Download P312 Ch05_PerceivingObjectsII

Chapter 5: Spatial Vision and Form Perception II
Note – The next chapter covered will be Chapter 8.
Why Is It So Difficult to Design a Perceiving Machine?
The Stimulus on the Receptors Is Ambiguous
Objects Can Be Hidden or Blurred
Objects Look Different From Different Viewpoints
The Gestalt Approach to Object Perception
The Gestalt Laws of Perceptual Organization
Pragnanz
Similarity
Good Continuation
Proximity (Nearness)
Common Region
Uniform Connectedness
Synchrony
Common Fate
Meaningfulness or Familiarity
Perceptual Segregation: How Objects Are Separated From the Background
What Are the Properties of Figure and Ground?
What Factors Determine Which Area is Figure?
The Gestalt “Laws” as Heuristics
Recognition by Components (RBC) Theory
Perceiving Scenes and Objects in Scenes
Perceiving the Gist of A Scene
Regularities in the Environment: Information for Perceiving
Physical Regularities
Semantic Regularities
The Role of Inference in Perception
Revisiting the Science Project: Designing a Perceiving Machine
The Physiology of Object and Scene Perception
Neurons That Respond to Perceptual Grouping and Figure-Ground
How Does the Brain Respond to Objects
Connecting Neural Activity and Perception
Something to Consider: Models of Brain Activity That Can Predict What a Person is
Looking At
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Biederman’s Recognition By Components (RBC) theory
RBC assumes that any view of an object can be represented as a collection of just a few, simple threedimension forms called geons (jee on).
A geon is a basic 3-dimensional shape with the following two characteristics . . .
1. It can be distinguished from other geons from almost any viewing perspective.
2. It is recognizable even if parts of it are obscured.
Example geons . . .
The theory assumes that we have neural processes, perhaps individual neurons, perhaps groups of neurons,
that respond specifically to the geon cones, cubes, cylinders, etc. These might be called cone detectors,
cube detectors, cylinder detectors, etc.
The presence of a cube shape in the visual scene activates a cube detector. The presence of a cone, for
example, activates a cone detector, etc.
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RBC theory assumes that visual scene is analyzed into the different geons present in the scene.
Analyzed – If a geon is present in the visual stimulus, a neuron or group of neurons in a specific part of the
brain – the “detector” for that geon responds. There are parts of the brain “looking” for geons. Different
parts “look” for different geons.
So for any visual scene many different “geon detectors” respond at the same time. This collection of
responses represents an analysis of the visual scene into its geon components.
Recognition involves comparing the collection of geons activated by an object with stored aggregations of
geons representing specific object.
RBC theory predicts that recognition of an object will be related to the extent to which its geons are visible.
If no geons of an object are visible in a scene, then object will not be able to be recognized.
But if the geons of an object in a scene are visible, then the object will be recognized.
So, a cup is a 5+3. A briefcase is a 5+2. A lamp is a 4+3. A bucket is a 5+3.
Whoops! How is a bucket different from a cup?
Clearly, this is a theory in its formative stages. There is much more to be understood.
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Evidence for RBC
1) There is some evidence of the existence of neurons that respond to specific volumetric shapes and
respond about the same regardless of the perspective or changes in properties that would usually accompany
rotation. (Kayaert, Biederman, & Vogels, 2003). These neurons could be the geon detectors.
2) Our ability to recognize objects depends to a certain extent on whether or not the geons are visible.
3) Our ability to recognize complex objects depends to a certain extent on their geon composition.
Evidence against RBC
RBC predicts that recognition will be independent of viewpoint, as long as the viewpoint allows perception
of the geons making up a figure.
But there is evidence that viewpoint DOES make a difference, even though geon representation is
unchanged.
Relationship to other theories
Similarity to Structuralists:
Assumes the visual scene is represented by specific components, analogous to the Structuralists’
sensations.
Assumes complex perceptions are a result of the synthesis of elementary components, just as the
Structuralists assumed that perception of complex objects resulted from synthesis of sensations.
Differences from structuralists:
Does not assume that the elements are available to consciousness.
Does not assume that we can introspect the elements or any part of the process.
Test essay question: Describe RBC theory. What are the components proposed by RBC theory? How are
complex objects perceived according to that theory? Describe one piece of evidence supporting it.
Describe one piece of evidence against it. How is it similar to Structuralism? How is it different from
Structuralism?
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Physiology of Object Perception G8, p. 120 ff
1. How does the brain respond to objects – G8 p. 121-128
Answer: It responds in multiple ways in many areas – at least 5 areas in response to a face.
Response to a face . .
Occipital Cortex: Initial processing in V1
Fusiform Gyrus: Identification in the FFA –
Fusiform Face Area
Amygdala: Emotional reaction in the Amygdala
(Ramachandran TED talks presentation – “that
person looks like my mother.”)
Superior Temporal Sulcus: Gaze direction area
Frontal Cortex: Evaluation of attractiveness
There is accumulating evidence that the perception of any object is the result of processing in many
different parts of the brain, with different parts signaling different aspects of the particular object being
perceived.
Recall that an ongoing question is the question of how the disparate responding in different parts of the
brain can be connected together to represent a single object.
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2. Connecting Neural Activity and Perception – G8 p. 122
This section is about research attempting to “read” peoples’ minds.
Grill-Spector (2004) study.
Examined activity in the Fusiform Face Area (FFA) in response to presentation of faces.
Precisely locate the FFA in each person.
Used an approach called the Region-of-Interest approach to identify exactly where the FFA was in
each participant. Presented pictures of faces and noted the specific area of the brain in and around
the FFA that was activated.
