Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Sensation and Perception > Chapter 4: Recognizing Visual Objects > Flashcards

Chapter 4: Recognizing Visual Objects Flashcards

1
Q

Object Familiarity

A

visual system ust match a mental representation of an object to a representation stored in memory

  • doesn’t have to be perfect image
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Image Clutter, Object Variety, and Variable Views

A

each view represents a complication for the visual systems to resolve in order to identify objects in the environment

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

Variable Views

A

different retinal images that can be projected by some object or category of objects

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

Image Clutter

A

characteristics of visual scenes in which many objects are scattered in 3D space, with partial occlusion of various parts of objects by other objects

  • can tell what’s happening without seeing all details
  • what we’re seeing sometimes have many missing details, but we still know what’s going on
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Object Variety

A

refers to fact that world contains enormous variety of objects

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

Representation

A

gives rise to subjective perceptual experience of that stimulus

  • contain info about increasingly complicated aspects of retinal image
  • visual system maintaining visual components (eg.color, edges)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Recognition

A

refers to the process of matching the representation of a stimulus to a representation stored in long-term memory

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

Perceptual Organization involves

A
  • identifying edges- abrupt/ elongated changes in brightness and/ or color
  • identifying regions bounded by those edges
  • determining what objects owns the boundaries (establishing figure and ground)
  • grouping similar regions (perceptual grouping)
  • handling missing sections by determining what to fill them with (perceptual interpolation)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Object Recognition

A

uses higher-level processes to represent objects fully enough to recognize them

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

Figure

A

region of image perceived as being part of object

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

Ground

A

region that is perceived as background

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

Visual System

A
  • must combine, or group together, the separate regions, based on similarity of properties– regions 13-20 are all the same color
  • must “fill in” the parts of the object that cannot be seen due to occlusion
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Perceptual Grouping

A

process by which visual system combines separate regions of retinal image that go together based on similar properties

  • separating things in visual system
  • combine image regions into wholes
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Perceptual Interpolation

A

process by which visual system fills hidden edges and surfaces in order to represent entirety of partially visible objects

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

Perceptual Organization

A

refers to visual system’s way of dealing with scenes containing multiple overlapping objects

  • makes object recognition which complex scenes possible
  • representing edges and regions
    - edge extraction
    - uniform connectedness
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Edge Extraction

A

process by which visual system determines location, orientation, and curvature of edges in retinal images

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

Uniform Connectedness

A

characteristics of regions of retinal image that have approximately uniform properties
- helps put things together

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

First step in perceptual organization is to represent scenes

A
  • neurons in areas V1, V2, and V4 of the “what” pathways are responsible for extracting edges from the visual field
    - lateral inhibition
    - uniform connectedness
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Edges and Simple Shapes in a Retinal Image

A

this illustrates the retinal image of a scene consisting of four dark gray shapes on a lighter gray background

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

Figure- Ground Organization: Assigning Border Ownership

A
  • Principle of figure-ground organization accomplishes edge extraction
    - determines which objects a border belongs to– critical to figure- ground segregation
  • Visual system principles used to assign border ownership and organize visual scenes into figure and ground
    - Depth, surroundedness, symmetry, convexity, meaningfulness, simplicity
  • Visual system principles
    - depth occurs when one region is perceived to be in front of another
    - a front region owns the border between regions and is perceived as the figure
    - other region is perceived as ground
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

Border Ownership

A

perception that edge/ border is owned by particular region of vertical image

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

V2 Processing

A

V1 and V2 include specialized networks that allow important info about border ownership and figure- ground organization to be computed and transmitted rapidly among cells whose combined receptive fields over contiguous areas of visual scene

  • V2 neurons play role in border assignment
23
Q

Gestalt Laws

A
  • while objects are often described as being grouped (belonging to a group), word “figure” could be easily substituted
  • in general, the figure is what the organism pays most attention to; the ground tends to be ignored
  • the neural basis for border assignment seems to stem from neurons in V2
  • similar results were found with humans in fMRI experiments
24
Q

Gestalt Principles used to group regions

A
  • Proximity- elements that are close together group more easily than elements that are far apart
  • Similarity- similar elements tend to group together
  • Common motion (common fate)- elements that move in unison are likely to be perceptually grouped
  • Symmetry and parallelism- things that are symmetrical or parallel group together
  • Good continuation- two edges that would meet if extended are perceived as single edge that has been partially occluded
25
Q

Good Continuation

A

visual systems idea that things that go together when they move together

26
Q

Neural bases for perceptual grouping

A

grouping may be due to neurons working together (synchronized neural oscillations)

  • research results suggest that three important principles of perceptual grouping may be represented by synchronized neural oscillations
    - similarity (or orientation), good continuation, and common motion
27
Q

Synchronized Neural Organization

A
  • neurons produce spike in temporal pattern

- produce clumps of spikes at same time

28
Q

Perceptual Interpolation

A

intelligently filling in edges and surfaces that aren’t visible, because they’re occluded by other elements, but sometimes they also blend in with the background

  • two different operations with somewhat different perceptual operations work in perceptual interpolation
    - finding edges
    - completing surfaces
29
Q

Edge Completion

A

perception of partially hidden edge as complete

30
Q

Illusory Contours

A

nonexistent but perceptually real edges perceived as result of edge completion

  • result of explicit perceptual representation early in visual stream
  • putting in edges that don’t actually exist
31
Q

