral occipital complex results
Responds more to obejcts Loc = first stage in visual hierarchy = where full objects explicitly represented - complete objects - whole Responds strongly to shape defined obejcts - doesn’t matter orientation, viewpoints, sizes, positions Partial invariance - doesn’t respond to specific image but just shape of object - also involved in figure ground segmentation and distinguishing object from background
73
Why is loc important for high level vsison
Bridges mid level feature processsing - v4, pit with high level object recognition = ita cortex, Ffa, Ppa Supports invariant object recognition - crucial for recognizing obejcts across diff contexts Provides whole object representations -making it a key step in ventral visual stream
74
What is loc for
Major hub for object recognition = makes it essential part of understating how brain transforms raw visual input into meaningful obejcts
75
Describe location of fusiform face area
In fusiform gyrus of ventral temporal lobe Usually only in right hemisphere Sometimes bilateral
76
Describe category selectivity of fusiform face area
Highly tuned to faces bit also respond to expert level recognition Preferential to faces
77
Describe invariant face recognition fusiform face area
Helps recognize faces across diff angles, lighting, expression = suggest view invariant representation
78
Describe damage to fusiform face area
Linked to prosopagnosia - cannot recognize faces anymore - do not know if ffa cares about identity - more research needed But this conditions doesn’t mean its linked to identity - bc could like identify based on info form ffa
79
What is still debated about fusiform face area
Some research argues ffa is not strictly for faces but instead specializes in fine grained within category visual recognition
80
Describe parahippocampal place area - gen
Region just posterior to hippocampus Responds preferentially to places
81
parahippocampal place area = first identification
As dedicated scene processing region
82
parahippocampal place area= challenges what
Idea that object recognition alone explains scene perception - scene not just a collection fo objects Spatial layout is key
83
parahippocampal place area Distinguishes what
Ppa from hippocampal spatial navigation = refines understanding of scene perception vs memory asked navigation
84
parahippocampal place area Provides what
Functional link between vision and spatial cognition - bridging perception and higher order place representation Also responds to other things too - not completely separated = all regions contribute some
85
V4 function
Extracts curves, textures and complex contours
86
V4 specialization
Sensitive to local features
87
Loc function
Encodes whole object representations
87
Pit function
Represent object parts
88
Pit specialization
Intermediate processing
89
Loc specialization
Sensitive to shape, invariant to texture and colour
90
Ffa function
Recognizes faces
91
Ffa specialization
Category selective
92
Ppa function
Recognizes scenes and places
93
Ppa specialization
Category selective
94
Describe Ffa and Ppa
Relieve projections from lower level regions to help them process info about category they prefer to respond to
95
Describe real world size
Small and big objects = projection onto brain medically and laterally to fusiform gyrus = contrast between sizes of objects Also on dorsal part = why does where care about obejcts - more than just spatial location in dorsal pathway
96
Describe role of context in object recognition
Contexts helps guide recognition of obejcts
97
Describe viewpoint and scale invariance
Many it neurons demonstrate invariance - at cellular level in neurons in itc = meaning they continue to respond to an object regardless of its size position or viewpoint = suggests that it neurons encode more abstract representations of objects rather than raw sensory features Invariance essential for object recognition Can rotate = neurons still respond. It if rotate too much =responses dampens
98
Describe decoding methods
Studying brain computer interface = brought machine learning Departed from invariant methods = ● Collect fMRI scans of a participant while they view images from multiple known categories.- show more images = better decoding area ● Train a computer model to recognize the brain activity patterns associated with each category. ● Test the model to see if it can correctly identify an unseen image based on learned brain activity patterns. - show image
99
Describe decoding based on similarity
One of first expos = even and odd runs = while doing exp = give break to ppl, between showing them houses and faces If do not cross categorical divisions = have strong correlation of distributed patterns on the 2 runs correlation drops = if switch categories = concept of distributed representations - remove group of voxels corresponding to ffa but still decode if looking at face = distributed system involved in object recognition
100
Describe encoding method = all steps
● Collect fMRI scans of a participant while they view images from multiple known categories. - build model ● Define a feature space, e.g. a gabor wavelet pyramid for visual stimuli. ● Fit weights that show how each feature contributes to the neural signal at each voxel. ● Once trained, encoding models can predict responses to new, unseen stimuli. Comparing the predicted responses with actual fMRI data allows researchers to assess the accuracy of the model and understand the representational structure of the brain region under study. - model has feature set that is rich enough to understand what brain processing
101
Describe voxelwise encoding models
Fmri activity Feature space - multi dimensional encodes orientations, contrast Multiple learned weights by feature space for unseen images —> then gives you the predicted fmri activity = look at performance correlated with predicted and measure fmri activity in that voxel and make map of where in brain mdoel can explain activity
102
Describe exp of identifying natural images from human brain activity
Stage 1 = model estimation = pyramidal hierarchy of Gabor patches = sinusoidal gratings with diff orientations spatial frequencies and positions in an image = gabors as feature space Stage 2 = image identification = measure brain activity for an image = can identify which image person looking at if mdoel successful at encoding right features Graph = correlation of measured voxel activity and predicted When pop = Megan taht mdoel has stronger correlation between predicted activity for one image and observed activity for same image Strong diagonal means mdoel very rarely better predicted response associated with other image
103
Describe second order isomorphism
Representational similarity analysis Similar obejcts in world must have simialr representations in mind Study = judge states of USA by shape = if simialr Saw these 2 things high correlated = also by name but still reate according = has to form mental image of the state = multidimensional representational space where similarity encoded