Presented trials in which observers saw either
a) picture of Harrison Ford,
b) a picture of someone else, or
c) a random texture.
Each presentation was about 50 ms, followed by a masking stimulus.
Observers responded by indicating “Harrison Ford”, “Other Face” , or “Nothing”.
They recorded brain activity occurring before the response of “Harrison Ford”.
Brain activity in the recorded spot was different, depending on which picture was actually shown.
The point of this study is that you can tell whether
or not the observer actually “perceived” Harrison
Ford by looking at the response curve.
If it’s above .3, then the observer saw HF.
If it’s below .2, the observer did not see HF.
If it’s between .2 and .3, then the observer saw a
face.
This is mind “reading” since the activity
represented by the response curves shown on the
left occurred before the observer’s response. So
they could have been used to predict the response
to each presentation.
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Description of the visual world in terms of spatial frequencies
(Not in the text.)
A different way of looking at the description of and processing of visual information
Old way. Let’s call it the Piece Coding method.
The visual world is broken down into individual pieces, like a jigsaw puzzle. Each piece is represented by a
group of neurons in a place in the cortex. Or to put it another way, each neuron represents what is in a
very small piece of the visual world.
For each piece of the visual world . . .
Some neurons represent how light or dark it is.
Other neurons represent whether it contains any edges.
Others represent whether it contains any corners..
Others represent whether it contains anything green.
So the description of the whole visual scene is built up of collections of small pieces of information, each
piece the activity of a neuron or group of neurons. In this view, neurons are kind of like pieces of a jigsaw
puzzle.
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Simplest possible Piece Coding of a stimulus. Each number represents the lightness of a piece of the visual
world.
The visual stimulus
Possible actvity
of visual
“lightness”
neurons.
Activity at a
place indicates
amount of light
at the
corresponding
location in the
visual field
Darkness
The point of this demonstration is to illustrate that the “place coding” representation of the visual stimulus
has each neuron responding to only what is happening (lightness or darkness in this example) at a particular
place in the visual field.
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Adding color.
Each spot is represented by 3 numbers, representing intensity in 3 “primary” wavelengths. – neurons
representing amount of Short wavelength, Medium wavelength, and Long wavelength light.
S, M, L
So 90,30,30
represents Blue
,90,30,30
,90,30,30
,90,30,30
,30,30,30
S, M, L
So 30,30,30
represents
darkness
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Now, add orientation, etc.
Hypercolummns,
each processing
several attributes.
Maybe 10,000
“numbers”
representing each
piece of the visual
scene..
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The piece coding view of the occipital lobe
Individual areas of
the visual field
Left occipital lobe
Right occipital lobe
Hypercolumns
processing light
from individual
pieces of left visual
field
Back of the brain
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New Way – Spatial Frequency Representation.
The external stimulus is considered to be a collection of alternating patterns of light and dark across the
whole visual field.
In this theory, each neuron responds not to what’s at a particular place, but instead to what’s happening
across the whole visual field – what kind of a pattern is distributed across the visual field.
Such neurons would be labeled spatial frequency detectors.
Each such a pattern responded to by a spatial frequency detector is called a grating.
A complex visual scene is composed of the overlay of many such gratings.
Some represent large scale changes in light and dark.
Others represent small scale changes, rapid alternations between light and dark.
Presumably we have detectors for each type of grating. Remember that a grating is an alternating pattern of
light and dark across the whole visual field, not just at a point.
Example
Low
Frequency
alternation
+
+
High
Frequency
alternation
Medium
Frequency
alternation
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Characteristics of grating stimuli
1. Spatial frequency: Number of alternations per degree of visual angle – cycles per degree.
High Frequency
Low Frequency
Thumb at arm’s length covers about 2 degrees of visual angle.
2. Orientation. The direction of the bands. 0 = vertical; 90=horizontal;
45 degreees
3. Phase. Starting point.
90 degrees out of phase with top grating
180 degrees out of phase with top grating
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4. Contrast
Contrast is the difference between the brightest bars of a grating and the darkest bars.
High contrast
Low contrast
Low contrast
High contrast
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Is it theoretically possible for a complex visual scene to be represented as the summary of various
repetitive whole field patterns?
Yes. Jean Baptiste Fourier (1768-1830) showed that any periodic complex waveform could be represented
by the sum of a set of sinusoidal waveforms. The breaking down of a complex waveform into its
sinusoidal components is called Fourier Analysis.
If a visual scene is viewed as a collection of complex waveforms of light vs. dark from left to right, then
each of those waveforms can be represented as the sum of sine wave components and the whole scene is,
therefore, the sum of an enormous collection of sine wave components.
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Some concepts associated with the spatial frequency representation
1. The spatial frequency spectrum of a stimulus. A representation which presents the frequencies and
contrasts of the components but not their phase or orientation.
Contrast
Frequency in cycles / degree of visual angle
Examples
Contrast
Frequency in cycles / degree of visual angle
The above is a spectrum of a stimulus with lots of sharp edges, but not much in the way of changes in fill - a
line drawing on homogenous paper, such as a cartoon drawing or text.
Contrast
Frequency in cycles / degree of visual angle
This is a figure with large expanses of solid, with no sharp edges – images in the fog.
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Examples of pictures with different spatial frequency spectra
Groucho Marx
Contrast
Frequency in cycles / degree of visual angle
Contrast
Frequency in cycles / degree of visual angle
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Another example
Leftmost picture
Contras
t
Frequency in cycles / degree of visual angle
Rightmost picture
Contras
t
Frequency in cycles / degree of visual angle
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