Surface Completion

A

perception of partially hidden surface as complete

32
Q

Neural Basis of Perceptual Interpolation

A

Neurons in are V2 have been shown to respond to illusory contours

  1. V2 neuron receptive field location and preferred orientation were determined
  2. Areas outside receptive field were presented two bars at preferred orientation that were moved up to induce illusory edge
33
Q

Perceptual Organization Reflects Natural Constraints

A
  • these were cases when perceptual organization may not be accurate. many such situations are human-made, often using heuristics
    - Heuristics- rules of thumb based on evolved principles and on knowledge of physical regularities
  • the principles of organization provide information needed to create camouflage
    - occurs whenever the figure blends into the background rather than stands out from it
34
Q

Perceptual Interference

A
  • interpretation of retinal image using heuristics
  • involves using heuristics to guide the interpretation of a retinal image based on knowledge of physical regularities in the world
35
Q

Object Recognition

A

Visual system may recognize an object as being the same despite changes in retinal image

Two approaches to object recognition:

    - Single representation is activated when an object is seen
    - Objects are represented in a view-specific manner
36
Q

Invariance

A

Visual system that can recognize object as being same despite changes in retinal image

37
Q

Recognition by Components

A

Proposes that recognizes an object depends on first identifying primitive geometric components that make up object

38
Q

Representation of a Curved Edge by a Neuron in Area V4

A

Complex contours could be represented by neurons in V4 that combines responses of multiple V1 neurons, perhaps with input from neurons in V2

39
Q

Properties to which neurons in area V4 responds

A

Evidence is presented about the stimuli neurons in V4 and the inferotemporal (IT) cortex responds to help with object recognition

40
Q

Individual neurons in area V4 respond mot strongly to […] that can be more complex than those in V1

A

Individual neurons in area V4 respond mot strongly to edges that can be more complex than those in V1

  • Edges to which V4 neurons respond can be straight or curved
  • V4 neurons have preferred orientation, but contour with preferred orientation will elicit strong response only if contour is at angular position relative to entire shape that contour belongs to
41
Q

A Shape-Tuned Neuron in Area V4

A
  • V4 neurons have preferred location in retinal image, but preferred location covers larger area of retinal image
  • Shapes are represented in V4 by combined activity of all neurons responding to contour fragments making up shape
42
Q

A V4 population code for shape

A
  • V4 neurons- tuned to specific curvatures and orientations, located in specific part of retinal image
  • IT neurons- respond most strongly to specific combinations of contour fragments, located almost anywhere in visual field

Based on structural description- description that specifies set of parts (contour fragments) and spatial relations

43
Q

Grandmother cell

A
  • exhibit high degree of invariance with respect to location
  • neuron that responds to particular object at a conceptual level, firing in response to the object itself, a photo of it, its printed name, and so on
  • cells (no one specific cell, but group of cells) that are so specifically tuned that they respond to a particular object
  • Individual neurons in medial temporal lobes
44
Q

Object recognition in brain

A

Areas V1 and V2 (detailed info about precise location) and V4 (curvature and orientation) create increasingly complex representations of edges regions and shapes

  • responses of neurons throughout visual hierarchy contribute to our experience
45
Q

Lateral Occipital Cortex

A

Activity in region is not dependent on size, position, or other features

46
Q

Ways the visual system represents objects

A

Modular coding: representation of object by module, region of brain is specialized for representing particular category of objects

Distributed coding: representations of objects by patterns of activity across many regions of brain

47
Q

Face- Selective Region in the Human Inferotemporal Cortex

A

An fMRI experiment measured activity in the human brain as subjects viewed alternating 30- second sequences of faces versus other types of objects

  • Fusiform face area (FFA)- found on fusiform gyrus along lower surface of temporal lobe
  • Parahippocampal place area (PPA)- activated by buildings and outdoor spaces
  • Extrastriate body area- activated by human and animal body parts
48
Q

Problems with Object Recognition

A
  • Visual form agnosia- impairment in object recognition
    - damage early on in ventral pathway
    - may come from trauma
  • Prosopagnosia- person is unable to recognize faces
  • Topographic agnosia- person is unable to recognize spatial layout such as building streets and landscapes
  • Neurons outside of category- specific modules carry info about whether viewed object belongs to that category
49
Q

Hierarchical Coding of Object Information

A

Brain uses combination of modular and distributed coding

50
Q

Top- Down Information

A
  • opposite of bottom-up processing
  • flow of info from higher to lower regions
  • emphasizes the perceiver’s goals, attention, knowledge, and expectations on perception
  • is combined with bottom- up information in ventral pathway to speed up the process of fully recognizing the objects in a scene
51
Q

Gist of a scene

A

-Improves recognition of the objects in the scene and at same time, recognition of objects in scene improves perception of gist

  • whenever object violated assumption between object and scene, object was more likely to be misidentified or missed
  • visual system creates representation of overall layout of scene and tries to match that with representations of layout of specific categories of scene
52
Q

Unconscious Interference and the Bayesian Approach

A

Bayesian approach- use of mathematical probabilities to describe the process of perceptual inference

  • Visual system of unconsciously combines two probabilities to infer what type of scene produced the currently experiences retinal image
    - prior probability of all possible scenes
    - for each possible scene, the probability that it produced the current retinal image
53
Q

Automatic Face Recognition

A

Feature- based approach- focuses on identifying most prominent anatomical features of face and spatial relations among them

Holistic approach- uses eigenfaces- face images generated from set of digital images of human faces under same lighting, normalized to line up eyes and mouths and rendered same spatial resolution

Sensation and Perception (14 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions